UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Evaluation of primary wood processing residues for bioenergy in British Columbia Kehbila, Atenkeng Taku 2010

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_fall_2010_kehbila_atenkeng.pdf [ 1.6MB ]
Metadata
JSON: 24-1.0071189.json
JSON-LD: 24-1.0071189-ld.json
RDF/XML (Pretty): 24-1.0071189-rdf.xml
RDF/JSON: 24-1.0071189-rdf.json
Turtle: 24-1.0071189-turtle.txt
N-Triples: 24-1.0071189-rdf-ntriples.txt
Original Record: 24-1.0071189-source.json
Full Text
24-1.0071189-fulltext.txt
Citation
24-1.0071189.ris

Full Text

 EVALUATION OF PRIMARY WOOD PROCESSING RESIDUES FOR BIOENERGY IN BRITISH COLUMBIA  by  ATENKENG TAKU KEHBILA B.Sc., Brandenburg University of Technology Cottbus, Germany, 2006  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE   in  THE FACULTY OF GRADUATE STUDIES (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  August, 2010  © Atenkeng Taku Kehbila, 2010   ii ABSTRACT  The growing energy demand in the world emphasizes the need for a more reliable energy source. To this end, a "fundamental” re-think is critical for an effective sustainable bioenergy production in the 21st century. BC has traditionally been the largest lumber and residue producing province in Canada, thus advancing bioenergy production in BC holds the potential to utilize residues from primary wood processing mills.  The main objectives of this study were: (1) to develop a database using important physical characteristics of residues from sawmills, and plywood mills in BC. (2) to estimate the total production, utilization and surpluses of primary residues within each forest region; and (3) to create a Dijkstra logarithm model partnered with a vector-based geographical information system (GIS) to investigate the optimal location of bio-based industrial plants using the available residues from BC’s mills.  Laboratory analysis of primary mill residues indicated that the higher heating values (HHV) ranged from 17 – 21.5 MJ/kg with a mean of 18.9MJ/kg. Ash contents were quite small for whitewood (0 – 2%) and higher for bark and hogfuel (2 – 6.5%) with a mean of 1.6%. The moisture content (MC) on wet mass basis ranged from 5 – 317% with a mean of 105%. The basic density ranged from 120 – 1151 kg/m3 with a mean of 352 kg/m3. The average particle size (Dp50) of the residues ranged from 0.2 – 49.7 mm.  Analyses of the availability of primary mill residues revealed that due to the demands of the pulp and paper industry, there were no available residues in BC for bioenergy production in 2006. Consequently, an artificial situation was created in order to demonstrate the potential benefits of the GIS-Dijkstra integrated modeling framework to determine the most preferred locations for bioenergy plants. Under this hypothetical scenario surplus residue generated in the Southern forest region had the lowest residue cost for bark at $15/BDt followed the coastal forest region at $46/BDt and the Northern forest region at $51/BDt. While the Southern interior forest region still had the cheapest whitewood residue cost at $31/BDt followed the Northern forest region at $78/BDt and the coastal forest region at $84/BDt.  iii TABLE OF CONTENTS   ABSTRACT....................................................................................................................... ii LIST OF TABLES........................................................................................................... vi LIST OF FIGURES........................................................................................................ vii CO-AUTHORSHIP STATEMENT ............................................................................... xi CHAPTER 1 RESEARCH OVERVIEW....................................................................... 1 1.1  INTRODUCTION ............................................................................................... 1 1.1.1 Research Objectives........................................................................................ 3 1.2 LITERATURE REVIEW: CONVERSION TECHNOLOGIES AND RESIDUE CHARACTERISTICS FOR BIOENERGY PRODUCTION.................... 5 1.2.1 Objective ......................................................................................................... 5 1.3 TECHNOLOGICAL CONVERSION PATHWAYS........................................ 6 1.3.1 Thermochemical conversion........................................................................... 6 1.3.2    Biochemical conversion...................................................................................... 7 1.4 COMBUSTION..................................................................................................... 8 1.4.2 Combined heat and power (CHP) ................................................................... 9 1.4.3 Cogeneration ................................................................................................. 10 1.4.4    Industrial scale biomass boilers ........................................................................ 10 1.5  GASIFICATION................................................................................................. 12 1.5.1 Producer gas.................................................................................................. 13 1.5.2    Types of Gasification Process........................................................................... 14 1.5.3    Types of gasifier ............................................................................................... 15 1.5.4 Integrated gasification combine cycle (IGCC) ............................................. 17 1.5.5    Commercial status of gasification..................................................................... 18 1.6 PYROLYSIS........................................................................................................ 19 1.6.1    Production process for pyrolysis....................................................................... 19 1.6.2 Products from pyrolysis ................................................................................ 21 1.6.3 Commercial status of pyrolysis..................................................................... 24 1.7 ETHANOL PRODUCTION .............................................................................. 25 1.7.1 Acid hydrolysis ............................................................................................. 25 1.7.2 Steam explosion pretreatment....................................................................... 28 1.7.3 Organosolv pretreatment............................................................................... 28 1.7.4 Enzymatic hydrolysis.................................................................................... 29 1.7.5 Commercial status of ethanol production from lignocellulose ..................... 30 1.7.6 Summary ....................................................................................................... 30 1.8    RESIDUE HANDLING FOR TECHNOLOGICAL PROCESSES.................. 31 1.8.1 Residue handling for combustion technology............................................... 32 1.8.2 Biomass handling for gasification technology.............................................. 33 1.8.3 Biomass handling for pyrolysis technology.................................................. 34 1.8.4    Residue handling for ethanol production.......................................................... 35 1.9 CONCLUSION ................................................................................................... 37 1.10 REFERENCES.................................................................................................... 38 CHAPTER 2 CHARACTERISTICS OF WOOD RESIDUES FOR BIOENERGY APPLICATIONS ............................................................................................................ 47 2.1 INTRODUCTION .............................................................................................. 47 2.1.1 Objectives ..................................................................................................... 49 2.2 LITERATURE REVIEW .................................................................................. 49  iv 2.2.1 Sources of industrial mill residues................................................................ 49 2.3 MATERIALS and METHODS ......................................................................... 55 2.3.1 Residue sampling region............................................................................... 55 2.2.2 Residue sampling type .................................................................................. 57 2.3.3 Residue sample preparation and analysis ..................................................... 60 2.3.4 Data Analysis ................................................................................................ 63 2.4 RESULTS ............................................................................................................ 64 2.4.1 Higher heating value data distribution.......................................................... 65 2.4.2 Ash content data distribution ........................................................................ 67 2.4.3 Basic density data distribution ...................................................................... 70 2.4.4 Moisture content data distribution ................................................................ 72 2.4.5 Total analysis of residues.............................................................................. 75 2.4.6 Particle size distribution................................................................................ 76 2.4.7 Relationship of basic density and particle size distribution.......................... 79 2.5 DISCUSSION...................................................................................................... 81 2.6 CONCLUSIONS ................................................................................................. 86 2.7 REFERENCES.................................................................................................... 87 CHAPTER 3 GEOGRAPHICAL DISTRIBUTION OF SAWMILL AND CHIPS MILL RESIDUES – ESTIMATES OF AVAILABILITY IN BRITISH COLUMBIA ........................................................................................................................................... 92 3.1 INTRODUCTION .............................................................................................. 92 3.1.1 Residue production in a sawmill................................................................... 93 3.1.2 Research objectives....................................................................................... 94 3.2 METHODS.......................................................................................................... 95 3.2.1 Estimating total residue production in BC.................................................... 96 3.2.2 Residue production from roundwood ........................................................... 97 3.2.3 Chip production in a chipping mill ............................................................... 99 3.2.4 Estimating total primary mill residue consumption in BC ......................... 100 3.3 RESULTS .......................................................................................................... 103 3.3.1 Northern Interior Forest Region.................................................................. 106 3.3.2 Southern Interior Forest Region.................................................................. 110 3.3.3 Coastal Forest Region ................................................................................. 114 3.4 DISCUSSION.................................................................................................... 117 3.4.1 Results interpretation .................................................................................. 117 3.5 CONCLUSIONS ............................................................................................... 124 3.6 REFERENCES.................................................................................................. 125 CHAPTER 4 USING GIS-DIJKSTRA INTEGRATED ASSESSMENT MODEL TO ASSESS THE DELIVERED RESIDUES TRANSPORTATION COSTS IN BRITISH COLUMBIA FOR BIOENERGY PRODUCTION ................................. 128 4.1 INTRODUCTION ............................................................................................ 128 4.1.2 Objective ..................................................................................................... 129 4.2 METHODS........................................................................................................ 130 4.2.1 Geographical net-surplus for bioenergy in BC and assumptions................ 130 4.2.2 Cost of residues........................................................................................... 131 4.2.3 Data analysis ............................................................................................... 133 4.2.4 Model Overview ......................................................................................... 134 4.2.5 Google Earth ............................................................................................... 134 4.2.6 ET Geowizard ............................................................................................. 135 4.2.7 ArcGIS 9.3 .................................................................................................. 136 4.2.8 Dijkstra model............................................................................................. 136  v 4.3 RESULTS .......................................................................................................... 141 4.3.1 Northern interior forest region residue quantification ................................ 142 4.3.3 Coastal forest region residue quantification ............................................... 153 4.4 DISCUSSION.................................................................................................... 158 4.5 CONCLUSIONS ............................................................................................... 162 4.6 REFERENCES.................................................................................................. 163 CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH ................................. 165 5.1 CONCLUSIONS ............................................................................................... 165 5.1.1 Phase I Analysis of the physical characteristics of mill residues in BC ..... 165 5.1.2 Phase II The availability of residues in BC ................................................ 166 5.1.3 Phase III A GIS model of surplus residues in BC ...................................... 167 5.2 LIMITATIONS................................................................................................. 168 5.4 REFERENCES.................................................................................................. 171 APPENDIX A: Summary of residue characteristics by type ........................................ 172 APPENDIX B: Table showing data on sieve analysis and basic density...................... 173 APPENDIX C: Abbreviations used for Northern interior forest regions...................... 174 APPENDIX D: Abbreviations used for Southern interior forest regions...................... 174 APPENDIX E: Abbreviations used for Coastal forest regions ..................................... 176 APPENDIX F: Road base_data..................................................................................... 177 APPENDIX G: Northern interior forest preferred whitewood mills............................. 185 APPENDIX H: Northern interior forest region preferred bark mills ............................ 187 APPENDIX I: Southern interior forest preferred bark mills......................................... 189 APPENDIX J: Southern interior forest region preferred whitewood mills .................. 193 APPENDIX K: Coastal forest region preferred bark mills ........................................... 197 APPENDIX L: Coastal forest region preferred whitewood mills ................................. 200    vi LIST OF TABLES    Table 1.1: Large-scale combustion systems ..................................................................... 12 Table 1.2: The difference between synthetic gas and producer gas ................................. 14 Table 1.3: Comparison of different gasification technology ............................................ 18 Table 1.4: Typical product (bio-oils, char and synthetic gas) yields (dry wood basis) obtained by different types of pyrolysis of wood ............................................................. 22 Table 1.5: Required feedstock characteristics for various thermochemical processes..... 32 Table 1.6: Required feedstock characteristics for ethanol production.............................. 36  Table 2.1: M1 (Plywood).................................................................................................. 59 Table 2.2: M2 (Shake mill) ............................................................................................... 59 Table 2.3: M3 (Pole mill).................................................................................................. 59 Table 2 4: M4   (Sawmill).................................................................................................. 60 Table 2.5: M5 (Sawmill)................................................................................................... 60 Table 2.6: Correlation matrix coefficient of residues analysis ......................................... 64 Table 2.7: A statistical table of total residues data analysis ............................................. 75 Table 2.8:  Particle distribution within the total residues by type .................................... 76 Table 2.9: Particle distribution of total residues ............................................................... 78 Table 2.10: Total analysis by residues.............................................................................. 80  Table 3.1: Residue generation from lumber production ................................................. 103 Table 3.2: Residue generation from chip mills in 2006.................................................. 105 Table 3.3: Total residue from both lumber mills and chip mill production.................... 105 Table 3.4: Residue consumers in the Northern interior forest region............................. 108 Table 3.5: Residue surplus in the Northern interior region ............................................ 108 Table 3.6: Residue consumers in Southern interior region............................................. 111 Table 3.7: Residue surplus in the Southern interior forest region .................................. 112 Table 3.8: Residue consumption in the Coast forest region ........................................... 115 Table 3.9: Residue surplus in the Coast forest region .................................................... 116  Table 4.1: A summary of residue cost in British Columbia (Bradley, 2009) ................. 132 Table 4.2: Interpretation of ET_geowizard to dijkstra algorithm................................... 137 Table 4.3: Residues in the Northern interior forest region ............................................. 142 Table 4.4: Residue surplus for bioenergy production in the Southern interior forest region ......................................................................................................................................... 147 Table 4.5: Total users of residues in Southern interior forest region.............................. 148 Table 4.6: Residue surplus for bioenergy production in Coastal forest region .............. 153   vii LIST OF FIGURES   Figure 1.1: Simplified technological conversion pathways of biomass to products........... 6 Figure 1.2: Schematic representation of combustion system.............................................. 8 Figure 1.3: Schematic representation of gasification process........................................... 13 Figure 1.4: Conceptual fast pyrolysis process .................................................................. 24 Figure 1.5:  Production of ethanol from lignocellulosic materials ................................... 26  Figure 2.1: Processing steps in the production of lumber................................................. 51 Figure 2.2: Processing steps in the production of plywood .............................................. 54 Figure 2.3: A map showing the locations of research sample sites .................................. 57 Figure 2.4: Histogram and box plot for higher heating value........................................... 65 Figure 2.5: Oneway analysis of higher heating value (MJ/kg) by residue type ............... 66 Figure 2.6: Oneway analysis of mean higher heating value (MJ/kg) by mills ................. 67 Figure 2.7: Histogram and box plot for ash content ......................................................... 68 Figure 2.8: Oneway analysis of ash content (%) by residue type..................................... 69 Figure 2.9: Oneway analysis of mean ash content (%) by mills....................................... 69 Figure 2.10: Histogram and box plot for basic density..................................................... 70 Figure 2.11: Oneway analysis of basic density (kg/m3) by residue type .......................... 71 Figure 2.12: Oneway analysis of mean basic density (kg/m3) by mills............................ 72 Figure 2.13: Histogram and box plot for moisture content............................................... 73 Figure 2.14: Oneway analysis of moisture content (%) by residue type .......................... 74 Figure 2.15: Oneway analysis of mean moisture content (%) by mills ............................ 75 Figure 2.16: Cumulative mean size distributions of chips from sawmill and plywood mills................................................................................................................................... 77 Figure 2.17: Bivariate fit of mean basic density (kg/m3) by mean particle size Dp50 (mm) ........................................................................................................................................... 79  Figure 3.1: Geographical location of sawmills in British Columbia in 2006 ................... 95 Figure 3.2: Typical softwood lumber recovery and byproduct production in BC............ 96 Figure 3.3: Geographical locations of residue users in British Columbia in 2006 ......... 102 Figure 3.4: BC residue distribution by type in 2004 and 2006....................................... 104 Figure 3.5: Northern interior forest region: proportions of residue production by type from sawmills.................................................................................................................. 107 Figure 3.6: Northern interior forest region residue estimates ......................................... 109 Figure 3.7: Southern interior forest region: proportion of residue production by type .. 111 Figure 3.8: Southern interior forest region residue estimates ......................................... 113 Figure 3.9: Coast forest region: residue production........................................................ 115 Figure 3.10: Coast forest region residue estimates ......................................................... 116 Figure 3.11: Major sawmill residue consumers in BC ................................................... 118 Figure 3.12: BC harvest forecast – coast and interior forest regions.............................. 120 Figure 3.13: Residue handling for bioenergy production ............................................... 121 Figure 3.14: Cost supply curve for various wood residues............................................. 123  Figure 4.1: Schematic diagram of the integrated GIS-dijkstra model used in this study. ......................................................................................................................................... 135 Figure 4.2: Organization of GIS data in layers ............................................................... 137  viii Figure 4.3: Schematic diagram describing the function of the dijkstra algorithm.......... 139 Figure 4.4: Total annual cost of transporting bark residues to existing Northern interior forest region users ........................................................................................................... 144 Figure 4.5: Total annual cost of transporting whitewood residues to existing Northern interior forest region users .............................................................................................. 145 Figure 4.6: Locations of mills and residue consumers in the Northern interior forest region .............................................................................................................................. 146 Figure 4.7: Total annual cost of transporting bark residues to existing Southern interior forest region users ........................................................................................................... 149 Figure 4.8: Total annual cost of transporting whitewood residues to existing Southern interior forest users ......................................................................................................... 150 Figure 4.9: Locations of mills and residue consumers in the Southern interior forest region .............................................................................................................................. 152 Figure 4.10: Total annual cost of transporting bark residues to existing Coastal users.. 155 Figure 4.11: Total annual cost of transporting whitewood residues to existing Coastal users ................................................................................................................................ 156 Figure 4.12: Locations of mills and residue consumers in the Coastal interior forest region .............................................................................................................................. 157   ix  ACKNOWLEDGEMENTS   The preparation of this thesis, and the research to which it refers, would not have been possible without the support, hard work and endless efforts of a number of individuals and institutions. From this premise, I am particularly indebted to my supervisor, Dr. Paul McFarlane, for providing resources and subjects, and offering direction, technical advice and constructive criticism. Your encouragement, guidance and support, from concept to completion, enabled me to develop an understanding of the subject.  Besides my supervisor, other members of my thesis committee, Dr. Robert Kozak, Dr. Steve Mitchell, Dr. Shahab Sokhansanj, and Dr. Taraneh Sowlati are especially thanked for providing valuable review, comments, and suggestions during the finalization of this thesis. Your worthy counsel and insightful comments were very fundamental to the successful realization of this work.  It is difficult to overstate my gratitude to the BC Ministry of Forest and Range for providing relevant information for this thesis. In particular, my appreciation goes to Rebecca Ewing who provided data for forest mill locations in BC. Gratitude is equally paid to Gorden Dunwell who granted us mill access and provided sawmill data and residues for laboratory analysis. My sincere thanks also to Dr. John Nelson and Simon Moreira who provided the Dijkstra algorithm software. I am heartily thankful to Jerome Alteyrac and Yu Li who assisted with mill sampling and residue analysis.  I am indeed grateful to Dr. Steve Mitchell and Dr. John Ruddick for giving me the opportunity to work for them as a teaching assistant. This opportunity broadened my researching skills and teaching scope. My sincere thanks to all my office mates and student colleagues from my research group for being the surrogate family during the many years I stayed at UBC and for providing a stimulating and fun environment in which to learn and grow. Thanks are due also to Josephine Fotabong, Alemagi Dieudonne, Emmanuel Ackom, and Hugues Petit-Etienne who have always been supportive and inspirational throughout my graduate study.  x  Financial support from the Sustainable Forestry Management Network (SFMN) has been instrumental in the successful realization of this project.  I am forever indebted to my parents, Sylvester Acheleke Taku and Marie Bezenche Taku, for giving me life in the first place, for educating me with aspects from both arts and sciences, and for their unconditional support and encouragement to pursue my interests even in the toughest of times. Big thanks as well to my siblings, Emily Atem, Ateawung Tateh and Njem Taku, for their understanding, endless patience and encouragement when it was most required.  Last, but not least, I owe my deepest gratitude to my dear husband, Anderson Gwanyebit Kehbila, for listening to my complaints and frustrations, and for believing in me. Your continuous support was very instrumental in the successful realization of this work.  xi CO-AUTHORSHIP STATEMENT   I hereby declare that this thesis, as approved by my thesis committee and the Graduate Studies office, is the product of my original work and that, to the best of my knowledge and belief, this thesis contains no material previously published or written by another person, except where due reference is made in the text of the thesis.  The core theme of this thesis is evaluation of primary wood processing residues for bioenergy in British Columbia. The ideas, data collection, laboratory analysis, model design, data interpretation and writing up of all the chapters in this thesis were the principal responsibility of myself, working within the department of Wood Sciences, under the direct supervision of Dr. Paul McFarlane. The thesis, and the research to which it refers, contains no material which has been accepted for the award of any other degree or diploma at any university or equivalent institution.  Chapters two, three and four of this thesis will eventually be revised into manuscripts and submitted for publication.               1 CHAPTER 1 RESEARCH OVERVIEW    1.1  INTRODUCTION   In BC, the primary wood processing industry is predominantly engaged in using roundwood to manufacture structural wood products such as lumber and plywood. In addition to producing lumber and plywood, this industry also creates substantial quantities of residues with a wide range of characteristics. Typically just over half of the incoming roundwood maybe converted to mill residues that maybe used for a variety of processes including pulp and paper, pellets and bioenergy. The utilization of mill residues as a raw material for bioenergy production requires an understanding of the availability, and basic feedstock characteristics to the various technological processes. Primary wood residues availability is a dynamic situation because residue production is a secondary product from the mills and its availability largely depends on the type and scale of the mill, the mill efficiency, and the current wood product market.  Although technological aspects are important for bioenergy production, the characteristics of the feedstocks also exert an important influence on the technological platforms. Thus, the choice of converting biomass into bioenergy depends on the nature of feedstock and the degree of homogeneity of the biomass. Since biomass is a very heterogeneous and chemically complex renewable resource, it is imperative that feedstock be examined to meet the minimum required standards for various bioenergy and bioproducts industries. Thus, testing methods for HHV, MC, ash content, basic density and particle size distribution for 33 samples collected were analyzed using descriptive statistical analyses.  While historical data are available to estimate the volume of biomass used by the pulp and paper sector in BC (McCloy and Associates, 2004), there is little up-to-date information on the quantities of biomass residues consumed for energy production at sawmill sites. Further, total estimates of residues available and/or consumed for energy  2 and non-energy use within BC are fundamentally lacking. The second phase of this study attempted to estimate the potential primary wood processing residues available in 2006 within different geographical regions in BC, in order to assess the surplus residues that maybe available to be utilized for bioenergy production. The study was based principally on the locations of primary processing plants in relation to existing residue consuming plants in order to estimate the quantity of residues available for use by new bioenergy plants.  As wood residue availability is spatially located, this study proposed a spatial model known as GIS-Dijkstra integrated assessment model that could be used as a tool to quantify residues geographically and to estimate total residue costs at preferred locations of bioenergy plants. Transport costs are a significant portion of delivered residue costs in all of BC’s forest regions, and this cost is dependent on the distances from the producer to the consumer. The dispersed geographical distribution of biomass supply has raised the interest in this research. To this end, this study undertook a systematic analysis of residues by determining the shortest $ weighted distance within the province to locate a bioenergy plant as well as creating preferred collection points in order to minimize total cost.  Understanding the residue availability by types and location within BC is an important element in facilitating bioenergy development.  The organization of this thesis is as follows: Chapter one focuses on the research overview and the literature review of all the available bioenergy conversion platforms existing in BC for both thermochemical and biochemical processes. Chapter two provides a detailed description of the physical characteristics of primary mill residues available in BC for bioenergy production. Chapter three focuses on the geographical distribution of sawmill and chip mill residues and the estimation of residue availability in BC. Chapter four presents the development of a GIS based system partnered with the Djitkstra model to analyze surplus residue available by type using the shortest path algorithm. Chapter five provides the conclusions from this research and indicates limitations as well as suggestions for future research activities.   3 1.1.1 Research Objectives   The objectives of this research were three folds:   1. Phase I: To analyze the physical characteristics of mill residues in BC. To realize this objective, samples were collected from a plywood mill (16 samples), two sawmills (14 samples), a shake mill (2 samples) and a pole mill (2 samples). The samples collected varied by species, age of pile, season, moisture condition, mill type and mill unit operation. The plywood mill, a shake mill and a pole mill were located in the coastal region while the two sawmills were located in the interior region of the Province. A total of 33 samples consisting of bark, hogfuel, chips, sander dust, trim ends, sawdust, and shavings were subjected to laboratory analysis. All samples were tested in triplicate for moisture content, higher heating value, ash content, basic density and sieve analysis.  2. Phase II: To assess the availability of primary wood processing residues in BC. Availability of biomass is perhaps one of the most difficult, but usually the most important, assessment to undertake (Rosillo-Calle, 2007). Up-to-date information on quantities of biomass residues produced and disposed of at wood products manufacturing sites for bioenergy consumption is usually lacking because previously sawmills segregated lumber products based on highest market values and thus less attention was devoted to quantify available by-products such as residues on-site (McCloy, 2004).  To achieve this objective, major primary timber processing data were collected from the BC Ministry of Forest including confidential individual actual lumber output data for all sawmills and chip mills in BC (BC MoF, 2006). The data obtained from the confidential report were used to estimate total residues generated from each mill by type in terms of sawdust, shavings, trim ends, bark and chips. This is a dynamic situation because residue production is a secondary product from the sawmills and its availability largely depends on the current lumber market.   4 3. Phase III: To develop an integrated GIS-Dijkstra model framework to locate the surplus residues in BC regionally. To accomplish this objective, GIS software was partnered with the Dijkstra logarithm model systems to evaluate residue produced from each forest regions in BC. The data on residue generation were incorporated with BC road network, transportation cost, and feedstock cost. The model used residue availability and demand together with mill location and transportation cost to determine the ideal collection site for residues, and the marginal cost of supplying residues to that location.   5 1.2 LITERATURE REVIEW: CONVERSION TECHNOLOGIES AND RESIDUE CHARACTERISTICS FOR BIOENERGY PRODUCTION   In 2004, bioenergy was the fourth largest source of energy in the world, supplying about 15% of primary energy at a global level (FAO, 2004).  Canada’s percentage of total primary energy generated from biomass is 13.5% (WEC, 2001).  Biomass efficiency has great growth potential and investments in bioenergy have been reported to create four times as many jobs as equivalent investments in conventional energy production and as well as helping fight against climate change (Suzuki, 2007).  Biomass can be converted into various forms of energy by numerous technical processes depending upon the raw material characteristics and the type of energy desired. This section outlines the various processes that may be used to convert woody biomass to bioenergy products and residue characteristics.   1.2.1 Objective   The specific objectives of this section include: (1) to summarize the current production of bioenergy from various technologies; and (2) to characterize primary wood processing residues for bioenergy applications.  6 >1000oC 1.3 TECHNOLOGICAL CONVERSION PATHWAYS   Biomass encompasses a variety of biological materials with distinctive chemical and physical characteristics (Figure 1.1). The conversion technologies vary in the actual amount of energy recovered from the biomass source and the form of that energy produced (McKendry, 2002).    Figure 1.1: Simplified technological conversion pathways of biomass to products (Adapted from: Oregon, 2007).  1.3.1 Thermochemical conversion   Thermochemical conversion processes can be subdivided into combustion, gasification, and pyrolysis. Pyrolysis and gasification are two widely studied thermochemical conversion technologies (He et al., c2000). The main advantage of pyrolysis over THERMOCHEMICAL CONVERSION COMBUSTION  GASIFICATION BIOMASS BIOCHEMICAL CONVERSION    PYROLYSIS ANAEROBIC DIGESTION FERMENTATION BIOGAS ETHANOL EXCESS OXYGEN LIMITED OXYGEN >750oC NO OXYGEN 300-800oC SYN FUEL &       CHAR STEAM,  HEAT & ELECTRICITY PRODUCER GAS: LOW & MEDIUM BTU  7 combustion and gasification is, it produces a wide variety of products, ranging from transportation fuel to chemical feedstock (e.g. adhesives, organic chemicals, and flavoring).  Pyrolysis plays a key role in the reaction kinetics of all thermal conversion processes and is always present in the initial stages of gasification and combustion (Cozzani et al., 1997). It also determines product distribution, composition, and properties (Raveendran, 1995). Combustion involves the direct burning of biomass in the presence of air (oxygen) while gasification and pyrolysis conversion involves the conversion of biomass into producer gas by burning the biomass with insufficient or no air (partial combustion) so that complete combustion does not occur and producer gas is formed.   1.3.2    Biochemical conversion    Biochemical conversion may be used to produce either ethanol or biogas by microbial hydrolysis and fermentation. The cellulose molecules in wood are systematically arranged, while the hemicellulose and lignin fill in the gaps between the cellulose molecules, giving the strength of wood. Among those components, only cellulose and hemicellulose have molecules composed of polymeric sugar molecules. These chains can be hydrolyzed to produce monomeric sugars which can then be converted to ethanol by fermentation.  Anaerobic reactors utilize mixed methanogenic bacterial cultures to produce a methane rich biogas. Anaerobic conversion processes are most effective with biomass that is very wet such as sludge, crop residues and animal dung to enable the efficient digestion of substrates by the bacteria. Anaerobic conversion using wood residues is not widely used and will not be considered in this study.   8 1.4 COMBUSTION   Combustion technologies play a major role throughout the world, and it has historically produced about 90% of the energy from biomass (Demirbas, 2001).  The simplest combustion technology is a furnace that burns biomass in a combustion chamber. As the biomass burns, hot gases are released (Figure 1.2). These hot gases contain about 85 percent of the fuel’s potential energy. Combustion occurs either directly (furnace) or indirectly, through a heat exchanger in the form of hot air or water (Oregon, 2007).  On a large industrial scale, there are various forms of combustion technologies, as described below.   Figure 1.2: Schematic representation of combustion system (Lee, 2006)  BIOMASS COOL EXHAUST EXHAUST FILTERING MATERIAL HANDLING AIR ASH, CHAR HOT EXHAUST: CO2 and H2O BIOMASS + AIR  HOT EXHAUST PRODUCTS C O M B U S TO R  BOILER  9 1.4.1    Direct-fire gas combustion   The direct-fired gas turbine is most common combustion technology for converting biomass to electricity, especially with retrofits of existing facilities to improve process efficiency. Direct firing refers to simply burning biomass in a boiler to raise steam in a boiler which can be expended through a stream turbine (or Rankine cycle) to generate power. Other prime movers include the Brayton cycle of gas turbines, Stirling engines, and thermoelectric and thermovoltaic possibilities. Power from these cycles can be used directly to drive a machine or to power an alternator to produce electricity. In CHP, the most common variant is when the electricity is generated first through a steam and the heat is taken from the exhaust of the electricity cycle.  Based on lower heating value (LHV) of the biomass, typical combustion efficiencies range from 65 percent in poorly designed furnaces and up to 99 percent in well-insulated, sophisticated combustion (Quaak et al., 1998).   1.4.2 Combined heat and power (CHP)   The CHP system uses the combustion process and it is widely used on various scales to burn biomass to produce heat, power and/or electricity. Production of heat, power and steam by means of combustion is applied to a wide variety of biomass, from very small scale (for domestic heating) up to large industrial scale. The major difference between combustion and CHP is that, all of the steam is condensed in the turbine cycle in combustion, while in CHP operation, a portion of the steam is extracted to provide process heat (EERE, c2000).    10 1.4.3 Cogeneration   There are two cogeneration arrangements which include: (1) Combining electric power generation with industrial steam production. Steam can be used in an industrial process first and then routed through a turbine to generate electricity. This arrangement is called a bottom cycle. (2) Alternatively, steam from the boiler may pass first through a turbine to produce electric power. The steam exhaust from the turbine is then used for industrial processes or for space and water heating. This arrangement is called a topping cycle. Of the two cogeneration arrangements, the topping cycle is more common (Oregon, 2007).  Two types of turbine may be utilized, namely a steam turbine which uses steam to make mechanical energy or a gas turbine that uses any gas (may be methane, natural gas, flue gas) to make mechanical energy. However, steam based systems provide low fuel to electrical output efficiencies due to the heat lost in the condensation phase, as a result fuel feed rates are high and the electricity outputs are low. The efficiency of cogeneration can be improved by modifying the steam turbine into a micro turbine which can further be developed into a biomass generator (Pritchard, 2005).  In recent years, advancement in turbine and gasification technologies have raised the efficiency of direct fired systems by using integrated gas turbine gasification technology. These gas turbines are compact, lightweight, and easy to operate, and come in several sizes ranging from couple hundred kilowatts to hundreds of megawatts. These gas turbines have become the premier electric generation system for peak and intermediate loads.   1.4.4    Industrial scale biomass boilers   Combustion technology is the most widely used thermochemical processes in BC. Large- scale combustion systems use mostly low-quality fuels, while high-quality fuels are more frequently used is small systems. Industrial boilers range from 100 to around 300 MWth output. Smaller scale versions are used in district heating and small processes down to as  11 low as 10 MWth, usually without the same level of emissions control. A boiler´s steam output typically contains 60 to 85 percent of the potential energy in biomass fuel (IAEA, 2006). The major types of biomass combustion boilers are fixed bed combustion (FxBC), fluidized bed combustion (FBC) and direct combustion (DC). The table below depicts the different boiler types and their residue specifications for large scale combustion systems (Table 1.1). Several cogeneration publications were reviewed to obtain representative values for the total output of power produced per hour (add references). To estimate the conversion efficiency at each plant, a 50% recovery factor was assumed for each production unit (BC Hydro, 2009).  There are two existing cogeneration plants in the Northern interior forest region of BC that utilize 100% residue to generate power. The Armstrong cogeneration plant, located at the Mackenzie District, had a total output of 20MW of energy (BC Hydro, 2009). The plant consumes approximately 100,000 BDt of residue on an annual basis. The second cogeneration plant in this forest region is Sandwell cogeneration plant, located in Mackenzie forest district, with an output of 14MW of energy. It consumes about 70,000 BDt of residue per year.  In the Southern interior forest region, the NWE Energy Corporation (EPCOR) is the biggest cogeneration plant in BC with an annual output of 60MW of power and consumes approximately 300,000 BDt of residues annually. The Purcell Power Project is located at Skookumchuck in the Rocky Mountain district with an output of 43MW of energy and it consumes approximately 215,000 BDt of residues annually. The smallest cogeneration plant in Southern interior is Riverside Forest Products Limited located in Okanagan Shuswap district. It utilizes about 100,000 BDt of residues annually and has a total output of 20MW.  In the coastal region, there is only one cogeneration plant that utilizes 100% wood residues, which is Seegen (Montenay Inc.) in Chilliwack district.  In addition, numerious boilers operate at primary mill processing plants in BC. Little information is presently available on the number, size and residue demand of these facilities.   12  Table 1.1: Large-scale combustion systems System Remarks Fixed-bed combustion (FxBC) -grate furnances -underfeed stokes   Grate furnaces are better for burning biomass fuels with moist, different particle sizes and high ash content. Usual capacity goes up to 20 MWth. Underfeed stokes represent a cheap, safe technology for small and medium scale systems up to 6 MWth Fluidized bed combustion (FBC) - bubbling (BFBC) - circulating (CFBC)  Biomass fuels are burned in a self-mixing suspension of gas and solid bed material in which air for combustion enters from below. FBC plants are better suited for large-scale applications, 30+ MWth. For smaller plants FxBC are usually more cost-effective. Direct Combustion (DC)      A mixture of fuel and primary combustion air is injected into the combustion chamber. DC is suitable for biomass fuels available in small dry particles such as wood dust.  Fuel-feeding needs particular control due to the explosion-like gasification process of the biomass. (compiled from material at www.ieabioenergy.com)   1.5  GASIFICATION   Gasification is a form of pyrolysis (Demirbas, 2001). There are two major differences between pyrolysis and gasification: (i) in pyrolysis the desired product is bio-oil, while in gasification it is producer gas. (ii) higher temperatures and pressures are involved in pyrolysis while gasification uses just high temperature in order to optimize gas production. During gasification, the amount of air supplied to the gasifier is carefully controlled so that only a small portion of the fuel burns completely. This “starved air” combustion process provides sufficient heat to pyrolyze and chemically break down the balance of the fuel into producer gas (Banerjee, 2006).  Typically, a gasification system is made up of three fundamental elements: (i) the gasifier, used to produce the combustible gas; (ii) the gas cleanup system, necessary to remove harmful compounds from the combustible gas; (iii) the energy recovery system. The system is completed with suitable sub-systems useful to control environmental impacts (air pollution, solid wastes production, wastewater) (Figure 1.3) (Belgiorno et al., 2003).  13   Figure 1.3: Schematic representation of gasification process (Lee, 2006)   1.5.1 Producer gas    During the gasification process, the main products formed include: producer gas, ash or char and oil or tar. The gas produced can be a low-Btu or medium-Btu gas, depending on the process used. For energy purposes, the major concerns of producer gas are its higher heating value (HHV), chemical composition, and possible contamination. Impurities like particulates and tars may be problematic for many producer gas applications.  The producer gas is passed through a cooling and cleaning sub-system that usually consists of a cyclone for particulate removal and a scrubber for cooling and removing the tar from the producer gas. Tar-free producer gas can be obtained in a properly designed biomass gasification process (Cao et al., 2006; Cummer and Brown, 2002). The typical composition of procucer gas is 20–22% CO, 15–18% H2, 2–4% CH4, 9–11% CO2 and 50–53% N2 (by volume) (Table 1.2) (Banerjee, 2006). BIOMASS CLEANED GAS TO PRODUCTS OR CHEMICALS GAS FILTERINGMATERIAL HANDLING OXYGEN ASH, CHAR HEATING SYNTHESIS GAS (H2, CO, CH4, H2O, CXHY) 1200° ~ 2000°F G A S IF IE R  STEAM G A S  C O N D IT IO N IN G  BIOMASS FUEL + STEAM + OXYGEN  GAS FUEL  14 Ash formed during the process by oxidation reactions, moves through the reduction zone and gets removed from the ash disposal system (grate and ash collection system). The clean gas produced can be standardized in its quality and is easier and more versatile to use than the original biomass. Medium-Btu producer gas has higher heating value than producer gas, and can be converted into methanol, a liquid fuel. Thermal efficiencies of up to about 50% can be reached by combining electric power generation with a gasifier (Demirbas, 2001).  Table 1.2: The difference between synthetic gas and producer gas Synthetic gas Producer gas A mixture of CO, H2 and N2 Mixture of CO, H2, CO2 and CH4 Product of high temperature gasification Product of low temperature gasification Main input material: biomass and oxygen/air Main input material: biomass and steam (starved air) Can be used to synthesize organic molecules such as synthetic natural gas and also liquid biofuels e.g. synthetic diesel Can be burned as fuel gas for heat or internal combustion gas engine for electricity (CHP) (Adapted from Nader and Padban, 2001 and Belgiorno et al., 2003).  1.5.2    Types of Gasification Process   Gasification can be integrated with several industrial processes, such as power generation systems. The gas can be burned directly for space heat or drying, or it can be burned in a boiler to produce steam. There are two types of gasification, direct and indirect gasification.  Direct gasification occurs when an oxidant gasification agent such as oxygen/air is used to partially oxidize the feedstock (Belgiorno et al., 2003). The oxidation reactions supply the energy needed to vaporize the volatile components of biomass, to keep the temperature of the process up ranging from 450oC to 600oC. The syngas consists of carbon monoxide, hydrogen, volatile tars, and nitrogen. The residue, about 10 percent to 25 percent of the original fuel mass, is charcoal. Direct gasification is inefficient with  15 syngas higher heating value ranging 4-7 MJ/Nm3. This inefficiency is because in the presence of nitrogen, the heating value is significantly affected.  Indirect gasification occurs in the absence of an oxidizing agent, thus an external energy source is needed. Steam is the most commonly used indirect gasification agent, because it is easily produced and increases the hydrogen content of the combustible gas. Indirect gasification occurs at temperatures of 700° to 1200°C. The charcoal residue from the pyrolysis stage reacts with oxygen, producing carbon monoxide to form producer gas. Producer gas contains 70 - 80 percent of the energy originally present in the biomass feedstock. Indirect gasification is efficient with producer gas higher heating value ranging 15-20 MJ/Nm3 (Belgiorno, et al., 2003).   1.5.3    Types of gasifier   The conversion of a feedstock into fuel gas occurs in a gasifier. There are two principal types of gasification systems: the fixed bed and fluidized bed with variation within each type.  These are the most suitable forms of gasifiers used for wood conversion. A third type, known as entrained suspension gasifier is well adapted for coal production (McKendry, 2002). Other gasifiers include pressurized gasifier, is not discussed in this paper, because they are only suitable for coal and oil gasification. Considerable research has been devoted on improving the design of gasifier reactors (Cummer and Brown, 2002).  Progress in the development of biomass-fired gas turbine technology may include integrated gasification combined-cycle (IGCC) for electricity generation. In IGCC facility, a gas-fired turbine generator produces primary power, and this technology is well documented in Finland and Sweden, where the main use of this technology is to produce medium-scale combined heat and electricity. This type of facility operates by using the waste heat from the turbine exhaust to produce high-pressure steam, which then drives a steam turbine to generate secondary power.   16 Fixed bed gasifer   The two basic types of fixed-bed gasifiers, depending on the direction of flow of the biomass and the gas are the updraft and downdraft.  Vertical fixed bed reactors (VFB) are the more competitive fixed-bed gasifiers and generally are used in small-scale energy production (<10 MWth) (Banerjee, 2006; Belgiorno et al., 2003b). The fixed bed gasifier has been the traditional process used for gasification, operated at temperatures around 1000°C. The residence time of the fuel in the gasifier can last for several hours and the gas velocity is low. The traditional fixed-bed gasifiers are suitable only for specifically sized feedstocks (3 – 5 mm), which have high enough basic density to guarantee stable fuel flow (Corella et al., 1999; Cummer and Brown, 2002; McKendry, 2002).  Updraft gasifier The updraft is a counterflow gasifier, where the biomass fuel is loaded from the top of the reaction chamber while steam and air (or oxygen) enters from the bottom of the reactor. (Bridgwater, 1994; Hamel et al., 2007).  Downdraft gasifier The downdraft gasifier is a co-flow reactor, where the biomass is fed in from the top, and the gas is introduced at the sides above the grate while the combustible gas is withdrawn under the grate. Basically, the biomass and the air move in the same direction. The gasifier requires drying the biomass fuel to a moisture content of less than 20 percent (Bridgwater, 1994; Hamel et al., 2007; McKendry, 2002).   Fluidized bed   A fluidized-bed gasifier typically contains a fixed bed of inert granular particles, usually silica sand or ceramic. Biomass fuel, reduced to a particle size of 5 – 7mm, enters at the bottom of the gasification chamber. A high velocity flow of air from below forces the fuel upward through the bed of heated particles. The thermal efficiency of a fluidized bed gasifier is about five times that of a fixed bed (Bingyan et al., 1994). This is because fluidized heated bed is isothermal and operates at a temperature around 700-900oC in the gasification zone, which gives an advantage over fixed bed gasifier. This temperature is  17 sufficient to partially burn and transforms the fuel into a liquid-like state by contact with an upward flowing gas (gasification agent). The two types of fluidized bed gasifiers are bubbling fluidized bed and circulating fluidized bed gasifiers.  Bubbling fluidized bed gasifier These gasifiers consist of a vessel with a grate at the bottom through which the preheated air is introduced, and the prepared biomass is fed into the reactor above the grate. Regulation of the bed temperature to 700–900°C is maintained by controlling the air/biomass ratio. The biomass is pyrolysed in the hot bed to form a char (Van der Drift et al., 2001, Hamel et al., 2007).  Circulating fluidized bed gasifier The bed material is circulated between the reaction vessel and a cyclone separator which allows the gasifiers to operate at elevated pressures (McKendry, 2002).   It is important for bioenergy providers interested in gasification technologies to know the advantages and disadvantages of each gasifier type in order to make sound technological decision. For example, the fluidized-bed design produces a gas with low tar content but a higher level of particulate compared with fixed-bed designs. The main advantages of both fixed bed and fluidized bed gasifiers are summarized in Table 1.3. From is table, circulating fludized bed appears to be the best gasifier with a higher number of asterisks than other competing technologies.   1.5.4 Integrated gasification combine cycle (IGCC)   IGCC technology represents an advanced configuration for converting solid feedstock- based energy production into syngas through a gas turbine. It is suitable for use in an internal combustion engine, gas turbine or other application requiring a high-quality gas. The gas turbine drives an electric generator and its exhaust gas is used to produce steam to drive a steam turbine. IGCC is one of the most efficient and environmentally friendly advanced commercial power generation technologies available.  18                 Table 1.3: Comparison of different gasification technology          Fixed bed      Fluidized bed Characteristics Updraft Downdraft Bubbling  Circulating Carbon conversion  **** **** ** **** Thermal efficiency ***** **** *** **** Cold gas efficiency ***** *** *** **** Turndown ratio *** ** **** **** Start-up facility * * *** *** Management facility **** **** ** ** Control facility ** ** **** **** Moisture feed elasticity **** ** *** *** Scale-up potential *** * *** ***** Ash feed elasticity * * **** **** Fluffy feed elasticity **** ** * *** Sintering safety * * *** ***** Mixing * * **** ***** Cost safety ***** **** ** ** Tar content * ***** ** *** Particulate content ***** *** *** ** LHV  * * * ** a. *poor, **fair, ***good, ****very good, *****excellent   (adapted from: Bridgwater, 1994; and Juniper 2000)   1.5.5    Commercial status of gasification   There are two commercial-scale gasification facilities operating within BC that consumes approximately 100% wood residues. Tolko Industry is currently using Nexterra technology to gasify woody biomass to produce steam, and electricity at Heffley plywood mill. The syngas generated displaces approximately 235,000 GJ per year of natural gas previously used at the mill to dry veneer and to produce hot water for log conditioning at the Heffley plywood mill. This is equivalent to the amount of natural gas required to heat approximately 1,900 residential homes in BC (Nexterra, 2006).  Kruger Products Limited tissue mill located in New Westminster uses direct-fired biomass gasification plant to provide process steam for the pulp and paper mill. The syngas generated at the Kruger mill displaces approximately 54% per year of the current natural gas consumption. (Kruger Products Limited, 2009).  19  1.6 PYROLYSIS   Pyrolysis is a process whereby fine, low-moisture biomass fuel particles are heated rapidly to a temperature in the range of 327o to 527oC in the absence of air (or partial conversion) resulting in a liquid pyrolytic oil, charcoal and non-condensable gases such as acetic acid, acetone and methanol (Bridgwater, 2005). Many research and development (R&D) studies have been carried out with regards to pyrolytic conversion with different biomass sources (Murwanashyaka et al., 2001; Minkova et al., 2000). Most of the studies focused on pyrolysis of lignin and cellulose containing materials (Baldock and Smernik, 2002; Beaumont and Schwob, 1984; Scott et al., 1985). Biomass pyrolysis for liquid is a promising technology (Dai et al., 2000). It has been argued that cellulose gives the highest yields at around 85-90 wt% on dry feed.  Pyrolysis produces energy fuels with high fuel-to-feed ratios, making it the most efficient process for biomass conversion method. The conversion of biomass to crude oil can have efficiency up to 70 percent for flash pyrolysis processes of the original fuel mass. However, the overall efficiency of pyrolysis is typically 55 to 75% of the original fuel mass (Bridgwater, 2005).   1.6.1    Production process for pyrolysis    For wood to pyrolyse, a temperature of 200oC is needed at the initial stage. The surface of the wood becomes dehydrated and this is known as the easily degrading zone. When temperatures of 200–260oC are attained, the wood evolves water vapor, carbon dioxide, formic acid, acetic acid, glyoxal and some carbon monoxide. The reactions to this point are mostly endothermic, the products are largely non-condensable and the wood becomes charred (Demirbas, 2001).   20 Pyrolysis actually occurs between 260o and 500oC. The reaction from now is exothermic, and unless heat is dissipated, the temperature will rise rapidly. Combustible gases, such as carbon monoxide from cleaving of the carbonyl group, methane, formaldehyde, formic acid, acetic acid, methanol and hydrogen are liberated and charcoal is formed. The primary products then react with each other before they escape the reaction zone. If the temperature continues to rise above 500oC, a layer of charcoal will be formed. This is the site of vigorous secondary reactions that are mostly endothermic, and pyrolysis is considered to be complete at temperatures of 400 – 600oC (Demirbas, 2001).   Type of pyrolysis   A number of different terminologies are used for the different types of pyrolysis. Pyrolysis depends on factors such as operating temperature, pressure, oxygen content and biomass feedstock type. In this section pyrolysis will be grouped into the fast pressurized (termed fast pyrolysis) and slow pressurized (termed slow pyrolysis) processes. Thus, all high temperature/pressure forms of pyrolysis are going to be grouped under fast pyrolysis. For this study, fast and slow pyrolysis are reviewed because, high temperatures and longer residence times increase the biomass conversion to gas and moderate temperature, while short vapour residence times are optimal for producing liquids. Fast pyrolysis is of interest currently as the liquids produced may be used as a transport fuel.   Fast pyrolysis   Fast pyrolysis seeks to convert low value woody biomass into bio-oils (major product: typically 70 wt.%); char (typically 15 wt.%) and synthetic gas (typically 15 wt.%).  These product proportions can be achieved by optimizing the process temperature and minimizing the exposure of compounds to the intermediate (lower) temperatures that favor the formation of charcoal.  Fast pyrolysis has received considerable innovation to devise reactor systems that provide the essential ingredients of high heating rates, moderate temperatures and short vapour  21 product residence times for liquids. Bridgwater (1999) also describes a variety of reactor configurations that have been investigated and tested in various regions in Europe and North America. These reactor configurations have been shown to achieve liquid yields of as high as 70-80% (w/w) based on the starting dry biomass. The critical technical challenge in every fast pyrolytic process is heat transfer to the reactor in commercial systems. The two important requirements for heat transfer in a pyrolysis reactor are: (i) to the reactor heat transfer medium (solid reactor wall in ablative reactors, gas and solid in fluid and transport bed reactors, gas in entrained flow reactors), (ii) from the heat transfer medium to the pyrolysing biomass.   Slow pyrolysis   The low temperature conversion of biomass to oil and activated char  attempts to mimic the formation of fossil fuels (Oki and Mezaki, 1998). The main chemical processes are elimination of heteroatoms like N, O, P, S and halogens whereas the C-C bonds are not cracked. The operating principle is to use low temperature (<350oC) and long reaction times (about 60 minutes) to give low oxygen content oils.   1.6.2 Products from pyrolysis   The product yield and type from a given lignocellulosic material depends on the reactor type and the pyrolysis conditions (Simitzis, 1994). Products from pyrolysis include: pyrolysis oil, char and gas. The gas produced is considered to be a byproduct and is normally recycled back into the system.  The process can be adjusted to favor charcoal, pyrolytic oil, gas or methanol production with a 95.5% fuel-to-feed efficiency (Bridgwater, 1999). If the purpose is to maximize the yield of liquid products, a low temperature, high heating rate, short gas residence time process would be required. For high char production, a low temperature, low heating rate process would be chosen. If the purpose is to maximize the yield of fuel gas resulting  22 from pyrolysis, a high temperature, low heating rate, long gas residence time process would be preferred (Table 1.4).  Table 1.4: Typical product (bio-oils, char and synthetic gas) yields (dry wood basis) obtained by different types of pyrolysis of wood Types of pyrolysis Conditions Liquid (bio-oil) Char Gas Fast Moderate temperature, around 500oC Short hot vapor residence time-1 second 75% 12% 13% Intermediate Moderate temperature, around 500oC, Moderate hot vapor residence time- 10- 20 seconds 50% 20% 30% Slow (Carbonization) Low temperature, around 400oC, Very long solids residence time (no time specified) 30% 35% 35% Gasification High temperature, around 800oC, Long vapor residence time (no time specified) 5% 10% 85% (Pyne, 2007)   Pyrolysis oil   The pyrolysis liquid is referred to by many names including pyrolysis oil, bio-oil, bio- crude-oil, bio-fuel-oil, wood liquids, wood oil, liquid smoke, wood distillates, pyroligneous tar, pyroligneous acid, and liquid wood. Crude pyrolysis bio-oil is dark brown and approximates to biomass in elemental composition. Bio-oils are multi- component mixtures comprised of different size molecules derived primarily from depolymerization and fragmentation reactions of three key biomass building blocks: cellulose, hemicellulose, and lignin (Bridgwater & Peacocke, 2000). The liquid is thus formed by rapidly quenching, and is composed of a very complex mixture of oxygenated hydrocarbons with an appreciable proportion of water and char.  The water forms a stable single phase mixture, ranging from about 15% w/w to an upper limit of about 30-50% w/w water, depending on how it was produced and subsequently collected (Chiaramonti et al., 2005). The bio-oil contains several hundred different chemicals in widely varying proportions, ranging from formaldehyde and acetic acid to  23 complex high molecular weight phenols, anhydrosugars and other oligosaccharides. Bio- oil liquid fuel is usually the preferred end product of pyrolysis and the quality control during liquid production is very critical. Intense studies have been undertaken on pyrolytic oil with regards to the methods of production, quality follow-up and characterization, but the meaning of flash point is not yet clear because it does not correlate with ignition properties like in the case with mineral oils (Oasmaa and Dietrich, 1999; Beaumont and Schwab, 1984; Yaman, 2004; Zanzi et al., 1996).  The pyrolytic oil can be used to power engines and turbines. Utilization as feedstock for refineries is also being considered. Analysis also suggests that pyrolytic oils have a significant value as chemicals for use in making other products (Demirbas, 1998). Some of the problems associated with the oil conversion process and use need to be overcome. These include poor thermal stability and corrosivity of the oil. Upgrading by lowering the oxygen content and removing alkalis by means of hydrogenation and catalytic cracking of the oil may be required for certain applications (Yaman, 2004).   Charcoal Production   For char to be produced during pyrolysis, it requires a temperature range of 200–260oC. In this temperature range, the reaction is endothermic. The wood evolves water vapor, carbon dioxide, formic acid, acetic acid glyoxal and some carbon monoxide. The products are largely non-condensable and the wood becomes charred along with other solid products such as ash and unchanged biomass material (Demirbas, 2001).  Usually, char has been considered to be the least desirable product from pyrolysis, however recent interest in the benefits of adding biochar to soil has lead to greater focus on char as a product (Woolf, 2008). The critical issue is to bring the reacting biomass particle to the optimum process temperature and minimize its exposure to the intermediate (lower) temperatures that favour formation of charcoal. One way this objective can be achieved is by using small particles, as in the case of fluidised bed.   24 The effects of some pyrolytic conditions, such as reactor temperature, heating rate, porosity, initial particle size and initial temperature, on char yields and conversion times have been investigated. High temperature and fast heating rates were determined to decrease the yield of char. Three pyrolysis regimes were identified: (1) initial heating, (2) primary reaction at the effective pyrolysis temperature, and (3) final heating. The relative durations of each regime are independent of the reaction temperature and are approximately 20%, 60% and 20% of the total conversion time (Ahuja et al., 1996).   1.6.3 Commercial status of pyrolysis   Fast pyrolysis productions of liquids have gained much attention but the least commercially developed of all thermochemical processes. There are no commercial plants that pyrolyze biomass in Canada. However, Dynamotive Energy Systems Corporation is the most researched and are currently operating on a pilot scale. Dynamotive employ fast pyrolysis technology to pyrolyze biomass to produce a primary liquid fuel and BioOil which is then hydro-reformed and utilized in blends with hydrocarbon fuels, or further upgraded to transportation grade liquid hydrocarbon fuels (gasoline/diesel) (IEA, 2007) (Figure 1.4). There are several challenges such as the compititiveness and uncertainty on both the production and utilization of this technology that has to be overcome on a commercial scale (Chiaramonti et al., 2005).    Figure 1.4: Conceptual fast pyrolysis process (IEA, 2007)  25 1.7 ETHANOL PRODUCTION   Fuel ethanol is already consumed in large quantities in Brazil and the United States. However, at present ethanol is largely produced from agricultural products such as sugarcane or corn, and the future supply of these commodities is not assured considering the increasing food demand (Wheals et al., 1999). Under these circumstances, the development of ethanol production technologies from woody biomass resources has the potential to be a valuable substitute for, or complement to, gasoline.  The conversion of lignocellulose to ethanol consists of four major unit operations: pretreatment, hydrolysis, fermentation, and product separation/purification. However, given the heterogeneity in feedstock and the influence of different process conditions on enzymes makes the biomass-to-ethanol process complex. Thus the main features of the different ethanol processes comprise the same main components: hydrolysis of the hemicellulose and the cellulose to monomer i.e. sugars; fermentation and product recovery; and finally concentration by distillation.  The main difference between the process alternatives is the hydrolysis steps, which can be performed by dilute acid, concentrated acid or enzymatically. Some of the process steps are more or less the same, independent of the hydrolysis method used (Figure 1.5).   1.7.1 Acid hydrolysis   The processes of acid hydrolysis from cellulosic materials have been studied extensively starting during World War II in Germany, and the further modified Scholler processes (0.4% H2SO4) in the former Soviet Union, Japan and Brazil (Keller, 1996). Many different acid hydrolysis approaches have been designed and tested, and some processes have been carried out at an industrial scale (Katzen, 1997). Acid hydrolysis can be performed with several types of acids, including sulphurous, sulphuric, hydrochloric, hydrofluoric, phosphoric, nitric and formic acid. These acids may be either concentrated or diluted.  26    Figure 1.5:  Production of ethanol from lignocellulosic materials (Galbe and Zacchi, 2002)   Concentrated acid hydrolysis The have been a widespread documentation on use of concentrated acid hydrolysis on cellulosic material (Katzen, 1997, Jones and Semrau, 1984, Wyman, 1996, and Larsson et al., 1999a). The overall goal of processes involving concentrated acids is to attain a high yield of soluble sugars at lower operating temperature and pressure while minimizing the breakdown of hemicellulose sugars into decomposition products.  The acid concentration used in concentrated acid hydrolysis process is in the range of 10–30% (Broder et al. 1995). The concentrated acid hydrolysis involves longer retention times and results in higher ethanol yields than the dilute acid hydrolysis process (Broder et al., 1995). The advantages of the concentrated acid process are that the reaction is fast and is carried out at lower temperatures and pressures than those using dilute acid. These advantages result in less unwanted degradation products. The traditional disadvantages have been Concentrated acid hydrolysis Dilute acid hydrolysis Pretreatment Recovery of acid Enzymatic hydrolysis Enzyme production Simultaneous saccharification and fermentation (SSF) Fermentation Distillation           Ethanol Lignocellulose  27 high costs of construction due to the concentrated acid, multiple process steps, and higher operating costs due to acid losses and high waste levels. Also the high acid concentration used causes problems associated with equipment corrosion and energy demanding acid recovery (Jones and Semrau, 1984). Furthermore, when sulphuric acid is used the neutralization process produces large amounts of gypsum (Keller, 1996). However, the process has attracted some new interest due to novel economic methods for acid recovery proposed by the companies Masada Resource Group (Birmingham, Ala), Arkenol (Mission Viejo, Calif.) and APACE (New South Wales, Australia). Dilute acid hydrolysis The main advantage of dilute acid hydrolysis is the relatively low acid consumption. Hemicellulose is readily hydrolyzed by dilute acids under moderate conditions, but much more extreme conditions are needed for hydrolysis of cellulose. Extreme conditions such as high temperatures are required to achieve acceptable rates of conversion of cellulose to glucose, and high temperatures also increase the rates of hemicellulose sugar decomposition and equipment corrosion (Jones and Semrau, 1984).  Sugar degradation products can also cause inhibition in the subsequent fermentation stage (Larsson et al., 1999a). The maximum yield of glucose is obtained at high temperatures (160oC), pressures (~10 atm), and short residence times (not specified), but even under these conditions the glucose yield is only between 50% and 60% of the theoretical value (Barrier and Bulls, 1992; Wyman, 1996). The acid concentration in the dilute acid- hydrolysis process is in the range of 2–5% (Taherzadeh et al., 1997). The main advantage of the low concentration acid in the hydrolysis process is that acid recovery may not be required and there will be no significant losses of acid. H2SO4 has been most widely studied, apparently because it is inexpensive and effective (Torget et al., 1990, 1991, 1996; Nguyen et al., 1998, 1999, 2000; Tengborg et al., 1998).   28 1.7.2 Steam explosion pretreatment   Steam explosion has come to be considered one of the most effective pretreatments, characterized by low use of chemicals and low energy consumption. Extensive research has been conducted on steam explosion (Brownell and Saddler, 1984; Avellar and Glasser, 1998; Glasser and Wright, 1998; Heitz et al., 1991; Abatzoglou et al., 1992; Ramos et al., 1992; Saddler et al., 1993; Hsu, 1996; McMillan, 1994). Uncatalyzed steam explosion refers to a pretreatment technique in which lignocellulosic biomass is placed in high-pressure stainless steel tube and exposed to steam under pressures ranging from 250-650 psi at 200 to 240oC for up to 20 minutes without addition of any chemicals. The sudden pressure releases causes an explosive decompression in order to physically and chemically modify the biomasss: where the hemicellulose is hydrolyzed, lignin is solubilized, and the accessibility of the cellulose to cellulose enzymes is improved. The biomass/steam mixture is held for a period of time to promote further hemicellulose hydrolysis.  1.7.3 Organosolv pretreatment   Organosolv is the fractionation of lignocellulosic materials using alcohol and water solvents. Lignol’s process involves the solution of all types of lignocellulose (softwood, hardwood, and agricultural residues) using 40-60% ethanol (w/w) as pulping liquor and sulphuric acid as catalyst. The reaction is carried out in a range of processing conditions (cooking temperature, 185-198oC; cooking time 30-60 minutes; liquor pH, 2.0-3.4; liquor wood ratio: 7-10:1. The feedstock needs to contain moisture and be hammer-milled. The pulps were separated from the pulping liquor and washed with aqueous 70% (w/w) ethanol. The pulping liquor and ethanol washes were combined and concentrated for further analysis. An organosolv lignin fraction was obtained as a precipitate by dilution of the concentrated pulping liquor with water.  29 There are some unique aspects involved in organosolv pretreatment process in that: (i) a wide variety of feedstocks including hardwoods, softwoods agricultural residues, and grain can be processed with the same conditions; (ii) the hydrolysis can process the hemicellulose and the cellulose at the same time without any significant degradation of the pentose sugars resulting in high yields; (ii) The process produces a concentrated sugar solution reducing recovery costs. Although many biological, chemical, and physical methods have been tried over the years, pretreatment advances are still needed for overall costs to become competitive with conventional commodity fuels and chemicals (Wyman, 1996). It should be pointed out that this process can be combined simultaneously to achieve a better yield.   1.7.4 Enzymatic hydrolysis   The enzymatic hydrolysis process can be designed in various ways. The steps following pretreatment, i.e. hydrolysis and fermentation, can be run as separate hydrolysis and fermentation (SHF) or as simultaneous saccharification and fermentation (SSF). Whether SHF or SSF is used to produce ethanol, two of the most critical issues are the performance and production cost of the cellulases. Today, cellulases are produced by a small number of large enzyme companies, e.g. Novozymes and Genencor, in small quantities for applications other than cellulose hydrolysis.  Enzymatic hydrolysis requires feedstock pretreatment, enzyme production, and enzyme recovery, which may make this option economically unfeasible. Problems associated with the hydrolysis process need more improvements in order to reduce the operating cost for an ethanol-producing plant that uses wood wastes as feedstock.  The advantage of SHF is the ability to carry out each step under optimal conditions, i.e. enzymatic hydrolysis at 45-50°C and fermentation at about 30°C. It is also possible to run fermentation in continuous mode with cell recycling. The major drawback of SHF is that the sugars released inhibit the enzymes during hydrolysis. In SSF, the glucose produced  30 is immediately consumed by the fermenting microorganism, e.g. Saccharomyces cerevisiae (ordinary baker’s yeast), which avoids end-product inhibition of glucosidase.   1.7.5 Commercial status of ethanol production from lignocellulose   Second generation biofuel technologies have been developed and some expectations have arisen that, in the near future, these biofuels will reach full commercialization (Sims, et al. 2009). Lignol is a Canadian company undertaking the development of biorefineries for the production of fuel-grade ethanol and other biochemical co-products from cellulosic biomass feedstocks. A thorough understanding of fundamental issues related to development and implementation of large scale-independent processes needs to be established in order to lower overall production cost for ethanol.  For second generation bioethanol production to reach commercial scale there is (i) a need for continued supply of reliable, secure access, inexpensive and high quality feedstock (ii) synergism between science and engineering disciplines along with participation by industry is crucial to the successful implementation of biorefineries.   1.7.6 Summary   Thermochemical conversion technologies convert biomass to electricity, chemicals, steam, and bio-oil; using conversion technologies such as combustion, gasification and pyrolysis. Combustion processes burn biomass with excess supply of oxygen. This process produces steam to turn a turbine to generate electricity. Gasification processes burn biomass with a limited supply of oxygen. This process produces a mixture of carbon monoxide and hydrogen, known as syngas. Pyrolysis burn biomass in the absence of oxygen to produce bio-oil.  Biochemical conversion technologies involve three basic steps: (1) converting biomass to sugar using one or a combination of pretreatments to produce sugars; (2) fermenting  31 these sugars using biocatalysts (microorganisms including yeast and bacteria); and (3) processing the fermentation product to yield fuel-grade ethanol.  The products from both thermochemical and biochemical conversion technologies heavily depend on the nature of the feedtsocks. The following section (section. 1.7) analyzes the optimum characteristics required for these feedstocks to suit the available technologies.   1.8    RESIDUE HANDLING FOR TECHNOLOGICAL PROCESSES   The utilization of biomass as a raw material for bioenergy production requires an understanding of the basic feedstock characteristics to the various technological processes. As presented in the previous section, technological aspects are important for bioenergy production. However, the characteristics of the feedstocks also exert an important influence on the technological platforms and this section explores these influences. The choice of converting biomass into bioenergy depends on the nature of feedstock and the degree of homogeneity of the biomass. For example, residue type causes major operational and maintenance problems during thermochemical processes and with knowledge, these problems can be reduced or avoided. Since biomass is a very heterogeneous and chemically complex renewable resource, it is imperative to examine the feedstock to meet the minimum required standards for various bioenergy and bioproducts industries (Table 1.5).  32  Table 1.5: Required feedstock characteristics for various thermochemical processes Gasification: Pyrolysis: Analysis Type Combustion:  Fixed Bed  Fluidized bed Ablative Circulating fluid bed Fluidized bed HHV 17 – 21MJ/kg 17 – 21MJ/kg 17 – 21MJ/kg 17 – 21MJ/kg 17 – 21MJ/kg 17 – 21MJ/kg Ash content >30% <6%e <25%e Unknown Unknown Unknown Moisture content 10 – 25%a  10 – 15%b 15 – 65 %b 0 – 10%d 0 – 10%d 0 – 10%d Basic density Blend residue for uniform density Blend residue for uniform density Blend residue for uniform density Blend residue for uniform density Blend residue for uniform density Blend residue for uniform density Particle size about 2mma However it is determined by limited grate size and feed opening  6 – 100mm  6 – 50mm Very large particlesc 0 - 6mmc 0 - 2mmc (aOregon.gov, 2007, bBridgewater, 1994; cPeacocke, 1994, dBridgwater, 1999, eQuaak, et al. 1998)   1.8.1 Residue handling for combustion technology   Differences in biomass density can influence the energy content of the fuel used for combustion processes (Li and Liu, 2000). In terms of biomass handling in combustion unit, densification therefore becomes an important parameter. Thus densified materials, such as pellets and briquettes, are more fuel efficient raw materials for combustion boilers (Heschel, 1999; Olsson, 2003). Their greater density also improves their ease of handling and they are more readily transported. In combustion technology, fuel pretreatment is generally used to reduce biomass to a particle size of less than 2 millimeters and a moisture content of less than 25 percent (Oregon. gov, 2007).   33  1.8.2 Biomass handling for gasification technology   The largest impediments facing the application of biomass gasification for energy production are the inabilities of the current ancillary systems to allow for economical production of a clean producer gas, free of contaminants. Ancillary systems are operations other than the actual gasification process and generally fall into two categories: biomass preparation and feeding of the feedstock (prior to gasification) and gas-cleaning systems (subsequent to gasification) (Cummer and Brown, 2002).  Pretreatment of the biomass feedstock is generally the first step in gasification. Pretreatment involves drying, pulverizing and screening. Optimal gasification requires dry fuels of uniform size, with moisture content no higher than 20% (Van der Drift et al., 2001).  However, the degree of pretreatment of biomass feedstock is dependent on the gasification technology used (McKendry, 2002). Thus it is important to know the specifications of the various gasifiers to be able to characterize the available residues to suit the various gasifiers. Fluidized-bed gasifiers can handle a wide range of biomass fuels, especially biomass with high ash content (Quaak et al., 1998).  Moisture content, particle size, and drying The biomass moisture content should be below 20% before gasification. Moist fuel with more than 30 percent moisture content is likely to clog the feeding system and it can lower the heating value of the producer gas, and thus makes ignition difficult. High moisture content reduces the temperature achieved in the oxidation zone, resulting in the incomplete cracking of the hydrocarbons released from the pyrolysis zone (McKendry, 2002).  The drying and sizing sequence depends on the drying and sizing equipment used. Some dryers may require the feedstock to be sized prior to drying, while some sizing equipment may require that the feedstock be dry. These constraints may require stages of drying and sizing in order to reach the desired size and moisture content.   34 Typically, feed particle sizes are in the range 20–80 mm (McKendry, 2002). The two most common devices for comminuting biomass to sizes appropriate for gasification are knife chippers and hammermills. Chippers are high-speed rotary devices, operating at speeds up to 1800 rpm, and are better suited for comminuting wood. While hammermills are also rotary devices, where a large metal hammers is used to crush the biomass. As biomass falls into the hammermill, larger pieces are crushed by the spinning hammers (Cummer and Brown, 2002). Cummer and Brown, (2002) carried out a well documented study on commercial dryers. In their opinion, the selection of an appropriate dryer depends on several factors such as the size of the particle to be dried, the type of fuel (whether woody or herbaceous), and the necessary drying capacity of the system are all important considerations.  The various dryers include: perforated floor bin dryer, conveyor dryer, rotary cascade dryer, fluid bed steam dryer, and pneumatic conveying steam dryer.   1.8.3 Biomass handling for pyrolysis technology   Most of the studies reported on pyrolysis technology pertain to woody biomass, but the influence of biomass characteristics on product properties remains relatively uninvestigated (Raveendran, 1996). In order to achieve favorable products from pyrolysis, the handling and the pretreatment of residues becomes imperative. Problems that need to be overcome are: (i) the selection of proper feedstock pre-treatment systems in relation to specific low cost reactor technology; (ii) the appropriate handling of the various biomass residues; (iii) a correlation between feedstocks (properties and characteristics) used and the products from pyrolysis.  Influence of particle size and moisture content Beaumont and Schwob (1984), investigated the influence of physical and chemical parameters of wood pyrolysis. In their study, coarser particles yielded more char and gas, and less oil than smaller particles. Also, the coarser the particle, the slower the heating, and pyrolysis was performed on average at a lower temperature.   35 In the case of moisture content, increased moisture promotes charring and lowers oil yield. The quantitative composition of oil remains unchanged. However, qualitative shifts are observed. The amount of water due to the thermal degradation is dependent on the moisture; more water is formed by the pyrolysis of dry wood. Higher water contents also cause a decrease in the yields of the organic products, especially methanol, formic and propionic acids, and hydroxyl-1-acetones.   1.8.4    Residue handling for ethanol production   Softwoods are generally recognized as being much more refractory than hardwoods or agricultural residues. This is due to the fact that softwoods have a more rigid structure and contain more lignin. Also, the content of acetylated groups is lower than in hardwoods and autohydrolysis cannot occur to the same extent. Before the cellulose from wood can be used as a substrate for ethanol production, it must be separated from the confines of its protective hemicellulose-lignin wrapper and the effective surface area available to cellulose enzymes must be increased. Several method of pretreatment, including biological, chemical, and mechanical procedures, have been investigated with vary degree of success.  Size reduction Size reduction adds value to the raw materials; however, this process has high energy requirements and can significantly add to the total process cost. Accordingly, considerable attention should be given to the choice of a chipping or grinding process such that the least expensive technology that yields a wood particle compatible with subsequent treatment is chosen. Mechanochemical pretreatment is most interested in residue size reduction. During this pretreatment, the wood is ground to approximately 20 μm, and it is thought to increase enzyme saccharification significantly (Table 1.6). Detailed analysis has shown that mechanochemical pretreatment destroys the strong network of woody components, which enhances the mobility of cellulose molecule bundles and lets enzymes approach more easily, therefore allowing accelerated enzymes saccharification.  36 Substrate particle size is considered a major limiting factor in the enzyme hydrolysis of lignocellulose. A limiting particle size carries with it the implication of surface area and not just the concept of a solid particle with specific dimensions. Particle size deals more with the implication of pores throughout the substrate particle whereby celluloses may or may not gain access to additional B-1, 4-glucosidic bonds. Table 1.6: Required feedstock characteristics for ethanol production Feedstock Steam  and enzyme hydrolysis Concentrated acid hydrolysis Acid catalyzed Organosolv Particle size Not specified Relatively small size particles Hammer-milled (very small) MC Not specified 10 - 20% 20- 30% Basic density Not specified Not specified Not specified Feedstock Suitable for agricultural residues and hardwood, less suitable for softwood Very clean, suitable softwood Wide variety of feedstock can be processed Sugar type Hexose and pentose  Hexose and pentose Hexose and pentose Total ethanol produced  per year 200 million liters  45 million Lab stage (Environment Canada, 1999)     37 1.9 CONCLUSION   Various research and development is ongoing in order to optimize both thermochemical and biological bioenergy technologies. These technological conversion schemes produce a variety of bioenergy products. These products depend on the raw material characteristics and the type of energy desired.  The key ingredients for improvements of bioenergy from woody biomass are technological innovations, residues handling and total residue cost (availability and transportation cost). In this study, both themochemical and biochemical technological platforms were reviewed.  A themochemical processes offer the key advantage of directly producing products such as electricity, bio-oils and steam using energy recovery processes such as gas turbine, engine and biolers. In the thermochemical process, gasification and pyrolysis are two most widely studied thermochemical conversion technologies while combustion is the most widely used technology. The main advantage of pyrolysis over gasification and combustion is that it produces a wider range of products. Each form of thermochemical process requires a defined residue specification to optimize yield. Commercially viable thermochemical platforms are well established in BC with combustion technology being the most commercially applicable of all technologies in the province. Both gasification and pyrolysis technology are suitable for biomass conversion but needs to overcome some of the technological hurdles to be commercially successful.  The biochemical conversion platforms typically need to overcome technical and economic challenges in order to become commercially viable. Concentrations of soluble substances are produced during ethanol pretreatment step. The technical challenge lies in scaling up not only the various pretreatment processes to increase the amount of sugar yield, but also on the fermentability of the concentrated solution. The economic challenges lie in reducing the overall processing cost and sourcing cheaper residue feedstocks.  38 1.10 REFERENCES  Abatzoglou, E. Chornet, Belkacemi, I, and Overend, R. Phenomenological kinetics of complex systems: the development of a generalized severity parameter and its application to lignocellulosics fractionation, Chemical Engineering Science 47 (1992) (5), pp. 1109–1122. Ahuja, P. Kumar, S. and Singh, P.C. (1996) A model for primary and heterogeneous secondary reactions of wood pyrolysis. Chem. Eng. Tech. 19, pp. 272–282 Avellar, B.K. and Glasser, W.G. (1998) Steam-assisted biomass fractionation I: process considerations and economic evaluation, Biomass and Bioenergy 14 (3), pp. 205– 218. Baldock, J. A., and Smernik, R. J. (2002). Chemical composition and bioavailability of thermally altered pinus resinosa (red pine) wood. Organic Geochemistry, 33(9), 1093-1109. Banerjee, R. (2006). Comparison of options for distributed generation in india. Energy Policy, 34(1), 101-111. Barrier, J.W., and Bulls M.M. (1992). Feedstock availability of biomass and wastes. In: Rowell RM, Schultz TO, Narayan R, editors. Proceedings of the ACS Symposium: Emerging Technologies for Materials and Chemicals from Biomass, American Chemical Society, Washington, DC, p. 410–21. BC MoF (2006).  Major Primary Timber Processing Facilities in British Columbia (MPTPFBC) 2005.  Economics and Trade Branch. Ministry of Forests & Range Victoria, B.C. Beaumont, O., and Schwob, Y. (1984). Influence of physical and chemical parameters on wood pyrolysis. Ind. Eng. Chem. Res. 23, 637. Belgiorno, V., De Feo, G., Della Rocca, C., and Napoli, R. M. A. (2003). Energy from gasification of solid wastes. Waste Management, 23(1), 1-15.  39 Bingyan, X., Zengfan, L., Chungzhi, W., Haitao, H., Xiguang, Z., (1994). Circulating Fluidized Bed Gasifier for Biomass. Integrated Energy Systems in China. The cold Northeastern Region Experience FAO. Bradley, D., (2006). Canada biomass – bioenergy report. Climate Change Solutions. National Team Leader – IEA Bioenergy, Task 40 – Biotrade. Bridgwater, A.V., (1994). Catalysis in thermal biomass conversion. Applied Catalysis A: General, 116(1-2), 5-47. Bridgwater, A. V., (1999). Principles and practice of biomass fast pyrolysis processes for liquids. Journal of Analytical and Applied Pyrolysis, 51(1-2), 3-22. Bridgwater, A.V., and Peacocke, G.V.C. (2000). Fast pyrolysis processes for biomass. Renewable and Sustainable Energy Reviews, 4(1), 1-73. Bridgwater, A.V., (2005). Fast pyrolysis of biomass: A handbook volume 3. Edited by Bridgwater, CPL Press 2005   ISBN 1872691927. Broder, J.D., Barrier, J.W. Lee K.P. and Bulls, M.M. (1995)  Biofuels system economics. World Resources Review 7 4, pp. 560–569. Brownell, H.H., and Saddler, J. N. (1984) Steam explosion pretreatment for enzymatic hydrolysis, Biotechnology and Bioengineering Symposium 14 pp. 55–68. Cao, Y. Wang, Y., Riley, J., and Pan, W. (2006). A novel biomass air gasification process for producing tar-free higher heating value fuel gas. Fuel Processing Technology, Volume: 87 Issue: 4 Pages: 343-353 Chiaramonti, D., Oasmaa, A., and Solantausta, Y. (2005). Power generation using fast pyrolysis liquids from biomass. Renewable and Sustainable Energy Reviews, 11(6), 1056-1086. Cozzani, V., Nicolella, C., Rovatti, M., and Tognotti, K. (1997). Influence of gas-phase reactions on the product yields obtained in the pyrolysis of polyethylene. Ind. Eng. Chem., 36 (2), pp 342–348.  40 Corella, J., Orío, A., and Toledo, J. (1999). Biomass Gasification with Air in a Fluidized Bed:  Exhaustive Tar Elimination with Commercial Steam Reforming Catalysts. American Chemical Society. Energy Fuels, 13 (3), pp 702–709 Cummer, K.R., and Brown, R.C. (2002). Ancillary equipment for biomass gasification. Biomass and Bioenergy, 23(2), 113-128. Dai, X.W., Wu, C.Z.. Li, H.B., and Chen, Y. (2000). The fast pyrolysis of biomass in CFB reactor. Energy Fuels 14, pp. 552–557 Demirbas, A. (1998). Yields of oil products from thermochemical biomass conversion processes. Energy Conversion and Management, 39(7), 685-690. Demirbas, A. (2000). Mechanisms of liquefaction and pyrolysis reactions of biomass. Energy Conversion and Management, 41(6), 633-646. Demirbas, A. (2001). Biomass resource facilities and biomass conversion processing for fuels and chemicals. Energy Conversion and Management, 42(11), 1357-1378. EERE, (c2000). U.S. DOE Energy Efficiency and Renewable Energy. Overview of biomass technologies. Retrieved from http://www1.eere.energy.gov/ba/pba/pdfs/bio_overview.pdf Verified April, 03th, 2009. Environment Canada, (1999). The "Wood-Ethanol Report" Retrieved from http://www.springerlink.com/content/94bkt3ntvflxgyyq/fulltext.pdf Verified April, 2009. FAO Forestry Department (2004): Global Wood Energy Information, i-WEIS 2004, Update and upgrade of the interactive Wood Energy Information System, Wood Energy Programme Galbe, M. and Zacchi. (2002). A review of the production of ethanol from softwood. Appl Microbiol Biotechnol 59:618-628  41 Glasser, W.G. and Wright, R.S. (1998) Steam-assisted biomass fractionation II: fractionation behavior of various biomass resources, Biomass and Bioenergy 14 (3), pp. 219–235 Hamel, S., Hasselbach, H., Weil, S., & Krumm, W. (2007). Autothermal two-stage gasification of low-density waste-derived fuels. Energy, 32(2), 95-107. He, B., Armando G., McDonald. (c2000) Thermochemical Processes for Biomass Conversion. Biorefinery - http://www.uidaho.edu/bhe/biorefinery/. 4-9. Muffle Furnace. Retort. Gas Collection Bottle. Retrieved from: www.webpages.uidaho.edu/~bhe/biorefinery/4.pdf Verified April, 2009 Heitz, M., Capek-Menard, E., Koeberle, P.G., Gagne, J., Chornet, E., Overend, R.P., Taylor, J.D., and Yu, E. (1991) Fractionation of Populus tremuloides in the pilot plant scale: optimization of steam pretreatment conditions using STAKE II technology, Bioresource Technology 35, pp. 23–32. Heschel, W., Rweyemamu, L., Scheibner, T., and Meyer (1999). Abatement of emissions in small-scale combustors through utilization of blended pellet fuels. [Amsterdam]: Elsevier Scientific Pub. Co. Fuel Processing Technology, Vol 61, pp223 – 242. Hsu, T.A. (1996) Pretreatment of Biomass In: C.E. Wyman, Editors, Handbook on Bioethanol, Production and Utilization, Taylor & Francis, Washington, DC. International Atomic Energy Agency (2006). IAEA.org: Energy and Environment Data Reference Bank (EEDRB) Retrieved from: https://www.cia.gov/library/publications/the-world-factbook/geos/ca.html. Verified June, 2009. International Energy Agency (IEA), (2007) Task 34: Bioenergy Research Group, Aston University. Retrieved from: http://www.dynamotive.com/assets/articles/2007/Task_34_Booklet.pdf, Verified June, 2009. Jones, J.L., and Semrau, K.T. (1984) Wood hydrolysis for ethanol production - previous experience and the economics of selected processes. Biomass 5:109-135  42 Juniper, 2000. Pyrolysis and Gasification of Waste. Worldwide Technology & Business Review. Juniper Consultancy Services Ltd. Katzen, R.A. (1997) 60 year journey through bioconversion of biomass to ethanol. In: Ramos LP (ed) Proceedings of the Fifth Brazilian symposium on the chemistry of lignins and other wood components, vol 6. Curitiba, PR, Brazil, pp 334-339 Keller, F.A. (1996) Integrated bioprocess development for bioethanol production. In: Wyman CE (ed) Handbook on bioethanol: production and utilization. Taylor & Francis, Bristol, Pa., pp 351-379 Kruger Products Limited – Gasification systems (2009) Retrieved from: http://www.nexterra.ca/PDF/Project_Profile_Kruger_20100111.pdf Verified June, 2009 Larsson, S, Palmqvist E, Hahn-Hägerdal B, Tengborg C, Stenberg K, Zacchi G, Nilvebrant, N-O. (1999a) The generation of fermentation inhibitors during dilute acid hydrolysis of softwood. Enzyme Microb Technol 24:151-159 Lee, A.W. (2006) Powering Corn Ethanol Production with Biomass Energy: Environmental and Economic Expectations. Chippewa Valley Ethanol Company 21st Annual Conference on the Environment: Addressing Current Environmental Needs Li, Y. and Liu, H. (2000) High-pressure densification of wood residues to form an upgraded fuel Biomass and Bioenergy. Volume 19, Number 3, September 2000, pp. 177-186 (10) Elsevier. McCloy, B. and Associates. (2004) Estimated production, consumption, surplus mill wood residue in Canada – 2004. Natural Resources Canada and Canadian Forest Service (NRCan/CFS) 60p McKendry, P. (2002). Energy production from biomass (part 1): Overview of biomass. Barking, Essex, England: Elsevier Applied Science McKendry, P. (2002). Energy production from biomass (part 3): Gasification technologies. Bioresource Technology, 83(1), 55-63  43 McMillan, J.D., Himmel, M. E., Baker, J.O., and Overend, R.P. (1994). Pretreatment of lignocellulosic biomass Editors, Enzymatic Conversion of Biomass for Fuels Production, ACS Symposium Series vol. 566, ACS, Washington, DC, pp. 292–324 Minkova, V.,  Marinov, S.P.,  Zanzi, R.,  Bjornbom, E., Budinova, T., Stefanova, M., (2000). Thermochemical treatment of biomass in a flow of steam or in a mixture of steam and carbon dioxide. Fuel Process. Technol. 62, pp. 45–52 Murwanashyaka, J.N. Pakdel, H. and Roy, C. (2001). Step-wise and one-step vacuum pyrolysis of birch-derived biomass to monitor the evolution of phenols. J. Anal. Appl. Pyrol. 60, pp. 219–231 Nader, A. and Padban, N. (2001). “PFB air gasification of biomass, Investigation of product formation and problematic issues related to ammonia, tar and alkaline”. 5th Bioenergy Conferences of America, Orlando, Florida, U.S. Nexterra – Tolko Gasifier (2006). Retrieved from http://www.nexterra.ca/industry/tolko.cfm Verified March, 10th, 2009 Nguyen, Q.A., Tucker M.P., Boynton, B.L., Keller F.A., Schell, D.J. (1998). Dilute acid pretreatment of softwoods. Appl Biochem Biotechnol 70-72:77-87 Oasmaa, A., and Dietrich, M., (1999). Characteristics and analysis. Retrieved from http://www.pyne2005.inter-base.net/docs/WP2A%20report.pdf Verified April, 09th, 2009 Oki, S. and Mezaki, R. (1998). Investigation of the rate-controlling step of the water gas shift reaction with use of various isotopic tracers. Chemical Engineering Department New York. American Chemical Society. 23pp. Olsson, M., Kjallstrand, J., and Petersson, G. (2003). Specific chimney emissions and biofuel characteristics of softwood pellets for residential heating in sweden. Oxford: Pergamon. Biomass and Bioenergy, Volume 24, Number 1, pp. 51-57(7)  Oregon. (2007). Biomass energy hompage: biomass energy technology.       http://www.oregon.gov/ENERGY/RENEW/Biomass/bioenergy.shtml. Verified June       09th, 2009  44 Peacocke, G.V.C., (1994). Effect of reactor configuration on the yields and structures of pine-wood derived pyrolysis liquids: A comparison between ablative and wire-mesh pyrolysis. Biomass and Bioenergy Vol. 7, 155-167. Pritchard, D. (2005) biomass fired micro turbine for small scale power generation. Final report on DTI NRE project (contract no: B/T1/00790/00/00) Talbott’s Heating Ltd Catalysis in thermal biomass conversion. Pyne - International Energy Agency (IEA) Bioenergy, (2007) Task 34: Pyrolysis principles. Retrieved from: http://www.pyne.co.uk/?_id=76 Verified June, 2009 Quaak, P., Knoef, H., Stassen, E. H. (1998). Energy from Biomass: A Review of Combustion and Gasification Technologies. Edition: Published by World Bank Publications, ISBN 0821343351,9780821343357. 78 pages Ramos, L.P., Breuil, C. and Saddler, J.N. (1992) Comparison of steam pretreatment of eucalyptus, aspen, and spruce wood chips and their enzymatic hydrolysis, Applied Biochemistry and Biotechnology, 34/35, pp. 37–48. Raveendran, K. (1995) Influence of mineral matter on biomass pyrolysis characteristics. London: Volume 74, Issue 12, Pages 1812-1822 Butterworths Scientific Publications. Raveendran, K. (1996) Pyrolysis characteristics of biomass and biomass components Volume 37, Number 5, September 1996 , pp. 360-360(1) Elsevier Rosillo-Callé, F., Groot, D.P., Hemstock, L.S. and Woods, J. (2007) The biomass assessment handbook. Bioenergy for a sustainable environment. Earth Scan. ISBN- 10: 1-84407-285-1 Saddler, J.N., Ramos, L.P., and Breuil, C. (1993) Steam pretreatment of lignocellulosic residues In: J.N. Saddler, Editors, Bioconversion of Forest and Agricultural Plant Wastes, C.A.B. International, Wallingford, UK, pp. 73–92. Scott, D.S., Piskorz, J., Radlein, D. (1985). Liquid products from the continuous flash pyrolysis of biomass. 5469720, Ind. Eng. Chem. Process Des. Dev. ; Vol/Issue: 24:3, Univ. of Waterloo, Ontario; Univ. of the West Indies, Mona, Jamaica  45 Simitzis, J. (1994). High temperature pyrolysis of novolac resin biomass composites. Amsterdam,: Elsevier Scientific Pub. Co. vol. 30, no 2, pp. 161-171. Sims, R., Taylor, M., Saddler, J., and Mabee, W. (2009) IEA's Report on 1st- to 2nd- Generation Biofuel Technologies.  Retrieved from http://www.renewableenergyworld.com/rea/news/article/2009/03/ieas-report-on-1st- to-2nd-generation-biofuel-technologies Verified April, 2009 Suzuki, D., (2007) Climate change: Kyoto protocol. Retrieved June, 2008. http://www.davidsuzuki.org/Climate_Change/Kyoto/ Stokes (2000c), Biorefinery/bioeenrgy program. USDA Forest Service R&D. Madison WI, USA Taherzadeh, J.M., Eklund, R., Gustafsson, L., Niklasson, C., and Lidén, G. (1997) Characterization and Fermentation of Dilute-Acid Hydrolyzates from Wood. Department of Chemical Reaction Engineering, Chalmers University of Technology, S-412 96 Gteborg, Sweden, Ind. Eng. Chem. Res., 1997, 36 (11), pp 4659–4665 Tengborg, C., Stenberg, K., Galbe, M., Zacchi, G., Larsson, S., Palmqvist, E., Hahn- Hägerdal, B. (1998) Comparison of SO2 and H2SO4 impregnation of softwood prior to steam pretreatment on ethanol production. Appl Biochem Biotechnol 70-72:3-15 Torget, R., Werdene, P., Himmel, M., Grohmann, K. (1990). Dilute acid pretreatment of short rotation woody and herbaceous crops. Appl Biochem Biotechnol 24-25:115- 126 Torget, R, Walter, P, Himmel, M, and Grohmann, K. (1991). Dilute-acid pretreatment of corn residues and short-rotation woody crops. Appl Biochem Biotechnol 28-29:75- 86 Torget, R., Hatzis, C., Hayward, T.K., Hsu, T-A, Philippidis, G.P. (1996). Optimization of reverse-flow, two-temperature dilute-acid pretreatment to enhance biomass conversion to ethanol. Appl Biochem Biotechnol 57-58:85-101 Van der Drift, A., Van Doorna, J. and Vermeulen, W. J. (2001). Ten residual biomass fuels for circulating fluidized-bed gasification. Biomass and Bioenergy. Volume 20, Issue 1, Pages 45-56  46 Wheals, A.E,. Basso L.C., Alves D.M.G., Amorim H.V. (1999). Fuel ethanol after 25 years. Trends Biotechnol 17:482-487 Woolf, D. (2008) Biochar as a soil amendment: A review of the environmental implications. 31pp www.orgprints.org/.../Biochar_as_a_soil_amendment_- _a_review.pdf World Energy Council (WEC) (2001) WEC Survey of Energy Resources 2001—Biomass (other than wood). Retrieved from www.worldenergy.org/wec- geis/publications/reports/ser/biomass/biomass.asp Verified July 11th, 2009. Wyman, C.E., (1996). Ethanol production from lignocellulosic biomass: overview. In: Wyman CE (ed) Handbook on bioethanol: production and utilization. Taylor & Francis, Bristol, Pa., pp 1-18 Yaman, S. (2004). Pyrolysis of biomass to produce fuels and chemical feedstocks. Energy Conversion and Management, 45(5), 651-671. Zanzi, R., Sjöström, K., & Björnbom, E. (1996). Rapid high-temperature pyrolysis of biomass in a free-fall reactor. Fuel, 75(5), 545-550.     47  CHAPTER 2 CHARACTERISTICS OF WOOD RESIDUES FOR BIOENERGY APPLICATIONS1   2.1 INTRODUCTION   Woody residue comprises a wide range of natural by-products and it is the most abundant lignocellulosic material that can be used to solve the growing demand for bioenergy energy in the 21st century. Traditionally, residue characterization was an insignificant component of bioenergy production and it is now believed to be one of the factors affecting their wide commercial success of bioenergy generation (Bushnell et al., 1989). A detailed understanding of bioenergy processes requires accurate characterization of the feedstocks available, and these characteristics are not generally available in the literature.  Although sources of wood residues may arise from forests, mills and demolition of buildings, this study focused on residues from primary wood processing mills. These mills process logs to manufacture plywood and lumber, and depending on the lumber recovery factor, more than half of the log is considered residue after processing (Dramm, c2000). These residues comprise a wide range of by-products such as chips, trim ends, sawdust, shavings, sander dust, hog fuel and bark. The commercial and industrial successes of bioenergy generation from wood residues is generally a function of residue availability, handling requirements, residue pre-treatment and optimal bioenergy operating conditions (Bradley, 2006; Carrasco, 2004; Mohan, et. al., 2006). This paper investigates the physical characteristics of 33 samples of residues including chips, sawdust, trim ends, shavings, sander dust, recycled pallet bark and hog fuel in BC.   1 “A version of this chapter will be submitted for publication. Taku-Kehbila, A.V. and McFarlane P.N.  (2010) Characteristics of wood residues for bioenergy applications”   48  It is important to understand the similarities and differences among the residue types, and how these factors influence a bioenergy process. Published data has reported major differences with respect to physical (moisture content and basic density); chemical (ash content); and morphological (size distribution) characteristics of residues (Quaak et al., 1998). Due to the large variability in residue characteristics as influenced by tree species, location within a tree, growth condition and handling environment, a proper understanding of residue characteristics will enhance the optimum design of bioenergy conversion systems.  This study provides information on higher heating value (HHV), ash content, basic density, moisture content (MC), and particle size distribution of samples collected from two mill sites in the coastal region of BC (20 samples); and two mill locations in the interior of the Province (13 samples). These analyses are important for effective residues utilization in that: (i) basic density indicates the mass of material for a given volume and this influences transportation cost; (ii) particle size distribution influences the handling and the combustion characteristics of solid particulate biofuels and the pre-treatment of residues for biochemical process (Hartmann, et. al., 2006); (iii) ash content includes various forms of incombustible impurities that accumulate into clinkering/slagging rocklike materials that affect the temperature in a boiler (McKendry, 2002); (iv) HHV at constant pressure measures the enthalpy change of combustion (with water condensed) on an oven dry basis and typically ranges from  17 -21.5 MJ/kg; (v) moisture content affects the heating values. The heating value of residues decreases with increasing moisture content. Higher MC requires increased residence times on the grate while drier residues lead to higher combustion temperatures.  The production of ethanol through biochemical pathways includes various conversion pathways such as enzymatic hydrolysis and acid hydrolysis that require residues with higher MC. This study analyzes the physical characteristics of residues and tabulates specific data characteristics.   49 2.1.1 Objectives   The objectives of this research were two fold: (1) to contribute to a database of important physical characteristics of residues from sawmills and plywood mills in BC which are needed for bioenergy systems; (2) to analyze the physical characteristics of mill residues in BC.   2.2 LITERATURE REVIEW  2.2.1 Sources of industrial mill residues   BC has traditionally been the largest lumber-producing region of Canada. According to McCloy and Associates (1999), BC alone accounted for about 7.75 million BDt of net mill wood residues produced and 0.98 million BDt of bark residues annually in 1999. More than half of this mass of residues (5.69 million BDt) was utilized, and 1.08 million was considered surplus. By 2004, the dynamics of wood residue production had shifted. Surplus mill residues were produced primarily by large interior sawmills, which at that time accounted for more than 84 percent of BC’s annual lumber production, while the coastal region did not produce any significant surplus residues. This was a result of the evolution of the coastal industry where the consumption of wood residues by pulp mills had traditionally been in balance with sawmill production of wood residues (McCloy, 2004). Efficient wood residue utilization is becoming more important in BC, as residues have become increasingly scarce as a result of reduced demand for forest products as a consequence of fewer housing starts in the United States, increased demand from the energy sector, and increasing environmental concerns. In order to categorize residues, their origin and source need to be traceable within the overall supply chain (Alakangas, et.  50 al., 2006). The following section describes the overall production chain in a sawmill and a plywood mill, and identifies the major sources of residues.  Sawmill   In 2006, the average lumber recovery factor (LRF) for a BC sawmill was 0.270 mfbm/m3 roundwood. Consequently on average, roundwood was converted into 48% lumber and 52% residues (37% chips, 15% sawdust and shavings) excluding bark (BC MoF, 2006). Few sawmills have the same lumber recovery factor and these are largely dependent on several factors such as log diameter, length, taper, and quality; sawing variation, rough green-lumber size, and size of dry-dressed lumber and kerf width (Steele, 1984). The Figure (2.1) highlights the important residues and where they are produced in a sawmill.  Factors influencing the design of a sawmill are the wood resource in terms of species, quality, log supply and log size; the markets; the mill location; and the available capital (Walker, 2006). There are various operational steps that occur in the processing of a log to lumber.  The basic steps include: (1) Logs are scaled either on the way to the mill or upon arrival at the mill from the forest. Decking occurs at the log yard where logs are stored according to species, diameter, length and end-use. Most of the log yards across BC are unpaved. Thus the bark residues are usually contaminated with sand, rocks and grit, which increase the ash content. (2) The debarker removes the bark from the logs. The most important benefit to debarking is that it eliminates the sand and grit along with the bark to prevent the saw being dulled; and to produce clean bark-free chips and slabwood which command a better price (Wingate-Hill and MacArthur, 1991).       51    Figure 2.1: Processing steps in the production of lumber (Bowyer et al., 2003)  (3) A saw is used for primary break-down of the logs (headrig) and the carriage or conveyor is used for handling logs during the sawing process and to position the log in such a manner as to allow the operator to achieve a sawing pattern, which will result in the optimum production of sawn timber with minimum residues produced (FAO, 1990). Circular saws, bandsaws, or framesaws may be used to generate the saw kerf. The wood in the kerf is reduced to coarse sawdust while the chipper canters chip the edges of thick slabs, cants or flitches to generate two parallel phases while                        debarking stacking sorting remanufacturing                      primary breakdown (headrig)           gang sawing           edging trimming sorting grading surfacing drying Bark Sawdust Trim ends Shavings Sawdust Hog fuel Sawdust Residue types Chips         Log storage  52 reducing the amount of chips that is generated. The thick slabs are then sawn into planks and the flitches and cants are sawn into planks and boards (FAO, 1990). (4) The edger is used to convert the trimming boards to produce smooth, parallel edges, and the trimmer cut the boards to square and precise lengths (Bowyer et al., 2003). The trim residues at this point are green with original moisture content. (5) The sawn and trimmed timber is sorted according to thickness, width, length, quality, grade and species depending on the market requirements and kiln dry. As kiln-drying of sawn timber accounts for some 70-90 percent of the total energy consumed in the sawmilling process, it is a widely accepted practice in the sawmilling industry to use its residues as a fuel source, the energy value of which may even be surplus to the mill's requirements (FAO, 1990). (6) The sawn timber passes through planers, where rotating cutting heads trim the pieces to their final dimensions, smooth all four surfaces, and round the edges. At this point, dry trim ends and shaving residues are generated. (7) The lumber is further graded by visual or mechanical inspection and grade according to the amount of defects present. They are sorted and packaged according to the market specifications.   Plywood mill   In a typical BC plywood mill, total veneer capacity grew by about 17% between 1990 and 2005. The veneer recovery factor varied across mills and it was largely dependent on several factors such as log quality, decision making by sawmill personnel, and the condition and maintenance of mill equipment. Figure 2.2 below highlights the important residues produced in a plywood mill. The basic processing steps in a plywood mill include: (1) The logs are generally sorted in the log-yard upon arrival; they are stacked in long piles known as log decks according to size and species.  53 (2) The debarking process facilitates the removal of bark along with dirt and debris, either with sharp-toothed grinding wheels or with jets of high-pressure water, while the log is slowly rotated about its long axis. The logs are cut by the circular saw to lathe fitting length, which is normally 2.5 - 2.6m long, suitable for making standard 2.4 m long sheets. Bark residues are produced during debarking and sawdust is generated when logs are cut to length. (3) The cut logs are conditioned by cooking in both heat and moisture or exposed to live steam to soften the wood in order to facilitate peeling and to produce an acceptable quality of plywood. Conditioning depends on the wood species and it is typically done for about 12 hours at a temperature of 60-80 oC to remove starch and to kill microscopic organisms (Uniply, 2008). (4) The conditioned log is then peeled on the lathe to the required thickness by rotating around its axis in a lathe and a log veneer sheet is cut by a knife mounted parallel to the block's axis. (5) The green veneer is then reeled and clipped to size, either manually or by high- speed knives. They are graded and stored in piles ready for drying. Any defects, such as knots and splits, are then cut out of the sheet to produce chip residues. (6) The drying of veneer reduces the moisture content aids the gluing process during the manufacture of the plywood. Drying is done at a uniform temperature and also destroys microorganisms that may have survived the cooking process. (7) The dry veneer is assembled in layers and glued to give the plywood greater strength and dimensional stability.   54   Figure 2.2: Processing steps in the production of plywood (FAO, 1990)   (8) The assembled dry veneer is pressed by passing it through hydraulic presses to bring the veneer into direct contact with the adhesive. The cold pre-pressing takes Bark Sawdust Ply-trim end Chips         Cutting Debarking Drying and sorting Peeling Cut-off saw Veneer upgrading Clipping Grading Trimming saws Pressing Assembly Conditioning Sawdust Cold Sanding, upgrading Hot Hog fuel         Logs Residue types Plywood to stock Sander dust  55 place at comparatively low pressures. This process ensures uniform spread of the specially formulated chemicals within the layers. The veneer passes through hot press of 80 – 180oC. This ensures uniform density at all points in the plywood. After hot-pressing the boards are cooled. (9) The boards are trimmed horizontally and vertically using the trimming saws and the plywood boards are then cut to required sizes. The cut boards are then sanded in wide-belt sanders so as to obtain the desired surface smoothness. A unique technique of pre-cutting is used before the final cutting to ensure dimensional accuracy. The plywood then comes in for the final quality inspection and they are loaded and transported to the market (FAO, 1990).  2.3 MATERIALS and METHODS   2.3.1 Residue sampling region   Sample collection was based on the cluster sample method. BC was divided into three geographical forest regions: Northern, Southern and coastal forest regions. Further, randomly selected district form each geographical region were chosen to represent the sampling site. However, the North east region was not fully represented in the overall analyses.  The mill types sampled were a plywood mill, two sawmills, a shake mill and a pole mill as shown on the map (Figure 2.3). A total of 20 samples were collected from the coastal region of British Columbia with Mill 1 (M1) being plywood mill (Table 2.1) with a total of 16 samples; and Mill 2 (M2) being a shake mill (Table 2.2) with a total of 2 samples. Mill 3 (M3) was a pole mill (Table 2.3) with a total of 2 samples.  A total of 14 samples were collected from the interior region of the Province, with Mill 4 (M4) being a sawmill (Table 2.4) with a total of 7 samples. Mill 5 (M5) was also a  56 sawmill (Table 2.5) with a total of 7 samples. The samples collected varied by species, age of pile, season, moisture condition, and mill type. The data were compiled and subjected to analysis of histogram, dot plot, box, and mean separation using the Analyse- it statistical package.  A total of 33 samples consisting of bark, hogfuel, chips, sander dust, trim ends, sawdust, and shavings were randomly sampled and collected across BC from conveyor belts, kilns, planers within the mills, and old selected piles that had been exposed to the weather for substantial periods. Given the variability of wood species and composition in a residue pile, obtaining representative sampling was often a difficult challenge. These samples were stored in air tight rubber containers and transported to the laboratory within 48 hours of collection. The samples were then stored at 4oC in the refrigerator until further testing.  57  Figure 2.3: A map showing the locations of research sample sites   2.2.2 Residue sampling type   In 2006, there were 67 lumber mills and 4 veneer mills in the coast; and 119 lumber mills and 16 veneer mills in the interior of BC (BCMoF, 2006). In 2006, sawmills in BC interior produced over 6.5 millions BDts of wood residue which was about 31% of the total wood residue production in Canada (21 million BDts) (Bradley, 2006). The residue  58 samples collected include: bark, hogfuel, chips, sawdust, sander dust, trim ends, and shavings.  Bark Bark is produced during debarking at both sawmills and plywood mills. Bark constitutes a considerable volume of a log, which normally ranges from 9 to 24% depending on the species and log diameter (Xing, et. al., 2006). Over half of the approximately 17 million m3 of bark produced annually by the Canadian wood processing industry is incinerated or land-filled (NFDP, 2005).  Hog fuel Hog fuel is a combustible wood residue, which is produced from the lumber, shingles, plywood and pulp industries. It consists of a variety of residues including sawdust, bark, planer shavings, chip fines, and in some cases, waste wood as trim ends and edgings which give it a varied size and shape. The composition of the hog fuel varies with the tree species utilized, the manufacturing process and the level of residue utilization at the manufacturing site (Reid and Associates, 1978)  Chips Chips are produced during sawing in sawmills and clipping in plywood mills. Wood chips are bark free residues that are produced to meet defined specifications according to their intended use (Bradley, 2007). They have subrectangular shape with a typical length of 4-40mm and are in large demand by the pulp and paper sector.  Sawdust Sawdust consists of fine wood particles that originate during almost all wood processing operations. They are either green (i.e. produced from green lumber before kiln drying) or dry from the planer. Sawdust is largely consumed by the pellet mills in BC and thermochemical bioenergy processes.  Sander dust Sander dust is produced during the sanding of plywood. It contains very fine particles of wood at a low moisture content which makes it very suitable for direct firing (FAO, 1990).  Trim ends and shavings These residues are generally clean and dry and originate during the smoothing of plywood and lumber at its finishing state. They are used in the particleboard and pellet sectors and are ideal for thermochemical bioenergy processes.   59 Recycled wood Some mills in urban areas import recycled wood  as a supplementary fuel for their boilers. This feedtstock may include size-reduced demolition wastes, pallet wastes and wood residues from secondary wood products manufacturers. In this  study, sander dust from a plywood mill was also categorized as recycled wood.   Origin of Coastal mill samples   Table 2.1: M1 (Plywood) Sample  Species Operation season Moisture Content Ply trim end 2 Unknown Trimming Winter Green Ply trim end 3 Unknown Trimming Winter Dry Chips 6 Unknown Chipping Winter Green Chips 7 Unknown Chipping Winter Green Chips 8 Unknown Chipping Winter Green Chips 9 Unknown Chipping Winter Green Sander dust 1 (Recycled wood 1) Unknown Sanding Winter Dry Chips recycle pallet 2 (Recycled wood 2) Unknown Unknown Winter Dry Demolition wood 3 (Recycled wood 3)  Unknown Unknown Winter Dry Hog fuel 3 Unknown Mixed Winter Green Hog fuel 4 Unknown Mixed Winter Green Hog fuel 5 Unknown Mixed Winter Green Bark 2 DF Debarking Winter Green Bark 3 B Debarking Winter Green Bark 4 B Debarking Winter Green Bark 5 DF Debarking Winter Green The wood species include: (B) Balsam, (DF) Douglas Fir. Recycled wood: sander dust is named as recycled wood 1.   Table 2.2: M2 (Shake mill) Sample  Species Operation season Moisture Content  Shake mill 1 Redcedar Saw Winter Green  Shake mill 2 Redcedar Saw Winter Green   Table 2.3: M3 (Pole mill) Sample  Species Operation season Moisture Content Log peel 1 Unknown Debarking Winter Green Log peel 2 Unknown Debarking Winter Green  60 Origin of interior mill samples  Table 2 4: M4   (Sawmill) Sample  Species Operation Season Moisture Content Sawdust 4 LP Edging, Trimming and Surfacing Summer Green Sawdust/Shav ings 5 LP Surfacing Summer Dry Trim ends Unknown Trimming Summer Green Chips 1  Unknown Gang saw, Headrig, Edgers Summer Green Chips 2 (D) DF, and L Headrig, Edgers, Gang saw Summer Dry Chips 3 Unknown Headrig, Edgers, Gang saw Summer Green Hogfuel 1 SPF Mixed Summer Green Hogfuel 2 DF and L Mixed Summer Green    D: Dry sample   The tree species are: (DF) Douglas fir; (L) Larch; (SPF) Spruce-Pine-Fir; and (LP) Lodgpole pine.   Table 2.5: M5 (Sawmill) Sample  Species Operation Season Moisture Content Sawdust Unknown Surfacing Summer Green Sawdust/Shavings Unknown Surfacing Summer Dry Chips 4 SPF Gang saw, Headrig, Edgers Summer Green Chips 5 DF Gang saw, Headrig, Edgers Summer Green Bark 1 DF Debarking Summer green D: Dry sample The tree species are: (DF) Douglas fir; (L) Larch; and (SPF) Spruce-Pine-Fir.  2.3.3 Residue sample preparation and analysis   Prior to performing the measurements, ten kilogram’s of each sample were air dried in the laboratory for 48 hours in order to minimize possible moisture variations. The samples used for MC, basic density and ash measurement, were not air dried. Each laboratory measurement was repeated in triplicate.   61  In order to obtain a representative sub-sample for analysis, the original sample was divided in halves, and then quartered. Each quarter was then further sub-divided. The result of each test sample was measured using a balance (OHAUS, EP 214C) with an accuracy of 0.0001g at constant temperature and humidity. All samples collected were analyzed for HHV, MC, ash content, basic density and particle size distribution.   Higher heating value   HHV is the absolute value of the specific energy of combustion, in joules, for a unit mass of a solid fuel burned in oxygen in calorimetric bomb under specific conditions.  The particle sizes have to be very fine to make the pellets that are used in the bomb calorimeter. The residue samples were ground in a Wiley mill and food processor to reduce the particles to a powder. The powder was then pelletized using a hand pelletizer and the pellets were used for testing. Using the ASTM D240 (2002), the heating value of these samples were determined experimentally by employing an adiabatic Parr 6100 oxygen bomb calorimeter. The result of combustion is assumed to consist of gaseous oxygen, nitrogen, carbon dioxide and sulphur dioxide of liquid water (in equilibrium with its vapour) saturated with carbon dioxide under conditions of the bomb reaction, and of solid ash, all at the reference temperature and at constant volume. The bomb was calibrated prior to use to determine its heat capacity (Cv).  Once calibrated, the bomb is may be used to determine the heat of combustion of any compounds using the equation: ΔHc = Cv. ΔT. Where: Cv = heat capacity of the bomb ΔT = temperature  change. The enthalpy change between reactants and products of the samples were printed automatically using the equation: HHV = LCV + Mm *  He Where: LCV: lower calorific value Mm: mass of water produced per unit mass of fuel He: latent heating of evaporation of water.   62 Moisture content   ASTM standard D4442 (2002), was used to calculate the moisture content of each residue. A representative sample of at least 20 – 40g was introduced in the oven at 103o ± 2oC for 24 hours. The MC of the samples was calculated as:      ((green weight (g) – oven dry weight (g)) MC of an oven dry basis (%) =                                                     X 100                                                                          (Dry weight (g))    Ash content   Ash content is defined as the mass of inorganic residue remaining after ignition of a fuel under specified conditions, expressed as a percentage of the dry matter in the fuel. It is an approximate measure of the mineral content and other inorganic matter in wood. Following the protocols outlined in ASTM D1102 (2002), a sample was placed in an oven at 100 to 150oC. After one hour, the sample was placed in a desiccator to cool and weigh. This procedure was repeated until the weight was constant at 0.0001g. The crucible was then ignited in a muffle furnace at 550oC and cooled in a desiccator. A 1g sample was placed in the crucible with an initial temperature of 250oC and then raised to a maximum furnace temperature of 550oC±10oC. The ash content was calculated after the carbon was completely ignited (ASTM (Reapproved 2001).   Basic density   The volume of the samples was determined by water displacement method (ASTM D2395-02). The oven dry weight was determined by placing the green samples in an oven at 103o ± 2oC for 24 hours. The basic density of the samples was calculated as:        (oven dry weight (g)) Basis density (kg/m3) =                       X 1,000                                            (Green volume (cm3))    63 Particle size distribution   During the analysis, the sieves with an aperture of between 0.125 – 90mm nominal opening were used. The sample type, its screenability and range of particle sizes were taken into consideration to determine the mass of the sample used. In all measurements, the sample weight ranged between 54 – 163g and it was placed in the top sieve. The wide variation in weight ranged was due to sample variability in some cases, and the limited quantity of the available residues to run triplicate tests. A Ro-tap sound enclosure sieve shaker (model number R-30050) was used. The sieves were stacked largest on top and progressively reduced in aperture size. The duration of the screening operation was set between 15 – 20 minutes. Any particles sticking on the sieve were brushed gently and added to the oversized fraction of the respective sieve.   2.3.4 Data Analysis   Comprehensive analysis was performed for the 33 residues samples. The laboratory data collected were analyzed using Analyse-it (version 2008) statistical software. Exploratory data analysis tools such as histograms, box plot, frequencies, standard deviation, median and means (with 95% confidence intervals) were computed to summarize the data. In addition, all the data were analyzed in aggregate and hierarchy of occurrence (i.e. level) within each residues type using one-way analyses of variance, correlation matrix table and t-test (α=0.05). The purpose of the descriptive statistics was to note if residue characteristics have any relevant differences within residue type and mill type.  There are three points to note in the results and discussion section: (1) all residues produced after kiln drying are represented with a capital D by their name; (2) there were insufficient samples for log peel mill (M2) and shake and shingle mill (M3) to undertake all analyses and as a result these mills were not fully represented in the analysis; (3) sander dust in all the analyses was considered an outlier and was added to the recycled wood group (Table 2.1).  64 2.4 RESULTS   Descriptive statistics were performed for the following physical characteristics: HHV, ash content, basic density, MC, and particle size distributions. These properties are relevant to quatify residue as an energy source. The correlation matrix was used to assess whether a predictive relationship that existed between the variables (HHV, ash content, basic density and MC). (Table 2.6).  Table 2.6: Correlation matrix coefficient of residues analysis   HHV Ash content Basic density MC HHV 1 Ash content 0.15 1 Basic density 0.34 -0.02 1 MC 0.24 0.32 -0.15 1   Due to the lack of a significant correlation amongst the variables, the key results and discussion of this study were presented in five subsections: (1) effect of HHV by residue and mill type; (2) effect of ash content by residue and mill type; (3) effect of basic density by residue and mill type; (4) effect of MC by residue and mill type; (5) particle size distributions of the total residues.  65 2.4.1 Higher heating value data distribution    The mean HHV values ranged between 17.3 – 21.5 MJ/kg (Figure 2.4; Table 2.10). The central tendency of the data can be summarized by the various arithmetic measurements which include the mean 18.9, and the median 19.2 MJ/kg. The measured range in the sample data had an interquartile range of 1.5, a standard deviation of 1.0 and the 95% confidence interval was 18.8 – 19.1 MJ/kg.  Histogram 0 5 10 15 20 25 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 HHV (MJ/kg) Fr eq ue nc y  17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 HHV (MJ/kg) 95% CI Notched Outlier Boxplot Median (19.19) 95% CI Mean Diamond Mean (18.93)  Figure 2.4: Histogram and box plot for higher heating value   Higher heating value by residue type  The HHV were determined on an oven dry basis. HHV did not vary extensively within the residue types, with mean values ranging from 17 MJ/kg to 21.5 MJ/kg. On average hog fuel (19 MJ/kg) and bark (19 MJ/kg) residues had slightly higher heating values than  66 whitewood samples (18.3 MJ/kg). Within the whitewood samples, trim ends (19 MJ/kg) had the highest value, followed by chips (18 MJ/kg) and sawdust/shavings (18 MJ/kg) (Figure 2.5).  These values are consistent with other reports (Ince, 1977; Demirbas, 1996). There was no significant correlation between HHV and ash content which disagrees with the results of Sheng and Azevedo (2005). Previous results indicated that HHV ranged from 18.6 MJ/kg to 23 MJ/kg for whitewood and 19 MJ/kg to 23.5 MJ/kg for bark (Forintek 1985), which are slightly higher values than the results of this study.  Figure 2.5: Oneway analysis of higher heating value (MJ/kg) by residue type   Higher heating value by mill type   Residues from plywood mills (M1) had significantly higher heating values of 19 MJ/kg than sawmills (M4 and M5) with 17.96 MJ/kg as determined by the Student t-test (Figure 2.6). Rosillo-Calle et al., (2007) reported that the net amount of energy available from biomass as heat depends on the amount of moisture and ash content; however the analyses were carried on oven dry basis thereby decreasing any significant effect that MC may have on HHV.   67  Figure 2.6: Oneway analysis of mean higher heating value (MJ/kg) by mills   2.4.2 Ash content data distribution   The ash content of the samples ranged between 0.1 and 6.1%. The distribution of the total ash content analysis is right skewed with the mean (1.6) greater than the median (0.9) (Figure 2.7; Table 2.10). Majority of the whitewood samples occurred within the range of 0 – 2, while non-white wood samples (bark and hog fuel) that were contaminated with sand and grit occurred within the range of 2 – 6.5%. The sample data had an interquartile range of 2.6, a standard deviation of 1.7 and the 95% confidence interval was 1.3 – 1.9. Key Residues +  Recycled Wood Y Log peel O Trim ends ▼ Bark ● Sawdust/shavings ♦ Chips * Hog fuel  68 Histogram 0 5 10 15 20 25 30 35 40 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 Ash content (%) Fr eq ue nc y  0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 Ash content (%) 95% CI Notched Outlier Boxplot Median (0.656) 95% CI Mean Diamond Mean (1.652)  Figure 2.7: Histogram and box plot for ash content   Ash content by residue type   Ash is an incombustible component of wood. The results were recorded on dry basis. The higher ash content value is primarily the result of impurities such as sand, grit and other inorganic contamination that may occur outside of log yard. The mean ash contents of all bark and hog fuel residues (3% and 4% respectively) were considerably higher than whitewood residues (0.6%). Almost all whitewood samples were below the mean of means with the exception of sander dust (1%) represented as recycled wood 1 (D) as presented in Figure 2.8. These effects were due largely to whitewood samples being significantly less exposed to inorganic contaminants.    69  Figure 2.8: Oneway analysis of ash content (%) by residue type   Ash content by mills   There were no significant differences in the mean ash content from each of the mills (Figure 2.9). The residues were highly heterogeneous which indicated that geographical regions, mill type, tree species had a negligible influence on the residue’s ash content as shown by the Student t-test. It was more likely that the handling of individual residue type influenced the ash content.  Figure 2.9: Oneway analysis of mean ash content (%) by mills Key Residues +  Recycled Wood Y Log peel O Trim ends ▼ Bark ● Sawdust/shavings ♦ Chips * Hog fuel  70  2.4.3 Basic density data distribution   Most samples displayed a basic density within the range of 120 – 500 kg/m3 with sander dust being the outlier (1010 kg/m3) (Figure 2.10; Table 2.10). The sample data had an interquartile range of 189, a standard deviation of 1.6 and the 95% confidence interval was 318 – 364 (Figure 2.10). Histogram 0 2 4 6 8 10 12 14 16 18 20 100 150 200 250 300 350 400 450 500 Basic density (kg/m3) Fr eq ue nc y  100 150 200 250 300 350 400 450 500 Basic density (kg/m3) 95% CI Notched Outlier Boxplot Median (373.83732979) 95% CI Mean Diamond Mean (328.51299528)  Figure 2.10: Histogram and box plot for basic density   Basic density by residue type   The basic density of the residues ranged from 120 – 1010 kg/m3. Lower basic densities were observed for the following residues: hog fuel 1 (120 kg/m3), hog fuel 2 (165 kg/m3), bark 1 (127 kg/m3), chips 1 (164 kg/m3), chips 4 (165 kg/m3), and chips 8 (168 kg/m3)  71 (Figure 2.11). Sander dust exhibited the highest basic density of 1010 kg/m3 and was considered to be an outlier and was not included in the overall analysis.   Figure 2.11: Oneway analysis of basic density (kg/m3) by residue type   Basic density by mills   There was a significant variation in basic density by mill type (Figure 2.12). Analysis of variance with student t-test showed that M1 differed significantly from the other regions and the variability decreased regionally as you move from the coastal to the interior region.    72  Figure 2.12: Oneway analysis of mean basic density (kg/m3) by mills   2.4.4 Moisture content data distribution   The samples displayed a wide range of MC and most of the values occurred between 9 – 170%. The MC of the total samples was right skewed as the mean (105%) was larger than the median (96%) (Figure 2.13; Table 2.10). The sample data had an interquartile range of 109, a standard deviation of 0.7 and the 95% confidence interval was 90 – 120. The outliers were bark and hog fuel from the coastal region that had been exposed to the environment for a considerable length of time.  Key Residues +  Recycled Wood Y Log peel O Trim ends ▼ Bark ● Sawdust/ shavings ♦ Chips * Hog fuel  73 Histogram 0 5 10 15 20 25 30 0 50 100 150 200 250 300 350 MC (%) Fr eq ue nc y  0 50 100 150 200 250 300 350 MC (%) 95% CI Notched Outlier Boxplot Median (96.20) 95% CI Mean Diamond Mean (104.85) Outliers > 1.5 and < 3 IQR  Figure 2.13: Histogram and box plot for moisture content   Moisture content by residue type  MC is influenced by a variety of factors such as the time of the year, the storage conditions and residue pile particle sizes. For example, small sized piles consisting of small particles form close layers and trap more moisture, compared to large sized particles which are more porous (Richardson, et. al. 2002). All chip residues produced after kiln drying displayed (MC’s) in the range of 5 to 15%. Residues from coastal regions tended to have higher MC values than interior mills (Figure 2.14)    74  Figure 2.14: Oneway analysis of moisture content (%) by residue type   Moisture content by mill type  The prevailing weather conditions at the time of sampling the residues varied from mill to mill. Samples from Mills 1, 2, and 3 were collected during winter while samples from Mill 4 and 5 were collected during summer (Figure 2.15). The significant difference within the seasons is clearly shown by the Student t-test.       75  Figure 2.15: Oneway analysis of mean moisture content (%) by mills   2.4.5 Total analysis of residues   The measures of statistical data distribution are summarized below for each given characteristic (Table 2.7). In addition, all the data were analyzed in aggregate according to hierarchy of occurrence (i.e. level indicated in alphabetical letters) within each residues type using one-way analyses of variance. The levels not connected by same alphabetical letter are significantly different (Table 2.10).   Table 2.7: A statistical table of total residues data analysis Parameters HHV (MJ/kg) Ash content (%) Basic density (kg/m3) MC (%) Range of data occurrence 17 - 21 0.1 – 6.1 120 - 1010 9 - 317 Mean 18.9 1.6 352 105 Median 19.2 0.9 371 96 Standard Deviation 1.0 1.7 1.6 0.75 Interquatile range 1.5 2.6 189 109 95% Confidence level 18.8 – 19.1 1.3 – 1.9 318 - 364 90 – 120 Key Residues + Recycled Wood Y Log peel O Trim ends ▼ Bark ● Sawdust/ shavings ♦ Chips * Hog fuel  76  2.4.6 Particle size distribution   The particle size distributions of the total residues were determined by the amount retained on each screen and divided by the total weight of the sample to give a percentage. During the data analysis, the particles were assumed as equivalent spheres thus the particles ranges were characterized by following particle sizes from Dp31.7mm, Dp50mm and Dp68.3mm. The r2 values were obtained by regressing a straight line against the transformed cumulative undersize percentage curve of the percent total mass on the sieve.  Approximately 50 percent of total sander dust was less than or equal to 0.2 mm. Sawdust and shavings ranged between 1.5 – 3.2 mm, trim ends ranged between 4 – 5.2 mm as presented in the Table 2.8. Particles within these size ranges can be burned directly without the need for further chipping. The size distributions of residues from the mills vary greatly because it depends on the processing type and equipment used especially in the case of chips (Table, 2.8).   Table 2.8:  Particle distribution within the total residues by type          Residue type Dp50 (mm) Dp31.7 - Dp68.3 (mm) r2 Sander dust  0.2 0.09 - 0.4 0.89 Shavings  3.2 1.7 – 4.9 0.95 Sawdust 1.5 – 3.2 0.7 – 5.3 0.85 – 0.87 Trim ends 4 – 5.2 2.2 –  6.9 0.79 – 0.98 Recycled wood 8.1 – 9 5.8 – 11.7 0.95 – 0.96 Chips 6.3 – 29.9 3.8 – 40 0.94 – 0.99 Hog fuel 4.3 – 43.7 2.5 –  76 0.89 – 0.99 Bark 3.8 – 49.7 2.1 – 67.7 0.95 – 0.98  77  Particle distribution within residues   The cumulative size distributions for all the particles were determined. Also, the cumulative size distribution for chips from the sawmills and plywood mills are presented in Figure 2.16. This figure revealed that the cumulative weight fractions of particles from sawmills (chips1, chips 2, chips 3, chips 4, and chips 5) showed similar distributions with residues from plywood mills (chips 6, chips 7, chips 8 and chips 9). Majority of the total particles occur within the Dp log sieve opening of 0.5mm to -1.0mm (~0.00001 - 3mm) which is an acceptable range (20 – 50mm) feedstock for thermochemical processes.     Figure 2.16: Cumulative mean size distributions of chips from sawmill and plywood mills   78  Table 2.9: Particle distribution of total residues                   Sieve Analysis (mm) Residues Dp 50 mm Dp 31.7 – Dp 68.3 mm r2 aRecycled wood (D) 1 0.2 0.09 - 0.4 0.9 Sawdust 4 1.5 0.7 – 2.5 0.9 Sawdust/Shavings 2 (D) 2.4 1.7 – 3.0 0.9 Sawdust 1 3.2 1.5 – 5.3 0.9 cRecycled wood 3 3.2 1.7 – 4.9 0.9 Sawdust/Shavings 3 (D) 3.2 1.7 – 4.9 0.9 Bark 2 3.8 2.1 - 5.8 0.9 Ply trim end 2 4 2.5 - 5.5 0.9 Bark 5 4.1 2.5 - 5.7 0.9 Hog fuel 4 4.3 2.5 - 6.2 0.9 Trim end 1 4.4 2.2 – 6.9 0.8 Hog fuel 5 4.4 2.6 - 6.3 0.9 Ply trim end 3 5.2 3.4 - 7 0.9 Bark 3 5.4 3.4 - 7.3 0.9 Bark 4 5.9 3.6 - 8.3 0.9 Hog fuel 3 6.2 3.8 - 8.7 0.9 Chips 9 6.3 3.8 - 9 0.9 Chips 7 7.6 5.7 - 9.2 0.9 bRecycled wood 2 8.1 5.8 - 10.2 0.9 Chips 4 10.9 7.6 – 13.9 0.9 Chips 8 14 11.4 - 16 0.9 Chips 5 16.5 11.5 – 21 0.9 Chips 3 16.9 11.5 - 22 0.9 Chips 6 16.9 11.5 - 22 0.9 Hog fuel 2 19.6 10.4 – 30 0.9 Chips 1 21.4 11.4 – 33 0.9 Chips 2 (D) 29.9 19.6 – 40 0.9 Hog fuel 1 43.7 19.5 – 76 0.9 Bark 1 49.7 31.7 – 67.7 0.9   79 2.4.7 Relationship of basic density and particle size distribution   The basic density had an inverse relationship with particle size (Figure 2.17). Particle size analyzes a mass range of particles distribution while basic density measures the dry mass to solid volume measurement which implies that smaller particles are more compact and hence exhibit a higher basic density than larger particles as presented in Figure 2.17.   Figure 2.17: Bivariate fit of mean basic density (kg/m3) by mean particle size Dp50 (mm)   Samples from the shake mill and log peel mill were collected by a third party. The volume of the available samples was not sufficient to carry out sieve analysis. Due to the variability of residues within a mill there is the need to analyze several fractions from different piles to reach an informed conclusion. The total sample numbers are not representative enough and thus do not have the same weighting factor as those from sawmills and plywood mill.    80 Table 2.10: Total analysis by residues  HHV (MJ/kg) Ash Content (%) Basic density (kg/m3) Moisture Content (%) Residues Mean SD Level Mean SD Level Mean SD Level Mean SD Level Sawdust/Shavings 3 (D) 19.4 0.5 A 0.6 0.0 A 483.8 9.3 A 9.4 0.1 C Sawdust 1 18.3 0.4 B  0.5 0.1 A 244.2 0.7 C 109.6 1.1 B Sawdust 4 17.8 0.3 C 0.5 0.2 A 445.8 10.4 B 150.3 3.9 A Sawdust/Shavings 2 (D) 17.7 0.1 C 0.1 0.1 B 224.4 1.9 D 9.4 0.1 C  Trim end 2 20.1 0.3 A 0.6 0.2 B 431.4 18.2 A 39.6 1.2 B Trim end 3 19.8 0.4 A-B 0.9 0.1 A 451.7 12.7 A 39.9 1.1 B Trim end 1 19.1 0.3 B 0.4 0.1 B 248.1 1.4 B 107.7 1.8 A  Chips 8 19.7 0.4 A 0.2 0.0 C-D 468.3 20.1 A-B 86.7 0.6 B Chips 6 19.4 0.2 A-B 0.1 0.1 D 484.3 14.1 A 89.7 2.5 A-B Chips 9 19.1 0.3 B 0.3 0.1 B-C-D 413.2 14.4 C 90.4 5.2 A-B Chips 7 19.0 0.3 B 0.2 0.1 D 459.7 14.5 B 93.1 6.5 A-B Chips 1 18.1 0.3 C 0.2 0.1 D 163.9 5.8 F 93.8 2.5 A Chips 4 18.0 0.1 C 0.3 0.1 B-C 165.3 0.9 F 27.6 1.9 D Chips 5 17.6 0.0 D 1.5 0.2 A 199.4 0.6 E 56.8 9.2 C Chips 2 (D) 17.6 0.1 D 0.2 0.0 C-D 254.4 2.6 D 13.2 0.8 E Chips 3 17.3 0.1 D 0.4 0.1 B 250.7 2.6 D 92.1 6.5 A-B  Shake mill 1 19.7 0.2 A 0.9 0.3 B 291.9 2.1 B 209.0 17.2 C Log peel 1 19.1 0.4 B 0.9 0.5 B 293.4 9.0 B 263.3 9.3 B Log peel 2  18.6 0.3 B 1.2 0.1 B 338.9 18.2 A 211.5 19.3 C Shake mill 2 18.6 0.1 B 3.2 1.3 A 246.8 8.8 C 317.3 21.3 A  Recycle wood 2 19.7 0.2 A 0.9 0.2 B 455.3 27.8 B 51.0 0.9 A Recycle wood 3 19.4 0.9 A 0.3 0.1 B 424.4 24.2 B 38.1 1.0 B Recycle wood 1(D) 18.1 0.5 B 2.7 0.6 A 1010.2 130.6 A 5.37 0.1 C  Hog fuel 5 20.0 0.5 A 2.2 0.1 C 417.9 3.1 A 141.6 6.1 A Hog fuel 4 19.9 0.1 A 3.3 0.1 B 416.3 10.9 A 146.2 4.3 A Hog fuel 3 19.8 0.1 A 3.1 0.6 B 419.0 15.3 A 104.8 4.5 B Hog fuel 1 18.3 0.1 B  4.6 0.6 A 120.6 1.3 C 96.6 12.9 B Hog fuel 2 17.3 0.3 C 4.9 0.1 A 165.1 6.9 B 68.5 12.9 C   Bark 2 20.3 0.9 A 4.4 0.2 B 382.6 10.4 A 164.5 7.1 B Bark 3 19.8 0.6 A-B 3.5 0.1 C 360.4 19.8 A 188.0 11.3 A Bark 4 19.7 0.3 A-B 3.0 0.2 C 362.6 9.6 A 167.6 2.7 B Bark 5 19.4 0.2 A-B 6.1 0.6 A 381.4 12.9 A 148.9 4.6 C Bark 1 18.9 0.6 B 2.1 0.1 D 127.2 5.5 B 29.1 2.9 D Level ABCD: All individual tests were carried out in triplicates. Means that are significantly different are paired according to their hierarchy of occurrence (in alphabetic letters) within each residue type. The levels not connected by same letter are significantly different.    81 2.5 DISCUSSION   It has been suggested that the variability of wood residue depends on the source of biomass feedstock, handling, and characterization (Schmidt, 1991). The establishment of an effective processing platform for biomass handling in BC could enhance combustion efficiency, reduce maintenance issues of the grate rolls, minimize contamination which can lower the refractory melting temperature during burning, and reduce overall operational costs (Donovan, 1994). Given that HHV, ash content, basic density, moisture content, and particle size distribution are reported to vary significantly, this research attempted to provide reliable information to narrow the information gap on the BC’s industrial mill residue properties pertaining to various bioenergy conversion systems.   Higher heating value   The analyses of whitewood and bark residues for the 33 samples showed that HHV does not differ significantly with review results (section 2.4). In addition, coastal HHV was higher than that of interior mills. The higher HHV values obtained for the bark samples may be explained by bark cellwall having higher concentrations of reduced compounds such as lignin, resins, phenols, and the waxy substance suberin, which are major contributors to the heat energy value of biomass (Demirbas, 2001; William, c2000; Lehtikangas, 2001; Jirjis and Theander, 1990). Chemical analysis indicated that bark typically has volatile matter (74.7%), fixed carbon (24%) and ash (1.3%) while whitewood has volatile matter (80%), fixed carbon (19.4%) and ash (0.6%) (Demirbas, 2000).  The small variations of HHV among primary wood residues indicate that, for thermochemical processes residues from different mill type could be assorted without compromising the residue quality (Senelwa and Sims, 1999). However, consideration should be given when mixing residues from different geographical regions (Figure 2.6).    82 Ash content   Ash is an incombustible component of wood. It is an important criterion for evaluating wood residue for thermochemical processes. The ash content of biomass affects both the handling and processing costs of the overall biomass energy conversion cost (Cuiping, et al. 2004).  Higher ash contents can cause combustion and gasification difficulties in that, the oxidation temperature of minerals is often higher than that of biomass ash oxidation temperature, leading to operational problems such as slagging, fouling, sintering and corrosion in the grate and subsequent feed blockages. The major concern with ash content is not solely linked to the technological maintenance but also to bioenergy produced.  Further, substantial amounts of roundwood processed in and around the coastal regions of BC are transported by the ocean which has been reported to increase the mineral content of the bark residue and thus may account for increase proportion of ash content for coastal residue than that of interior residue. To this end, appropriate handling of wood residue is important to optimize bioenergy production.   Moisture content  The effect of varying MC of samples is as a result of seasonality and residue handling at mill site. Also, MC of the manufacturing residues depends very much on the processing stage at the mill. For example chips produced before kiln drying will be higher in MC than shavings produced after kiln drying. There are two types of moisture content in biomass: (1) intrinsic moisture which is the moisture content of the residue without the influence of external effects; and (2) extrinsic moisture which is the influence of prevailing weather conditions during harvesting or storage on the overall biomass moisture content (McKendry, 2002). However, because moisture content of the residue analyzed did not depend on the intrinsic property but on the origin and seasons, variability amongst the residues is not significant to the processing platform.  83 The fact that MC is very difficult to control at the mill site, it is always advisable to dry residues before storing to enhance fuel quality for thermochemical processes. Residues with MC above 50% will be subjected to increased decomposition and mould formation, and storage in dry conditions is always recommended. The ideal MC range for thermochemical bioenergy processes is 15 – 30%, which implies most feedstock types would need to be dried to maximize the total energy produced. Biomass with moisture contents above 30% makes ignition difficult and reduces the net calorific value produced due to the need to evaporate the additional moisture before combustion/gasification can occur (McKendry, 2002). The conversion of residues through biochemical processes requires a residue with a moderate to high MC in order to facilitate the penetration of chemicals and enzymes into the residues. The production of ethanol from feedstock includes various conversion pathways such as enzymatic hydrolysis and acid hydrolysis that are moisture intensive processes. Unlike thermochemical bioenergy processes, bio-conversion requires feedstocks with moisture content greater than 25%, thus samples from all mills (M1, M2, M3, M4 and M5) are more suitable for bio-conversion. The net calorific value of biomass (on a non-oven dry basis) is greatly influenced by the MC. Given the known percentage of moisture for every given residues type, the net HHV can be calculated using the equation below: Hu(w) = [Hu(wf) (100 – w) – 2.44 w]/100 Where in the equation, Hu(w) defines the net calorific value (MJ/kg) of the biomass at a specific total moisture, Hu(wf) the net calorific value of the fully dry biomass, and w the total moisture (in %). This equation demonstrates that at high MC, the net HHV reduces significantly and at 88% MC, the net HHV is zero. This implies more than half of the total analyzed samples had net HHV of zero (Smith et al., 2001).     84 Basic density   Basic density is the weight of biomass per unit of volume. The potential heat energy available per unit volume of residue is directly dependent on ash and basic density. Basic density also is a direct function to residue transportation cost. Biomass of low basic density is expensive to handle, transport and store. Apart from handling and storing behavior, the basic density is important for the performance of the biomass as a fuel inside the reactor (Stassen and Knoef, 2004). Residue with high basic density and low moisture content has always been preferred because of its high energy content per unit volume and slow burning rate (Senelwa and Sims, 1999).  Very low basic density may reduce the residence time in a thermochemical reactor resulting by lowering the conversion efficiency. It may also give rise to poor mixing characteristics and a nonuniform temperature distribution, both of which create unfavorable operating conditions of the thermochemical conversion systems (Bilbao et al., 1988).   Particle size distribution  The size and shape of the residue particles plays a major role in selecting the operational platform for bioenergy production. Beaumont and Schwob (1984) investigated the various sizes of particles and their influence on thermochemical processes. It was revealed that coarser particles yield more of char, gas, and less oil during pyrolysis. Ideal particle size suitable for thermochemical processes ranges between 10 – 20 mm which is suitable for residues such as recycled wood and chips. Larger particles such as hog fuel (Dp50 4.3 – 43.7mm) and bark (Dp50 3.8 – 49.7mm) can form bridges which prevent the feed moving down, while smaller particles such as sander dust (Dp50 0.2mm), shavings (Dp50 3.2) and sawdust (Dp50 1.5 – 4.9mm) may clog the available air voidage, leading to a high pressure drop and the subsequent shutdown of the gasifier (McKendry, 2002) (Table 2.12).  85 Generally speaking, small boilers (<250 kW) require a high quality wood fuel with low moisture content (<30%) and a small, even residue with few, if any, oversize or overlong particles. Medium boilers (250 kW) are more tolerant of moisture content (30 – 40%) and can handle a coarse particle than small boilers. Further, the amount of oversize and overlong particles should be limited in medium boilers. Large boilers (>250 kW) are tolerant of both a higher moisture content (30 – 50%) and chip quality (Kofman, 2002).   Decreasing particle size of residues for biochemical processes increases their surface area for fermentation. Only whitewood samples are suitable substrate for biochemical conversion processes. Lignin loss and digestibility was studied in a range of residue type and it was revealed that smaller particles within a range of 0 – 7 mm such as sander dust, shavings, sawdust, and trim had increase lignin loss and digestibility than chips which showed a significant decrease in lignin digestion (Reid, 1989).   86 2.6 CONCLUSIONS   Residues were collected from five different sources representing the coastal and interior regions of BC. The physical properties for bioenergy processes of mill residues were analyzed in triplicates for 33 samples. The test results included HHV, ash content, MC, basic density and particle size distribution. The HHV ranged from 17 – 21.5 MJ/kg. Ash contents were quite small for whitewood (0.1 – 4.5%) and higher for bark and hogfuel (2.0 – 6.5%). MC which was determined on an oven dry basis, ranged from 5 – 317%. The basic density was expressed as weight per unit volume ranged from 120 – 498 kg/m3, and sander dust (Recycled wood1) was an outlier with a value of 1157 kg/m3. The average particle size (Dp50) ranged from 0.2 – 49.7mm.  This research does not give a complete review of all available physical characteristics of wood residues but rather provides useful data on primary wood processing residues available for thermochemical and biochemical bioenergy processes within the province of BC. The provision of suitable handling sites to minimize dirt, prepared storage of the residues to minimize MC, and suitable reduction of particle sizes to the required specifications, will contribute towards achieving the optimum bioenergy conversion efficiency.  Understanding the different types of mill residues and their acceptable characteristics will enhance the optimization of thermochemical and biochemical processes and enable more efficient technological design for bioenergy production.    87 2.7 REFERENCES  Alakangas, E., Valtanen, J., and  Levlin, J. (2006) CEN technical specification for solid biofuels – Fuel specification and classes. Biomass and Bioenergy 30 (2006) 908-914 American Society for Testing and Materials (ASTM). (2002) Annual book for ASTM standard, section 4, volume 04.10: Wood, ASTM, West Conshohocken, Pennsylvania. BC MoF, (2006).  Major Primary Timber Processing Facilities in British Columbia (MPTPFBC) 2005.  Economics and Trade Branch. Ministry of Forests & Range Victoria, B.C. Beaumont, O. and Schwob, Y., (1984) Influence of physical and chemical parameters on wood pyrolysis. Ind. Eng. Chem. Process Des. Dev. 23, pp. 637–641 Bilbao, R., Lezaun, J.L., Menendez, M., and Abanades, J.C.  (1988). Model of mixing/segregation for sand-straw mixtures in fluidized beds. Powder Technology 56:149-155. Bowyer, L.J., Shmulsky, R., and Haygreen, G.J. (2003) Forest Wood Products and Wood Science: An Introduction. 4th Edition, ISBN 0813826543. Iowa State Press-USA 564p Bradley, D. (2006). Canada biomass – bioenergy report. Report Climate change solutions for IEA Task 40. 402 Third Avenue, Ottawa, Ontario – Canada. Bradley, D. (2007) Canada - Sustainable forest biomass supply chains. Climate change solutions for IEA Task 40. 402 Third Avenue, Ottawa, Ontario – Canada. Bushnell, J., Haluzok, C., and Dadkhah-Nikoo, A. (1989) Biomass Fuel Characterization: Testing and Evaluating the Combustion Characteristics of Selected Biomass Fuels, Bonneville Power Administration, Corvallis, OR.  88 Carrasco, F., (2004) Thermo-mechano-chemical pretreatment of wood in a process development unit. Wood Science and Technology. Pp. 413-428 Springer Berlin / Heidelberg. Cuiping, L. Chuangzhia, W. Yanyongjieb and Haitao, H. (2004) Chemical elemental characteristics of biomass fuels in China. Biomass and Bioenergy. Volume 27, Issue 2, August 2004, Pages 119-130. Demirbas, A. (1996) Calculation of higher heating values of biomass fuels Fuel Vol. 76, No. 5, pp. 431-434, 1997 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0016-2361/97 $17.00+0.00 Educational Faculty, Technical University of the Black Sea , 61335 Akgaabat-Trabzon, Turkey Demirbas, A. (2000) Biomass resource facilities and biomass conversion processing for fuels and chemicals P.K. 216, TR-61035- Trabzon, Turkey Demirbas, A. (2001) Biomass resources for energy and chemical industry. Energy Edu Sci Technol 5 (2000), pp. 21–45. Elsevier Science Ltd. Donovan, C.T. and Associates, (1994) A sourcebook on wood waste recovery and recycling in the Southeast, U.S. Department of Energy South Eastern Regional Biomass Energy Program, Tennessee Valley Authority, Alabama Dramm, J. (c2000) Small log sawmilling 101 forest products utilization specialist. USDA forest service, State and private forestry. Madison Wisconsin. Retrieved from http://www.forestprod.org/smallwood02_dramm.pdf. Verified April 29th 2009.  Food and Agriculture Organization of the United Nations Rome, (FAO) (1990). Energy conservation in the mechanical forest industries. ISBN 92-5-10, FAO Forestry Paper  - 93, T0269/E Hartmann, H., Bohm, T., Jensen P.D., Temmerman, M., Rabier, F., and Golser, M. (2006). Methods for size classification of wood chips. Biomass and Bioenergy 30, 944-953  89 Ince, P.J. (1977) Estimating effective heating value of wood or bark fuels at various moisture contents. Statistical Assistant Forest Products Laboratory, 1/ Forest Service U.S. Department of Agriculture Jirjis, R., and Theander, O. (1990). The effect of seasonal storage on the chemical composition of forest residue chips. Scandinavian Journal of Forest Research 53, pp. 437–448. Kofman, D.P. (2002). Quality wood chip fuel. Harvesting/ Transporting No. 6. COFORD connects. Socio-Economic Aspects of Forestry Lehtikangas, P. (2001) Quality properties of pelletised sawdust, logging residues and bark Department of Forest Management and Products, Swedish University of Agricultural Sciences, SLU, Box 7060, 75007 Uppsala, Sweden McCloy, B., and Associates. (1999) Canada’s wood residues: A profile of current surplus and Regional concentrations. Prepared for National Climate Change Process Forest Sector Table. 13pp McCloy, B. and Associates. (2004) Estimated production, consumption, surplus mill wood residue in Canada – 2004. Natural Resources Canada and Canadian Forest Service (NRCan/CFS) McKendry, P. (2002) Energy production from biomass (part 1): overview of biomass. Applied Environmental Research Centre Ltd, Tey Grove, Elm Lane, Feering, Colchester CO5 9ES, UK Mohan, D., Pittman Jr, U.C., and Steele, H.P. (2006) Pyrolysis of wood-biomass for bio- oil: a critcal review. Department of Chemistry, Mississippi State University, Mississippi State, Mississippi 39762, USA, Environmental Chemistry Division, Industrial Toxicology Research Centre, Lucknow, India, and Forest Products Department, Mississippi State University, Mississippi State, Mississippi 39762, USA Energy Fuels, 2006, 20 (3), pp 848–889 National Forest Database Program (NFDP). (2005) Wood Supply in Canada The Canadian Council of Forest Ministers. 2005 Report.  ISBN 0-662-41792-5; Cat no.: Fo4-5/2005E-PDF. Forests and forestry – Canada – Statistics.  90 Quaak, P., Knoef, H., Stassen, E. H. (1998). Energy from Biomass: A Review of Combustion and Gasification Technologies. Edition: Published by World Bank Publications, ISBN 0821343351,9780821343357. 78 pages Reid, C. and Associates. (1978). Hog Fuel availability in B.C. Prepared by British Columbia Wood-Waste Energy Coordinating Committee., Reid, Collins & Associates. Ministry of Forest Library, call no. 338.4766265 H714 Reid, I.D. (1989). Optimization of solid-state fermentation for selective delignification of aspen wood with Phlebia tremellosa. Enzyme Microb. Technol. 11 12, pp. 804–809. Richardson, J. Björheden, R. Hakkila, P. Lowe, A. T. Smith, C. T. (2002). Bioenergy from sustainable forestry: guiding principles and practice. Published by Springer, 2002; ISBN 1402006764, 9781402006760; 344 pages. Rosillo-Callé, F., Groot, D.P., Hemstock, L.S. and Woods, J. (2007) The biomass assessment handbook. Bioenergy for a sustainable environment. Earth Scan. ISBN- 10: 1-84407-285-1 Schmidt, K. and Associates, (1991) Biomass design manual industrial size systems, U.S Department of Energy South Eastern Regional Biomass Energy Program, Tennessee Valley Authority, Alabama. Senelwa, K. and Sims, R.E.H. (1999) Fuel characteristics of short rotation forest biomass. Biomass and Bioenergy 17 (1999), pp. 127–140. Sheng, C., and Azevedo, J.L.T. (2005) Estimating the higher heating value of biomass fuels from basic analysis data. Mechanical Engineering Department, Instituto Superior Técnico, Pavilhão de Mecãnica I, 2° Andar, Av. Rovisco Pais, 1049-001 Lisbon, Portugal. ISBN: 0961-9534, pp 499 – 507, Elsevier Ltd Smith, K.R., Kaltschmitt, M., Thrän, D. (2001). Renewable Energy from Biomass. Contribution for the Encyclopedia of Physical Science and Technology. Academic Press, San Diego, California, USA.  91 Steele, H.P. (1984). Factors determining lumber recovery in sawmilling. Gen. Tech. Rep. FPL – 39. Madison, WI. United State Department of Agriculture, Forest Service, Forest Products Laboratory. 8pp. Stassen, H.E.M. and Knoef, H.A.M. (1994) Small scale gasification systems. Biomass Technology Group BV P.O. Box 217, 7500 AE Enschede, The Netherlands. Uniply (2008). Plywood manufacturing process, Quality assurance. http://www.uniply.in/quality/process.html#cutting. Retrieved 3rd/04/2009 Walker, J.C.F., (2006) Primary Wood Processing: Principles and Practice Edition: 2, illustrated Published by Springer, 2006 ISBN 1402043929, 9781402043925, 596 pages William R.C. (c2000) Why Do Animals Eat the Bark and Wood of Trees and Shrubs? Professor of Tree Physiology Department of Forestry and Natural Resources Purdue University, West Lafayette, IN 47907 Wingate-Hill, R. and MacArthur, I.J. (1991) Debarking small-diameter eucalypts. In: The Young Eucalypt report: some management options for Australia's regrowth forests. C. M. Kerruish and W. H. M. Rawlins (eds). East Melbourne, CSIRO: 107-151. Wood Energy (2006) List and values of wood fuel parameters part 1 Ireland’s Natural and renewable Energy Source COFORD. Retrieved from http://www.woodenergy.ie/iopen24/defaultarticle.php?cArticlePath=5_29 Verified April 20th 2009. Xing, C., Deng, J., and Zhang, S.Y. (2006) Effect of thermo-mechanical refining on properties of MDF made from black spruce bark. Wood Science and Technology, Volume 41, Number 4, April 2007 , pp. 329-338(10).  92  CHAPTER 3  GEOGRAPHICAL DISTRIBUTION OF SAWMILL AND CHIPS MILL RESIDUES – ESTIMATES OF AVAILABILITY IN BRITISH COLUMBIA2   3.1 INTRODUCTION   British Columbia has traditionally been the largest lumber producing region in Canada. This province has also historically been the largest producer of primary wood processing residues. A study of residue inventories in BC from 1990-1998 revealed a large decrease in the annual surplus within the province (McCloy and Associates, 1999).  During this period (1990 to 1998), about 7.75 million BDt of primary wood processing residues were produced in BC each year, of which 5.69 million BDt were utilized, and 2.06 million BDt were considered surplus (McCloy and Associates, 1999).  In 2004, a comprehensive survey of BC’s sawmill residue generation was conducted. The results revealed that at that time, 6.55 million BDt of primary wood processing residues were produced annually, of which 4.38 million BDt were utilized, and 1.81 million BDt were considered surplus. The study also revealed that, surplus mill residues were produced primarily from large interior sawmills, while the coastal region did not produce any significant surplus residues (McCloy and Associates, 2004).  This study attempts to estimate the potential primary wood processing residues available within different geographical regions in BC, in order to assess the surplus residues that maybe available to be utilized for bioenergy production.  2 “A version of this chapter will be submitted for publication. Taku-Kehbila, A.V. and McFarlane P.N. (2010) Geographical distribution of sawmill and chips mill residues – estimates of availability in British Columbia”   93  Estimates of residue production in 2006 were developed, and the findings were compared to the 2004 survey of residues in BC (McCloy and Associates, 2004). The study was based principally on the locations of primary processing plants, such as chip mills and sawmills, in relation to existing residue consuming plants in order to estimate the quantity of residues available for use by new bioenergy plants.  In BC, the primary wood processing industry is predominantly engaged in using roundwood to manufacture structural wood products such as lumber and plywood. While historical data are available to estimate the volume of biomass used by the pulp and paper sector in BC (McCloy and Associates, 2004), there is little up-to-date information on the quantities of biomass residues consumed for energy production at sawmill sites.   3.1.1 Residue production in a sawmill  Due to the different ecoregions (Pacific Maritime, Montane Cordillera and Boreal Cordillera) in BC, and the associated variation in tree species and wood quality, there are two general types of sawmills within the province. Coastal mills process large high-grade logs to produce a wide range of specialty products while interior mills use smaller, lower quality logs to produce commodity-type products such as dimension lumber. Interior sawmills focus primarily on North American markets while coastal mills serve more diversified markets. The interior sawmills are the largest producers of mill residues in BC (McCloy and Associate, 2006). During wood processing, residue is generated in the form of sawdust, shavings, trim ends, chips and bark. These residues are generated at every sawmill and they can be converted directly for bioenergy production or non-energy use such as composite products production, pulp and paper production, pellet manufacturing, and animal bedding.    94 3.1.2 Research objectives   Underpinning the goal of quantifying surplus residues in BC, specific factors such as residue utilization, and actual feedstock availability by region were analyzed. To this end, the research sought to answer the following questions:  -Biomass supply: Sawmills and chip mills  What was the location of all the existing sawmills and chip mills in BC in 2006?  What was the capacity of these mills in 2006?  What were the various quantities of residues produced by type in 2006?  What amount of the total residues produced was used by consumers in 2006?  What percentage of the residue surplus is available for bioenergy production?  - Biomass demand: Bioenergy plants  Are there enough wood processing residues available to supply the bioenergy sector over time?  What was the capacity of each bioenergy plant in BC?  Who are the major residue consumers in BC?  What quantity of residue was consumed by each bioenergy plant on an annual basis?    95 3.2 METHODS   In 2006, there were a total of 193 sawmills in BC (BC MoF, 2008). In this study sawmills were categorized according to the estimated annual lumber production based on a standardized operation of 240 days per year, two 8 hour shifts per day. This schedule may vary in some cases. Small sized mills ranged from 0 – 10 million board feet of lumber and large sized mills ranged over 10 million board feet (BC MoF, 2008). The map below presents all the sawmills within BC by location (Figure 3.1).  Figure 3.1: Geographical location of sawmills in British Columbia in 2006   In 2006, the average lumber recovery factor (LRF) for the interior forest region was, 0.279 mfbm/m3 lumber (or 47% yield on a volumetric basis). In 2006, the interior mills  96 also converted 38% v/v of the incoming whitewood to chips, and 15% v/v to shavings and sawdust (BC MoF, 2008).  In the coastal forest region LRF for an average 100% roundwood produced in 2006 was 0.234 mfbm/m3 lumber (or 43% yield on a volumetric basis). In 2006, the coastal mills also converted 34% v/v of the incoming whitewood to chips, and 23% v/v to shavings and sawdust (BC MoF, 2008) (Figure 3.2). Given the above estimates, it is possible to estimate the total residues produced on a regional basis.    Figure 3.2: Typical softwood lumber recovery and byproduct production in BC (photo from USDA Forest Service by Steele. 1984)  3.2.1 Estimating total residue production in BC   This section estimates the amount of residues available for energy and non-energy use within BC. There are various methods (theoretical, technological and economical) used to determine the economics of residues availability for bioenergy production (Voivontas et. al. 2001). In this study the theoretical analysis of biomass potential was adopted were the total production of residues from chip mills and sawmills in BC was determined.  38% Chips 15% Shavings &       Sawdust 47% Lumber Interior forest region 23% Shavings &       Sawdust 34% Chips 43% Lumber Coastal forest region  97 Due to a lack of detailed residue data generated at mill sites, major primary timber processing data were collected from the BC Ministry of Forest (BC MoF, 2008) and confidential individual actual lumber output data for all sawmills and chip mills in BC. The ratio deduced from the confidential report was used to extrapolate total residues generated from each mill by type in terms of sawdust, shavings, trim ends, bark and chips.  The general approach employed followed four phases: (1) the sawmills and chip mills that were operating in BC in 2006 were identified and confidential output data were obtained; (2) confidential data were used to estimate the total residue production for all mills in 2006; (3) regional estimates were made of the quantity of each type of residue produced, consumed and surplus; (4) the results were compared with McCloy and Associates, 2004 residue survey.   3.2.2 Residue production from roundwood   The confidential mill data were used to develop residue production equation, and these equations were applied to all the lumber mills operating in 2006. The total residue production is the sum of the individual estimates of product recovered from a roundwood regionally. The estimated theoretical analysis indicated that the total amount of residues generated from lumber and chip mills in 2006 breakdown by residue type was as follows:  Total Residue = confidential lumber production output (mfbm) * (Mean of  confidential residue breakdown (BDt))/(Mean of confidential lumber output (BDt))  In the interior forest region Chips (BDt) = Lumber (mfbm) * 0.38 (BDt/mfbm) Sawdust (BDt) = Lumber (mfbm) * 0.06 (BDt/mfbm) Shavings (BDt) = Lumber (mfbm) * 0.06 (BDt/mfbm) Trim ends (BDt) = Lumber (mfbm) * 0.02 (BDt/mfbm) Bark (BDt) (produced at sawmills) = Lumber (mfbm) * 0.16 Bark (BDt) (left on the forest during logging) = Lumber (mfbm) * 0.05  98  In the coastal forest region Chips (BDt) = Lumber (mfbm) * 0.34 (BDt/mfbm) Sawdust (BDt) = Lumber (mfbm) * 0.1 (BDt/mfbm) Shavings (BDt) = Lumber (mfbm) * 0.1 (BDt/mfbm) Trim ends(BDt)  = Lumber (mfbm) * 0.03 (BDt/mfbm) Bark (BDt) (produced at sawmills) = Lumber (mfbm) * 0.16 Bark (BDt) (left on the forest during logging) = Lumber (mfbm) * 0.05    In the interior forest region: both Northern and Southern forest regions  The residue ratios used in section 3.2.2 are bound to change when the mills process mountain pine beetle (MPB) killed wood. For example, sawmill trials using MPB infected logs yielded 12.5% less lumber and 17.5% less value compared to normal green logs, because MPB roundwood has reduced moisture content (Barrett and Lam, 2007). Due to the MPB crisis in BC, the annual allowable cut has increased in the Northern interior forest region, which over time would generate additional sawmill residue to feed the bioenergy sector (Ralevic and Layzell, 2006).   In the coastal forest region   The original ratios for the representative interior mill were used to determine the proportion of roundwood converted to chips, sawdust, shavings and trim ends. At the end of the initial model run, the output from this model underestimated the volume of chips produced by coastal sawmills by approximately 13% compared to the data produced by BC MoF (2008) report. The ratios of residues produced by the coastal mills were adjusted slightly to the corresponding total residue equation in order to achieve a good estimate of residue produced in the coast.  99 3.2.3 Chip production in a chipping mill   BC had 13 chip mills in 2008. Chip mills were considered amongst all other primary mill type because they are the major supplier of chips to the pulp and paper industry, and pulp and paper mills are the biggest province’s consumer of primary residues. Chip mills may vary in size from large whole-log chippers down to very small units used for the chipping of sawmill residues. The quality of chips is very important in pulp industries because it affects the uniformity of the pulp and the productivity of a pulp mill (Fuller, 1991).  In this study, it was assumed that the recovery from a chip mill consuming 100% roundwood produced approximately 90%v/v chips, 5%v/v sawdust and 5%v/v shavings. Given the above estimates, it is possible to calculate total residues produced from the chip mill.  In the chips mill: Chips (from chip mill) = Roundwood * 0.90% Sawdust = Roundwood * 0.05% Shavings = Roundwood * 0.05%  These equations provided estimates of trim ends, sawdust, shavings, bark, chips and chips from the chip mill that can be used for energy and non-energy consumption. The following chapter analyzes the amount of residues consumed within BC by region.    100 3.2.4 Estimating total primary mill residue consumption in BC   The following residue consumption categories have been assumed:   Pulp and paper   Pulp and paper industries consume primarily chips, and are the oldest and the largest residue users in BC. With the increased closure of mills in BC, the pulp mills are experiencing large chip shortages. In 2006, there were 21 pulp and paper mills in BC and they consumed approximately 1,294,000 BDt chips annually.  The back calculation for pulp and paper was challenging in that the efficiency yield came from a credible but out-dated source (Forintek, 1985). According to Forintek, a typical bleached kraft pulp mill had an estimated yield of 0.39%, unbleached kraft pulp mill had an estimated yield of 0.45%, while mechanical mill had a yield of 0.94%. However there is the expectation that the conversion efficiencies have improved over the years compared to what was reported in Forintek (1985). The equation was derived as: Residues (BDt/yr) Input = ((total output of pulp (ADMT) /estimated yield) * (0.9                            (1 tonne at 10%MC) /estimated yield) * 1.12 (conversion factor to BDt))   Cogeneration and gasification   Cogeneration generally uses the lowest quality residues (i.e. bark or hog fuel) for energy generation. Consequently, thermochemical facilities have the added advantage of less residues competition than bio-chemical refineries. Traditionally, sawmills in BC have used natural gas or steam from on-onsite boilers for kiln drying lumber. Due to data limitations, not all combustion facilities operating on-site at sawmill have been accounted  101 for in this study. Consequently, this study over-estimated the surplus bark and hogfuel residues available.  There are six independent power and heat producers (IPP) and some sawmill on-site cogeneration plants in BC producing approximately 184 MW of electricity and heat annually. The largest IPP in BC is EPCOR cogeneration plant at Williams lake, which produces 65MW of power and utilizes approximately 300,000 – 400,000 BDt residues annually.  Several cogeneration websites were researched to get the total output of power produced per hour. To estimate the conversion efficiency at each plant, a 50% recovery factor was assumed for each production unit as illustrated in the equation below (BC Hydro, 2009). Residue (BDt/yr) Input = ((total energy output (BDt) * (106 * 0.5) * 24 * 345) (BC Hydro, 2009; Bradley, 2009; and Capital Power Income L.P. 2009).   Wood pellets   Pellets are produced mostly from sawdust which is used for animal bedding and home heating. In 2006, there were 8 pellet plants in BC with a capacity of approximately 1,195 thousand tonnes of pellets (BC MoF, 2008). At this time, the leading producer was Pinnacle Pellets which operated 4 plants in BC, with a total annual capacity of 355 thousand tonnes of pellets. To calculate the total amount of residue consumed at a pellet site, a high recovery factor (of 0.97%) was designated to convert whitewood to pellet which was: Residue (BDt/yr) Input = ((Capacity (output)/345) * 0.97%) (Pinnacle pellet INC. (2007)) (Figure 3.3).   102  Figure 3.3: Geographical locations of residue users in British Columbia in 2006  103 3.3 RESULTS   This study estimated that the BC sawmills produced 11.7 million BDt of total residues production in 2006 (Table 3.1). In order to assess the long-term residue production trend in BC, the theoretical estimates of this study were analyzed using the previous research results of McCloy (1999 and 2004). As McCloy excluded the total production of chips and trim ends in his calculations, the values estimated in this study were modified to enable a direct comparison with McCloy’s data (see BC2 in Table 3.1). The total estimated for 2006 was 4.81 million BDt compared to 6.55 million BDt reported in 2004 and 7.75 million BDt reported in 1999 mill survey.  These results suggest that the total residue production trend in BC is declining over time (McCloy and Associates, 2004, 1999) (Table 3.1). The declining trend is associated with the continuous increase in residue utilization. The long-term trend of residue availability is worsened by the collapse of the housing starts in US that have created a drastic impact on the lumber mills in BC; and especially the interior sawmill that focus primarily on North American lumber markets (NAHB, 2008).  Table 3.1: Residue generation from lumber production Actual Lumber Production (MMfbm) Production of Residue by type (BDt) Regions Actual Lumber Production % total production Sawdust Shavings Trim ends Chips Bark Total Northern interior forest region 7,211 42.5 481,410 481,410 140,337 2,806,749 1,016,585 4,926,491 Southern interior forest region 7,191 42.3 426,076 426,076 142,025 2,840,513 1,136,205 4,970,897 Coast forest region 2,583 15.2 164,300 164,300 53,910 1,037,140 410,750 1,830,400 BC1 16,985 100 1,071,786 1,071,786 336,274 6,684,404 2,668,373 11,727,788 BC2  16,985 100 1,071,786 1,071,786   2,668,373 4,811,945 BC3 13,994 100 2,057,163 1,514,958   2,981,633 6,553,754 1BC Theoretical total, 2006 analysis 2BC Theoretical total, 2006 analysis: comparing results with that of McCloy (2004) 3BC McCloy, 2004 survey   104  In 2004, the total lumber output in BC was 17,237 mfbm from 196 sawmills. While in 2006, total lumber output in BC was 16,985 mfbm from 193 sawmills and total production for chip mills was 1,357 million BDt. This study estimated that the total amount of residues produced in 2006 was 11.7 million BDt. About 41.9% of the total residues came from Northern interior region, 42.4% were from the Southern interior region and 15.6% were generated in the coastal region of BC (Figure 3.4). Figure 3.4 presents three principal residue types produced in 2006 and compares the results with the survey by McCloy and Associates 2004 (McCloy and Associates, 2004). The results indicate that between 2004 and 2006 the drop in lumber production in BC has led to a decrease in the total residues produced.  Residue inventory in 2004/2006 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 Sa wd us t Ba rk Sh av ing s R es id ue s pr od uc ed  (B D t) Theoretical inventory, 2006 McCloy, 2004  Figure 3.4: BC residue distribution by type in 2004 and 2006   Residues generated from chip mills  The total amount of residues produced in the theoretical survey for 2006 at chip mills was 887,000 BDt. About 11.1% of the total chip residues came from Northern interior region,  105 14.5% from the Southern interior region and 72.4% was generated from the coastal region of BC (Table 3.2).  Table 3.2: Residue generation from chip mills in 2006 Regions Chips (BDt) Sawdust (BDt) Shavings (BDt) Total (BDt) Northern  Region 87,597 4,866 4,866 97,329 Southern Region 130,670 7,259 7,259 145,188 Coastal Region 571,408 31,744 31,744 634,896 Final total, 2006 789,675 43,869 43,869 877,413   Wood residues are produced at specific locations and thus this section estimates the total quantity of residues produced from both sawmills and chip mills (Table 3.3). The province of BC is divided into three forest regions: Northern interior forest region, Southern interior forest region, and coast forest region (BC MoF, 2008); and the forest regions are further subdivided into districts (BC MoFR, 2009). This study has adopted the same approach to simplify the analysis of hauling costs as presented in chapter five of this study.   Table 3.3: Total residue from both lumber mills and chip mill production Regions Chips (BDt) Sawdust (BDt) Shavings (BDt) Trimends  Bark Total (BDt) Northern Region 2,894,346 486,276 486,276 140,337 1,016,585 5,023,820 Southern Region 2,971,183 433,335 433,335 142,025 1,136,205 5,116,083 Coastal Region 1,608,548 196,044 196,044 53,910 410,750 2,465,296 Final total, 2006 7,474,077 1,115,655 1,115,655 336,272 2,563,540 12,605,199   106 3.3.1 Northern Interior Forest Region   The Northern interior forest region is made up of the following forest districts:  Fort Nelson,  Fort St. James,  Kalum,  Mackenzie,  Nadina,  Peace,  Prince George,  Skeena Stikine,  Vanderhoof.  There was no residue generated for Fort Nelson, as there were no primary mills. There are 38 sawmills and 2 chip mills within the Northern region, and in 2006, the two largest sawmills were located in Nadina (525 MMfbm annually) and Mackenzie districts (420 MMfbm annually). In 2006, lumber production in the Northern interior forest region was 7,211 million board feet which accounted for 42.5% of the Province’s lumber production. The annual production of residues in 2006 from the sawmill industry was estimated to be 4,926,000 BDt, of which 2,806,000 BDt were chips, 481,000 BDt sawdust, 1,016,000 BDt bark, 140,000 BDt trim ends, 481,000 BDt shavings and 48,000 BDt chips from the chip mill (Table 3.1; and Figure 3.5).  In 2006, the total residue generated from the chip mills in the Northern interior forest region was estimated to be 97,000 BDt, of which 88,000 BDt were chips, 5,000 BDt were sawdust and 5,000 BDt were shavings.   107 shaving s 9 % chip s 56 % sawd ust 9 % b ark 2 3 % t r im end s 3 %  Figure 3.5: Northern Interior forest region: proportions of residue production by type from sawmills   Residue Consumers   In 2006, the major residue consumers of wood residues in the Northern interior forest region were: eight pulp and paper mills that consumed an estimated 4,695,000 BDt; three wood pellet producers that consumed an estimated 403,000 BDt; and two cogeneration plants that consumed an estimated 258,421 BDt (Table 3.4).  This study acknowledged that there were other consumers such as mill onsite energy systems, oriented strain board (OSB) and a medium density fibre board (MDF) mill that have not been accounted for due to limitation of available data. Forest districts such as Peace, Prince George, and Kalum that are deficient of a cogeneration or gasifier facility may be experiencing large piles of hog fuel and bark on-site.       108  Table 3.4: Residue consumers in the Northern interior forest region Regions Pulp and paper (BDt) Cogeneration and gasification (BDt) Pellets (BDt) Total (BDt) Fort Nelson Fort St James     0 Mackenzie 702,190 170,000   872,190 Nadina     145,500 145,500 Peace 425,880     425,880 Prince George 2,494,460   130950 2,625,410 Skeena Stikine       0 Vanderhoof   88,421 126,100 126,100 Kalum 1,072,480     1,072,480 Total 4,695,010 258,421 402,550 5,267,560   Estimation of surplus residues   Between 2004 and 2006, there was a large increase in residues consumption in the Northern forest district, which has led to a net deficit in available surplus from 1,358,000 BDt in 2004 to -244,000 BDt in 2006. The major deficit districts are Kalum with an estimated deficit of 953,000 BDt, Prince George with an estimated deficit of 781,000 BDt, Mackenzie with an estimated deficit of 363,000 BDt (Table 3.5).  Table 3.5: Residue surplus in the Northern interior region Produced (BDt) Produced (BDt) Regions (Sawmills) (chips mill) Total consumed (BDt) Surplus (BDt) Fort Nelson 0   0 0 Fort St James 356,265   0 356,265 Mackenzie 458,871 50,000 872,190 -363,319 Nadina 891,724   145,500 746,224 Peace 470,400   425,880 44,520 Prince George 1,843,967   2,625,410 -781,443 Skeena Stikine 234,500   0 234,500 Vanderhoof 598,095   126,100 471,995  109 Produced (BDt) Produced (BDt) Regions (Sawmills) (chips mill) Total consumed (BDt) Surplus (BDt) Kalum 72,669 47,330 1,072,480 -952,481 Total 4,926,491 97,330 5,267,560 -243,739  Nadina showed significant net surplus residues of 746,000 BDt, Vanderhoof of 472,000 BDt, followed by Skeena Stikine (235,000 BDt), Fort St. James (356,000 BDt) and Peace (45,000 BDt). Most of the surplus wood residues constitute hogfuel which is often problematic to handle. The hog fuel is either: (i) consumed by on-site wood residue incinerators, (ii) dumped on-site open area, and (iii) signed short-term contracts to residue consumers (Figure 3.6) (Bradley, 2009). It most be noted that, not all combustion facilities operating on-site at sawmill have been accounted for in this region. Consequently, the bark and hogfuel available are over-estimated.   -1500000 -1000000 -500000 0 500000 1000000 1500000 2000000 2500000 3000000 Fort Nelson Fort St James Mackenzie Nadina Peace Prince George Skeena Stikine Vanderhoof Kalum R es id ue  m as s (B D t) Produced Consumed Surplus/deficit  Figure 3.6: Northern interior forest region residue estimates  110 3.3.2 Southern Interior Forest Region   The Southern interior Forest region is made up of the following forest districts:  Arrow Boundary,   Cascades,  Central Cariboo,  Chilcotin,  Columbia,  Headwaters,   Kamloops,  Kootenay Lake,  Okanagan Shuswap,  Quesnel,  Rocky Mountain,   100 Mile House.  In 2006, there were 83 sawmills and 4 chip mills within the Southern region. The largest sawmill was located in Central Cariboo (541 mfbm annually). In 2006, lumber production in this region was 7,191 million board feet which accounted for 42.3% of the total provincial lumber production. The annual production of residues in 2006 from the sawmill industry was estimated to be 4,971,000 BDt, of which 2,841,000 BDt were chips, 426,000 BDt were sawdust, 1,136,000 BDt were bark, 142,000 BDt were trim ends, and 426,000 BDt were shavings (Figure 3.7 and Table 3.1).  In 2006, the total residue produced from the chip mills in Southern interior forest region was estimated to be 145,000 BDt, of which 131,000 BDt were chips, 7,000 BDt were sawdust and 7,000 BDt were shavings.   111 shavings 9% chips 56% trim ends 3% bark 23% sawdust 9%  Figure 3.7: Southern interior forest region: proportion of residue production by type    Residue consumers    In 2006, the major consumer of Southern interior wood residues from the mills included: five pulp and paper mills that consumed an estimated 4,148,610 BDt; four wood pellet producers that consumed an estimated 757,000 BDt; three cogeneration plants that consumed an estimated 1,257,500 BDt. The Kamloops, Arrow Boundary, and Rocky Mountain forest districts may be experiencing large piles of bark and hog fuel on-site, because the consumers (such as pulp and paper mills, pellet mills) within these region are more interested in the whitewood than bark or hog fuel (Table 3.6).  Table 3.6: Residue consumers in Southern interior region Regions Pulp and paper (BDt) Cogeneration and gasification (BDt) Pellets (BDt) Total consumed (BDt) 100 Mile House    0 Kamloops 1,093,270   1,093,270 Arrow Boundry 1,172,210   1,172,210 Cascades   87,300 87,300 Central Cariboo  625,000 485,000 1,110,000  112 Regions Pulp and paper (BDt) Cogeneration and gasification (BDt) Pellets (BDt) Total consumed (BDt) Chilcotin    0 Columbia  317,500  317,500 Kootenay lake    0 Headwaters    0 Okanagan shuswap  100,000 97,000 197,000 Quesnel 1,214,790  87,300 1,302,090 Rocky mountain 668,340 215,000  883,340 Total 4,148,610 1,257,500 756,600      6,162,710   Estimation of surplus residues   In the Southern interior forest region there were no net available surpluses in 2006. The net deficit for the Southern region was estimated at 1,032,627 BDt. Surplus residues have declined significantly in the Kamloops region since the last survey conducted in 2004. The Kamloops region is estimated at 205,000 BDt in 2004, while the 2006 surplus residues has decreased further to an estimated deficit of 707,000 BDt (Table 3.7).   Table 3.7: Residue surplus in the Southern interior forest region Produced (BDt) Produced (BDt) Regions (Sawmills) (Chips mill) Total consumed (BDt) Surplus (BDt) 100 Mile House 370,987   0 370,987 Kamloops 357,287 15,000 1,093,270 -706,983 Arrow Boundry 388,725   1,172,210 -783,485 Cascades 562,478   87,300 475,178 Central Cariboo 745,515 121,000 1,110,000 -243,485 Chilcotin 151,900   0 151,900 Columbia 58,800   317,500 -258,700 Kootenay Lake 65,771   0 65,771 Headwaters 206,068   0 206,068 Okanagan Shuswap 865,262 9,189 197,000 677,451 Quesnel 755,300   1,302,090 -546,790 Rocky mountain 442,801   883,340 -440,539 Total 4,970,894 145,189 6,162,710 -1,032,627   113 The available residue surplus of the Cariboo regional survey in 2004 was 207,000 BDt, where the total production was 1,187,000 BDt and consumption was 980,000 (McCloy and Associates). In 2006 analysis, Central Cariboo appeared to have decreased its total production (746,000 BDt), total consumption (1,110,000 BDt) and total deficit (244,000 BDt) from what was reported in 2004 (Figure 3.8).   -1,000,000 -500,000 0 500,000 1,000,000 1,500,000 100 M ile House Kam loops Arrow Boundry Cascades Central Cariboo Chilcotin Colum bia Kootenay lake Headw aters O kanagan shusw ap Q uesnel Rocky m ountain R es id ue  m as s (B D t) Produced Consumed Surplus/deficit  Figure 3.8: Southern interior forest region residue estimates   114 3.3.3 Coastal Forest Region   The coast forest region is made up of the following forest districts:  Campbell River,  Chilliwack,  North Coast,  North Island,  South Island,  Squamish,  Sunshine Coast.  The coastal forest region had the smallest sawmilling industry amongst the three forest regions. In 2006, there were 73 sawmills and 8 chip mills within the coast region. Within the 73 sawmills, 14 were above 100 and below 200 mfbm, while the remaining 59 mills were below 100 mfbm annual production. In 2006 total lumber production was 2,583 million board feet which accounted for 15.2% of the Province’s total lumber production. The annual production of residues in 2006 from the sawmill industry was estimated at 1,830,000 BDt, of which 1,037,000 BDt were chips, 164,000 BDt were sawdust, 411,000 BDt were bark, 54,000 BDt were trim ends, and 164,000 BDt were shavings (Table 3.1; and Figure 3.9).  In 2006, the total residue production from the chip mill in the coastal forest region was estimated at 635,000 BDt, of which 571,000 BDt were chip, 32,000 BDt were sawdust, and 32,000 BDt were shavings.  115 shavings 9% trim ends 3% bark 22% sawdust 9% chips 57%  Figure 3.9: Coast forest region: residue production   Residue consumers   The major consumers of wood processing residues in the coastal region are: eight pulp and paper mills that consume an estimated 4,108,000 BDt; and one cogeneration plant that consumes an estimated 110,000 BDt (Table 3.8). In addition to the net regional production, the coastal consumers are forced to import large amount of residues from the interior forest region to support the existing high residue demand (McCloy, 2004).  Table 3.8: Residue consumption in the coast forest region   Regions Pulp and Paper Cogeneration and  gasification Total Campbell River 436,520   436,520 Chilliwack 171,160 110,000 281,160 North island     0 South island 1,598,610   1,598,610 Sunshine coast 1,901,994   1,901,994 Squamish     0 Queen Charlotte     0 Total 4,108,284 110,000 4,218,284   116 Estimation of surplus residues   There are no surplus mill residues available in the coast forest region (Table 3.8). The sawmill surplus residues have declined significantly over the years especially in the Sunshine coast and are expected to further decline in the coming years due to significant close down of mills around the coastal region (Figure 3.9).   Table 3.9: Residue surplus in the coast forest region Produced (BDt) Produced (BDt) Regions (Sawmill) (Chips mill) Total consumed (BDt) Surplus (BDt) Campbell River 136,589 144,300 436,520 -155,631 Chilliwack 968,495 252,129 281,160 939,464 North Island 0.67022   0 0.67022 South Island 711,843 120,560 1,598,610 -766,207 Sunshine coast 6,916 117,818 1,901,994 -1,777,260 Squamish 5,704   0 5,704 Queen Charlotte 0.18538   0 0.18538 Total 1,829,548 634,807 4,218,284 -1,753,929  -2,000,000 -1,500,000 -1,000,000 -500,000 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 Cam pbell River Chilliw ack North island South island Sunshine coast Squam ish Q ueen Charlotte R es id ue  m as s (B D t) Produced Consumed Surplus/deficit  Figure 3.10: Coast forest region residue estimates  117 3.4 DISCUSSION   The discussion section is divided into two subsections: (1) discussion and interpretation of results in this section of the thesis (2) other factors influencing residue production in BC.   3.4.1 Results interpretation   This study revealed that there were no excess residues produced in all the three forest regions of BC. The Northern interior forest region presented the smallest net annual residue deficit (at 244,000 BDt) of all the three forest regions. A bioenergy plant is most suited in the Nadina and Vanderhoof forest districts which exibited the largest surpluses (Figure 3.6). In the Peace forest district a balanced situation existed were most of the residues produced were consumed.  Approximately 34.8% of the Southern interior forest region’s surplus residues were located in the Okanagan Shuswap forest district which is a viable potential site for bioenergy market, followed by Cascades 24.4% surplus, and 100 Mile House at 19% surplus. Districts such as, Headwaters and Chilcotin’s available residue may not support an economically viable bioenergy project; however can be used to replace natural gas at the sawmill for kiln drying the lumber (Figure 3.8). The Southern interior forest region had the second largest net annual deficits at 1,033,000 BDt.  The coastal forest region had the largest net annual deficits at 1,754,000 BDt. Thermochemical plants are noted to use the lowest cost feedstock; however locating a thermochemical plant in the coastal forest region that will operate solely on sawmill residues is completely unfeasible based on the scarcity of residues (Figure 3.10). The most cost effective situation may arise if a pulp mill shuts down, as that will divert some of the residue to support a new bioenergy process.    118 Theoretical model for residue sourcing The theoretical results of residue availability in BC, was developed by equating residue producers (chip mills and sawmills) and consumers (pellet plants, bioenergy facilities, and pulp and paper) (Figure 3.11). Residue quantification is expanded in chapter five where the GIS-Dijkstra integrated assessment model is employed to quantify the cost and supply of residue availability.   Figure 3.11: Major sawmill residue consumers in BC  To analyze the above Figure 3.11, a scenario may occur where, with more than one pulp mill shutting down, chip consumption may shift from the pulp mill to a bioenergy plant.     Combustion plant  Pellet plant Ethanol plant  Pulp & paper Chips      Trim ends     Shavings  Hog fuelBark Sawdust +7,474,000 BDt      + 1,116,000 BDt +336,000 BDt +1,116,000 BDt +2,564,000 BDt  -10,911,054 BDt -1,110,650 BDt      -1,220,000 BDt ZERO  119 3.4.2 Other factors influencing residue production   Residue supply within BC  Since residue availability is a vital component for bioenergy to thrive, the supply component account on key issues that can affect residue security in the long run.  a) Allowable Annual Cut (AAC) in BC The AAC is defined as “the rates of timber harvest permitted each year from a specified crown land from define areas of the forest in BC” (BC MoF, 2010). As the MPB devastated the interior forests of BC, the provincial government increased the AAC in most of the forest areas that were highly affected with MPB. The interior forest region, especially forest districts like Prince George, is at its record highest with AAC about 14,944,000 cubic meters, effective since October 1, 2004. With the increase in AAC over time would generate additional forest residue to feed the bioenergy sector (Ralevic and Layzell, 2006).  In the coast forest region, some forest regions such as Arrowsmith had their AAC dropped from 418,769 cubic meters to 373.300 cubic meters during the period 1996 to 2002 (BC MoF, 2007) (Figure 3.12). Thus, the area available for roundwood harvesting has decreased and as such reduction in AAC leads to reduction in roundwood harvest and lumber processing and consequently a decrease in sawmill residue production in the coast.  After the MPB epidemic has run its course, there will be a significant drop in the wood residue production (BC MoF, 2010). Thus residue modeling to determine the net available residue with fibre should be established before investing in a new bioenergy plant within the interior forest region. In the coastal forest region, there is a shortage of residues and the consumers are forced to import residues from the interior which inflates the overall transportation cost and is not a suitable scenario for bioenergy production (McCloy, 2004).   120  Figure 3.12: BC harvest forecast – coast and interior forest regions (BC MoF, 2006)   b) Residue supply chain development – residue handling Sawmill residues especially the hogfuel and bark are less valuable. In other for bioenergy industry to reach its full potential in BC, there should be proper residue handling on-site to meet specific requirements at bioenergy facilities.  Wood residues can be obtained from a wide range of sources with certain inherent physical and chemical characteristics. However, proper handling of these residues is very critical to the quality of the end product. Handling process at a mill site needs to convert the inherent characteristics such as particle sizes, and moisture content into requirements needed for the various conversion devices specifications, so as to reduce on-site costs. Biomass preparation may vary, but the flowchart presented a Figure 3.13 outlines the major steps required for a standard preparation site.  Antares group (2003) analyzed the cost effectiveness of biomass handling for bioenergy consumer. He revealed that, the manual approach to feedstock handling steps reduces overall capital costs but increase labor requirements. The manual approach to feedstock  121 handling would be primarily used for smaller facilities. While, a fully automated prep- yard, is more capital intensive but requires less labor. An automated system is only cost- effective for large biomass conversion systems. Below are the various residue handling steps:  Receiving - trucking tipper, conveyor, and radial stacker  Processing - reclaim feeder, conveyor, metal separator, dryer, screener, and                           grinder  Buffer storage - storage bin  Fuel metering - conveyors, meters, and pneumatic transport  Figure 3.13: Residue handling for bioenergy production (Antares group, 2003)  Gasify? Storage Bin Conveyed to Boiler Dryer Storage Bin Drying? Grinder/ Hogger    Yes Oversize No Yes No Receiving/ Uploading  Woodpile No Receiving Processing     Yes Buffer StorageFuel Metering Conveyed to Gasifier  122 Residue demand within BC   The collection and the use of residues for bioenergy processes is a complex process. For commercial bioenergy to thrive in any given geographic region, the following hurdles must be overcome.  a) Increased residue demand Residue inventories in BC from 1990-1998 revealed a large decrease in the annual surplus within the province (McCloy and Associates, 1999). During this period (1990 to 1998), about 7.75 million BDt of primary wood processing residues were produced in BC each year, of which 5.69 million BDt were utilized, and 2.06 million BDt were considered surplus (McCloy and Associates, 1999). In 2004, 6.55 million BDt of primary wood processing residues were produced annually, of which 4.38 million BDt were utilized, and 1.81 million BDt were considered surplus. In 2006, the total estimated residue produced from sawmills had decreased to 4.81 million BDt and surplus residues had decreased significantly to a deficit.  b) The residue affordability In order to maintain competitiveness, residue has to be available at a very affordable rate to enhance the cost effectiveness of bioenergy production. Woody residues such as sawmill residues, roadside residues and mountain pine beetle infested tree residues were analyzed in BC based on their delivery cost. The results showed sawmill residues ($17.0/Odt) were by far the cheapest biomass feedstocks available than roadside ($43.7/Odt) and MPB killed trees ($99.7/Odt) as presented in Figure 3.14 (Verkerk, 2008).   123  Figure 3.14: Cost supply curve for various wood residues (Verkerk. 2008)  c) Adequate residue volume and economic viability As the market slump continues without evidence of a turnaround, it may lead to elasticity of residue demand in the consumer sector. Given that there is a high demand for residues in BC, price elasticity may occur where the pulp and paper industry may experience inelastic demand for chips in that a small change in chips quantity can led to a large movement in the price.  The economics of biomass procurement, collection and transportation is a very significant cost in the establishment of a bioenergy plant. The transportation network cost is discussed in detail in Chapter five of this thesis.  c) Societal acceptance of bioenergy Many technical articles have been published on converting biomass to bioenergy, however the public is largely unaware of biomass as an option for bioenergy (Bradley, 2007). Real change starts from the bottom where forest industry as a whole must operate sustainably throughout the value chain (McFarlane, 2007).    124 3.5 CONCLUSIONS   Data have always been a limiting factor for the analysis of the supply of primary mill residues. However in this study a theoretical approach was adopted and compared with the results of a residue survey in 2004. The result presents a significant decrease in residue availability from 2004 (+1,810,000 BDt) to 2006 (-636,505 BDt). Biomass availability is complicated to sample and a theoretical approach is a low cost and yet an effective method of estimating residue availability provided robust data are available.   The total estimated residue production in 2006 from sawmills and chip mills were 12,605,000 BDt. The Southern forest region produced (5,116,000 BDt) more than the Northern forest region (5,024,000 BDt) and the coast forest region produced (2,465,000 BDt) the least amount. There were no surplus residues in any of the three regions. The coastal forest region had the largest deficit of 1,754,000 BDt, followed by the Southern forest region with a deficit of 1,033,000 BDt. The Northern forest region had the least deficit of 244,000 BDt.    125  3.6 REFERENCES   Antares Group Incorporated (2003). Assessment of power production at rural utilities using forest thinning and commercially available biomass power technologies. The United States Department of Agriculture (USDA), The United States Department of Energy (DOE), and The National Renewable Energy Laboratory (NREL) Under Task Order No. TOA KDC-(-29462-19. Barrett, J.D. and Lam. F. (2007). Stud mill lumber grade and value yields from green spruce-pine-fir and grey-stage dry mountain pine beetle attacked logs 32p. Prepared for Forestry Innovation Investment, Vancouver, BC. BC Hydro. (2009). BC hydro – About independent power projects. Retrieved from: http://www.bchydro.com/etc/medialib/internet/documents/planning_regulatory/acqui ring_power/current_ipp_supply.Par.0001.File.20090401_current_ipp_supply_list_in _bc.pdf Verified June, 2009. BC MoF (2007) British Columbia Ministry of Forest. AAC levels. Retrieved from: http://www.for.gov.bc.ca/hts/aactsa.htm Verified August, 2007. BC MoF (2008) Major primary timber processing facilities in British Columbia. Ministry of Forest and Range Economics and Trade Branch (2006). 50p BC MoFR (2009) Forest regions and districts websites. Retrieved from: http://www.for.gov.bc.ca/mof/REGDIS.HTM Verified June, 2009. BC MoFR (2010) Prince George TSA timber supply analysis public discussion paper. Forest analysis and unventory branch. Ministry of Forest and Range. Retrieved from: http://www.for.gov.bc.ca/hts/tsa/tsa24/tsr4/24ts10pdp.pdf. Verified January, 2010. Bradley, D. (2007). Canadian-Sustainable forest biomass supply chains. Climate change solutions, IEA Task 40. Retrieved from: http://www.for.gov.bc.ca/hts/tsa/tsa24/ Verified July 2008.  126 Bradley, D. (2009) Canada report on bioenergy 2009. Canadian Bioenergy Association (CANBIO). Climate Change Solutions “Consulting in strategy, business investment, bioenergy, policy and climate change issues”. Environment Canada and IEA Bioenergy Task 40. Capital Power Income L.P. (2009) Williams Lake Power Plant: Williams Lake, B.C. Retrieved from: http://www.capitalpowerincome.ca/enca/operations/Canada/Pages/williams.aspx. Verified June, 2009. Forintek  (Nielson, R.W. Dobie, J. and Wright, D.M.) (1985) Corp. Conversion factors for the forest products industry in western Canada. . Special Publication No. SP-24R Fuller, S. W. (1991) Weyerhaeuser company Tacoma, Wa., U.S.A. Presented at the 24th Pulp and Paper Annual Meeting, ABTCP, Sao Paulo, SP – Brazil. Retrieved from: http://frmconsulting.net/articles/chip_quality.pdf Verified June, 2009 McCloy, B. and Associates. (1999) Canada’s wood residues: A profile of current surplus and Regional concentrations. National Climate Change Process McCloy, B. and Associates. (2004) Estimated production, consumption, surplus mill wood residue in Canada – 2004. Natural Resources Canada and Canadian Forest Service (NRCan/CFS) 60p. McCloy, B. and Associate. (2006) Reducing impediments to pulp and paper mill biomass cogeneration. A report for Environment Canada and the Forest Products Association. Retrieved  from: http://www.canbio.ca/documents/publications/EC_FPAC_Cogen_Study_Feb_21_20 06.pdf.  Verified October, 2007 McFarlane, P. (2007). A course in Environmental Facilities Design - Forest certification (WOOD 491). UBC CAWP, Vancouver, BC, Canada. National Association of Home Builders (NAHB) (2008) Long term housing forecasts. Retrieved from: http://www.nahb.org/showpage_details.aspx?showPageID=311&sectionID=1163 Verified October, 2008  127 Pinnacle pellet INC. (2007) Retrieved from: http://www.pinnaclepellet.com/ Verified October, 2010. Ralevic, P. and Layzell, B. D. (2006) An inventory of the bioenergy potential of British Columbia. BIOCAP Canada Foundation, Queen’s University, 156 Barrie Street, Kingston, Ontario, Canada. 8p Steele, H.P. (1984) Factors determing lumber recovery in sawmilling. United State department of Agriculture, forest service, forest products laboratory. Powerpoint presentation. Retrieved from:  http://www.fpl.fs.fed.us/tmu/publications.html Verified October, 2007 Verkerk, B. (2008). Current and future trade opportunities for woody biomass end- products from British Columbia, Canada. M.Sc. student Sustainable Development; track Energy and Resources; Department of Science, Technology and Society, Utrecht University Voivontas, D. Assimacopoulos, D. and Koukios E. G. (2001) Assessment of biomass potential for power production: A GIS based method Biomass and Bioenergy (20) 101-112        128 CHAPTER 4 USING GIS-DIJKSTRA INTEGRATED ASSESSMENT MODEL TO ASSESS THE DELIVERED RESIDUES TRANSPORTATION COSTS IN BRITISH COLUMBIA FOR BIOENERGY PRODUCTION3  4.1 INTRODUCTION   As pressure from climate change protocols and green energy develops, there is a growing need for efficient collection and utilization of residues for bioenergy processes. Producing economically-competitive bioenergy from sawmill residues depends on residue availability and price. Based on this premise, a GIS-Dijkstra integrated assessment model was developed to locate all major residues from sawmills within the Province of BC, and to ascertain the cheapest mill deposit locations in BC for wood residue-based bioenergy processes.  As wood residue availability is spatially located, the GIS-Dijkstra integrated assessment model captured the surplus residues generated by sawmills geographically. This model analyzed the geographical variation and determined the supply cost of biomass within each major forest region in BC.  This research evaluated sawmills residue availability for bioenergy in BC. In the past BC has been reported to have a greater abundance of underutilized wood residue than any other Province in Canada (Bradley, 2007). The focus on BC was to evaluate regional variations across the province and provide specific residue type estimates for present and new facilities interested in locating in BC.    3 “A version of this chapter will be submitted for publication. Taku-Kehbila, A.V. and McFarlane P.N. (2010) Using GIS-Dijkstra integrated assessment model to assess the delivered residues transportation costs in British Columbia for bioenergy production”   129 Sawmill residues are considered to be the cheapest wood residue compared to roadside residues and mountain pine beetle (MPB) resource because the harvest and transportation costs from the forest have largely been covered by the mills (Verkerk, 2008).  The analysis presented in Chapter 3 demonstrated that, in 2006 there were no surplus residues in any of BC’s three major forest regions. In order to demonstrate the benefits of adopting a GIS-Dijkstra integrated assessment modeling approach, the research in this Chapter has intentionally undersupplied pulp mills with chips to create a surplus of whitewood and bark. Based on this assumption, the optimum collection sites for residues are determined for each of BC’s three major forest regions.  In summary, this study seeks to develop a total that may be used to optimize the location of bioenergy plants to redress gaps in knowledge by geographically quantifying total residue cost and transportation cost for hypothetical surplus sawmill residues regionally in BC. The outcome of this study will be the idenfication of the preferred sites for bioenergy producers.  4.1.2 Objective   This chapter seeks to develop a GIS-Dijkstra integrated assessment model to estimate the availability of wood residues by the amount, location and cost within the major forest regions in BC for bioenergy. The GIS-Dijkstra model provided an integrated framework using the scientific knowledge from previous chapters and builds on the Arc GIS 9.3 Support Systems. The residue-generated data were incorporated into the GIS-Dijkstra integrated assessment model based on four relevant factors: (1) the BC road network, (2) transportation cost, (3) the feedstock cost and (4) residue source (i.e. primary wood processing mill location). The model used these inputs to ascertain the optimum collection sites for residues and to determine the marginal cost of supplying residues to specified locations that are suitable for the available technological platforms. For each collection point, the analysis provided extensive information including road maps, distance data, residues types, transportation costs, and the net-available quantities generated per mill. By using these themes as a starting point for model integration and by  130 linking the model to policy relevant drivers and indicators, this chapter seeks to provide policy makers such as government officials, the wood processing industry, and bioenergy project developers with relevant information and a valuable visual database designed to support the decision-making process with respect to resource availability and feedstock utilization within BC.  The following section described the methods used to estimate both availability and production cost of woody biomass. This is followed by a section of results and discussions and finishes with brief conclusions.   4.2 METHODS  4.2.1 Geographical net-surplus for bioenergy in BC and assumptions   The forest industry continues to struggle in Canada with many forest companies making the difficult decisions to close plants permanently. In Western Canada, pulp mills and sawmills continue to be dependent on each other for survival, with pulp mills being the major residue consumers in BC (Alteyrac et al., 2008). In 2005, pulp mills in BC consumed 95% of the chips produced by sawmills. However, by 2008 because of decreased availability of chips from sawmills, many pulp mill companies in the interior were using 30-40% roundwood from beetle-killed timber to provide chip requirements on-site (International Forest Industries, 2008). This new chip source will continue to be very important until lumber production increases in response to increasing housing construction in the US.  This section explains how the net-available residues were calculated and then demonstrates how surplus residues can be calculated on a geographically explicit basis using the GIS-Dijkstra integrated assessment model for 2006 data. For this study to be feasible the following assumptions were made:   131 (1) All chips produced by chip mills and sawmills were used to satisfy the demand of pulp and paper mills. However, in 2006 the total the total chip supply from chip mills and sawmills did not satisfy total pulp mill consumption. Thus, this study intentionally forced a pulp and paper fibre deficit to create excess residue for the model analysis. This artificial scenario was required to demonstrate the potential benefit of GIS-Dijkstra integrated assessment model.  (2) Incremental 5 km residue collection buffer zones were established for each of the eight pellet plants, six cogeneration plants that were operating in the province in 2006. The objective of these zones was to create a catchment radius of mills closest to the collection point to satisfy each plant’s consumption.  Based on the above assumptions, the excess available residue for 2006 has been termed “surplus residues” for new bioenergy processes. Supply curves were created for various forest regions of the province by residue cost and type.   4.2.2 Cost of residues   Transportation cost - Road   The marginal cost of transporting biomass is determined by the fixed cost (loading and unloading costs) and the variable cost (cost per km transported). This study has only taken into account the variable cost, which is the cost of residues and the distances transported in kilometers. Although the purchasing cost, costs of transportation and handling of residues differ across the province, this study has used published average values because commercial sensitivity has prevented the use of real costing data.  Road types and road terrain (road slope) were not considered in this study and only major roads were considered. Thus, a single value for the road transportation cost was assumed for all regions in BC which was $21.5/BDt.km (Bradley, 2009).  132  All the model runs in the interior forest regions and for road transportation in the coastal forest region were made on a per kilometer basis and the cost to transport each bone dry tonne of residue to a preferred collection point was determined using the following equation: transportation cost (per BDt) = ((Distance (m)/1000)*(21.5cents/km)).   Transportation cost - Water   In the coastal forest region, the model runs across water from Vancouver - Island to the Mainland. The terminals used for this research are Port Nanaimo, Port McNeill, and Port Courtney, to No. 6 Road in Richmond. All the transportation distances covered with roads were multiplied by 21.5cents/100km while distances covered with water were multiplied by 3.02cents/km. In the GIS integrated Dijkstra logarithm model for the coast, a minimum of 3 days barge rental at 600.00/per day (1 day loading, 1 day towing, and 1 day unloading) was included in the analysis. Very often, a distant district such as Campbell River will need 12 hours for the tug to stand-by while the barge is loading. Any additional stand-by time would be factored into the overall towing cost. transportation cost (per BDt) = ((Distance (m)/1000)*(0.215cents/km)) for land transportation cost (per BDt) = ((Distance (m)/1000)*(3.02 cents/km)) for water   Residue cost   The cost of residues for both the interior and the coastal forest regions is summarized in Table 4.1. The costs of sawdust, shavings, and trim ends were averaged to determine the cost of whitewood residues (Bradley, 2009).  Table 4.1: A summary of residue cost in British Columbia (Bradley, 2009) Residue type Interior ($/BDt) Coast ($/BDt) Whitewood 37.8 59.0 Bark 13.80 22.50   133 Basic density (BD), moisture content (MC) and residue sizes were other important parameters considered in the transportation cost. These themes have been discussed extensively in Chapter 2. For sawmills to determine the total amount of residue to be transported in one load, the price is calculated on a volume-limited basis rather than the truck weight, enabling the volumetric payload to be constant for all MC (MacDonald, 2006). Wood residues generally have low basic density and depending on the handling of residue on-site, the moisture content maybe low or high (Chapter 2).   Total residue cost   Total cost is the sum of residue cost ($/BDt) and transportation cost ($/km.BDt). This section uses a modeling system to minimize the cost of transporting residues within forest regions in BC. The modeling system had five fundamental components. The first component provided an overview of all potentially available mill sites and their near distances in km as they could also serve as a potential bioenergy plant site. The second component mapped mill capacities in relation to roundwood production in order to estimate residue supply. The third component calculated net surplus residue regionally (same method as chapter 3). The fourth component calculated near transportation costs distances ($/km.BDt) and mapped the marginal cost of delivering specific quantities of feedstock ($/BDt) to any mill within a given forest region. The fifth component identified mills according to cost ranks and highlighted where existing and new bioenergy facilities might be best co-located according to the cost curve.   4.2.3 Data analysis   Geographic Information Systems (GIS) have been developed for various scientific disciplines (Arnberg et al., 2003). In this study, a GIS-Dijkstra integrated modeling framework was developed employing ArcGIS 9.3. As the integrity and quality of data are very important for all credible GIS studies, this study was very particular about data  134 analysis, which included extensive data preparation and error checking. The total data were available both in spatial and non-spatial formats for the analysis.  4.2.4 Model Overview   In this study, a GIS-Dijkstra integrated modeling framework was used. The GIS tool provided a full coverage of the geographical area and it was used to identify primary mill locations and preferred spots for bioenergy generation in BC. Three forest regional synoptic analyses were computed using Google Earth, ET Geowizard, ArcGIS 9.3. The Dijkstra algorithm, on the other hand, was fundamental in quantifying and computing the shortest road distance and the total net-surplus residues produced per region. A schematic representation of how the integrated GIS-Dijkstra modeling framework operated is presented in Figure 4.1.  4.2.5 Google Earth   Google Earth was employed to generate the coordinates of 193 sawmills, 8 pellet mills, and six cogeneration plants using the ‘fly to and place mark’ tool (BC MoF, 2008). Each coordinate included individual fields of point locations that were linked to a series of data such as annual capacities, quantity of residue available, quantity used and various residue type. These data were imported into ArcGis 9.3 map using the ‘add XY data’ tool to generate the respective shape files.  135       Figure 4.1: Schematic diagram of the integrated GIS-dijkstra model used in this study.  4.2.6 ET Geowizard   Spatial data on the road network was obtained from the UBC GIS website and the roads were represented as lines. In order to establish a road network, all the roads (lines) must be connected. However, the data received had discontinued line segments and some of the lines had multiple lines in different orientations (pseudo lines). Due to these data irregularities the network had to be cleaned to ease analysis. ET GeoWizard, a powerful tool designed to analyze and modify spatial data, was employed to identify and connect every missing link by snapping. The GeoWizard cleaned up all the dangling, pseudo lines and connected all lines to a clean join line. It also splits any loops in the roads. After the MILL CAPACITIES:  Chips  Sawdust  Shavings  Trim ends  Bark COMPUTATION:  Google earth,  ET Geowizard,  GIS 9.3  Dijkstra algorithm      Regional boundary MILL LOCATION:  Sawmills  Pellet mills  Pulp mills  Cogeneration RESULT:  Surplus yield regionally  Yield type  Total cost  Distances  136 existing roads were cleaned and transformed into a network, the lines were renoded and imported into the Dijkstra algorithm model.   4.2.7 ArcGIS 9.3    The data embodied in ArcGis 9.3 included roads as lines, mills as points, and regions as polygons, which were vector based and were organized into thematic layers and tables (Figure 4.2). The newly created tables (cogeneration plants, sawmills, and gasification plants) were imported into ArcGIS and connected to each layer based on their spatial characteristics. Also, a digitalized map of BC including roads, and political boundaries were used. This spatial information was composed of attribute files and geographic entity.   4.2.8 Dijkstra model   The Dijkstra algorithm is a network model that is used to compute the shortest road network (Dijkstra, 1959) by at every step, selecting the best choice available at any given step without regards to future consequences (Gass and Harris, 1996). It has been labeled as a node greedy algorithm.  Sawmills, pellet mills, and cogeneration plants coordinates were imported into ArcGis map. The roads were connected using ET_Geowizard to create ET_ID, ET_TNODE and ET_FNODE. The collection points (e.g. a cogeneration plant) were computed using the near tool. The near tool computes the distance from each point in the input feature class to the nearest polyline within a maximum search radius.       137     Figure 4.2: Organization of GIS data in layers Source: ArcGIS desktop help  For the Dijkstra algorithm to run smoothly, the computer generated numbers (based on points and links) must have the information presented below, which is translated from ET_Geowizard to Djitkstra algorithm (Table 4.2).  Table 4.2: Interpretation of ET_geowizard to Dijkstra algorithm ET_Geowizard Dijkstra algorithm ET_ID ID ET_TNODE (created by near tool) ENTRY_NODE ET_FNODE EXIT_NODE COST ($/BDt) or DISTANCE ($/km) COST ($/BDt) or DISTANCE ($/km)   The model runs by selecting connected points to an existing road network. Nodes must be positive integers and ID can be a code to distinguish the start from the exit point (i.e. mill). Also, there should not be a null node because null values signify some missing  138 distances. Once a connection is established, the algorithm notes it in the network and selects another point. This process occurs repeatedly until the run is completed. Each run took at least 2 hours to complete.  The model runs by presenting the result in three columns: type, rank and total cost. Type = 1: selected arcs; 2: non-selected arcs; and 3: non-connected to the network. Rank = Ranking of the order of arcs selected as the shortest path. Total cost = Total cost/length of moving from any point to the mill (exit).  A schematic diagram describing the operation of the Dijkstra algorithm is presented in Figure 4.3. The mills (j) are represented by the nodes and the distances (i) are displayed as arcs. In cases where there is a link (i) to a mill (j) then the D(i,j) fill color reflects red for a selected arc. In cases where there is a link (i) to a mill (j) but it is a non-selected arc because of distance, the fill color reflects yellow. In cases where there is no link (i) to a mill (j) then D(i,j) for the non-connected arc is represented by the fill color blue.  During the analysis, some off-road factors were ignored such as the gradient of the slope, hydrology, and the standard of the roads (either a major or a minor road). These factors were ignored because the road network data did not provide all the details of the roading system. It was assumed that all transportation occurred by the road for the interior region and both road and water for the coastal region. Also, no vehicle classes were distinguished during the analysis.   How the integrated model works    For the model to run properly, the data phase preparation is very important. The input information must contain the following four distinct columns: (1) ID (2) ENTRY_NODE (3) EXIT_NODE (4) COST or DISTANCE. In order to generate these four columns, all the mills must connect to a node in the road network.  Mill identification Data on sawmill residue production, availability and use were compiled. First, the mill data preparation included creating a table of all the mills with  139 their output and location. The mill ID was coded to differentiate the mills from roads. Then the mill code was linked with numbers that were easy to understand. Using the NEAR tool in ArcGIS, the ENTRY_NODE was then created.                            Figure 4.3: Schematic diagram describing the function of the Dijkstra algorithm   Given the example in Table 3, mill M1 and M2 are connected to the road network at node 4 and 2 respectively. The mills were assigned bigger numbers than any value from the road network, so it is easy to differentiate.  Road preparation The roads were copied directly from ArcGIS and the NODES were checked to ensure that there was not any NODE with a zero or a negative value. The ground transportation cost was then determined as =         Shape_Length/100000*21.5cents/km.  The ground/water transportation was then determined as =       Shape_Length/100000*21.5cents/km and Shape_Length/1000*3.02cents/BDt/km. 10 10 10 10 10 20 10 1 2 3 4 5 5 6 7 8 9 10 20 10 9 10    8  1 2  3  6 5 4 10 7 10 5 10 10 10 10 20 10 20  Mills (j)       Roads (i) Non-computed network Computed network  140  Distance The NEAR_DISTANCE from ArcGIS were copied into an excel sheet. The distance values were multiplied by 1000 to convert meters to kilometers.  Data query To query the data, a query template was created in a MACRO excel programming. The unique ID and variable columns (ENTRYNODE, EXITNODE and DISTANCE) were bind to the coded query template (Table 3).  To query the data, a target plant was selected for a particular forest region, and all residues were procured at a given transportation cost. Each time a query was executed the data were validated and the connecting nodes were established. The established nodes were loaded in a new row of data in the execution sheet. During computing, the nodes must be fully connected in the network to run and generate optimum location for a bioenergy plant. The computing distance was the shortest path distance in the whole network from the nearest node to the delivery point. In the execution sheet, where a link was established - the fill color reflected red to indicate connected arc (1st class road). In cases where there was a connection but it was a non-preferred arc because of increased road distance - the fill color reflected yellow (2nd class road), and in a non-connection arc - the fill color reflected blue. Thus, from the two road classes, cost of transporting one BDt of residue was calculated on the output worksheet. These output sheets contained a list of individual mills sorted according to delivered residue cost in BDt.   141 4.3 RESULTS   In this study, the optimum location of a bioenergy generation facility in BC was sought. In sourcing a bioenergy plant, a very significant factor taken into consideration is proximity to biomass production. This section focuses on determining the delivered residue cost of new and existing bioenergy plants in BC.  The volumes measured in this study were intended to estimate the net amount of residue available for bioenergy after existing consumers have been deducted. The supply and demand of wood residues are both dynamic and this study was conceived as a project that would demonstrate the potential benefits of this type of methodology. Wood residue consumers are more concerned with security of supply and having it delivered on-site at the cheapest cost and they are not necessarily concerned about the origin of the residue. In this study, the shortest distances of the surplus residue were estimated for the three forest regions within BC. Transportation cost is one of the largest cost components in biomass logistics and is key to developing a successful bioenergy industry from wood residues (Ileleji, 2007). All costs curves have been calculated in current Canadian dollars.      142 4.3.1 Northern interior forest region residue quantification   Available residue   Historically, approximately 80% of the total residues produced from sawmills and chip mills in BC have been used to supply the pulp and paper mills (McCloy, 1999). For this study, the pulp mills were undersupplied intentionally in order to create a surplus of whitewood residues for bioenergy production. Consequently, a substantial fibre deficit of 1.79 million BDt of chips that would be usually consumed by the pulp sector was created (Table 4.3). As discussed above, this artificial situation was required in order to demonstrate the potential benefits of the GIS-Dijkstra integrated modeling framework to determine the most favored locations for bioenergy plants.  Table 4.3: Residues in the Northern interior forest region Northern interior region Chips from sawmills (BDt) Whitewood (BDt)1 Bark (BDt)2 Total (BDt)   Production   2,904,079 1,103,000 1,016,585 2,119,585  Pulp and Paper 4,695,010     0 Demand Cogeneration     170,000 170,000   Pellet mills   402,550   402,550   Surplus    -1,790,931 700,450 846,585 1,547,035 1 = sawdust, shavings, and trim ends from sawmills. All chips have been allocated to pulp mills. 2 = no bark has been allocated to on-site energy production at sawmills   The purchase cost of residue at existing cogeneration plant   There are two existing cogeneration plants in the Northern interior forest region of BC that utilize 100% residue to generate power. The Armstrong cogeneration plant, located at the Mackenzie district, had a total output of 20MW of energy (BC Hydro, 2009). The plant consumes approximately 100,000 BDt of residue on an annual basis. Assuming all the surplus residue in the Northern interior forest region has to be transported to this plant,  143 the GIS-Dijkstra integrated assessment model predicted that it would have cost the plant $87.8/BDt for bark, and $96.8/BDt for whitewood (Figures 4 and 5).  The second cogeneration plant in this forest region is Sandwell cogeneration plant, located in Mackenzie forest district, with an output of 14MW of energy. It consumes 70,000 BDt of residue per year. The integrated assessment model calculated that it would have cost $78.9/BDt to transport all bark, and $90.2/BDt to transport all whitewood to this plant (Figures 4.4 and 4.5).   The purchase cost of bark in Northern interior forest region   When applying the GIS- Dijkstra model system, it was assumed that 1,016,000 BDt of bark residues were produced at mill sites, and that existing bioenergy facility consumed 170,000 BDt of residues per year. Consequently, there were 846,000 BDt of bark residues available in this region. Under this scenario, the analysis illustrated that there were 1,550,000 BDt residues available that could support new bioenergy facilities in the Northern interior forest region (Table 4.3).  The use of the integrated GIS-Dijkstra model facilitated the identification and ranking of sawmills on the cost curve within the province are based on the distances travelled and the amount of residue moved. The cheapest districts were Prince George and Nadina with a cost under $50/BDt, with this price varying depending on the amount of residue available onsite and the distances transported (Figure 4.4).  The residue supply cost curve is flat for mills located in Prince George (PG), and Nadina (ND) (Figure 4.4). In the Northern interior forest region, a comparison of Prince George (PG) and the Fort Nelson (FN) forest regions clearly demonstrated that lower feedstock costs are associated with large mills that are located at proximate distances to each other, while higher feedstock costs are associated with small mills located further apart from each other. From this premise, it would cost the bioenergy plants over $50/BDt to transport feedstock to districts that are located furthest away (such as Skeena Stikine in  144 the North, Kalum in the West, and Fort St John in the East) to supply a large bioenergy facility (Figure 4.4).  - 20 40 60 80 100 120 140 Ca na dia n F or es t P ro du cts  Lt d. (P G) Ho us ton  Fo res t P ro du cts  C o. (N D) Ca na dia n F or es t P ro du cts  Lt d. (N D) Ca na dia n F or es t P ro du cts  Lt d. (P C) Ca rri er  Lu mb er  Lt d. (P G) Wi nto n G lob al (P G) Ch es lat ta Fo res t P ro du cts  Lt d. (N D) Bu rn s L ak e C om mu nit y F or es t (N D) Ca na dia n F or es t P ro du cts  Lt d. (P G) Wo od lan d F or es t P ro du cts  Lt d. (P G) Co rw oo d T im be r P ro du cts  Lt d. (N A) Po pe  an d T alb ot Ltd . (J A) BC  C us tom  Ti mb er Pr od uc ts Ltd . (V A) Ap oll o F or es t P ro du cts  Lt d. (JA ) Ca na dia n F or es t P ro du cts  Lt d. (V A) Ab itib i-C on so lid ate d C om pa ny  of  C an ad a ( MK ) We st Fr as er  M ills  Lt d. (K M) We st Fr as er  M ills  Lt d. (S S) Ca na dia n F or es t P ro du cts  Lt d. (FN ) D el iv er ed  re si du e co st  ($ /B D t) Transportation Cost Total Cost Armstrong Sandwell  Figure 4.4: Total annual cost of transporting bark residues to existing Northern interior forest region users [y-axis label is mill name and forest district]. Forest district abbreviations are presented in Appendix number B.1.    The purchase cost of whitewood in Northern interior forest region   As for the results for bark, the costs for whitewood residues are dependent on the distances travelled and the amount of residue moved (Figure 4.5). The supply curve revealed that the preferred bioenergy location for whitewood residues would include regions such as Fort St James (JA), Prince George (PG), Vanderhoof (VA) and Mackenzie (MK). It would have cost less than $90/BDt to move about 700,000 BDt of all whitewood residues in the Northern interior forest region to the optimal location regions. The more remote the mill’s location, the steeper was the purchased residue cost. In this  145 case, the Northern interior forest region had the most expensive residue within BC, as reflected by the overall delivered cost curve which reached a value of $175/BDt.  0 20 40 60 80 100 120 140 160 180 200 Po pe  an d T alb ot Ltd . (J A) Ca na dia n F or es t P ro du cts  Lt d. (P G) Ca na dia n F or es t P ro du cts  Lt d. (P G) Ap oll o F or es t P ro du cts  Lt d. (JA ) Ab itib i-C on so lid ate d C om pa ny  of  C an ad a ( MK ) Ca na dia n F or es t P ro du cts  Lt d. (V A) BC  C us tom  Ti mb er Pr od uc ts Ltd . (V A) We st Fr as er  M ills  Lt d. (K M) We st Fr as er  M ills  Lt d. (V A) Ca na dia n F or es t P ro du cts  Lt d. (P C) We st Fr as er  M ills  Lt d. (S S) St ua rt La ke  Lu mb er Co . L td . (J A) PG  So rt Ya rd  (P G) Ki tw an ga  M ills  Lt d. (S S) Wo od lan d F or es t P ro du cts  Lt d. (P G) Ba bin e F or es t P ro du cts  Li mi ted . (N D) Ca rri er  Lu mb er  Lt d. (P G) Ch es lat ta Fo res t P ro du cts  Lt d. (N D) Co rw oo d T im be r P ro du cts  Lt d. (N D) D el iv er ed  re si du e co st  ($ /B D t) Transportation Cost Total Cost Sandwell Armstrong  Figure 4.5: Total annual cost of transporting whitewood residues to existing Northern interior forest region users [y-axis label is mill name and forest district]. Forest district abbreviations are presented in Appendix number B.1.   Preferred location for bioenergy production in the Northern interior forest region   In mapping the potential locations of multiple bioenergy facilities, one must account for the fact that feedstock resources used for one facility are not available to another facility. The map below suggested that the regions of Prince George (PG), Mackenzie (MK), Fort St James (JA) and Vanderhoof (VA) were the preferred sites to locate a large bioenergy facility within the Northern interior forest region of BC. Potential bioenergy facility locations were selected successively based on lowest delivered marginal cost for bark and whitewood (Figure 4.6). The preferred lowest cost sites for bioenergy production from  146 bark were Canfor Forest Products Limited located in Prince George and Babine Forest Products Limited located in Nadina with an actual residue cost of $51.BDt for both regions. While the preferred lowest cost sites for bioenergy production from whitewood were Pope and Talbot Limited located in Fort St James and Winton Global located in Prince George with an actual residue cost of $78/BDt and $83/BDt respectively.   Figure 4.6: Locations of mills and residue consumers in the Northern interior forest region  147  4.3.2 Southern interior forest region residue quantification   Of the net 5.1 million BDt of residues produced in the Southern interior forest region in 2006, about 60% of the total residues (i.e. chips from sawmills and chip mills) were allocated to the pulp and paper industries by the integrated model. Assigning this quantity of feedstock to the pulp industry created a deficit of 1.29 million BDt of chips for the region’s pulp and paper industry. Under this scenario, the net-surplus of 758,000 BDt of residue was created and artificially used to determine potential sites for bioenergy production using the integrated model (Table 4.4).   Table 4.4: Residue surplus for bioenergy production in the Southern interior forest region Southern interior forest region Chips from sawmills (BDt) Whitewood (BDt)1 Bark (BDt)2  Total (BDt)   Production   2,840,513 994,177 1,136,205 2,130,382 Pulp and Paper 4,134,610   Cogeneration     615,000 615,000     Demand   Pellet mills   756,600   756,600   Surplus   -1,294,097 237,577 521,205 758,782 1 = sawdust, shavings, and trim ends from sawmills. All chips have been allocated to pulp mills. 2 = no bark has been allocated to on-site energy production at sawmills   The purchase cost of whitewood in Southern interior forest region   In the Southern interior forest region, there are three cogeneration plants that utilize 100% wood residues. The distance covered by truck for each BDt of residue from the production site to the utilization site varied for each cogeneration plant.   NWE Energy Corporation (EPCOR) is the biggest cogeneration plant in BC with an annual output of 60MW of power. According to the estimates used in this study, this plant utilizes 300,000 BDt of sawmill residues annually. The integrated model estimated  148 that the total delivered residue cost to the EPCOR site was $31/BDt for bark and $47/BDt for whitewood.  Purcell Power Project located at Skookumchuck in the Rocky Mountain district has the cheapest delivered residue cost. This is because the proximity of this power plant to the large sawmills in the region. The integrated model estimated the residue costs for this plant were $29/BDt for bark and $37/BDt for whitewood.  The smallest cogeneration plant in Southern interior forest region is Riverside Forest Products Limited located in Okanagan Shuswap district and it utilizes 100,000 BDt of residues annually. This plant is the most expensive of the three as the model predicted it would cost this plant $40/BDt to purchase bark and $76/BDt to purchase whitewood (Table 4.5).  Table 4.5: Total users of residues in Southern interior forest region    The purchase cost of bark in Southern interior forest region   The purchased residue cost curve for the Southern interior forest region is generally cheaper than that for the Northern interior forest region. This is because the Southern interior forest regional mills are more closely located with better road connections so that it has a substantially lower overall delivery cost. The integrated model predicted that the cost of bark is less than $30 at Purcell Power cogeneration plant at Rocky Mountain and reached a maximum of approximately $44 at Riverside Forest cogeneration plant in Okanagan Shuswap. The preferred new bioenergy production locations for bark in the Consumer type Districts Process Output BDt/MW NWE Energy Corporation (EPCOR) Central Cariboo Cogeneration 60MW 300,000 Purcell Power Project (Tembec Enterprises Inc. Skookumchuck) Rocky Mountain Cogeneration 43MW 215,000  Riverside Forest Products Limited (Armstrong Wood Waste Co-Gen) Okanagan Shuswap Cogeneration 20MW 100,000  149 Southern interior forest region would include Headwaters, Columbia, Kamloops and Okanagan Shuswap (Figure 4.7).  - 5 10 15 20 25 30 35 40 45 50 Gi bb s C us to m Sa wm ill   (H W ) We ye rh ae us er  C om pa ny  Lt d.  (O S) Ha ue r B ro s. Lu mb er  Lt d.  (H W ) Te mb ec  In du str ies  Lt d.  (R M) En id La ke  L og gin g L td.    ( RM ) To lko  In du str ies  L td.    ( CC ) To lko  In du str ies  L td.    ( QU ) To lko  In du str ies  L td.   (C C) W es t F ra se r M ills  L td.   (C C) W es t F ra se r M ills  Lt d.  (M H) He rri dg e T ru ck ing  an d S aw mi llin g L td  (A B) To lko  In du str ies  L td.   (O S) Wa dle gg er Lo g &  C on str . C o.  (H W) We st Ch ilc oti n F or es t P ro du ct Lt d.  (C H) Jo ne s T ies  n .P . (1 97 8) Lt d.  (A B) Ru ss o S aw m ills , (O S) Ar de w Wo od  Pr od uc ts Ltd .  ( CS ) Sc ha po l L og gin g L td.   (O S) Lin de  B ro s L um be r L td .  ( CC ) No rth  S tar  Pl an in g C o. Ltd .  ( RM ) Pa rag on  V en tur es  Lt d.  (O S) Ma rs h B ro s L um be r a nd  S up ply  Lt d.  (H W) Sig ur ds on  B ro s. Lo gg ing  C om pa ny   (C H) Po pe  &  Ta lbo t L td.   (A B) Ka les nik off  Lu mb er  C o. Ltd .  ( AB ) Po pe  &  Ta lbo t L td.    ( AB ) D el iv er ed  re si du e co st  ($ /B D t) Transportation Cost Total Cost Purcell Power Project NEW Energy Corporation Riverside Forest Products Ltd  Figure 4.7: Total annual cost of transporting bark residues to existing Southern interior forest region users [y-axis label is mill name and forest district]. Forest district abbreviations are presented in Appendix number 4.II.   The purchase cost of whitewood in Southern interior forest region   The cost trend of delivered whitewood residue in the Southern interior forest region is steeper compared to the curve trend in the Northern interior forest region although absolute values are lower. This is because the efficient road connectivity and proximity of the Southern interior sawmills are critical to the delivered price. In this scenario, the model predicts that the Southern interior forest region is the most challenging region to locate a power plant because given the geographical proximity to the sawmills, there will  150 be significant variation in residue consumption within the region. Thus, and an extensive study is required on a plant-based approach to minimize transportation cost and maximize output.   The optimum cheapest consumer site was Purcell Power Project cogeneration plant at Rocky Mountain. This is because it seems Purcell Power is well located and has ready access to residues from mills at proximate distances. The preferred new bioenergy production locations for whitewood in the Southern interior forest region would include 100 Mile House, Central Cariboo, Okanagan Shuswap, and Kamloops (Figure 4.8).  0 10 20 30 40 50 60 70 80 90 100 We st Fr as er  M ills  Lt d. (M H) Gi lbe rt Sm ith  Fo r P ro d L td.  (K A) To lko  In du str ies  Lt d. (C C) To lko  In du str ies  Lt d. (O S) To lko  In du str ies  Lt d. (Q U) Te mb ec  In du str ies  Lt d. (R M) We st Fr as er  M ills  Lt d. QU ) Jo e K oz ek  Sa wm ills  Lt d. (C O) We st Fr as er  M ills  Lt d. (M H) La ke sid e T im be r L td.  (O S) J H  H us cro ft L td.  (K L) Mc Do na ld Ra nc h &  Lu mb er Ltd . (R M) Wi ldw oo d F or es t P ro du cts  Lt d. (C C) Po pe  &  Ta lbo t L td.  (A B) We st Ch ilc oti n F or es t P ro du ct Lt d. (C H) Be ar  Lu mb er Ltd . (R M) No tch  H ill Fo res t P ro du cts  Lt d. (O S) Ma rsh  B ro s L um be r a nd  Su pp ly Ltd . (H W) Ru ss o S aw mi lls , L  (O S) No rth  O ka na ga n C ed ar Lt d. Ka les nik off  Lu mb er Co . L td.  (A B) Po pe  &  Ta lbo t L td.  (A B) Co rw oo d T im be r P ro du cts  Lt d. (H W) En id La ke  Lo gg ing  Lt d. (R M) Po pe  &  Ta lbo t L td.  (A B) Sig ur ds on  B ro s. Lo gg ing  C om pa ny  (C H) D el iv er ed  re si du e co st  ($ /B D t) Transportation Cost $/Total Cost Pucell Power NEW Energy Corporation Riverside Forest Products  Figure 4.8: Total annual cost of transporting whitewood residues to existing Southern interior forest users [y-axis label is mill name and forest district]. Forest district abbreviations are presented in Appendix number B.2.    151 Preferred location for bioenergy in the Southern interior forest region   In a region like the Southern interior forest region of BC, where the road connections are highly dense, the model indicated that the preferred delivered residue cost would be spotted throughout the region for either bark or whitewood (Figure 4.9). The three lowest cost sites for bioenergy production from bark were Gibbs Custom Sawmill located in Headwaters, Joe Kozek Sawmills Limited located in Columbia, and Weyerhaeuser Company Limited located in Kamloops. While the three lowest cost sites for bioenergy production from whitewood were West Fraser Mills Limited located in 100 Mile House, West Fraser Mills Limited located in Central Cariboo, and Larry Buff Sawmills Limited located in Okanagan Shuswap. By allocating sawmill residues to those bioenergy plants with the lowest overall transportation cost; the overall cost of producing bioenergy can be minimized. Thus it pays to situate a bioenergy plant closest to a large mill and enjoy economies of scale, as shown in the case of the Purcell Power project located close to the Canal Flats and Elko mills.  The map below suggested that the regions of Headwaters, Columbia, Kamloops and Okanagan Shuswap for bark and 100 Mile House, Central Cariboo, Okanagan Shuswap, and Kamloops for whitewood could be the potential sites to locate large bioenergy facilities within the Southern interior forest region of BC.   152  Figure 4.9: Locations of mills and residue consumers in the Southern interior forest region   153   4.3.3 Coastal forest region residue quantification    The transportation scenario in the coastal region of BC is different from that of the interior forest regions. The coast forest region is made up of both the Mainland and Vancouver Island with its mills scattered along the coastal region. However, residues produced by coastal sawmills may be used on-site for energy generation or towed across the protected coastal waterway from all the Island regions to Vancouver or specific locations on the Island where the residues may be used for energy generation. The integrated approach to analyze the residue transportation cost for the coastal region provides a challenge for this section of the thesis because there were so many transportation options by water.  In the coastal forest region, the total residue produced from sawmills and chip mills have been allocated to the pulp and paper mills. Given that there is a proportionality larger demand for residue in the coast than any other region in BC, this scenario has created a large fibre shortage for the coastal pulp and paper industry, estimated to be -2.4 million BDt (Table 4.6). Thus, an artificial scenario was forced in to create excess residue supply of 682,000BDt in order to demonstrate the potential benefits of the GIS-Dijkstra integrated model framework. There are no pellet mills in the coastal region.   Table 4.6: Residue surplus for bioenergy production in coastal forest region Coast forest region Coast forest region Chips from sawmills and total production from chip mills (BDt) Whitewoo d (BDt)1 Bark (BDt)2 Total (BDt)  Total production  1,672,036 382,510 410,330 792,840 Pulp and Paper 4,108,284 Cogeneration     110,000 110,000     Pellet mills       0 Both regions      Surplus  -2,436,248 382,510 300,330 682,840 1 = sawdust, shavings, and trim ends from sawmills. All chips have been allocated to pulp mills. 2 = no bark has been allocated to on-site energy production at sawmills   154  Existing Cogeneration plant   In the coastal region, there is only one cogeneration plant that utilizes 100% wood residues, which is Seegen (Montenay Inc.) in Chilliwack district. The integrated GIS- Dijkstra model predicted that it would cost the Segeen plant $28/km to truck and tow 300,000 BDt of bark residue from all the coastal sawmills to its plant site at a cost of $50/BDt   The purchase cost of bark in coastal forest region   The preferred bioenergy location for bark in the coastal forest region would include Chilliwack and South Island (Figure 4.10). Bark residue in the coast is sold at $22.5/BDt as compared to $13.80 in the interior regions of the province which increases the overall purchased residue cost curve (Bradley, 2009). However, the higher bark residue cost in the coast is compensated with cheaper water transportation, hence a gentle increase in delievered residue cost.  The GIS-Dijkstra integrated assessment model predicted that the Seegen cogeneration plant was strategic in sourcing its power plant in Chilliwack, and thus benefited in terms of economics of scale (Figure 4.10 and 4.11). The future uncertainty of this plant will include feedstock security over its lifetime.  155 0 10 20 30 40 50 60 70 80 90 Te rm ina l F or es t P ro du cts  Lt d. (C K) Er rin gto n C ed ar Pr od uc ts Ltd . (S I) Ra inb ow  Lu mb er (S I) Lo ng  H oh  En ter pri se s C an ad a L td (S I) We ste rn  Fo res t P ro du cts  (S I) Int ern ati on al Fo res t P ro du cts  Lt d. (C K) Pr o C ut Lu mb er Co rp . (S I) Int ern ati on al Fo res t P ro du cts  Lt d. (C K) We ste rn  Fo res t P rod uc ts (C K) Ye ' O ld Do gw oo d L um be r ( SI ) Fu  So  En ter pr ise s L td.  (C K) Da le Ed wa rd s ( SI) We ste rn  Fo res t P ro du cts  (S I) We ste rn  Fo res t P ro du cts  (S I) No ble  C us tom  C ut Ltd . (C K) A J F or es t P ro du cts  ltd . (S Q) An de rse n P ac ific  Fo res t P ro du cts  Lt d. (C K) Th om so n B ros . L um be r C o. Ltd . (C R) Bl ac kta il E nte rp ris es  (C R) Su nc oa st Lu mb er an d M illi ng  (S C) Isl an d P ac ific  W oo d P ro du cts  (C R) SC G Fo res t In c. (C R) Jo hn  Sa lo (N I) D el iv er ed  re si du e co st  ($ /B D t) Transportation Cost Total Cost Seegen cogeneration plant  Figure 4.10: Total annual cost of transporting bark residues to existing coastal users [y-axis label is mill name and forest district]. Forest district abbreviations are presented in Appendix number B.3.   The purchase cost of whitewood in coastal forest region   In the integrated GIS-Djitskra logarithm model for the coast, the number of barge days depends on loading and unloading facilities, which are difficult to control. However, a 3- day minimum was adopted to determine the overall costs used in the integrated model. For whitewood the preferred bioenergy region was the Chilliwack district. The difference between hauling cost curve ($/km) and total residue cost curve ($/BDt) was relatively higher in coastal whitewood transportation than all other regions in BC. This is because the coastal forest region has the most expensive cost of whitewood at $59/BDt as compared to $37.8/BDt in the interior forest region (Bradley, 2009) (Figure 4.11). This price reflects the high competition for chips within the coastal forest region.   156 0.0 20.0 40.0 60.0 80.0 100.0 120.0 Te rm ina l F ore st Pro du cts  Lt d. (CK ) Go ldw oo d I nd us trie s L td.  (C K) Alb ion  Al de r a nd  M ap le Co . (C K) We ste rn Fo res t P rod uc ts (CK ) Mi ll &  Ti mb er Pro du cts  Lt d. (CK ) S &  R Sa wm ills  Lt d. (CK ) Na ga ard  Sa wm ills  Lt d. (SI ) Tim be rW es t F ore st Co rp.  (C R) De lta  Ce da r P rod uc ts Ltd . (C K) Da le Ar de n L og  Ha uli ng  Lt d. (SI ) A J  Fo res t P rod uc ts ltd . (S Q) An de rse n P ac ific  Fo res t P rod uc ts Ltd . (C K) We ste rn Fo res t P rod uc ts (SI ) We ste rn Fo res t P rod uc ts (SI ) Pro  Cu t L um be r C orp . (S I) We ste rn Fo res t P rod uc ts (SI ) Sil va Se rvi ce s L td.  (Q U) Lo is L um be r L td.  (S C) Su nc oa st Lu mb er an d M illin g ( SC ) C.V . C ed ar Sa les  Lt d ( CR ) Ed ge gra in (CR ) Gr een  Fo res t P rod uc ts Ltd . (C R) Sp ike  To p C ed ar Ltd . (N I) D el iv er ed  re si du e co st  ($ /B D t) Transportation Cost Total Cost Seegen cogeneration plant  Figure 4.11: Total annual cost of transporting whitewood residues to existing coastal users [y-axis label is mill name and forest district]. Forest district abbreviations are presented in Appendix number B.3.   Preferred location for coastal forest region bioenergy production   For efficient transportation of residues, a large barge (1400 X 200ft3) size was assumed in the input to ease the GIS-Dijkstra integrated assessment model. The idea of equating distance to transportation cost will be more misleading in the coastal scenario because tugs are relatively slow in towing and are dependent on the calmness of the sea. The lowest cost sites for bioenergy production for both bark and whitewood were located in Chilliwack district.  Given the complexity of coastal transportation system, the results of this study needs to be combined with plant-to-mill-based approach, knowledge on tug navigational effect, and differences in weather along the coast before locating a bioenergy plant (Figure 4.12).  157   Figure 4.12: Locations of mills and residue consumers in the coastal interior forest region   158 4.4 DISCUSSION   The GIS-Dijkstra integrated assessment model approach was used in this study to analyze the residue transportation network within BC and to determine the preferred sites for bioenergy production within the province. GIS was utilized as a supporting tool to establish transportation networks so that accurate estimates of hauling distances and costs can be determined. Other tools such as Dijkstra macro programming written in Microsoft Excel was used to allow point-wise analysis of the data.  The handling and transportation of woody biomass for bioenergy generation in BC has been often overlooked. To date, the substantial interest has generally been limited to bioenergy in large pulp and paper facilities. There is a dearth of empirical research with no systemic findings in the literature to ascertain the interplay between residue availability and geographical surplus. Feedstock security is an important requirement for any biobased industrial facility. Thus, residue consumers are very interested in long term supply security, in having the residue delivered at the mill site at the cheapest rates. As discussed below, this study investigated the costs of moving residue volumes to particular points for bioenergy production within the province.  (1) Elasticity in supply and demand It has been recognized that quantification of wood residue availability is a dynamic situation and is directly related to lumber processing and inversely related to production efficiencies. Currently in BC, wood residue generation is on the decline as sawmills are downsizing and several have shut down permanently.  Supply - Historically, most mills had long term contracts to sell their residue at little or nothing to consumers. Currently, some of the contracts maybe near expiring or the mills are closed leaving residue consumers with little or no options. With the current financial climate, bioenergy plants (depending on their production capacities) have to come up with creative ways to procure residues in an increasingly competitive market.  This may be achieved by improving the overall transportation and logistics of feedstock source and creating a mutual relationship with sawmills where mills would benefit from reduced energy costs from bioenergy plants, with bioenergy plants benefiting from a well priced,  159 secure and clean residue supply, thus securing a long-term feedstock delivery in order to hedge against risks, and thus an increase in the overall profit margin.  Demand - Despite the current financial climate and recent mill closures, there is a significant increase in residue demand across the province. Residue demand is even greater with the growing interest in bioenergy initiatives and new pellet producers. Different residues types command different prices geographically and sawmill keep their residue prices confidential because of competition amongst mills. Matters are made worse if transportation distances are further apart from the consumer site. The variability of residue prices geographically is presented such as: the cheapest total cost of whitewood residue in the Southern interior forest region is $31/BDt, in the Northern interior forest region is $78/BDt; and the coastal forest region is $80/BDt.  (2) Snapshot of residue availability in 2006 – undersupplied pulp and paper mills In this study, the optimization process was only possible by undersupplying chips to the pulp and paper industries. All three forest regions studied had a fibre deficit for the pulp and paper industry with the coastal region having the greatest chip deficit. This research demonstrates how GIS integrated Djitskra logarithm model can be used to address residue availability and to calculate the purchased residue cost across mills.  (3) Model runs Dijkstra algorithm analysed the cost of delivered wood residues at each sawmill within BC including existing cogeneration sites. This analysis was made possible by using ET geowizards software to generate the nodes and GIS systems to generate the near field identity (FID). The results were used to generate a delivered residue supply curve for bark and whitewood for each forest region in BC. It should be pointed out that GIS-Dijkstra integrated assessment model is not a static model and it can be modified and used in other regions provided there is available spatial and non-spatial data to run the model. It can also be used to run collection points for large regions as well as small forest regions or in an individual bioenergy-based approach.  Further, this study can be used to assess the available biomass within a region by adding up the total residue produced by category from each sawmill within that region as well as its relation to new and existing bioenergy facilities. This work may be a starting point for  160 provincial decision-makers, businesses, and individuals to undertake more in-depth studies of this topic.  (4) Transportation cost $/BDt Transportation cost has often been considered as one of the most important elements in bioenergy production costs. Purchased residues costs were calculated for three forest regions of BC (Figures 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 4.10 and 4.11). In order to understand the purchase cost model, Northern and Southern interior forest regions with a similar transportation type (road) were discussed. The differences are reflected in the slope and shape of the curves for both bark and whitewood in the two forest regions.  In the case of the Northern interior forest region, the purchased residue cost curve is flat in the lower cost zone associated with large sawmills with annual lumber outputs of over 10 million board feet. The curve eventually steepens at a higher cost zone associated with smaller mills averaging from 0 – 10 million board feet of lumber (BCMoF, 2006). Although, strategic location is very important for every total cost calculation, the Northern interior forest region exhibited one of the most expensive total cost within the province of $51/BDt for bark and $78/BDt for whitewood. This was attributed to the fact that most of the Northern regions had large road connection distances with most of the small sized sawmills being further away from the large sawmills. These distances which automatically increased the overall transportation cost (Figures 4.4 and 4.5).  The overall total residue cost for the Southern interior forest region was the cheapest ($15/BDt for bark and $31/BDt for whitewood) of all three forest region. The lower cost in the Southern interior forest region can be explained by the fact that the large and small sized mills are evenly distributed across the region and the region has shortest road distances between mills, which have substantially lowered the overall transportation cost (Figures 4.7 and 4.8).  The total residue costs for the coastal forest region were ($46/BDt for bark and $80/BDt for whitewood) comparable to the total residue cost of the Northern interior forest region (at bark $51/BDt and whitewood $78/BDt) despite having the highest purchase costs per BDt of residues at mill gate. This is because the long road transport distances in the Northern interior forest region have been compensated by the higher residues prices in  161 the coastal forest region. Thus, economics of a large scale thermochemical plant are better located in the Southern interior forest region followed by the Northern interior forest region. Smaller bioenergy plants are better suited in the coastal forest region because of its largest residue deficit but the lowest pulp and paper demand of the three forest regions.  (5) The cost of residue for bioenergy Public data on the purchase price of sawdust, bark and chip are not available within the province because of market sensitivity. To estimate the total cost of delivering all total residues within the regions at each existing site, GIS was used to position biomass consumers and production sites in order to determine the shortest $ weighted transportation distance. Thus, the transportation distances were combined with the summarized residues estimates provided by Bradley (2009).  The decision to locate a bioenergy plant is very sensitive to the plant’s scale and plant location. This study would benefit an investor who has a significant large investment capital and is interested to source residues for a large bioenergy plant (say >40MW) within a particular forest region.  Bioenergy facilities that produce electricity are still faced with the challenge of locating their plants closest to a gridline to be able to sell the electricity they generate to BC Hydro. To minimize overall operational cost, both the mills and residue consumers should be proactive to safeguard residues for bioenergy by purchasing residues from mills that properly handle and store their residues on-site. Also, cogeneration plants have to be located along electricity grid lines to ease accessibility of their products.  6) The dynamic nature of supply and demand This study highlights a snapshot of residue production in 2006. However, it must be remembered that this is a hypothetical scenario, and it is aimed at demonstrating the potential of the approach used in this study. This scenario can change quite quickly driven by AAC changes within the regions, mill locations and the market condition.   162 4.5 CONCLUSIONS   Transport costs are a significant portion of delivered residue costs in all of BC’s forest regions, and this cost is dependant on the distances from the producer to the consumer. The dispersed geographical distribution of biomass supply has raised the interest in this research. Understanding the residue availability by types and location within BC will facilitate bioenergy development potential. The purpose of this section was to depict how a GIS-Dijkstra integrated assessment model could be utilized to quantify residues geographically, to estimate total residue costs and to determine the preferred locations of bioenergy plants.  This study undertook a systematic analysis of residues by determining the shortest $ weighted distance within the province to locate a bioenergy plant as well as creating preferred collection points in order to minimize total cost.  The available net-surplus residue was analyzed for each region and the results revealed that cost of residues available at the preferred locations were significantly cheaper than non-preferred site which indicated that residue sourcing must be taken into consideration before locating a bioenergy plant at any given region.  With the hypothetical scenario created in the three forest regions, surplus residue generated in the Southern interior forest region had the cheapest total residue cost at $15/BDt for bark followed the coastal forest region at $46/BDt and the Northern forest region at $51/BDt. While Southern interior forest region had the cheapest total residue cost at $31/BDt for whitewood followed the Northern forest region at $78/BDt and the coastal forest region at $84/BDt.  Irrespective of the units used in quantifying residue to the consumers, biomass availability will impact the size of any new plant. A bioenergy plant’s profitability is influenced by the location, thus it is important to analyze the location of a plant on a site- specific basis were the plant will be situated in close proximity to unused residues that meet the specification of the plant year round so as to gain strong economic advantage by minimizing its overall transportation cost. However, with the dynamic changes in residue availability, it is difficult to forecast the lifetime resources of a plant.  163 4.6 REFERENCES  Alteyrac, J., Li, Y. and McFarlane P.N. (2008). Material flow and employment in British Columbia’s wood products and paper manufacturing sub-sectors. Presented at Arbora: 9th Conference of Science and Wood Industries. Bordeaux, France. November 21. Arnberg, W., Arnborg, S., Eklundh, L., Harrie, L., Hauska, H., Olsson, L., Pilesjö, P., Rystedt, B. & Sandgren, U. (2003). Geographical Information handling: methods and applications. Landscape and Urban Planning, Stockholm. 351 pp. BC MoF (2008). Major primary timber processing facilities in British Columbia. Ministry of Forest and Range Economics and Trade Branch (2006). 50pp. BC Hydro (2009) Armstrong woodwaste cogeneration plant. Retrived from: http://www.bchydro.com/planning_regulatory/acquiring_power/customer_generation /project_updates/armstrong_woodwaste.html Verified September 10th, 2009. Bradley, D. (2007). Canadian-Sustainable forest biomass supply chains. Climate change solutions, IEA Task 40. Retrieved from: http://www.for.gov.bc.ca/hts/tsa/tsa24/ Verified July 2008. Bradley, D. (2009) Canada report on bioenergy 2009. Canadian Bioenergy Association (CANBIO). Climate Change Solutions “Consulting in strategy, business investment, bioenergy, policy and climate change issues”. Environment Canada and IEA Bioenergy Task 40. Dijkstra, E. W. (1959), A Note on Two Problems in Connexion with Graphs, Numerische Mathematik, Vol. 1, pp. 269-271. Gass, S. I. and Harris, C.M. (1996), Encyclopedia of operations research management science. Kluwer, Boston, Mass. Ileleji, K. (2007) Transportation logistics of biomass for industrial fuel and energy enterprises. 7th Annual Conference on Renewable Energy from Organics Recycling,  164 Indianapolis, USA. Retrieved from http://www.jgpress.com/bcre07/t10.pdf Verified April 29th 2009. International Forest Industries. (2008) Canadian pulpmills are paying higher wood costs as supply tightens, WRI reports. Retrieved from http://www.internationalforestindustries.com/2008/10/30/canadian-pulpmills-are- paying-higher-wood-costs-as-supply-tightens-wri-reports/ Verified April 20th 2009. MacDonald, A. J. (2006) Estimating costs for harvesting, comminuting, and transporting beatle-killed pine in the Quesnel/Nazko area of central British Columbia. Forest Research Institute of Canada (FERIC). Retrieved from http://www.for.gov.bc.ca/hts/bioenergy/Link_Rep/FERIC_MPB%20report.pdf Verified August 20th 2009. McCloy, B., and Associate. (1999) Canada’s wood residues: A profile of current surplus and Regional concentrations. National Climate Change Process Verkerk, B. (2008). Current and future trade opportunities for woody biomass end- products from British Columbia, Canada. M.Sc. Thesis in Energy and Resources; Department of Science, Technology and Society, Utrecht University   165  CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH  5.1 CONCLUSIONS   This research developed an inventory mapping system of the sawmill residue producers in BC, and charted the residue masses available geographically for bioenergy consumption. In this study, the costs of transporting sawmill residues from the mills to a biomass processing site were analyzed for the whole of BC using a GIS-Dijkstra integrated assessment model to examine the surplus residues generated by sawmills geographically. This model analyzed the geographical variation and determined the supply cost of biomass within each major forest region in BC.  Further, the primary mill residues were analyzed for HHV, MC, ash content, basic density and their particle size distribution from different mill types. These sections elaborate on the conclusions that were drawn from each chapter and they are sub-divided according to the research objective phases stated in section 1.9.   5.1.1 Phase I Analysis of the physical characteristics of mill residues in BC   The samples were collected from a plywood mill, two sawmills, a shake mill and a pole mill. A total of 20 samples were collected from the coastal region of BC. Mill 1 was a plywood mill with a total of 16 samples; Mill 2 was a shake mill with a total of 2 samples and Mill 3 was a pole mill with a total of 2 samples.  Fourteen samples were also collected from the interior region of the Province. Mill 4 was a sawmill with a total of 7 samples and Mill 5 was a sawmill with a total of 7 samples.  166 The samples collected varied by species, age of pile, season, moisture condition, mill type and mill unit operation.  A total of 33 samples consisting of bark, hogfuel, chips, sander dust, trim end, sawdust, and shavings were subjected to laboratory analysis. The physical characteristics test carried out included: an adiabatic Parr 6100 oxygen bomb calorimeter to determine HHV; the ash content was determined using a muffle furnace; the MC was analyzed on oven dry basis; the basic density was analyzed using the volume displacement method; and the particle size distribution was determined using the sieve tests using aperture sizes ranging between 0.125 – 90mm nominal opening size.  The HHV ranged from 17 – 21.5 MJ/kg with a mean of 18.9MJ/kg. Ash contents were quite low for whitewood (0 – 2%) and higher for bark and hogfuel (2 – 6.5%) with a mean of 1.6%. MC ranged from 5 – 317% with a mean of 105%. The basic density was expressed as weight per unit volume ranged from 120 – 1151 kg/m3 with a mean of 352kg/m3. The average particle size (Dp50) of the mill residues ranged from 0.2 – 49.7mm.  Due to the large variability in residue characteristics caused by a range of factors including species type, geographical location, growth conditions, manufacturing process, and a more detailed understanding of residues characteristics will require a more comprehensive investigation than was undertaken in this study.   5.1.2 Phase II The availability of residues in BC   For bioenergy generation in BC to reach its full potential, there is the need to understand residue available by cost, quantity, type, availability, and location. This section of the thesis attempted to provide a theoretical inventory of the province’s residues. The total availability of residues for bioenergy is quite difficult to evaluate. Confidential mill data were used to develop representation residue production equations for BC sawmills, and these equations were applied to all the lumber mills operating in 2006.  167  In 2006, the total lumber output in BC was 16,985 mfbm from sawmills and the total capacity of the chip mills was 877,000 million BDt. This study estimated that the total amount of residues produced by sawmills and chip mills in 2006 were 12.6 million BDt. About 41.9% of the total residues came from Northern interior region, 42.4% were from the Southern interior region and 15.6% were generated from the coastal region of BC. There were no surplus residues in any of the three forest regions. The Southern forest region had a deficit of 1,032,000 BDt, the Northern forest region had the smallest deficit of 244,000 BDt while the coast forest region had the largest deficit of 1,754,000 BDt.   5.1.3 Phase III A GIS model of surplus residues in BC   This section of the research focused on evaluating the total transportation costs curve of residue available geographically (i.e. The Northern interior, Southern interior and coastal forest region) using a model methodology. Minimizing residue cost is an important issue to enable a bioenergy plant to compete with other energy sources.  The results revealed that, in the case of the Northern interior forest region, the purchased residue cost curve is flat in the lower cost zone associated with large sawmills with annual lumber outputs of over 10 million board feet. The curve eventually steepens at a higher cost zone associated with smaller mills averaging from 0 – 10 million board feet of lumber (BC MoF, 2006). The hypothetical scenario created in the three forest regions, revealed that surplus residues generated in the Southern interior forest region had the cheapest total residue cost at $15/BDt for bark followed the coastal forest region at $46/BDt and the Northern forest region at $51/BDt. While Southern interior forest region had the cheapest total residue cost at $31/BDt for whitewood followed the Northern forest region at $78/BDt and the coastal forest region at $84/BDt. The lower cost in the Southern interior forest region could be explained by the fact that the large and small sized mills are evenly distributed across the region and the region has shortest road distances between mills.   168  Using such a modeling approach, this chapter sought to provide policy makers and bioenergy project developers with relevant information and a valuable visual database designed to support the decision-making process with respect to resource availability and feedstock utilization within BC. Thus, sawmill manufacturers, hauling companies and bioenergy plants could develop the most cost effective ways of producing bioenergy.   5.2 LIMITATIONS   This research focused on analyzing the characteristics and the total amount of residues available for bioenergy production in BC. This study has several limitations; to achieve these goals a number of assumptions were made.  First, the data collected were for 2006 and this study was concluded in 2009. This was because 2006 data from the BC MoF for all major primary mills available in BC were released in 2008 and they were the only available data. The residue conversion ratios were assumed to be unchanging across the regions and as a function of mill size. More accurate data needs to be collected from the sawmills by initiating a  comprehensive survey of residue production type in terms of residue demand, amount of residue left on- site, and the variation of residue cost in order to increase the assessment accuracies.  Second, the model could have been improved in many aspects. Data acquisition was a huge part of this study, and key information, such as the quantity of biomass combusted at mill sites was lacking, and such information could sway the net regional shortage and surplus to large deficit. Improved geographical data on BC’s updated road network, and mill production forecasts could be used to increase the accuracy of this study.  Third, the bioenergy users were analyzed in this study as being inflexible to residue consumption. However, the assumption was true for bioenergy users such as ethanol plants and pulp and paper industries that consume principally high quality chips. While for thermochemical facilities the assumption is false because they consume a wide range of residue types.  169  Fourth, residue samples used in the laboratory analysis were collected from two sawmills in the Southern interior and one plywood mill, one pole mill and one shake and shingle mill in the coast forest region. The goal of this study was to initiate a database of residue characteristics within the province. A systematic approach to residue collection and characterization in BC should be established to complete this database.  Fifth, GIS software was partnered with Dijkstra model to estimate the delivering cost of residue available for bioenergy. To achieve this goal, several assumptions were made. The same transportation cost ($21.5/BDt) was used in the entire forest region irrespective of the road type. If the Dijkstra logarithm could have worked with individual regional transportation costs, more precise cost benefit estimates could have been developed. Also, the fixed cost (loading and unloading cost), the cost to store feedstock, and the cost to transport biomass in all the four seasons to the processing site were not computed.  Sixth, this study demonstrated the use of integrated GIS-Dijkstra assessment model to examine the surplus residues generated by sawmills geographically in 2006. Residue supply and demand is a dynamic situation and this study highlight residue production only for one year. Consequently, the results are not applicable to present circumstances.   5.3 FUTURE WORK   In the future, this research could pursue several directions.  Phase I: Analysis of the physical characteristics of mill residues in BC: Due to the biogeoclimatic conditions in BC and the natural anisotropic nature of wood material, a detailed analysis of residue properties is required to provide baseline technical data to assess residues characteristics for all bioenergy industries. It is likely that the tree species, the growth conditions, and time of the year that the tree is cut, will influence the density, heating value, moisture content, radial and tangential diameter, and fibre length which may affect the residue market  potential (Prescott and Vesterdal, 2005).   170 Phase II: The availability of residues in BC: This work is based on estimates from 2006 roundwood production from the mills in BC.  This study updated information on the quantities of biomass residues produced and the physical characteristics of these residues. However, a more detailed analysis of residues produced at manufacturing site by specific categories such as bark, chips, sawdust, shavings and trim ends is needed. In order for this goal to be achieved, the industry needs to be more proactive in collecting residue production data and annual surveys should be conducted to assess the quantities of residues produced and disposed of at wood products manufacturing sites.  Phase III: A GIS model of surplus residues in BC: Collection cost varies widely with the various logistics. Further investigations such as creating a GIS based network that analyzes the cost of all possible transportion options for wood residues within BC will be beneficial to the bioenergy plants by minimizing the overall residue transportation cost. This study did not consider rail transportation because there is presently a very limited use of rail for transporting residues. In terms of ownership investment, truck transport of biomass often requires little or no investment by the shipper, because trucks are owned by the carrier, not the shipper. The situation is different for rail transport in that the rail carrier typically owns the main tracks but the shipper owns the siding and all equipment located there, i.e., the shipper is responsible for loading the railcars. In addition, for any long term project such as a power plant supplied by dedicated trains, the shipper typically owns the rail wagons which add to the overall transportation cost (Mahmudi and Flynn, 2006). A detailed comparative study on all the existing logistics would be beneficial to the bioenergy sector in BC.  Finally, a comprehensive up-to-date GIS model could be expanded into a dynamic model where information on the mills currently operating could be updated yearly. Other detailed information such as: amount of residue produced at the mill, the price at which the mill is prepared to sell it’s residue by type, the current users of its residues, and how much is consumed on site could to be incorporated in the model for every region. This will enable the creation of a more accurate residue database for bioenergy facilities.   171 5.4 REFERENCES  Mahmudi, H. and Flynn, C. P (2006) Rail vs truck transport of biomass. Applied Biochemistry and Biotechnology. Volume 129, Numbers 1-3. Department of Mechanical Engineering, University of Alberta, T6G 2G8 Edmonton, Alberta, Canada Prescott E. C. and Vesterdal (2005) Effects of British Columbia tree species on forest floor chemistry. Faculty of Forestry, University of Bristish Columbia, Vanvcouver, British Columbia, Canada. Forest and Landscsspe, Royal Veterinary and Agricultural University,  Honholm, Denmark   172 APPENDICES  APPENDIX A: Summary of residue characteristics by type   Mill Residues HHV (MJ/kg) Ash content (%) Moisture content (%) Basic density (kg/m3)  Mean Range Mean Range Mean Range Mean Range Sander dust dry (Recycled wood 1) 18.1 17.7- 18.7 2.7 2.2- 3.3 5.3 5 - 5 1010 893 - 1157 Sawdust 18.1 17.6– 19.7 0.5 0.1- 0.6 130 108 - 155 345 244 - 489 Trim ends 19.7 18.8–20.1 0.6 0.4- 1.0 62.4 39 - 109 377 247 - 463 Shavings 18.5 17.6– 19.7 0.4 0.1- 0.6 9.4 9 - 10 354 223 - 490 Bark 19.6 18.3– 21.5 3.8 2.0- 6.5 140 26 - 198 323 122 - 396 Chips 18.4 17.2- 20 0.3 0.1- 1.7 91.5 27 - 110 332 158 - 498 Chips (dry) 17.6 17.5– 17.6 0.2 0.2 13.2 12 - 14 254 252 - 257 Hog fuel 19.1 17.1– 20.5 3.6 2.0- 5.2 111 61 - 151 308 119 - 433  173 APPENDIX B: Table showing data on sieve analysis and basic density Sieve Analysis (mm) Basic Density (kg/m3) Residues Dp 50 Dp 31.7 - Dp68.3 R2 SD Level Mean SD aRecycled wood (D) 1 0.2 0.09 - 0.4 0.9 0.56 A 1010 130.6 Sawdust 4 1.5 0.7 – 2.5 0.9 0.15 B 446 10.4 Sawdust/Shavings 2 (D) 2.4 1.7 – 3.0 0.9 0.06 D 224 1.9 Sawdust 1 3.2 1.5 – 5.3 0.9 0.06 C 244 0.7 cRecycled wood 3 3.2 1.7 – 4.9 0.9 0.13  B 424 24.22 Sawdust/Shavings 3 (D) 3.2 1.7 – 4.9 0.9 0 A 484 9.34 Bark 2 3.8 2.1 - 5.8 0.9 0.21 A 383 10.37 Ply trim end 2 4 2.5 - 5.5 0.9 0.17 A 433 18.16 Bark 5 4.1 2.5 - 5.7 0.9 0.61 A 381 12.97 Hog fuel 4 4.3 2.5 - 6.2 0.9 0.07 A 416 10.91 Trim end 1 4.4 2.2 – 6.9  0.8 0.06 A 448 1.4 Hog fuel 5 4.4 2.6 - 6.3 0.9 0.13 A 418 3.1 Ply trim end 3 5.2 3.4 - 7 0.9 0.12 A 452 12.65 Bark 3 5.4 3.4 - 7.3 0.9 0.06 A 360 19.75 Bark 4 5.9 3.6 - 8.3 0.9 0.2 A 363 9.62 Hog fuel 3 6.2 3.8 - 8.7 0.9 0.25 A 419 15.26 chips 9 6.3 3.8 - 9 0.9 0.12 C 413 14.41 chips 7 7.6 5.7 - 9.2 0.9 0.06 B 459 14.52 bRecycled wood 2 8.1 5.8 - 10.2 0.9 0.15  B 455 27.8 chips 4 10.9 7.6 – 13.9 0.9 0.06 F 165 0.95 chips 8 14 11.4 - 16 0.9 0 F 168. 20.07 chips 5 16.5 11.5 – 21 0.9 0.17 E 199 0.55 chips 3 16.9 11.5 - 22 0.9 0.12 D 251 2.55 chips 6 16.9 11.5 - 22 0.9 0.06 D 255 14.05 Hog fuel 2 19.6 10.4 – 30 0.9 0.12 B 165 6.85 chips 1 21.4 11.4 – 33 0.9 0.06 F 164 5.75 chips 2 (D) 29.9 19.6 – 40 0.9 0 D 254 2.6 Hog fuel 1 43.7 19.5 – 76 0.9 0.55 C 120 1.3 Bark 1 49.7 31.7 – 67.7 0.9 0.12 B 127 5.45 shake mill 1  0.25 B 292 2.10 log peel 1  0.45 B 293 9.01 log peel 2   0.06 A 339 18.16 shake mill 2  1.26 C 247 8.75 (a) There was a shortage of residue from the shake mill and log peel mill    174  APPENDIX C: Abbreviations used for Northern interior forest regions FN JA KM MK ND PC PG SS VA KEY Fort Nelson Fort St. James Kalum Mackenzie Nadina Peace Prince George Skeena Stikine Vanderhoof     175  APPENDIX D: Abbreviations used for Southern interior forest regions MH AB CS CC CH CO HW KA KEY 100 Mile House Arrow Boundary Cascades Central Cariboo Chilcotin Columbia  Headwaters Kamloops    KL OS QU RM Kootenay Lake Okanagan Shuswap Quesnel Rocky Mountain  176  APPENDIX E: Abbreviations used for coastal forest regions CR CK NC NI QC SI SQ SC KEY Campbell River Chilliwack North Coast North Island - Central Coast Haida Gwaii South Island Squamish Sunshine Coast    177 APPENDIX F: Road base_data Roads2_Proj ect Also in: Base_Data.mdb Roads_Project OBJE CTID BC_L INE_ BC_L INE_I D FCODE ET_ID ET_FNod e ET_TNo de Enable d LOC Shape_Length 1 1953 1953 DA24900010 1378 1373 1 1 MAINLAND 8784.573196 2 1956 1956 DA24900010 1 1373 2 1 MAINLAND 5362.894046 3 1957 1957 DA24900010 2 2 3 1 MAINLAND 6327.893188 4 1958 1958 DA24900010 3 3 4 1 MAINLAND 20713.59939 5 1959 1959 DA24900010 4 4 5 1 MAINLAND 13695.15514 6 1970 1970 DA24900010 5 6 7 1 MAINLAND 3444.616385 7 1971 1971 DA24900010 6 7 1373 1 MAINLAND 9364.129992 8 1972 1972 DA24900010 7 5 8 1 MAINLAND 10999.77404 9 1973 1973 DA24900010 8 8 9 1 MAINLAND 12407.15808 10 1973 1973 DA24900010 9 9 10 1 MAINLAND 11132.4145 11 1973 1973 DA24900010 10 10 11 1 MAINLAND 15973.83841 12 1974 1974 DA24900010 11 11 12 1 MAINLAND 3897.483476 13 1975 1975 DA24900010 12 12 13 1 MAINLAND 12920.0367 14 1976 1976 DA24900010 13 13 14 1 MAINLAND 9685.317069 15 1977 1977 DA24900010 14 14 15 1 MAINLAND 1090.854001 16 1978 1978 DA24900010 15 15 16 1 MAINLAND 11113.31336 17 1978 1978 DA24900010 16 16 17 1 MAINLAND 11664.4941 18 1978 1978 DA24900010 17 17 18 1 MAINLAND 2830.920677 19 1979 1979 DA24900010 18 18 19 1 MAINLAND 9766.295363 20 1979 1979 DA24900010 19 19 20 1 MAINLAND 11942.62889 21 1979 1979 DA24900010 20 20 21 1 MAINLAND 11006.17231 22 1979 1979 DA24900010 21 21 22 1 MAINLAND 10313.04135 23 1979 1979 DA24900010 22 22 23 1 MAINLAND 4318.298461 24 1980 1980 DA24900010 23 23 24 1 MAINLAND 10657.36121 25 1980 1980 DA24900010 24 24 25 1 MAINLAND 8018.66373 26 1981 1981 DA24900010 25 25 26 1 MAINLAND 10254.91968 27 1981 1981 DA24900010 26 26 27 1 MAINLAND 8463.274512 28 1982 1982 DA24900010 27 27 28 1 MAINLAND 1614.992121 29 1983 1983 DA24900010 28 28 29 1 MAINLAND 7309.678198 30 1984 1984 DA24900010 29 29 30 1 MAINLAND 7523.768366 31 1985 1985 DA24900010 30 30 31 1 MAINLAND 10802.96076 32 1985 1985 DA24900010 31 31 32 1 MAINLAND 6835.691996  178 Roads2_Proj ect Also in: Base_Data.mdb Roads_Project OBJE CTID BC_L INE_ BC_L INE_I D FCODE ET_ID ET_FNod e ET_TNo de Enable d LOC Shape_Length 33 1985 1985 DA24900010 32 32 33 1 MAINLAND 9694.167401 34 1986 1986 DA24900010 33 33 34 1 MAINLAND 10801.08484 35 1986 1986 DA24900010 34 34 35 1 MAINLAND 10881.74706 36 1986 1986 DA24900010 35 35 36 1 MAINLAND 8912.297145 37 1987 1987 DA24900010 36 36 37 1 MAINLAND 5983.625381 38 1987 1987 DA24900010 37 37 38 1 MAINLAND 8408.672527 39 1988 1988 DA24900010 38 38 39 1 MAINLAND 9530.475443 40 1988 1988 DA24900010 39 39 40 1 MAINLAND 7318.619151 41 1989 1989 DA24900010 40 40 41 1 MAINLAND 1945.989176 42 1990 1990 DA24900010 41 41 42 1 MAINLAND 10239.57121 43 1991 1991 DA24900010 42 42 43 1 MAINLAND 4133.260166 44 1992 1992 DA24900010 43 43 44 1 MAINLAND 10751.33962 45 1992 1992 DA24900010 44 44 45 1 MAINLAND 5652.298666 46 1993 1993 DA24900010 45 45 46 1 MAINLAND 1317.832939 47 1994 1994 DA24900010 46 46 47 1 MAINLAND 5447.543999 48 1995 1995 DA24900010 47 47 48 1 MAINLAND 5380.577195 49 1996 1996 DA24900010 48 49 50 1 MAINLAND 888.2639448 50 1997 1997 DA24900010 49 50 51 1 MAINLAND 1046.407561 51 1998 1998 DA24900010 50 48 49 1 MAINLAND 7766.191595 52 1999 1999 DA24900010 51 51 52 1 MAINLAND 4031.66789 53 2000 2000 DA24900010 52 52 53 1 MAINLAND 13487.35907 54 2001 2001 DA24900010 53 53 54 1 MAINLAND 5387.521611 55 2002 2002 DA24900010 54 54 55 1 MAINLAND 498.0662761 56 2003 2003 DA24900010 55 55 56 1 MAINLAND 6630.293295 57 2004 2004 DA24900010 56 56 57 1 MAINLAND 5476.618305 58 2005 2005 DA24900010 57 57 58 1 MAINLAND 696.6241871 59 2006 2006 DA24900010 58 58 59 1 MAINLAND 10324.90485 60 2007 2007 DA24900010 59 59 60 1 MAINLAND 4845.609563 61 2008 2008 DA24900010 60 60 61 1 MAINLAND 3617.931125 62 2009 2009 DA24900010 61 61 62 1 MAINLAND 1535.581403 63 2010 2010 DA24900010 62 62 63 1 MAINLAND 5872.609794 64 2011 2011 DA24900010 63 63 64 1 MAINLAND 2793.568052 65 2012 2012 DA24900010 64 65 66 1 MAINLAND 10086.32659 66 2012 2012 DA24900010 65 66 67 1 MAINLAND 4520.385991  179 Roads2_Proj ect Also in: Base_Data.mdb Roads_Project OBJE CTID BC_L INE_ BC_L INE_I D FCODE ET_ID ET_FNod e ET_TNo de Enable d LOC Shape_Length 67 2013 2013 DA24900010 66 67 62 1 MAINLAND 459.7194248 68 2014 2014 DA24900010 67 64 68 1 MAINLAND 2449.440071 69 2015 2015 DA24900010 68 69 70 1 MAINLAND 7175.169043 70 2016 2016 DA24900010 69 68 71 1 MAINLAND 1046.696541 71 2017 2017 DA24900010 70 71 72 1 MAINLAND 936.40951 72 2018 2018 DA24900010 71 73 74 1 MAINLAND 1816.501963 73 2019 2019 DA24900010 72 74 65 1 MAINLAND 2168.915297 74 2020 2020 DA24900010 73 70 75 1 MAINLAND 398.1133498 75 2021 2021 DA24900010 74 75 76 1 MAINLAND 3129.849604 76 2022 2022 DA24900010 75 76 77 1 MAINLAND 6685.259466 77 2023 2023 DA24900010 76 77 73 1 MAINLAND 559.9710584 78 2024 2024 DA24900010 77 78 69 1 MAINLAND 9843.247369 79 2025 2025 DA24900010 78 72 79 1 MAINLAND 506.0555502 80 2026 2026 DA24900010 79 79 80 1 MAINLAND 1254.527983 81 2027 2027 DA24900010 80 81 82 1 MAINLAND 485.0844496 82 2028 2028 DA24900010 81 82 83 1 MAINLAND 9985.156347 83 2028 2028 DA24900010 82 83 78 1 MAINLAND 3166.191922 84 2029 2029 DA24900010 83 84 85 1 MAINLAND 461.5444967 85 2030 2030 DA24900010 84 85 86 1 MAINLAND 12698.7147 86 2030 2030 DA24900010 85 86 81 1 MAINLAND 10027.19196 87 2031 2031 DA24900010 86 87 84 1 MAINLAND 516.510351 88 2032 2032 DA24900010 87 88 87 1 MAINLAND 1088.026152 89 2033 2033 DA24900010 88 80 89 1 MAINLAND 10076.48672 90 2033 2033 DA24900010 89 89 90 1 MAINLAND 4359.532642 91 2034 2034 DA24900010 90 90 91 1 MAINLAND 7088.791015 92 2035 2035 DA24900010 91 91 92 1 MAINLAND 1691.210974 93 2036 2036 DA24900010 92 92 93 1 MAINLAND 1417.893076 94 2037 2037 DA24900010 93 93 94 1 MAINLAND 2506.14939 95 2038 2038 DA24900010 94 95 96 1 MAINLAND 966.1481975 96 2039 2039 DA24900010 95 96 97 1 MAINLAND 5518.848454 97 2040 2040 DA24900010 96 97 98 1 MAINLAND 10277.32478 98 2040 2040 DA24900010 97 98 99 1 MAINLAND 11040.65433 99 2040 2040 DA24900010 98 99 100 1 MAINLAND 9993.526993 100 2040 2040 DA24900010 99 100 101 1 MAINLAND 1991.597274  180 Roads2_Proj ect Also in: Base_Data.mdb Roads_Project OBJE CTID BC_L INE_ BC_L INE_I D FCODE ET_ID ET_FNod e ET_TNo de Enable d LOC Shape_Length 101 2041 2041 DA24900010 100 102 103 1 MAINLAND 3847.602962 102 2041 2041 DA24900010 101 103 104 1 MAINLAND 2731.243033 103 2041 2041 DA24900010 102 104 105 1 MAINLAND 10606.23397 104 2042 2042 DA24900010 103 105 95 1 MAINLAND 3314.617616 105 2043 2043 DA24900010 104 106 107 1 MAINLAND 7677.658268 106 2044 2044 DA24900010 105 107 108 1 MAINLAND 223.6158808 107 2045 2045 DA24900010 106 108 109 1 MAINLAND 10860.31297 108 2045 2045 DA24900010 107 109 102 1 MAINLAND 371.9164287 109 2046 2046 DA24900010 108 110 111 1 MAINLAND 1727.186459 110 2047 2047 DA24900010 109 111 112 1 MAINLAND 8172.403597 111 2048 2048 DA24900010 110 112 106 1 MAINLAND 1646.023233 112 2049 2049 DA24900010 111 113 114 1 MAINLAND 7663.623371 113 2049 2049 DA24900010 112 114 115 1 MAINLAND 10633.97077 114 2049 2049 DA24900010 113 115 110 1 MAINLAND 1018.907582 115 2050 2050 DA24900010 114 116 117 1 MAINLAND 1940.569328 116 2051 2051 DA24900010 115 117 113 1 MAINLAND 2268.831687 117 2052 2052 DA24900010 116 118 116 1 MAINLAND 23.84338474 118 2053 2053 DA24900010 117 119 120 1 MAINLAND 2031.426573 119 2054 2054 DA24900010 118 121 119 1 MAINLAND 1625.000469 120 2055 2055 DA24900010 119 122 123 1 MAINLAND 5854.082216 121 2056 2056 DA24900010 120 123 124 1 MAINLAND 5738.429996 122 2056 2056 DA24900010 121 124 118 1 MAINLAND 334.1180575 123 2057 2057 DA24900010 122 125 122 1 MAINLAND 998.1148027 124 2058 2058 DA24900010 123 126 125 1 MAINLAND 3148.390126 125 2059 2059 DA24900010 124 127 128 1 MAINLAND 2510.541723 126 2060 2060 DA24900010 125 128 129 1 MAINLAND 11748.41683 127 2060 2060 DA24900010 126 129 130 1 MAINLAND 1102.110803 128 2061 2061 DA24900010 127 130 131 1 MAINLAND 9921.682939 129 2061 2061 DA24900010 128 131 121 1 MAINLAND 10443.8421 130 2062 2062 DA24900010 129 120 132 1 MAINLAND 11324.11463 131 2062 2062 DA24900010 130 132 133 1 MAINLAND 10033.77521 132 2062 2062 DA24900010 131 133 134 1 MAINLAND 7345.692967 133 2063 2063 DA24900010 132 134 135 1 MAINLAND 2321.826882 134 2064 2064 DA24900010 133 135 136 1 MAINLAND 329.6247479  181 Roads2_Proj ect Also in: Base_Data.mdb Roads_Project OBJE CTID BC_L INE_ BC_L INE_I D FCODE ET_ID ET_FNod e ET_TNo de Enable d LOC Shape_Length 135 2065 2065 DA24900010 134 136 137 1 MAINLAND 762.479965 136 2066 2066 DA24900010 135 138 139 1 MAINLAND 5786.760248 137 2067 2067 DA24900010 136 139 140 1 MAINLAND 8750.919502 138 2068 2068 DA24900010 137 140 127 1 MAINLAND 7365.579424 139 2069 2069 DA24900010 138 137 141 1 MAINLAND 2563.314638 140 2070 2070 DA24900010 139 141 142 1 MAINLAND 3222.262089 141 2071 2071 DA24900010 140 142 143 1 MAINLAND 10764.29347 142 2071 2071 DA24900010 141 143 144 1 MAINLAND 7296.417019 143 2072 2072 DA24900010 142 144 145 1 MAINLAND 1631.782838 144 2073 2073 DA24900010 143 146 147 1 MAINLAND 2524.028755 145 2074 2074 DA24900010 144 147 148 1 MAINLAND 13022.29068 146 2074 2074 DA24900010 145 148 149 1 MAINLAND 12263.26186 147 2074 2074 DA24900010 146 149 150 1 MAINLAND 11119.98644 148 2074 2074 DA24900010 147 150 126 1 MAINLAND 6580.039989 149 2075 2075 DA24900010 148 145 151 1 MAINLAND 3763.556979 150 2075 2075 DA24900010 149 151 152 1 MAINLAND 127.052156 151 2075 2075 DA24900010 150 152 153 1 MAINLAND 613.5414973 152 2076 2076 DA24900010 151 153 154 1 MAINLAND 4660.858319 153 2077 2077 DA24900010 152 154 155 1 MAINLAND 5810.272355 154 2078 2078 DA24900010 153 155 156 1 MAINLAND 2260.388315 155 2079 2079 DA24900010 154 157 158 1 MAINLAND 1345.767749 156 2080 2080 DA24900010 155 158 159 1 MAINLAND 1872.575108 157 2081 2081 DA24900010 156 159 160 1 MAINLAND 10452.0136 158 2082 2082 DA24900010 157 160 146 1 MAINLAND 4735.128294 159 2083 2083 DA24900010 158 161 162 1 MAINLAND 1264.788997 160 2084 2084 DA24900010 159 162 163 1 MAINLAND 1332.99016 161 2085 2085 DA24900010 160 163 164 1 MAINLAND 4655.257397 162 2086 2086 DA24900010 161 164 165 1 MAINLAND 1388.415233 163 2087 2087 DA24900010 162 165 166 1 MAINLAND 433.4616892 164 2088 2088 DA24900010 163 166 167 1 MAINLAND 528.7521568 165 2088 2088 DA24900010 164 167 168 1 MAINLAND 5668.929438 166 2089 2089 DA24900010 165 168 169 1 MAINLAND 7726.040918 167 2090 2090 DA24900010 166 169 170 1 MAINLAND 7772.790016 168 2091 2091 DA24900010 167 170 171 1 MAINLAND 10295.15822  182 Roads2_Proj ect Also in: Base_Data.mdb Roads_Project OBJE CTID BC_L INE_ BC_L INE_I D FCODE ET_ID ET_FNod e ET_TNo de Enable d LOC Shape_Length 169 2091 2091 DA24900010 168 171 172 1 MAINLAND 1855.220904 170 2092 2092 DA24900010 169 172 173 1 MAINLAND 7714.808507 171 2093 2093 DA24900010 170 173 174 1 MAINLAND 10798.16853 172 2093 2093 DA24900010 171 174 138 1 MAINLAND 11234.81689 173 2094 2094 DA24900010 172 175 176 1 MAINLAND 115.1722595 174 2095 2095 DA24900010 173 176 177 1 MAINLAND 9040.012046 175 2096 2096 DA24900010 174 177 178 1 MAINLAND 8838.352843 176 2096 2096 DA24900010 175 178 157 1 MAINLAND 646.4481974 177 2097 2097 DA24900010 176 156 179 1 MAINLAND 5528.406807 178 2098 2098 DA24900010 177 179 180 1 MAINLAND 8138.625848 179 2099 2099 DA24900010 178 180 181 1 MAINLAND 10244.14443 180 2099 2099 DA24900010 179 181 182 1 MAINLAND 10940.59086 181 2099 2099 DA24900010 180 182 183 1 MAINLAND 2557.458732 182 2100 2100 DA24900010 181 183 184 1 MAINLAND 10343.60854 183 2100 2100 DA24900010 182 184 185 1 MAINLAND 8864.585979 184 2101 2101 DA24900010 183 185 186 1 MAINLAND 3401.637933 185 2102 2102 DA24900010 184 186 187 1 MAINLAND 6952.558061 186 2103 2103 DA24900010 185 187 188 1 MAINLAND 3031.785236 187 2104 2104 DA24900010 186 189 175 1 MAINLAND 5051.833536 188 2105 2105 DA24900010 187 190 191 1 MAINLAND 5832.407462 189 2106 2106 DA24900010 188 191 192 1 MAINLAND 10887.28875 190 2106 2106 DA24900010 189 192 193 1 MAINLAND 4313.960242 191 2107 2107 DA24900010 190 193 161 1 MAINLAND 4062.079493 192 2108 2108 DA24900010 191 194 195 1 MAINLAND 4386.380111 193 2109 2109 DA24900010 192 195 196 1 MAINLAND 12713.74238 194 2109 2109 DA24900010 193 196 197 1 MAINLAND 812.0276372 195 2110 2110 DA24900010 194 198 199 1 MAINLAND 11029.30579 196 2110 2110 DA24900010 195 199 189 1 MAINLAND 3822.453034 197 2111 2111 DA24900010 196 197 200 1 MAINLAND 698.2064117 198 2112 2112 DA24900010 197 200 201 1 MAINLAND 6568.916442 199 2113 2113 DA24900010 198 202 203 1 MAINLAND 3604.40147 200 2114 2114 DA24900010 199 203 204 1 MAINLAND 8744.535197 201 2115 2115 DA24900010 200 204 205 1 MAINLAND 10252.59829 202 2115 2115 DA24900010 201 205 206 1 MAINLAND 6352.924303  183 Roads2_Proj ect Also in: Base_Data.mdb Roads_Project OBJE CTID BC_L INE_ BC_L INE_I D FCODE ET_ID ET_FNod e ET_TNo de Enable d LOC Shape_Length 203 2116 2116 DA24900010 202 206 190 1 MAINLAND 4905.901209 204 2117 2117 DA24900010 203 207 208 1 MAINLAND 10533.46419 205 2117 2117 DA24900010 204 208 209 1 MAINLAND 114.7740149 206 2118 2118 DA24900010 205 209 202 1 MAINLAND 2098.815381 207 2119 2119 DA24900010 206 210 211 1 MAINLAND 1421.132678 208 2119 2119 DA24900010 207 211 212 1 MAINLAND 5296.643288 209 2120 2120 DA24900010 208 212 214 1 MAINLAND 6835.837865 210 2121 2121 DA24900010 209 214 215 1 MAINLAND 8884.114262 211 2121 2121 DA24900010 210 215 216 1 MAINLAND 3128.818015 212 2122 2122 DA24900010 211 216 217 1 MAINLAND 934.2845951 213 2123 2123 DA24900010 212 217 218 1 MAINLAND 485.8952358 214 2124 2124 DA24900010 213 201 219 1 MAINLAND 5791.70095 215 2125 2125 DA24900010 214 219 220 1 MAINLAND 10259.58904 216 2125 2125 DA24900010 215 220 221 1 MAINLAND 2081.875133 217 2126 2126 DA24900010 216 221 222 1 MAINLAND 9424.903749 218 2127 2127 DA24900010 217 218 223 1 MAINLAND 10150.44603 219 2127 2127 DA24900010 218 223 224 1 MAINLAND 3116.535172 220 2128 2128 DA24900010 219 224 225 1 MAINLAND 10263.29145 221 2128 2128 DA24900010 220 225 207 1 MAINLAND 3803.644264 222 2129 2129 DA24900010 221 226 227 1 MAINLAND 10330.82638 223 2129 2129 DA24900010 222 227 228 1 MAINLAND 2468.581279 224 2130 2130 DA24900010 223 228 229 1 MAINLAND 1560.849717 225 2131 2131 DA24900010 224 229 198 1 MAINLAND 3299.80547 226 2132 2132 DA24900010 225 230 226 1 MAINLAND 845.0215413 227 2133 2133 DA24900010 226 231 232 1 MAINLAND 2307.565387 228 2134 2134 DA24900010 227 233 231 1 MAINLAND 1359.604263 229 2135 2135 DA24900010 228 232 234 1 MAINLAND 925.7915437 230 2136 2136 DA24900010 229 234 235 1 MAINLAND 4027.857773 231 2137 2137 DA24900010 230 236 237 1 MAINLAND 9230.886842 232 2138 2138 DA24900010 231 237 238 1 MAINLAND 18876.59549 233 2140 2140 DA24900010 232 235 239 1 MAINLAND 2982.356822 234 2141 2141 DA24900010 233 239 240 1 MAINLAND 814.5458287 235 2142 2142 DA24900010 234 240 236 1 MAINLAND 11182.05852 236 2144 2144 DA24900010 235 238 241 1 MAINLAND 18850.78896  184 Roads2_Proj ect Also in: Base_Data.mdb Roads_Project OBJE CTID BC_L INE_ BC_L INE_I D FCODE ET_ID ET_FNod e ET_TNo de Enable d LOC Shape_Length 237 2147 2147 DA24900010 236 242 243 1 MAINLAND 895.5388939 238 2148 2148 DA24900010 237 243 230 1 MAINLAND 7445.574957 239 2149 2149 DA24900010 238 244 245 1 MAINLAND 969.6773134 240 2150 2150 DA24900010 239 245 246 1 MAINLAND 10727.05616 241 2150 2150 DA24900010 240 246 247 1 MAINLAND 5831.904244 242 2151 2151 DA24900010 241 248 249 1 MAINLAND 10131.02277 243 2151 2151 DA24900010 242 249 250 1 MAINLAND 4165.433592 244 2152 2152 DA24900010 243 250 244 1 MAINLAND 3319.639006 245 2153 2153 DA24900010 244 251 242 1 MAINLAND 1906.494213 246 2154 2154 DA24900010 245 252 251 1 MAINLAND 3963.746004 247 2155 2155 DA24900010 246 253 254 1 MAINLAND 1319.6811 248 2156 2156 DA24900010 247 254 252 1 MAINLAND 262.1921257 249 2157 2157 DA24900010 248 255 253 1 MAINLAND 850.8029975 250 2158 2158 DA24900010 249 247 256 1 MAINLAND 10746.22743 251 2159 2159 DA24900010 250 256 255 1 MAINLAND 1918.514189 252 2160 2160 DA24900010 251 257 251 1 MAINLAND 2907.893107 253 2161 2161 DA24900010 252 241 258 1 MAINLAND 11056.34959 254 2162 2162 DA24900010 253 258 259 1 MAINLAND 8583.097411 255 2163 2163 DA24900010 254 260 261 1 MAINLAND 2159.508463 256 2164 2164 DA24900010 255 261 257 1 MAINLAND 2510.131252 257 2165 2165 DA24900010 256 259 262 1 MAINLAND 11279.3321 258 2166 2166 DA24900010 257 262 263 1 MAINLAND 12214.05089 259 2167 2167 DA24900010 258 263 264 1 MAINLAND 9175.171129 260 2168 2168 DA24900010 259 265 257 1 MAINLAND 9200.247761 261 2169 2169 DA24900010 260 266 260 1 MAINLAND 6882.287801 262 2170 2170 DA24900010 261 264 267 1 MAINLAND 2843.639878 263 2171 2171 DA24900010 262 267 268 1 MAINLAND 11100.64822 264 2172 2172 DA24900010 263 268 269 1 MAINLAND 6713.181732   185 APPENDIX G: Northern interior forest preferred whitewood mills  Northern_Interior_Forest_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  Moved Mass   $Distance  $Cost 133 Pope and Talbot Ltd. Fort St James 7028 1439159 25037850.6   29,879,859    54,917,709  700450 43  78 162 Winton Global Prince George 7035 1481760 24995250   29,870,764    54,866,014  661250 45  83 153 Canadian Forest Products Ltd. Prince George 7015 2021544 24455466   29,637,637    54,093,103  646970 46  84 158 Dunkley Lumber Ltd. Prince George 7020 2862972 23614038   29,847,502    53,461,540  624710 48  86 97 Canadian Forest Products Ltd. Prince George 7007 0 26477010   34,611,456    61,088,466  700450 49  87 144 L & M Lumber Ltd. Vanderhoof 7024 1084860 25392150   33,757,560    59,149,710  671750 50  88 137 Apollo Forest Products Ltd. Fort St James 7002 703836 25773174   34,548,659    60,321,833  681830 51  88 128 Abitibi-Consolidated Company of Canada Makenzie 7000 1243620 25233390   34,423,928    59,657,318  667550 52  89 129 Abitibi-Consolidated Company of Canada Makenzie 7001 1026648 25450362   34,541,841    59,992,203  673290 51  89 160 Canadian Forest Products Ltd. Prince George 7013 0 26477010   36,207,230    62,684,240  700450 52  89 140 Canadian Forest Products Ltd. Vanderhoof 7012 1663918 24813091.8   34,370,089    59,183,181  656431 52  90 182 Terrace Lumber Company Ltd. Kalum 7030 65053.8 26411956.2   36,593,482    63,005,438  698729 52  90 10 BC Custom Timber Products Ltd. Vanderhoof 7004 10584 26466426   36,760,781    63,227,207  700170 53  90 130 Canadian Forest Products Ltd. Makenzie 7011 2225437 24251572.8   34,405,627    58,657,200  641576 54  91 183 West Fraser Mills Ltd. Kalum 7034 484331.4 25992678.6   36,361,285    62,353,964  687637 53  91 166 Canadian Forest Products Ltd. Prince George 7014 1746360 24730650   34,807,472    59,538,122  654250 53  91 532 West Fraser Mills Ltd. Vanderhoof 7032 1762236 24714774   34,820,405    59,535,179  653830 53  91 552 West Fraser Mills Ltd. Peace 7031 1296540 25180470   35,764,353    60,944,823  666150 54  91 127 Canadian Forest Products Ltd. Peace 7006 1222452 25254558   35,937,714    61,192,272  668110 54  92 122 Canadian Forest Products Ltd. Peace 7008 1037232 25439778   36,005,568    61,445,346  668110 54  92 191 West Fraser Mills Ltd. Skeena Stikine 7033 1635228 24841782   36,100,119    60,941,901  657190 55  93  186  Northern_Interior_Forest_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  Moved Mass   $Distance  $Cost 710 Northern Lumber Solutions Ltd Prince George 7026 0 26477010   39,576,357    66,053,367  700450 57  94 136 Stuart Lake Lumber Co. Ltd. Fort St James 7029 550368 25926642   40,371,160    66,297,802  700450 58  95 149 Lakeland Mills Ltd. Prince George 7025 775278 25701732   39,966,707    65,668,439  679940 59  97 732 PG Sort Yard Prince George 7027 0 26477010   41,458,850    67,935,860  700450 59  97 193 Canadian Forest Products Ltd. Nadina 7009 0 26477010   42,299,066    68,776,076  700450 60  98 184 Kitwanga Mills Ltd. Skeena Stikine 7023 137592 26339418   43,121,492    69,460,910  696810 62  100 135 Canadian Forest Products Ltd. Prince George 7010 0 26477010   44,579,468    71,056,478  700450 64  101 672 Woodland Forest Products Ltd Prince George 7036 0 26477010   47,126,338    73,603,348  700450 67  105 530 Houston Forest Products Co. Nadina 7022 0 26477010   51,539,706    78,016,716  700450 74  111 213 Babine Forest Products Limited Nadina 7003 0 26477010   51,593,400    78,070,410  700450 74  111 121 Canadian Forest Products Ltd. Fort Nelson 7021 0 26477010   51,611,617    78,088,627  700450 74  111 150 Carrier Lumber Ltd. Prince George 7016 0 26477010   63,543,094    90,020,104  700450 91  129 181 Decker Lake Nadina 7019 0 26477010   65,033,233    91,510,243  700450 93  131 737 Cheslatta Forest Products Ltd. Nadina 7017 0 26477010   65,571,908    92,048,918  700450 94  131 917 Burns Lake Community Forest Nadina 7005 0 26477010   87,450,565   113,927,575  700450 125  163 742 Corwood Timber Products Ltd. Nadina 7018 0 26477010   96,268,883   122,745,893  700450 137  175 COGENERATION PLANT 10021 Sandwell Makenzie 10021  26477010   36,690,740  63167750.35  700,450  52.38166943  90.18 10020 Armstrong  cogeneration plant Makenzie 10020  26477010   41,340,728  67817737.68  700,450  59.02024081  96.82    187 APPENDIX H: Northern interior forest region preferred bark mills Northern_Interior_Forest Region_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  Moved Mass  $/Dist ance   $/BDt 97 Canadian Forest Products Ltd. Prince George 7007 393024 11289849  30,529,719  41,819,568    818,105            37            51 213 Babine Forest Products Limited Nadina 7003 580704 11102169  30,062,025  41,164,194    804,505            37            51 530 Houston Forest Products Co. Nadina 7022 752928 10929945  29,951,289  40,881,234    792,025            38            52 160 Canadian Forest Products Ltd. Prince George 7013 604992 11077881  30,551,083  41,628,964    802,745            38            52 193 Canadian Forest Products Ltd. Nadina 7009 1159200 10523673  29,754,245  40,277,918    762,585            39            53 158 Dunkley Lumber Ltd. Prince George 7020 1194528 10488345  30,064,261  40,552,606    760,025            40            53 127 Canadian Forest Products Ltd. Peace 7006 510048 11172825  32,154,602  43,327,427    809,625            40            54 122 Canadian Forest Products Ltd. Peace 7008 432768 11250105  32,608,410  43,858,515    815,225            40            54 150 Carrier Lumber Ltd. Prince George 7016 441600 11241273  32,838,865  44,080,138    814,585            40            54 166 Canadian Forest Products Ltd. Prince George 7014 728640 10954233  32,152,093  43,106,326    793,785            41            54 162 Winton Global Prince George 7035 618240 11064633  32,809,671  43,874,304    801,785            41            55 181 Decker Lake Nadina 7019 154560 11528313  35,649,086  47,177,399    835,385            43            56 737 Cheslatta Forest Products Ltd. Nadina 7017 134964 11547909  36,320,307  47,868,216    836,805            43            57 153 Canadian Forest Products Ltd. Prince George 7015 843456 10839417  34,642,267  45,481,684    785,465            44            58 917 Burns Lake Community Forest Nadina 7005 15014.4 11667858.6  37,445,225  49,113,083    845,497            44            58 552 West Fraser Mills Ltd. Peace 7031 540960 11141913  39,501,469  50,643,382    846,585            47            60 135 Canadian Forest Products Ltd. Prince George 7010 565800 11117073  38,522,659  49,639,732    805,585            48            62 710 Northern Lumber Solutions Ltd Prince George 7026 13027.2 11669845.8  41,440,617  53,110,463    845,641            49            63 672 Woodland Forest Products Ltd Prince George 7036 3312 11679561  41,738,530  53,418,091    846,345            49            63 144 L & M Lumber Ltd. Vanderhoof 7024 55200 11627673  41,581,582  53,209,255    842,585            49            63 742 Corwood Timber Products Ltd. Nadina 7018 15359.4 11667513.6  41,936,195  53,603,709    845,472            50            63  188 Northern_Interior_Forest Region_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  Moved Mass  $/Dist ance   $/BDt 149 Lakeland Mills Ltd. Prince George 7025 323472 11359401  42,184,910  53,544,311    823,145            51            65 133 Pope and Talbot Ltd. Fort St James 7028 600465.6 11082407.4  42,095,253  53,177,660    803,073            52            66 732 PG Sort Yard Prince George 7027 31726.2 11651146.8  48,531,758  60,182,905    844,286            57            71 10 BC Custom Timber Products Ltd. Vanderhoof 7004 0 11682873  50,299,406  61,982,279    846,585            59            73 136 Stuart Lake Lumber Co. Ltd. Fort St James 7029 0 11682873  50,534,538  62,217,411    846,585            60            73 137 Apollo Forest Products Ltd. Fort St James 7002 0 11682873  53,979,249  65,662,122    846,585            64            78 532 West Fraser Mills Ltd. Vanderhoof 7032 0 11682873  54,775,157  66,458,030    846,585            65            79 140 Canadian Forest Products Ltd. Vanderhoof 7012 0 11682873  54,983,805  66,666,678    846,585            65            79 129 Abitibi-Consolidated Company of Canada Makenzie 7001 0 11682873  61,642,134  73,325,007    846,585            73            87 128 Abitibi-Consolidated Company of Canada Makenzie 7000 0 11682873  64,221,256  75,904,129    846,585            76            90 130 Canadian Forest Products Ltd. Makenzie 7011 0 11682873  64,788,275  76,471,148    846,585            77            90 183 West Fraser Mills Ltd. Kalum 7034 202073.4 11480799.6  69,979,609  81,460,409    831,942            84            98 182 Terrace Lumber Company Ltd. Kalum 7030 27130.8 11655742.2  75,460,518  87,116,260    846,585            89          103 191 West Fraser Mills Ltd. Skeena Stikine 7033 682272 11000601  89,948,311  100,948,912    846,585          106          119 184 Kitwanga Mills Ltd. Skeena Stikine 7023 57408 11625465  91,310,465  102,935,930    842,425          108          122 121 Canadian Forest Products Ltd. Fort Nelson 7021  11682873  101,970,518  113,653,391    846,585          120          134 COGENERATION PLANTS 10021 Sandwell Makenzie 10021  11682873  55,158,064  66840936.96   846,585  65.1536 78.9536 10020 Armstrong  cogeneration plant Makenzie 10020  11682873  62,619,712  74302585.23   846,585  73.96742 87.76742    189 APPENDIX I: Southern interior forest preferred bark mills Southern_Interior_Forest_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  Moved Mass  $Distance  $Cost 32 Gibbs Custom Sawmill Headwaters 7015 0 1992237     6,490,537      7,950,121  521205           12  15 47 Joe Kozek Sawmills Ltd. Columbia 7023 0 1992237     6,434,788      8,098,033  521205           12  16 74 Weyerhaeuser Company Ltd. Kamloops 7071 0 1992237     6,502,485      8,137,716  521205           12  16 27 Weyerhaeuser Company Ltd. Okanagan Shuswap 7072 357006 1635231     6,378,357      8,370,594  495335           13  17 20 Tolko Industries Ltd. Okanagan Shuswap 7054 532653 1459584     6,361,713      8,342,731  482607           13  17 68 Tolko Industries Ltd. Okanagan Shuswap 7056 527118 1465119     6,435,960      8,427,879  483008           13  17 172 Hauer Bros. Lumber Ltd. Headwaters 7018 328992 1663245     6,582,256      8,574,493  497365           13  17 665 North Okanagan Cedar Ltd.  7037 38419 1953818     6,595,568      8,587,805  518421           13  17 11 Eagle River Industries Inc. Okanagan Shuswap 7011 0 1992237     6,610,128      8,602,365  521205           13  17 79 Tembec Industries Ltd. Rocky Mountain 7052 11219 1981018     6,649,386      8,641,623  520392           13  17 45 Downie Timber Ltd. Columbia 7010 317 1991920     6,719,436      8,711,673  521182           13  17 14 Gorman Bros Lumber Ltd. Okanagan Shuswap 7017 0 1992237     6,941,326      8,933,563  521205           13  17 734 Enid Lake Logging Ltd. Rocky Mountain 7012 4706 1987531     6,964,025      8,956,262  520864           13  17 113 West Fraser Mills Ltd. Quesnel 7069 0 1992237     6,983,192      8,975,429  521205           13  17 86 Galloway Lumber Co. Ltd. Rocky Mountain 7014 99912 1892325     7,528,870      8,966,899  513965           15  17 103 Tolko Industries Ltd. Central Cariboo 7055 554208 1438029     6,510,828      8,503,065  481045           14  18 452 Chimney Creek Lumber Co Ltd. Central Cariboo 7008 504059 1488178     6,530,588      8,522,825  484679           13  18 99 Ukass Logging Ltd. Rocky Mountain 7062 0 1992237     7,543,574      9,535,811  521205           14  18 98 Tolko Industries Ltd. Quesnel 7061 0 1992237     7,847,933      9,840,170  521205           15  19 82 Tembec Industries Ltd. Rocky Mountain 7053 0 1992237     7,859,596      9,851,833  521205           15  19 29 Weyerhaeuser Company Ltd. Cascades 7073 0 1992237     7,865,044      9,857,281  521205           15  19  190 Southern_Interior_Forest_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  Moved Mass  $Distance  $Cost 107 Tolko Industries Ltd. Central Cariboo 7058 0 1992237     7,946,312      9,938,549  521205           15  19 25 Tolko Industries Ltd. Cascades 7060 0 1992237     8,362,472    10,354,709  521205           16  20 95 West Fraser Mills Ltd. 100 Mile House 7067 0 1992237     8,375,106    10,364,472  521205           16  20 114 West Fraser Mills Ltd. Central Cariboo 7068 0 1992237     8,349,204    10,320,023  521205           16  20 66 Gilbert Smith For Prod Ltd. Kamloops 7016 2870 1989367     8,375,924    10,268,249  520997           16  20 255 Larry Buff Sawmills Ltd. Okanagan Shuswap 7028 21418 1970819     8,956,843    10,949,080  519653           17  21 214 West Fraser Mills Ltd. 100 Mile House 7066 0 1992237     9,203,080    11,195,317  521205           18  21 602 T.L. Timber Ltd. Okanagan Shuswap 7051 0 1992237     9,319,098    11,299,481  521205           18  22 526 West Fraser Mills Ltd. Quesnel 7070 0 1992237     9,455,963    11,448,200  521205           18  22 518 Herridge Trucking and Sawmilling Ltd Arrow Boundary 7019 11854 1980383     9,434,161    11,418,118  520346           18  22 88 J H Huscroft Ltd. Kootenay Lake 7022 0 1992237     9,613,470    11,605,707  521205           18  22 30 Pope & Talbot Ltd. Arrow Boundary 7043 0 1992237   10,341,801    11,806,920  521205           20  23 67 Tolko Industries Ltd. Okanagan Shuswap 7057 8280 1983957     9,853,183    11,845,420  520605           19  23 70 International Forest Products Ltd. Kamloops 7021 0 1992237     9,921,579    11,903,549  521205           19  23 173 Valemount Forest Products Ltd. Headwaters 7063 0 1992237   10,075,235    12,067,472  521205           19  23 229 Wadlegger Log & Constr. Co. Headwaters 7064 10267 1981970   10,281,510    12,273,747  520461           20  24 101 Tolko Industries Ltd. Central Cariboo 7059 0 1992237   10,543,984    12,529,197  521205           20  24 597 Lakeside Timber Ltd. Okanagan Shuswap 7027 0 1992237   10,861,471    12,853,708  521205           21  25 639 West Chilcotin Forest Product Ltd. Chilcotin 7065 7024 1985213   11,119,341    13,095,459  520696           21  25 698 Wildwood Forest Products Ltd. Central Cariboo 7074 0 1992237   11,216,957    13,209,194  521205           22  25 197 McDonald Ranch & Lumber Ltd. Rocky Mountain 7033 16118 1976119   11,239,673    13,231,910  520037           22  25 247 Jones Ties n .P. (1978) Ltd. Arrow Boundary 7024 0 1992237   11,798,290    13,286,468  521205           23  25 64 Canadian Forest Products Headwaters 7007 0 1992237   11,376,931    13,369,168  521205           22  191 Southern_Interior_Forest_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  Moved Mass  $Distance  $Cost Ltd. 26 281 Karl Beattie Cont Ltd. Columbia 7026 0 1992237   12,086,136    14,078,373  521205           23  27 36 Russo Sawmills, L Okanagan Shuswap 7046 0 1992237   12,274,593    14,266,830  521205           24  27 684 Corwood Timber Products Ltd. Headwaters 7009 0 1992237   12,408,159    14,400,396  521205           24  28 498 Aspen Planers Ltd. Cascades 7002 0 1992237   12,479,004    14,471,241  521205           24  28 207 Ardew Wood Products Ltd. Cascades 7001 0 1992237   12,612,640    14,604,877  521205           24  28 93 Wynndel Box & Lumber Co. Ltd. Kootenay Lake 7075 0 1992237   12,901,758    14,893,664  521205           25  29 626 Porcupine Wood Products Ltd. Kootenay Lake 7044 0 1992237   12,936,486    14,928,723  521205           25  29 480 Schapol Logging Ltd. Okanagan Shuswap 7047 331 1991906   13,121,672    15,113,909  521181           25  29 271 Rouck Brothers Sawmill Ltd. Okanagan Shuswap 7045 0 1992237   13,108,984    15,091,064  521205           25  29 7 Adams Lake Development Corporation Kamloops 7000 0 1992237   13,241,804    15,234,041  521205           25  29 702 Linde Bros Lumber Ltd. Central Cariboo 7029 10157 1982080   13,195,525    15,149,343  520469           25  29 603 Meadow Creek Cedar Ltd. Kootenay Lake 7034 0 1992237   13,691,298    15,683,535  521205           26  30 514 Simpcw Development Co Ltd. Kamloops 7050 0 1992237   14,361,222    16,353,459  521205           28  31 199 North Star Planing Co. Ltd. Rocky Mountain 7038 0 1992237   14,650,282    16,642,519  521205           28  32 482 Notch Hill Forest Products Ltd. Okanagan Shuswap 7005 0 1992237   15,087,999    17,080,236  521205           29  33 643 Bear Lumber Ltd. Rocky Mountain 7003 0 1992237   15,087,999    17,080,236  521205           29  33 713 Paragon Ventures Ltd. Okanagan Shuswap 7040 0 1992237   15,112,600    17,104,837  521205           29  33 482 Notch Hill Forest Products Ltd. Okanagan Shuswap 7039 0 1992237   15,217,550    17,209,787  521205           29  33 286 Hilmoe Forest Products Ltd. Arrow Boundary 7020 0 1992237   15,229,424    17,221,661  521205           29  33 542 Marsh Bros Lumber and Supply Ltd. Headwaters 7031 0 1992237   15,241,366    17,233,603  521205           29  33 116 Lytton Lumber Ltd. Cascades 7030 0 1992237   15,575,768    17,568,005  521205           30  34 750 Sigurdson Bros. Logging Company Chilcotin 7048 0 1992237   16,130,527    18,122,764  521205           31  35  192 Southern_Interior_Forest_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  Moved Mass  $Distance  $Cost 740 Sigurdson Bros. Logging Company Chilcotin 7049 0 1992237   17,670,089    19,662,326  521205           34  38 90 Canadian Forest Products Ltd. Rocky Mountain 7006 0 1992237   17,720,248    19,712,485  521205           34  38 252 C & C Wood Products Ltd. Quesnel 7004 0 1992237   17,716,246    19,703,405  521205           34  38 31 Pope & Talbot Ltd. Arrow Boundary 7041 0 1992237   17,711,187    19,698,718  521205           34  38 169 McBride Forest Ind. Ltd. Headwaters 7032 5078 1987159   18,342,487    20,334,724  520837           35  39 12 Federated Co-op Ltd. Okanagan Shuswap 7013 0 1992237   18,375,780    20,368,017  521205           35  39 50 Kalesnikoff Lumber Co. Ltd. Arrow Boundary 7025 0 1992237   18,375,780    20,368,017  521205           35  39 662 Munson Equipment Ltd. Kamloops 7035 0 1992237   18,375,780    20,368,017  521205           35  39 618 North Enderby Timber Ltd. Okanagan Shuswap 7036 0 1992237   18,375,780    20,368,017  521205           35  39 62 Pope & Talbot Ltd. Arrow Boundary 7042 0 1992237   20,411,854    22,404,091  521205           39  43 COGENERATION PLANT SD3 Purcell Power Project (Tembec Enterprises Inc.) Rocky Mountain 10003  1992237   13,251,926  15244163 521205 25 29.2 SD2 NWE Energy Corporation Central Cariboo 10002  1992237   13,937,117  15929354 521205 27 30.6 SD4 Riverside Forest Products Limited Okanagan Shuswap 10004  1992237   20,729,555  22721792 521205 40 43.6  193 APPENDIX J: Southern interior forest region preferred whitewood mills Southern_Interior_Forest_Region_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  MovedVolume $/Dist ance $/BDt 95 West Fraser Mills Ltd. 100 Mile House 7067 1301719 3607443      2,662,116  6,269,559  203,140 13 31 114 West Fraser Mills Ltd. Central Cariboo 7068 1280664 3628498      2,692,360  6,320,858  203,697 13 31 255 Larry Buff Sawmills Ltd. Okanagan Shuswap 7028 0 4909162      2,708,479  7,617,640  237,577 11 32 66 Gilbert Smith For Prod Ltd. Kamloops 7016 0 4909162      2,714,649  7,623,811  237,577 11 32 247 Jones Ties and Poles (1978) Ltd. Arrow Boundary 7024 2116.8 4907045      2,855,289  7,762,334  237,521 12 33 29 Weyerhaeuser Company Ltd. Cascades 7073 19467 4889695      2,900,613  7,790,307  237,062 12 33 107 Tolko Industries Ltd. Central Cariboo 7058 0 4909162      2,911,105  7,820,266  237,577 12 33 25 Tolko Industries Ltd. Cascades 7060 0 4909162      2,916,006  7,825,168  237,577 12 33 93 Wynndel Box & Lumber Co. Ltd. Kootenay Lake 7075 156114 4753048      3,059,772  7,812,819  233,447 13 33 20 Tolko Industries Ltd. Okanagan Shuswap 7054 1328292 3580870      3,114,740  6,695,609  202,437 15 33 82 Tembec Industries Ltd. Rocky Mountain 7053 957852 3951310      3,127,055  7,078,365  212,237 15 33 99 Ukass Logging Ltd. Rocky Mountain 7062 378 4908784      3,065,067  7,973,851  237,577 13 34 98 Tolko Industries Ltd. Quesnel 7061 0 4909162      3,094,320  8,003,482  237,567 13 34 27 Weyerhaeuser Company Ltd. Okanagan Shuswap 7072 0 4909162      3,146,731  8,055,892  237,577 13 34 14 Gorman Bros Lumber Ltd. Okanagan Shuswap 7017 0 4909162      3,207,580  8,116,741  237,577 14 34 79 Tembec Industries Ltd. Rocky Mountain 7052 0 4909162      3,239,386  8,148,548  237,577 14 34 45 Downie Timber Ltd. Columbia 7010 0 4909162      3,242,874  8,152,036  237,577 14 34 11 Eagle River Industries Inc. Okanagan Shuswap 7011 418068 4491094      3,301,957  7,793,050  226,517 15 34 113 West Fraser Mills Ltd. Quesnel 7069 1778112 3131050      3,337,984  6,469,033  190,537 18 34 602 T.L. Timber Ltd. Okanagan Shuswap 7051 0 4909162      3,328,143  8,237,304  237,577 14 35 172 Hauer Bros. Lumber Ltd. Headwaters 7018 0 4909162      3,369,186  8,278,348  237,577 14 35  194 Southern_Interior_Forest_Region_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  MovedVolume $/Dist ance $/BDt 47 Joe Kozek Sawmills Ltd. Columbia 7023 0 4909162      3,487,960  8,397,122  237,577 15 35 74 Weyerhaeuser Company Ltd. Kamloops 7071 0 4909162      3,503,151  8,412,312  237,577 15 35 526 West Fraser Mills Ltd. Quesnel 7070 0 4909162      4,438,345  9,347,507  237,577 19 39 214 West Fraser Mills Ltd. 100 Mile House 7066 1502928 3406234      4,538,244  7,944,477  197,817 23 40 32 Gibbs Custom Sawmill Headwaters 7015 216972 4692190      4,600,737  9,292,927  231,837 20 40 103 Tolko Industries Ltd. Central Cariboo 7055 0 4909162      4,611,130  9,520,292  237,577 19 40 597 Lakeside Timber Ltd. Okanagan Shuswap 7027 529.2 4908632      5,953,534  10,862,167  237,563 25 46 67 Tolko Industries Ltd. Okanagan Shuswap 7057 0 4909162      6,221,335  11,130,496  237,577 26 47 518 Herridge Trucking and Sawmilling Ltd Arrow Boundary 7019 0 4909162      6,228,406  11,137,568  237,577 26 47 88 J H Huscroft Ltd. Kootenay Lake 7022 0 4909162      6,241,030  11,150,191  237,577 26 47 70 International Forest Products Ltd. Kamloops 7021 0 4909162      6,321,745  11,230,907  237,577 27 47 101 Tolko Industries Ltd. Central Cariboo 7059 0 4909162      6,911,388  11,820,549  237,577 29 50 197 McDonald Ranch & Lumber Ltd. Rocky Mountain 7033 0 4909162      6,934,375  11,843,536  237,577 29 50 173 Valemount Forest Products Ltd. Headwaters 7063 415.8 4908746      7,038,211  11,946,957  237,566 30 50 229 Wadlegger Log & Constr. Co. Headwaters 7064 0 4909162      7,100,204  12,009,365  237,577 30 51 698 Wildwood Forest Products Ltd. Central Cariboo 7074 0 4909162      7,621,715  12,530,877  237,577 32 53 86 Galloway Lumber Co. Ltd. Rocky Mountain 7014 0 4909162      7,725,587  12,634,749  237,577 33 53 498 Aspen Planers Ltd. Cascades 7002 0 4909162      7,747,059  12,656,221  237,577 33 53 30 Pope & Talbot Ltd. Arrow Boundary 7043 0 4909162      7,867,874  12,777,035  237,577 33 54 207 Ardew Wood Products Ltd. Cascades 7001 0 4909162      7,931,755  12,840,917  237,577 33 54 603 Meadow Creek Cedar Ltd. Kootenay Lake 7034 907.2 4908254      8,619,833  13,528,087  237,553 36 57 639 West Chilcotin Forest Product Ltd. Chilcotin 7065 0 4909162      8,762,425  13,671,586  237,577 37 58 199 North Star Planing Co. Ltd. Rocky Mountain 7038 0 4909162      9,630,186     237,577 41 61  195 Southern_Interior_Forest_Region_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  MovedVolume $/Dist ance $/BDt 14,539,347 68 Tolko Industries Ltd. Okanagan Shuswap 7056 0 4909162    10,041,062  14,950,224  237,577 42 63 643 Bear Lumber Ltd. Rocky Mountain 7003 0 4909162    10,286,543  15,195,704  237,577 43 64 713 Paragon Ventures Ltd. Okanagan Shuswap 7040 0 4909162    10,286,543  15,195,704  237,577 43 64 281 Karl Beattie Cont Ltd. Columbia 7026 0 4909162    10,305,512  15,214,674  237,577 43 64 482 Notch Hill Forest Products Ltd. Okanagan Shuswap 7039 1058.4 4908103    10,308,662  15,216,765  237,549 43 64 286 Hilmoe Forest Products Ltd. Arrow Boundary 7020 0 4909162    10,403,088  15,312,250  237,577 44 64 64 Canadian Forest Products Ltd. Headwaters 7007 0 4909162    11,135,851  16,045,013  237,577 47 68 542 Marsh Bros Lumber and Supply Ltd. Headwaters 7031 0 4909162    11,565,954  16,475,116  237,577 49 69 116 Lytton Lumber Ltd. Cascades 7030 0 4909162    11,576,697  16,485,858  237,577 49 69 480 Schapol Logging Ltd. Okanagan Shuswap 7047 0 4909162    12,487,859  17,397,021  237,577 53 73 36 Russo Sawmills, L Okanagan Shuswap 7046 0 4909162    12,505,691  17,414,853  237,577 53 73 702 Linde Bros Lumber Ltd. Central Cariboo 7029 0 4909162    13,055,770  17,964,932  237,577 55 76 7 Adams Lake Development Corporation Kamloops 7000 0 4909162    13,094,816  18,003,977  237,577 55 76 665 North Okanagan Cedar Ltd. 7037 0 4909162    13,217,048  18,126,210  237,577 56 76 626 Porcupine Wood Products Ltd. Kootenay Lake 7044 0 4909162    13,218,761  18,127,922  237,577 56 76 12 Federated Co-op Ltd. Okanagan Shuswap 7013 1587.6 4907574    13,225,531  18,133,105  237,535 56 76 50 Kalesnikoff Lumber Co. Ltd. Arrow Boundary 7025 0 4909162    13,255,488  18,164,650  237,577 56 76 662 Munson Equipment Ltd. Kamloops 7035 0 4909162    13,255,488  18,164,650  237,577 56 76 618 North Enderby Timber Ltd. Okanagan Shuswap 7036 0 4909162    13,255,488  18,164,650  237,577 56 76 31 Pope & Talbot Ltd. Arrow Boundary 7042 0 4909162    13,255,488  18,164,650  237,577 56 76 271 Rouck Brothers Sawmill Ltd. Okanagan Shuswap 7045 0 4909162    13,360,859  18,270,020  237,577 56 77 452 Chimney Creek Lumber Co Ltd. Central Cariboo 7008 0 4909162    13,463,475  18,372,637  237,577 57 77  196 Southern_Interior_Forest_Region_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportation Cost  Total projected cost  MovedVolume $/Dist ance $/BDt 684 Corwood Timber Products Ltd. Headwaters 7009 0 4909162    13,463,475  18,372,637  237,577 57 77 90 Canadian Forest Products Ltd. Rocky Mountain 7006 0 4909162    13,768,522  18,677,683  237,577 58 79 734 Enid Lake Logging Ltd. 7012 0 4909162    13,805,626  18,714,787  237,577 58 79 734 Enid Lake Logging Ltd. Rocky Mountain 7032 0 4909162    13,810,193  18,719,355  237,577 58 79 252 C & C Wood Products Ltd. Quesnel 7004 13230 4895932    13,813,628  18,709,560  237,227 58 79 514 Simpcw Development Co Ltd. Kamloops 7050 0 4909162    14,477,346  19,386,507  237,577 61 82 62 Pope & Talbot Ltd. Arrow Boundary 7041 0 4909162    15,087,158  19,996,320  237,577 64 84 110 Canadian Forest Products Ltd. Quesnel 7005 0 4909162    15,393,022  20,302,184  237,577 65 85 750 Sigurdson Bros. Logging Company Chilcotin 7048 0 4909162    16,382,761  21,291,922  237,577 69 90 740 Sigurdson Bros. Logging Company Chilcotin 7049 0 4909162    16,881,827  21,790,989  237,577 71 92 COGENERATION PLANTS SD3 Purcell Power Project (Tembec Enterprises Inc.) Rocky Mountain 10003  4909162 3854074.797  8,763,236  237,577 16 37 SD2 NWE Energy Corporation Central Cariboo 10002  4909162 6208476.167  11,117,638  237,577 26 47 SD4 Riverside Forest Products Limited Okanagan Shuswap 10004  4909162 13088460.11  17,997,622  237,577 55 76   197  APPENDIX K: Coastal forest region preferred bark mills Coastal_Forest_Region_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportatio n Cost  Total projected cost  Moved Volume  $/Dist ance  $/BDt 396 Terminal Forest Products Ltd. Chilliwack 9064 0 682427 726068.0346 1408495.035 30330.09 23.9 46 0 Terminal Forest Products Ltd. Chilliwack 9065 0 682427 727134.2407 1409561.241 30330.09 24.0 47 738 Chalwood Forest Products LTD. South Island 9021 1080 681347 734703.1404 1416050.14 30282.09 24.3 47 301 Errington Cedar Products Ltd. South Island 9025 3132 679295 739726.0473 1419021.047 30190.89 24.5 47 408 Nagaard Sawmills Ltd. South Island 9030 1080 681347 797290.3672 1478637.367 30282.09 26.3 49 320 Western Forest Products South Island 9035 56160 626267 802319.5426 1428586.543 27834.09 28.8 51 744 Rainbow Lumber South Island 9032 23.4 682403 812747.9803 1495151.58 30329.05 26.8 50 194 Mike Gogo Cedar Products South Island 9029 144 682283 823960.0203 1506243.02 30323.69 27.2 50 376 Western Forest Products South Island 9037 51120 631307 828050.2676 1459357.268 28058.09 29.5 52 712 Long Hoh Enterprises Canada Ltd South Island 9028 6480 675947 832393.3624 1508340.362 30042.09 27.7 50 531 Western Forest Products South Island 9038 49446 632980 835256.7505 1468237.391 28132.47 29.7 52 546 Western Forest Products South Island 9036 53640 628787 838659.2912 1467446.291 27946.09 30.0 53 398 Western Forest Products Chilliwack 9067 0 682427 843038.516 1525465.516 30330.09 27.8 50 393 Western Forest Products South Island 9034 41400 641027 845317.0121 1486344.012 28490.09 29.7 52 461 International Forest Products Ltd. Chilliwack 9053 39960 642467 845846.2741 1488313.274 28554.09 29.6 52 512 Jemico Enterprises Ltd. South Island 9027 1476 680951 848121.2518 1529072.25 30264.49 28.0 51 23 Shannon Lumber Chilliwack 9060 900 681527 848198.7598 1529725.76 30290.09 28.0 51 535 Pro Cut Lumber Corp. South Island 9031 216 682211 848845.3788 1531056.379 30320.49 28.0 51 19 Albion Alder and Maple Co. Chilliwack 9042 0 682427 849762.7786 1532189.779 30330.09 28.0 51 537 Goldwood Industries Ltd. Chilliwack 9049 0 682427 850295.7726 1532722.773 30330.09 28.0 51 283 International Forest Products Ltd. Chilliwack 9050 55440 626987 852108.8762 1479095.876 27866.09 30.6 53 297 International ForestProducts Ltd. Chilliwack 9051 51120 631307 852251.0096 1483558.01 28058.09 30.4 53 330 TimberWest Forest Corp Campbell 9014 65880 616547 852339.9818 1468886.982 27402.09 31.1 54  198 Coastal_Forest_Region_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportatio n Cost  Total projected cost  Moved Volume  $/Dist ance  $/BDt River 298 Western Forest Products Chilliwack 9068 11520 670907 852636.3821 1523543.382 29818.09 28.6 51 403 Mll & Timber Products Ltd. Chilliwack 9057 0 682427 861833.4457 1544260.446 30330.09 28.4 51 539 Stag Timber Ltd. Chilliwack 9062 0 682427 862159.6382 1544586.638 30330.09 28.4 51 347 Ye' Old Dogwood Lumber South Island 9040 345.6 682081.4 864395.0391 1546476.439 30314.73 28.5 51 538 Fraser Pulp Chips Ltd. Chilliwack 9047 0 682427 864947.3997 1547374.4 30330.09 28.5 51 361 International Forest Products Ltd. Chilliwack 9052 13983.48 668443.5 2 867714.575 1536158.095 29708.60 29.2 52 319 Fu So Enterprises Ltd. Chilliwack 9048 1440 680987 868273.1483 1549260.148 30266.09 28.7 51 454 S & R Sawmills Ltd. Chilliwack 9059 0 682427 871477.8167 1553904.817 30330.09 28.7 51 73 Dale Arden Log Hauling Ltd. South Island 9023 180 682247 887833.4756 1570080.476 30322.09 29.3 52 719 Dale Edwards South Island 9024 93.6 682333.4 923540.3764 1605873.776 30325.93 30.5 53 377 Coulson Manufacturing Ltd South Island 9022 6721.2 675705.8 928141.3985 1603847.199 30031.37 30.9 53 16 Cascades West Forest Products Inc. Chilliwack 9044 0 682427 965811.9687 1648238.969 30330.09 31.8 54 528 Western Forest Products South Island 9033 46800 635627 998463.7763 1634090.776 28250.09 35.3 58 100 J.S. Jones Chilliwack 9054 48600 633827 999125.2315 1632952.232 28170.09 35.5 58 26 Edge Grain Cedar Chilliwack 9046 0 682427 1007219.364 1689646.364 30330.09 33.2 56 392 Western Forest Products South Island 9039 34200 648227 1007805.489 1656032.489 28810.09 35.0 58 326 Delta Cedar Products Ltd. Chilliwack 9045 0 682427 1008064.532 1690491.532 30330.09 33.2 56 714 Franklin Forest Products Ltd. South Island 9026 5677.2 676749.8 1009596.464 1686346.264 30077.77 33.6 56 22 Noble Custom Cut Ltd. Chilliwack 9058 0 682427 1013836.581 1696263.581 30330.09 33.4 56 5 Abfam Enterprises Ltd. Queen Charlotte 9019 519.021 681907.9 79 1033511.234 1715419.213 30307.02 34.1 57 336 Mill & Timber Products Ltd. Chilliwack 9056 20520 661907 1044434.2 1706341.2 29418.09 35.5 58 9 A J Forest Products ltd. Squamish 9041 2880 679547 1052186.093 1731733.093 30202.09 34.8 57 631 Silvermere Forest Prod Ltd. Chilliwack 9061 3240 679187 1054447.665 1733634.665 30186.09 34.9 57 453 Twin River Cedar Products Ltd. Chilliwack 9066 0 682427 1061021.71 1743448.71 30330.09 35.0 58 8 Andersen Pacific Forest Products Ltd Chilliwack 9043 0 682427 1061464.659 1743891.659 30330.09 35.0 58 670 Silva Services Ltd. Queen 9020 93.6 682333.4 1098321.049 1780654.449 30325.93 36.2 59  199 Coastal_Forest_Region_Preferred_Bark_Mills ID Mill Location NODE Purchased cost Total residue cost  Projected transportatio n Cost  Total projected cost  Moved Volume  $/Dist ance  $/BDt Charlotte 445 Beaver Forest Products Ltd Campbell River 9001 144 682283 1111498.037 1793781.037 30323.69 36.7 59 96 Thomson Bros. Lumber Co. Ltd. Campbell River 9013 43.2 682383.8 1119880.511 1802264.311 30328.17 36.9 59 730 Hart Creek Forest Products Campbell River 9007 144 682283 1147886.316 1830169.316 30323.69 37.9 60 582 Quadra Island Forest Products Ltd. Campbell River 9010 54 682373 1158137.311 1840510.311 30327.69 38.2 61 905 Blacktail Enterprises Campbell River 9002 90 682337 1175923.152 1858260.152 30326.09 38.8 61 399 Lois Lumber Ltd. Sunshine Coast 9055 1296 681131 1229232.58 1910363.58 30272.49 40.6 63 444 C.V. Cedar Sales Ltd Campbell River 9003 10.8 682416.2 1255981.227 1938397.427 30329.61 41.4 63.9 24 Suncoast Lumber and Milling Sunshine Coast 9063 2196 680231 1278807.481 1959038.481 30232.49 42.3 64.8 907 Saratoga Speedway Mills Campbell River 9011 25.2 682401.8 1280541.002 1962942.802 30328.97 42.2 64.7 741 Dove Creek Timber Corp. Campbell River 9004 1710 680717 1283635.482 1964352.482 30254.09 42.4 64.9 708 Island Pacific Wood Products Campbell River 9009 360 682067 1301823.129 1983890.129 30314.09 42.9 65.4 717 Edgegrain Campbell River 9005 144 682283 1339241.538 2021524.538 30323.69 44.2 66.7 705 Heartwood Ventures Campbell River 9008 90 682337 1348760.801 2031097.801 30326.09 44.5 67.0 731 SCG Forest Inc. Campbell River 9012 180 682247 1369388.227 2051635.227 30322.09 45.2 67.7 567 Green Forest Products Ltd. Campbell River 9006 90 682337 1391389.97 2073726.97 30326.09 45.9 68.4 716 Rocky Mountain Salvage North Island 9017 108 682319 1623718.22 2306037.22 30325.29 53.5 76.0 743 John Salo North Island 9016 7.2 682419.8 1626150.737 2308570.537 30329.77 53.6 76.1 654 Spike Top Cedar Ltd. North Island 9018 43.2 682383.8 1643292.459 2325676.259 30328.17 54.2 76.7 679 G.C. Williams Milling North Island 9015 180 682247 1654101.793 2336348.793 30322.09 54.6 77.1 COGENERATION PLANTS  Seegen Cogen plant Chilliwack 10001  682427 835,859  1518285.984    30,330.09 27.6 50.1   200 APPENDIX L: Coastal forest region preferred whitewood mills Coastal_Forest_Region_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Total projected cost  Moved Volume  $/BDt $/ Distance 396 Terminal Forest Products Ltd. Chilliwack 9064 79998 2190827  795,712  539.27  37,132.66 80.4 21.4 0 Terminal Forest Products Ltd. Chilliwack 9065 96701 2174124  796,739  2,970,862.72  36,849.56 80.6 21.6 398 Western Forest Products Chilliwack 9067 124609 2146216  864,559  3,010,775.47  36,376.54 82.8 23.8 537 Goldwood Industries Ltd. Chilliwack 9049 17406 2253419  867,781  3,121,199.56  38,193.54 81.7 22.7 461 International Forest Products Ltd. Chilliwack 9053 97580 2173245  869,229  3,042,473.93  36,834.66 82.6 23.6 23 Shannon Lumber Chilliwack 9060 2198 2268627  871,920  3,140,547.23  38,451.31 81.7 22.7 19 Albion Alder and Maple Co. Chilliwack 9042 440 2270385  873,893  3,144,278.21  38,481.11 81.7 22.7 283 International Forest Products Ltd. Chilliwack 9050 135381 2135444  877,490  3,012,934.07  36,193.96 83.2 24.2 297 International Forest Products Ltd. Chilliwack 9051 124832 2145993  877,623  3,023,615.64  36,372.76 83.1 24.1 298 Western Forest Products Chilliwack 9068 28131 2242694  877,667  3,120,360.31  38,011.76 82.1 23.1 539 Stag Timber Ltd. Chilliwack 9062 56262 2214563  882,190  3,096,752.81  37,534.96 82.5 23.5 538 Fraser Pulp Chips Ltd. Chilliwack 9047 10817 2260008  886,331  3,146,339.62  38,305.23 82.1 23.1 403 Mill & Timber Products Ltd. Chilliwack 9057 25054 2245771  888,790  3,134,560.47  38,063.91 82.3 23.3 361 International Forest Products Ltd. Chilliwack 9052 34147 2236678  890,219  3,126,897.05  37,909.80 82.5 23.5 319 Fu So Enterprises Ltd. Chilliwack 9048 3516 2267309  890,738  3,158,046.37  38,428.96 82.2 23.2 454 S & R Sawmills Ltd. Chilliwack 9059 124832 2145993  892,429  3,038,421.91  36,372.76 83.5 24.5 738 Chalwood Forest Products LTD. South Island 9021 2637 2268188  930,196  3,198,384.00  38,443.86 83.2 24.2 301 Errington Cedar Products Ltd. South Island 9025 7648 2263177  936,624  3,199,801.13  38,358.93 83.4 24.4 408 Nagaard Sawmills Ltd. South Island 9030 2637 2268188                                    85.3 26.3  201 Coastal_Forest_Region_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Total projected cost  Moved Volume  $/BDt $/ Distance 1,009,652  3,277,840.04  38,443.86 744 Rainbow Lumber South Island 9032 57 2270768  1,036,263  3,307,030.81  38,487.59 85.9 26.9 16 Cascades West Forest Products Inc. Chilliwack 9044 879 2269946  1,057,025  3,326,970.47  38,473.66 86.5 27.5 330 TimberWest Forest Corp. Campbell River 9014 160875 2109950  1,070,455  3,180,405.05  35,761.86 88.9 29.9 100 J.S. Jones Chilliwack 9054 118679 2152147  1,099,618  3,251,764.40  36,477.06 89.1 30.1 26 Edge Grain Cedar Chilliwack 9046 88 2270737  1,109,390  3,380,127.18  38,487.07 87.8 28.8 326 Delta Cedar Products Ltd. Chilliwack 9045 32527 2238298  1,109,952  3,348,250.33  37,937.26 88.3 29.3 22 Noble Custom Cut Ltd. Chilliwack 9058 6281 2264544  1,117,670  3,382,213.94  38,382.11 88.1 29.1 320 Western Forest Products South Island 9035 137140 2133685  1,139,932  3,273,616.92  36,164.16 90.5 31.5 73 Dale Arden Log Hauling Ltd. South Island 9023 440 2270385  1,142,278  3,412,663.05  38,481.11 88.7 29.7 336 Mill & Timber Products Ltd. Chilliwack 9056 50109 2220716  1,156,462  3,377,178.64  37,639.26 89.7 30.7 194 Mike Gogo Cedar Products South Island 9029 352 2270473  1,165,718  3,436,191.65  38,482.60 89.3 30.3 9 A J Forest Products ltd. Squamish 9041 7033 2263792  1,167,316  3,431,108.08  38,369.36 89.4 30.4 631 Silvermere Forest Prod Ltd. Chilliwack 9061 4747 2266078  1,169,049  3,435,126.94  38,408.10 89.4 30.4 453 Twin River Cedar Products Ltd. Chilliwack 9066 19340 2251485  1,176,869  3,428,354.07  38,160.76 89.8 30.8 8 Andersen Pacific Forest Products Ltd Chilliwack 9043 12307 2258518  1,177,579  3,436,096.66  38,279.96 89.8 30.8 719 Dale Edwards South Island 9024 229 2270596  1,187,588  3,458,184.67  38,484.69 89.9 30.9 377 Coulson Manufacturing Ltd South Island 9022 16413 2254412  1,193,769  3,448,181.60  38,210.38 90.2 31.2 376 Western Forest Products South Island 9037 124832 2145993  1,195,858  3,341,851.04  36,372.76 91.9 32.9 712 Long Hoh Enterprises Canada Ltd South Island 9028 15824 2255001  1,200,578  3,455,579.53  38,220.36 90.4 31.4 531 Western Forest Products South Island 9038 120745 2150080                                    92.1 33.1  202 Coastal_Forest_Region_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Total projected cost  Moved Volume  $/BDt $/ Distance 1,205,161  3,355,240.81  36,442.03 546 Western Forest Products South Island 9036 130986 2139839  1,209,676  3,349,514.61  36,268.46 92.4 33.4 393 Western Forest Products South Island 9034 101097 2169729  1,217,961  3,387,689.78  36,775.06 92.1 33.1 512 Jemico Enterprises Ltd. South Island 9027 3604 2267221  1,220,454  3,487,674.48  38,427.47 90.8 31.8 535 Pro Cut Lumber Corp. South Island 9031 527 2270298  1,220,971  3,491,268.84  38,479.62 90.7 31.7 347 Ye' Old Dogwood Lumber South Island 9040 844 2269981  1,241,078  3,511,058.91  38,474.26 91.3 32.3 5 Abfam Enterprises Ltd. Queen Charlotte 9019 1267 2269558  1,283,449  3,553,006.38  38,467.08 92.4 33.4 528 Western Forest Products South Island 9033 114283 2156542  1,293,881  3,450,422.71  36,551.56 94.4 35.4 392 Western Forest Products South Island 9039 83515 2187311  1,305,216  3,492,526.50  37,073.06 94.2 35.2 714 Franklin Forest Products Ltd. South Island 9026 13863 2256962  1,305,896  3,562,857.49  38,253.59 93.1 34.1 670 Silva Services Ltd. Queen Charlotte 9020 229 2270596  1,365,666  3,636,262.83  38,484.69 94.5 35.5 445 Beaver Forest Products Ltd Campbell River 9001 352 2270473  1,409,686  3,680,159.01  38,482.60 95.6 36.6 96 Thomson Bros. Lumber Co. Ltd. Campbell River 9013 105 2270720  1,437,786  3,708,505.06  38,486.77 96.4 37.4 399 Lois Lumber Ltd. Sunshine Coast 9055 3165 2267660  1,449,415  3,717,074.86  38,434.92 96.7 37.7 730 Hart Creek Forest Products Campbell River 9007 352 2270473  1,456,844  3,727,317.34  38,482.60 96.9 37.9 582 Quadra Island Forest Products Ltd. Campbell River 9010 132 2270693  1,494,101  3,764,794.28  38,486.33 97.8 38.8 24 Suncoast Lumber and Milling Sunshine Coast 9063 5363 2265462  1,512,406  3,777,868.33  38,397.67 98.4 39.4 905 Blacktail Enterprises Campbell River 9002 220 2270605  1,516,675  3,787,280.43  38,484.84 98.4 39.4 907 Saratoga Speedway Mills Campbell River 9011 62 2270763  1,610,487  3,881,250.49  38,487.52 100.8 41.8 444 C.V. Cedar Sales Ltd Campbell River 9003 26 2270799  1,612,886  3,883,684.98  38,488.11 100.9 41.9 741 Dove Creek Timber Campbell 9004 4176 2266649                                    101.7 42.7  203 Coastal_Forest_Region_Preferred_Whitewood_Mills ID Mill Location NODE Purchased cost Total residue cost  Total projected cost  Moved Volume  $/BDt $/ Distance Corp. River 1,640,349  3,906,998.41  38,417.79 708 Island Pacific Wood Products Campbell River 9009 879 2269946  1,652,227  3,922,173.04  38,473.66 101.9 42.9 717 Edgegrain Campbell River 9005 352 2270473  1,698,706  3,969,179.34  38,482.60 103.1 44.1 705 Heartwood Ventures Campbell River 9008 220 2270605  1,711,758  3,982,362.86  38,484.84 103.5 44.5 731 SCG Forest Inc. Campbell River 9012 440 2270385  1,737,949  4,008,334.66  38,481.11 104.2 45.2 567 Green Forest Products Ltd. Campbell River 9006 220 2270605  1,764,876  4,035,481.15  38,484.84 104.9 45.9 716 Rocky Mountain Salvage North Island 9017 264 2270561  2,032,588  4,303,149.46  38,484.09 111.8 52.8 743 John Salo North Island 9016 18 2270807  2,035,669  4,306,476.46  38,488.26 111.9 52.9 654 Spike Top Cedar Ltd. North Island 9018 105 2270720  2,057,424  4,328,143.76  38,486.77 112.5 53.5 679 G.C. Williams Milling North Island 9015 440 2270385  2,071,152  4,341,537.26  38,481.11 112.8 53.8 COGENERATION PLANT  Seegen Cogen plant  10001  2270825 892650.6685  3,163,475.67  38488.56 82.2 23.2  

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.24.1-0071189/manifest

Comment

Related Items