Applied Science, Faculty of
Chemical and Biological Engineering, Department of
DSpace
UBCV
Oveisi-Fordiie, Ehsanollah
Doctor of Philosophy - PhD
University of British Columbia
In the published literature, there is a lack of detailed data relevant to full-scale gasification plants that operate on variable quality of biomass feedstock, especially those originating from urban sources. The objectives of this study are to characterize the spatial and temporal variations in the physical properties of waste wood supplied to the UBC industrial gasification system; and to conduct an analysis of variations in fuel properties vs. gasifier performance in terms of system reliability, steam production, syngas quality, tar formation, and gasification efficiencfy. The measured values of fuel properties were in compliance with the fuel specifications. Large variation in steam production during the earlier years of operation was attributed partly to the variations in feedstock moisture content and particle size. Subsequent data collection and analysis revealed that fuel moisture content affects the production of steam the most. Fuel moisture content (mc), gasifier bed temperature, and fuel feeding rate were identified to be the key factors that affect syngas quality, tar formation, and gasifier efficiency. Multi-variable regression models were developed to quantify these relationships. Despite a wide variation of the data collected from the industrial gasifier, our results are in line with the trends reported in the literature based on lab-scale and pilot-scale studies. Research findings indicated that even for a commercial updraft gasifier, syngas quality and steam production would be enhanced if the fuel moisture content could be maintained around 20% (wb). With air as the gasifying agent, when fuel moisture content decreased to 20%, the carbon monoxide (CO) concentration (~30%) and calorific value (>4 MJ/m³) would be higher, whereas tar concentration (<60–70 g/m³) would be lower. This near-optimal level of fuel moisture content (20% wb) is recommended for the operation of BRDF. The 20% mc also coincides with the typical equilibrium moisture content of air-dried wood fuel under ambient air conditions in Vancouver if the fuel is protected from rain. As such, high temperature drying of the wood chips can be avoided for saving energy. The higher CO concentration in syngas is also beneficial when the syngas is used to produce other industrial and chemical products.
LOGISTICS OF WOOD CHIPS SUPPLY AND PERFORMANCE ANALYSIS OF AN INDUSTRIAL UPDRAFT GASIFIER by Ehsanollah Oveisi-Fordiie B.A.Sc., University of British Columbia, 2003 M.Sc., University of British Columbia, 2011 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Chemical and Biological Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2019 ©Ehsanollah Oveisi-Fordiie, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Logistics of Wood Chips Supply and Performance Analysis of An Industrial Updraft Gasifier submitted by Ehsanollah Oveisi Fordiie in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Chemical and Biological Engineering Examining Committee: Shahab Sokhansanj, Chemical and Biological Engineering Co-supervisor Anthony Lau, Chemical and Biological Engineering Co-supervisor Jim Lim, Chemical and Biological Engineering Supervisory Committee Member Paul Watkinson, Chemical and Biological Engineering University Examiner Patrick Kirchen, Mechanical Engineering University Examiner iii Abstract In the published literature, there is a lack of detailed data relevant to full-scale gasification plants that operate on variable quality of biomass feedstock, especially those originating from urban sources. The objectives of this study are to characterize the spatial and temporal variations in the physical properties of waste wood supplied to the UBC industrial gasification system; and to conduct an analysis of variations in fuel properties vs. gasifier performance in terms of system reliability, steam production, syngas quality, tar formation, and gasification efficiencfy. The measured values of fuel properties were in compliance with the fuel specifications. Large variation in steam production during the earlier years of operation was attributed partly to the variations in feedstock moisture content and particle size. Subsequent data collection and analysis revealed that fuel moisture content affects the production of steam the most. Fuel moisture content (mc), gasifier bed temperature, and fuel feeding rate were identified to be the key factors that affect syngas quality, tar formation, and gasifier efficiency. Multi-variable regression models were developed to quantify these relationships. Despite a wide variation of the data collected from the industrial gasifier, our results are in line with the trends reported in the literature based on lab-scale and pilot-scale studies. Research findings indicated that even for a commercial updraft gasifier, syngas quality and steam production would be enhanced if the fuel moisture content could be maintained around 20% (wb). With air as the gasifying agent, when fuel moisture content decreased to 20%, the carbon monoxide (CO) concentration (~30%) and calorific value (>4 MJ/m3) would be higher, whereas tar concentration (<60–70 g/m3) would be lower. This near-optimal level of fuel moisture content (20% wb) is recommended for the operation of BRDF. The 20% mc also coincides with the typical equilibrium moisture content of air-dried wood fuel under ambient air conditions in Vancouver if the fuel is protected from rain. As such, high temperature drying of the wood chips can be avoided for saving energy. The higher CO concentration in syngas is also beneficial when the syngas is used to produce other industrial and chemical products. iv Lay Summary Gasification is the thermal decomposition of biomass into producer gas by partial oxidation. This thesis describes an industrial gasification system where solid biomass is converted to non-condensable gases. The system begins with the fuel source and ends with its final utilization in steam production. A recycling yard collects woody biomass and processes them to produce feedstock required by the gasifier. Fuel properties or operational conditions of the gasifier were not adjusted throughout this project. Fuel properties such as moisture content, ash content, wet bulk density, calorific value, and percentage of undersize and oversize materials were measured to evaluate the biomass feedstock temporal variability. The effects of biomass quality on the industrial gasifier system reliability, steam production, tar formation and syngas quality were assessed. Multi-variable regression analysis on syngas heating value and tar formation was also performed. The gasifier efficiency based on fuel properties, gas profiles, and operational conditions was calculated. v Preface This dissertation presents the original work carried out by the author, Ehsanollah Oveisi Fordiie, during his Ph.D. program. Defining the research, the scope and objectives of the study, as well as the selection of the methodology were supervised by my research supervisors, Dr. Shahab Sokhansanj and Dr. Anthony Lau, and the supervisory committee members Dr. Jim Lim and Mr. Cliff Mui. The author made periodic site visits and had numerous communications with industrial experts in order to gather the required data and information to perform this research. Industrial experts include Nexterra System Corp., management of the biomass depot (Cloverdale Fuel), and Dr. Fernando Preto of NRCan CanmetENERGY-Ottawa. Journal manuscripts published or to be published are based on the analysis and case studies presented in Chapters 2, 3, 4, and 5 of this thesis. Chapter 2: Oveisi, E.; Sokhansanj, S.; Lau, A.; Lim, C.J.; Bi, X.; Ebadian, M.; Preto, F. 2018. Mui, C.; Gill, R. In-depot upgrading the quality of fuel chips for a commercial gasification plant. Biomass Bioenergy 2018, 108, 138–145. Chapter 3: Oveisi, E.; Sokhansanj, S.; Lau, A.; Lim, C.J.; Bi, X.; Mui, C. 2019. The influence of particle size and moisture content of wood fuel on the performance of a commercial updraft gasifier, in preparation. Chapter 4: Oveisi, E.; Sokhansanj, S.; Lau, A.; Lim, C.J.; Bi, X.; Preto, F.; Mui, C. 2018. Characterization of recycled wood chips, syngas yield, and tar formation in an industrial updraft gasifier. Environments 2018, 5, 84, 1-13. Chapter 5: Oveisi, E.; Sokhansanj, S.; Lau, A.; Lim, C.J.; Bi, X.; Preto, F.; Mui, C. 2019. In-situ validation of a simple model for integration of fuel quality and a commercial updraft gasification system, in preparation. For Chapters 2 – 5, I was the main investigator. I was responsible for the concept formation of the research topics., experimental design, data collection, data analysis, model development, and the composition of manuscripts. Dr. Sokhansanj and Dr. Lau were the supervisory authors of these manuscripts. They contributed to the early concept formation, experimental design, data analysis and manuscript edits. Dr. Lim and Dr. Bi gave valuable suggestions and comments on these projects. Mr. Mui is the industrial mentors who helped me get familiar with the concepts in the commercial gasification system and contributed to data vi analysis for these manuscripts. For Chapters 2, 4 and 5, Dr. Preto provided the tar sampling equipment and involved in discussion of tar and syngas related topics. For Chapter 2, Dr. Ebadian provided the project with valuable comments to improve the discussion section. Mr. Gill gave access to samples and data from the depot, and he helped me understand the preprocessing on fuel in the depot. vii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................. iv Preface............................................................................................................................................. v Table of Contents .......................................................................................................................... vii List of Tables ................................................................................................................................. ix List of Figures ................................................................................................................................ xi List of Symbols ............................................................................................................................. xv Acknowledgements ..................................................................................................................... xvii Dedication .................................................................................................................................. xviii Introduction ................................................................................................................. 1 1.1 Background ........................................................................................................................... 1 1.2 System description ................................................................................................................ 2 1.2.1 Description of the gasifier .............................................................................................. 3 1.2.2 Description of fuel preparation ...................................................................................... 6 1.2.3 Description of transporting biofuel from CFL to gasifier ............................................ 12 1.3 Solid biofuel qualification ................................................................................................... 13 1.4 Thesis objectives ................................................................................................................. 18 1.5 Structure of the thesis .......................................................................................................... 20 In-depot Upgrading the Quality of Fuel Chips for a Commercial Gasification Plant ……………………………………………………………………………………………………21 2.1 Introduction ......................................................................................................................... 21 2.2 Overview of fuel supply and gasification system ............................................................... 24 2.3 Size reduction at depot ........................................................................................................ 26 2.4 Measurements at the depot .................................................................................................. 27 2.5 Measurements at the gasification plant ............................................................................... 32 2.6 Conclusions ......................................................................................................................... 35 The Influence of Particle Size and Moisture Content of Wood Fuel on the Performance of a Commercial Updraft Gasifier ........................................................................... 36 3.1 Introduction ......................................................................................................................... 36 3.2 Observations ....................................................................................................................... 39 3.2.1 Overall annual performance ......................................................................................... 39 3.2.2 Wood fuel supply and physical properties ................................................................... 42 3.2.3 Gasifier performance in terms of steam production .................................................... 45 viii 3.3 Conclusions ......................................................................................................................... 50 Characterization of Recycled Wood Chips, Syngas Yield, and Tar Formation in an Industrial Updraft Gasifier ............................................................................................................ 51 4.1 Introduction ......................................................................................................................... 51 4.2 Materials and methods ........................................................................................................ 53 4.2.1 Gasifiers ....................................................................................................................... 53 4.2.2 Gas and tar sampling .................................................................................................... 54 4.2.3 Biomass fuel sampling ................................................................................................. 55 4.2.4 Test procedure .............................................................................................................. 55 4.2.5 Multi-variable regression analysis ............................................................................... 57 4.3 Results and discussion ........................................................................................................ 58 4.3.1 Fuel properties ............................................................................................................. 58 4.3.2 Syngas composition ..................................................................................................... 60 4.3.3 Syngas composition and fuel properties ...................................................................... 62 4.3.4 Tar concentration ......................................................................................................... 64 4.3.5 Correlation coefficients ................................................................................................ 66 4.3.6 Multi-linear regression analysis ................................................................................... 67 4.4 Conclusions ......................................................................................................................... 76 Modeling and Analysis of the Efficiency of a Commercial Updraft Gasifier Based on Plant Data ................................................................................................................................. 78 5.1 Introduction ......................................................................................................................... 78 5.2 Methodology ....................................................................................................................... 81 5.2.1 Modeling ...................................................................................................................... 81 5.2.2 Gasifier efficiencies ..................................................................................................... 83 5.2.3 Multi-variable regression analysis on cold gas efficiency ........................................... 85 5.3 Results and discussion ........................................................................................................ 85 5.4 Conclusions ......................................................................................................................... 89 Overall Conclusions and Future Work ..................................................................... 90 6.1 Overall conclusions ............................................................................................................. 90 6.2 Recommendations for future work ..................................................................................... 94 References ..................................................................................................................................... 94 Appendix ..................................................................................................................................... 105 ix List of Tables Table 1.1 Variation of fuel properties among the four providers. They provide over 400 tonnes of biomass annually to UBC BRDF. The variation of moisture content (9% -18%), ash content (0.8% – 1.3%) and higher heating value, HHV, (18.9 – 19.3 MJ/kg) among the primary suppliers are quite small. .............................................................................................................................. 15 Table 1.2 Measurements of fuel properties with respect to specs by Nexterra Systems Corp. for fuel qualification purposes. The three batches of samples are mostly in compliance with the Nexterra specifications, with only a few exceptions. For example, batches 1, 2 and 3 had mass fraction of particles (10.7%, 10% and 8.7%) that exceed the maximum length allowed. ............ 15 Table 1.3 Feedstock properties of clean wood from working mills and green wood from tree trimmings. Number of samples and standard deviations are shown. The blending ratio (green wood: clean wood) ranges from 1:1 to 3:2. Moisture content creates the largest variation when the drier clean wood and wetter green wood (trimmings) are mixed. .......................................... 18 Table 2.1 List of physical and compositional properties and their values specified for the biomass gasification plant. The important parameters include length, fines and moisture content. These parameters are determined by the characteristics of the gasification system. .................... 25 Table 2.2 Measurements of feedstock properties from the grinder and after the screens. The properties measured include ash content, calorific value and moisture content. The ash content of the undersize feedstock fraction was several times higher than the ash content in the midsize and oversize fractions .......................................................................................................................... 28 Table 2.3 Characterization of feedstock supplied from CFL to UBC. Long term properties with 871 samples collected over two operating years 2012-2014. Coefficient of variation (CV) indicates the percent variation in a single property among the samples. Fraction of particles <6.4 mm particles had the largest calorific value and smallest CV. Measured biomass moisture content ranged from 10% to 57.4% during this period. ................................................................ 33 Table 3.1 Overall annual performance of the gasifier from 2012 to 2016. The annual is from June to next July. The total yearly working hours increased from 6,060 hours to 7602 hours in 2016. The plant utilization rate increased from 69% in the first year of operation to 87.9% in 2015 and 86.5% in 2016. .............................................................................................................................. 40 Table 3.2 Characterization of major feedstock and suppliers to CFL. The capacity of transport container is 41.3 m3. The average delivered wet biomass was 3 tonnes per truckloads. Assuming each container was full to 75% capacity, the bulk density of wood in a load was estimated to be 96 kg/m3. ....................................................................................................................................... 42 x Table 4.1 Mean and standard deviation of the variables used in the analysis. Cold gas efficiency will be discussed in detail in Chapter 5. ....................................................................................... 58 Table 4.2 Properties of fuel based on samples taken at University of British Columbia. Fuel MC ranged from 20.6 to 33.3% wb with an average of 26.8% and standard deviation of 4.3%. Bulk density and calorific value were 150.7 ± 17.3 kg/m3 and 19.2 ± 0.65 MJ/kg, respectively. ........ 61 Table 4.3 Ultimate and proximate analysis of fuel taken at UBC. The fuel’s specifications are compared with the expected values that the gasifier specifies. Having impact on the higher heating value, volatiles have a concentration of 83% which is well within the expected range between 70% to 85%. ................................................................................................................... 61 Table 4.4 Comparison of gas composition using the impingers and the CE-O sampling techniques at the tissue plant. During each sampling event, syngas was collected using sampling bags. Insignificant differences in the syngas composition were observed for the two methods. . 62 Table 4.5 Correlations between syngas heating value, tar concentration and cold gas efficiency and the parameters that contribute to them. .................................................................................. 66 Table 4.6 Results of the F-test for regression, which indicates the goodness of fit and degree of significance ................................................................................................................................... 68 Table 4.7 Sensitivity analysis on tar concentration using regression model Eq. 4.8. ................... 73 Table 4.8 Comparison of experimental conditions and results between this study and previous research works. ............................................................................................................................. 75 Table 4.9 Results of verification of the regression equations using data collected at the tissue plant............................................................................................................................................... 76 Table 5.1 Properties of the fuel samples utilized in the gasifier at the time when the syngas and tar were sampled. .......................................................................................................................... 86 Table 5.2 Composition of the fuel during the sampling of syngas and tar. The numbers in the brackets (first column) are the ranges of values required by UBC-Nexterra specifications. ....... 86 Table 5.3 Syngas composition for fuel moisture content from 20% to 35% (wb). Nitrogen content was high as the gasifier is air blown ................................................................................ 86 Table 5.4 Gasifier performance. The cold-gas, hot-gas and net gasifier efficiencies were calculated using field data. ............................................................................................................ 87 xi List of Figures Figure 1.1 Schematic diagram illustrating the Cloverdale Fuel Ltd. (CFL) recycling yard and the UBC Bioenergy Research and Demonstration Facility (BRDF) operation. CFL collects the urban wood wastes from the various sources. Upon arrival at the CFL, a series of fuel preparation steps are undertaken on the raw biomass materials to produce wood chips biofuel. The biofuel is then stored under a shed for protection from precipitation before being trucked to the BRDF. ............ 3 Figure 1.2 Examples of the effect of fuel quality on the gasifier plant and the equipment: ........... 6 Figure 1.3 Sources of biomass, products after pre-processing, and the end users. The source providers are within 100 km from the CFL recycling yard. After the materials are processed at CFL, the woody biomass is shipped to the BRDF which is 50 km away. The materials used by BRDF are the product from blending wood chips and hog fuel. .................................................... 8 Figure 1.4 As received raw materials are piled before processing in the CFL recycling yard.(a) Green wood: The woody parts of the tree trimmings collected from Metro Vancouver’s curbsides and landfills; (b) Clean wood: The lumber grade wood with low variability in their properties. The green wood materials have high average moisture content of 40.2% as compared to the average moisture content of 16.7% for the drier clean wood materials. Pictures were taken by the author. ............................................................................................................................................. 9 Figure 1.5 Distribution of suppliers that provide waste wood to CFL. All the suppliers are located around the recycling yard. UBC-BRDF is located at the northwest corner in the map. Google Earth is used for computing the distance between CFL and its source providers and the BRDF. ........................................................................................................................................... 10 Figure 1.6 Histogram of distance between the suppliers and CFL. Over 40% of the suppliers are located at a distance of 20-40 km from CFL. Another 40% of the suppliers are within 20 km away from CFL. Only approximately 20% of suppliers are farther than 40 km from CFL. ........ 10 Figure 1.7 Side-by-side comparison of wood chips (left) and hog fuel (right) before they are mixed at CFL. “Chipped wood waste” refers to the woody biomass generated from a chipper. Wood chips have more uniform (narrower range) particle sizes and geometry with less dust. “Ground wood waste” refers to the woody biomass (generally called hog fuel) generated from a hammer mill grinder. Picture was taken by the author. ................................................................ 13 Figure 1.8 Annual quantity of waste wood supplied by the 114 source providers to CFL. Most suppliers deliver less than 200 tonnes of woody waste materials to CFL. There are six main suppliers that provide more than 2,000 tonnes of woody materials per year. The fact that a small number of providers contribute most of the wood results in a high consistency in biomass properties....................................................................................................................................... 14 xii Figure 1.9 Structure of this thesis based on the research objectives ............................................. 20 Figure 2.1 Diagram of the updraft gasifier plant at the University of British Columbia. Note the mechanism of feeding feedstock that flows from underneath into the gasification chamber. Within gasifier, the fuel is piled up in a cone shape, and the air comes through nuzzles from outside in order to provide oxygen for partial combustion. The syngas is directed from the top of gasifier to oxidizer. (Adopted from https://nexterra.ca/files/environmental-advantages.php) ..... 24 Figure 2.2 The relationship between: (a) ash content and particle size; (b) calorific value (or higher heat value) and particle size. This plot shows a high amount of the ash in the feedstock which could belong to the dirt content (soil dust) mixed with biomass. ...................................... 29 Figure 2.3 Relationship between the higher heating value and ash content of the biomass. The data for this plot is extracted from the data obtained from Figure 2.2. The high ash content does not represent the biogenic (inherent) ash in biomass but rather in the fraction of debris mixed with biomass. ................................................................................................................................ 29 Figure 2.4 Size distribution for the particles that fell through the screen configurations (S12, S22, S32) installed in the Hogzilla grinder. The graph shows that the three screen configurations had a similar performance in terms of removing small particles of less than 2 mm from the feedstock. S12: Two 76 mm grates; S22: Two 127 mm grates; S32: One 76 mm and one 127 mm. ............................................................................................................................................... 30 Figure 2.5 The distribution of particle size for unscreened output and midsize output. The bar graphs show that fractionation using three sets of screens (two screens 76 mm and 127 mm perforations and that pan) was successful in removing small pieces (<6.7 mm) and large pieces greater than 25 mm. About 75% of the midsize fraction met the gasifier’s size specifications. .. 31 Figure 2.6 The record of precipitation, humidity, temperature and biomass moisture content (MC) for the period from July 2012 to May 2015. Biomass moisture content had similar trend with precipitation and humidity. Moisture of the biomass delivered to the BRDF increased during rainy periods and dropped during no-rain periods. Temperature has a negative correlation with MC. It is not clear whether the slight shift between maximum precipitation and moisture content is due to a lag between when material got wet due to rain at the depot and when the moisture was measured at the point of delivery at the gasification plant. .................................... 34 Figure 3.1 The components of the UBC gasification plant (BRDF) with the boiler and emission control system. The syngas produced in the gasifier is supplied to a thermal oxidizer. Heat recovery is in the form of steam generation by the boiler. Emissions control is used for removing fine particles from the flowing gas. (Adopted from http://www.nexterra.ca/files/media-resources.php) ............................................................................................................................... 37 xiii Figure 3.2 The bar graph and the trend line showing monthly utilization rate (percent operational hours) of the gasification plant. The utilization rate increased from an average of 70% in 2012 to over 90% in 2015. The regular annual preventive maintenance shut down is in May to June. Other low utilization rates are due to breakdowns. ....................................................................... 41 Figure 3.3 The frequency of fuel moisture content over a period of three years. Percentage of the fuel lower than 30% MC was improved from 47.7% in the first half of this period to 53.1% in the second half. For fuel lower than 25% MC, the percentage of the fuel is improved by 7% in this period. ........................................................................................................................................... 41 Figure 3.4 Frequency and tonnage of wood fuel delivery to the gasification plant during January 2015 and January 2016. Each data point represents a truckload. 2-3 truckloads were delivered daily. The delivered amount (710 tonnes, January 2015) was increased to (834 tonnes, January 2016). The corresponding average dry truckloads were 11 tonnes in 2015 and 13 tonnes in 2016. It appears in a few instances large truck loads followed by a smaller truckload and vice versa. . 43 Figure 3.5 The daily moisture content of wood chips received at the BRDF. The data is taken when unloading the truck during two months (February and March 2014). The set of data shown in this graph represents a sample of temporal variations in moisture content. Similar to the data in Figure 3.3, two sequential values of moisture content may have a difference greater than 20 percentage points. ......................................................................................................................... 44 Figure 3.6 Measured bulk density of wood fuel received at the gasification plant over two years (July 2012 - June 2014). As expected, the bulk density increased with the fuel moisture content. However, this increase varied widely, probably due to variations in the particle size of the wood chips. Overall, the average bulk density increased from 105 kg/m3 at 10% moisture content to 210 kg/m3 at 50% moisture content. ............................................................................................. 44 Figure 3.7 Monthly steam generation at the UBC gasification plant from April 2012 to July 2015. The overall monthly steam production increased substantially. The three low points in 2012, 2013 and 2014 are indicative of the annual shutdown/maintenance services during May-June. .............................................................................................................................................. 46 Figure 3.8 Monthly steam production and monthly total dry biomass during the sub-interval April 2013 to December 2014. Steam generation increased from 3400 to 4350 tonnes/month while the consumption of biomass stayed constant, indicating gradual improvement of the gasifier performance during this time period. ............................................................................... 46 Figure 3.9(a) Steam production per tonne wet biomass vs. fuel moisture content based on ........ 48 Figure 4.1 Side-by-side syngas and tar sampling arrangement using impingers and the CE-O (CanmetENERGY-Ottawa) device. The sampling port is located about 1/3 distance from the xiv bottom of the reactor. The goal of this setup is to ensure the same syngas sample is collected by both sampling device. ................................................................................................................... 56 Figure 4.2 Fuel samples collected during tar sampling. These fuel samples were separated into five sizes using round-hole screens. Particle size (x) from the top left to the bottom: x > 25 mm, 12.5 < x < 25 mm, 6.3 < x < 12.5 mm, 3.15 < x < 6.3 mm, and x < 3.15 mm. Pictures were taken by the author. ................................................................................................................................ 59 Figure 4.3 Mass fraction of the biomass particles and their size distribution for each day of the test conducted at the tissue plant. Almost half of the materials were large pieces (greater than 25 mm) on day 2, whereas 40% of the mass was between 12.5 and 25 mm on day 1. The mass fraction of small pieces (below 3.15 mm) was less than 2% on both sampling days. .................. 59 Figure 4.4 Variation of gas contents with fuel moisture content: (a) CO, (b) H2, (c) CH4, (d) CO2. The gas content is on a volume (v/v) basis. Tests were done at the UBC gasifier. As MC increases from 20% to 35%, CO concentration drops from about 30% to 20% (v/v). CO2 concentration increases mildly. H2 and CH4 show a mild decrease. ............................................ 63 Figure 4.5 Syngas calorific value vs. fuel moisture content. Despite a wide variation in the calorific values vs. fuel moisture content, the calorific value shows a mild overall decrease from about 5.5 to 3.8 MJ/m3 as MC increases from 20% to 35%. ........................................................ 64 Figure 4.6 Tar concentration vs. fuel moisture content. Tests done at the UBC gasifier. Overall, tar concentration increased slightly with fuel MC, but the correlation coefficient was low at around 0.50. .................................................................................................................................. 65 Figure 4.7 Tar concentration vs. percent particles with a smaller size range. Tests done at the UBC gasifier. The mass fraction of particles in the small size range (3.15–6.3 mm) had a weak correlation with tar concentration. ................................................................................................ 65 Figure 4.8 Predicted vs. measured values for (a) syngas heating value, (b) tar concentration and (c) cold gas efficiency using multi-variable regression analysis. The R2 values of these fittings are 0.48, 0.44, and 0.65. The Critical F-values of the regression are 0.012, 0.004 and 0.0002. The average absolute residual errors of the predicted values are 9.8%, 20.9% and 12.2%, respectively. Cold gas efficiency will be discussed in detail in Chapter 5. .................................. 70 Figure 5.1 Inputs and outputs of the gasification system. Syngas is combusted in the oxidizer, and hot flue gas is fed into a boiler for steam generation. ............................................................ 78 Figure 5.2 Energy balance around a gasifier and the definitions for cold, hot and net energy efficiencies. ................................................................................................................................... 80 xv List of Symbols AO2 Volumetric fraction of oxygen in air CFL Cloverdale Fuel Ltd. Cp Specific heat, MJ/kg K Cpi Specific heat of syngas component i, MJ/kg.K Cp,CO Specific heat of syngas component CO, MJ/kg.K Cp,CO2 Specific heat of syngas component CO2, MJ/kg.K Cp,H2 Specific heat of syngas component H2, MJ/kg.K Cp,N2 Specific heat of syngas component N2, MJ/kg.K Cp,CH4 Specific heat of syngas component CH4, MJ/kg.K Ctar Tar concentration, g/m3 Er Energy recovered through burning tar, MJ HT Enthalpy of water at T , MJ/kg Htar Heat content of tar HTg Enthalpy of water at Tg, MJ/kg LHVf LHV of fuel, MJ/kg LHVg LHV of syngas, MJ/m3 LHVCO Lower heating value of CO, MJ/m3 LHVH2 Lower heating value of H2, MJ/m3 LHVCH4 Lower heating value of CH4, MJ/m3 mA Mass flow rate of air, kg/h mf Mass flow rate of fuel, kg/h mH,in Mass of hydrogen fuel, kg mH,g Mass flow rate of hydrogen in syngas, kg/h mNA Mass of nitrogen supplied from air, kg mNF Mass of nitrogen supplied from fuel, kg mw Mass flow rate of moisture in syngas, kg/h Mg Molar flow rate of syngas, mol/h MH2 Molar flow rate of hydrogen, mol/h xvi MN2 Molar flow rate of nitrogen, mol/h Qi Sensible heat of syngas component i rair Mass flow rate of air, kg/h SCH4 Volumetric fraction of CH4 in syngas, vol% SH2 Volumetric fraction of H2 in syngas, vol% SN2 Volumetric fraction of N2 in syngas, vol% SCO Volumetric fraction of CO in syngas, vol% SCO2 Volumetric fraction of CO2 in syngas, vol% T Gas temperature of the fuel entering the gasifier, K Tg Gas temperature at the gasifier exit, K Vg Volumetric flowrate of syngas, m3/h WO2 Molecular weight of O2, kg/kmol WN2 Molecular weight of N2, kg/kmol WH2O Molecular weight of H2O, kg/kmol x Moisture content of fuel, % wb XO2 Mass fraction of oxygen in air XN2 Mass fraction of nitrogen in air YH Mass fraction of hydrogen in fuel YN Mass fraction of nitrogen in fuel ηcg Cold-gas efficiency, % ηhg Hot-gas efficiency, % ηnet Gasifier net efficiency, % xvii Acknowledgements There were many people who contributed to the operation and successful completion of the experimental work of this thesis. I would first like to thank my thesis supervisors Dr. Shahab Sokhansanj and Dr. Anthony Lau for providing me with the tools to successfully complete this project. They dependably allowed this thesis to be my own work whilst steering me in the right direction wherever necessary throughout my thesis research. I would also like to thank the experts who were involved in this project as supervisory committee members: Dr. Jim Lim and Mr. Cliff Mui. Without their passionate participation and input, this project could not have been successfully conducted. I also owe thanks to the BRDF staff and management, James Torcov, for providing guidance and open access to their data and plant for this research to push forward. Moreover, I would like to acknowledge all of the Biomass and Bioenergy Research Group (BBRG) members, and in particular Bahman Ghiasi, for their unwavering support and comradery throughout these years. It has been a pleasure to work with the BBRG team in this and other related projects. I would like to thank Robby Gill, manager of Cloverdale Fuel Ltd. for providing samples and access to the recycling yard; he is always eager to discuss the recycling yard processes with me to deepen my understanding. Furthermore, I owe thanks to Dr. Fernando Preto (NRCan CanmetENERGY-Ottawa) for providing the tar sampling probe and guidance for how to use it properly, Serge Milaire (electronics technician) for setting up the probe for my experiments, as well as Indah Andriani for her assistance with the tar and syngas sampling events. Nothing has been more encouraging to me in the pursuit of this project than the members of my family. I would like to thank my parents who have been my constant guidance in whatever I pursue. My wife continues to be my rock and unending inspiration through all things, and I owe her special thanks for all her patience and encouragement throughout these years. My two daughters, Niki and Sufi, have provided me with the comic relief and infinite passion necessary to remain inspired throughout this time, and for this they have my gratitude. Finally, the financial support by BioFuelNet Canada and Natural Sciences and Engineering Research Council towards the research study is gratefully acknowledged. xviii Dedication To my beloved wife, Farzaneh 1 Introduction 1.1 Background The worldwide energy consumption derived from the combustion of fossil fuels was 80.4% of total fuels (Worldbank, 2018), and fossil fuels are increasingly more expensive. Furthermore, air pollution caused by the combustion of fossil fuels is high. The pollutants include greenhouse gases (GHG: CO2, N2O, and CH4), NOx, SOx, particulate matters (PM), and volatile organic compounds (VOCs). To cope with the scarcity of conventional fuels and reduce air pollution, bioenergy and biofuels are becoming more attractive to the public. These types of fuels are derived from living and recently dead biological materials, biomass (McKendry, 2002a). Biomass can be used to produce different forms of energy. Bioethanol, biodiesel, pure plant oil, bio-kerosene, syngas, renewable natural gas, and solid biofuel are the most well known examples of bioenergy. Panwar et al. (2011) published a comprehensive review of renewable energy sources. They showed that renewable energy sources would reduce greenhouse gas emission and lessen global warming by replacing conventional energy sources. Canada has abundant biomass resources that can be converted to biofuels, chemicals, and bio-products. About 70 biomass-based power plants were operated in Canada in 2015 with a total capacity of over 2000 MW (NEB, 2017). Almost all levels of governments have policies and programs to reduce GHG emission. For example, the federal target is to reduce national GHG emissions by 17% from 2005 levels by 2020 (Office of the Auditor General of Canada, n.d.). To meet this target, Canada is supporting the development of next-generation technologies, including those related to bio-energy. The province of British Columbia (BC) has set a GHG reduction target of 33% by 2020 (Act, 2018). At present, approximately 3% of the total energy demand is met by biomass in Canada. The contribution from biomass is expected to increase to 6-10% within the next decade. To meet this target, BC introduced a carbon tax that is based on GHG emissions generated from transportation, industry, residential, and agriculture. The tax imposes a price on each tonne of GHG emitted. The City of Vancouver has also stipulated a GHG reduction target of 33% by 2020 compared to the 1990 level. The University of British Columbia (UBC) has also set the 2 following GHG reduction targets: 33% below 2007 levels by 2015; 67% below 2007 levels by 2020; and eliminate 100% of GHGs by 2050 (University of British Columbia, 2018). In 2017, UBC used 8800 dry tonnes of wood waste. This resulted in a reduction equivalent to 8400 tonnes of CO2 compared to emissions from natural gas. At the time of writing this thesis, the UBC Vancouver campus’ GHG emission was 30% less compared to that in 2007, although building floor space has expanded by 21% and student population has increased by 30%. UBC installed a fixed-bed updraft biomass gasification system at the Point Grey Campus in Vancouver in 2012, with an aim to reduce campus’ GHG emissions. The gasification facility is called Bioenergy Research & Demonstration Facility (BRDF). The UBC-BRDF facility consists of feedstock receiving area, feeding system, silo for temporary storage, gasifier, thermal oxidizer, boiler for steam generation, electrostatic precipitator for removing fine particles, and stack to exhaust the flow gas to the atmosphere. UBC has a limited term contract with Cloverdale Fuel Ltd. (CFL) to deliver chipped wood waste fuels to the gasification plant based on a set of schedule and feedstock specifications. CFL has a recycling depot operation in Langley, a suburban community approximately 50 km from the Vancouver campus. More than 200,000 tonnes of wood materials recycled from urban waste wood (collected from 114 locations) are processed in the depot every year. The depot also collects green wood from the City of Vancouver. 1.2 System description Figure 1.1 shows a schematic diagram illustrating the CFL recycling yard (the depot) and UBC-BRDF gasification operations. The gasification system was designed to process approximately 13,000 bone-dry tonnes (BDT) per year of wood chips. This amount of biomass represents about 10% of CFL’s wood handling business. 3 Figure 1.1 Schematic diagram illustrating the Cloverdale Fuel Ltd. (CFL) recycling yard and the UBC Bioenergy Research and Demonstration Facility (BRDF) operation. CFL collects the urban wood wastes from the various sources. Upon arrival at the CFL, a series of fuel preparation steps are undertaken on the raw biomass materials to produce wood chips biofuel. The biofuel is then stored under a shed for protection from precipitation before being trucked to the BRDF. 1.2.1 Description of the gasifier The gasifier (4.9 m diameter, 4.9 m height) was manufactured by Nexterra Systems Corp. (Vancouver, BC). The gasifier was designed to provide up to 5% of 40 MW campus power and 15% of 40 MW thermal based heat (Staley, 2012). In the gasification system, fuel is fed by an auger system into the center of gasifier from the bottom of the unit. The fuel then passes through several stages before it is converted to syngas. The major stages are drying, partial oxidation (combustion), pyrolysis, gasification, and reduction to ash. The details of these steps are described in (Molino et al., 2016). The gasifier temperature is kept constant by controlling feed rate, combustion air ratio, and fuel pile height. For partial combustion in the gasifier, air is injected to the fuel pile, where partial oxidation happens at 1,500-1,800oF (815-980oC). The fuel is converted into two major components: syngas 4 that contains tar and non-combustible ash. Syngas is directed to the thermal oxidizer where it is burned to generate heat, which in turn is used to generate steam in a boiler. Bottom ash is removed by an automated system. During the period of study, it was shown that the fuel quality has a significant impact on plant operation and maintenance cost. Figure 1.2(a) shows the tar deposition at the ports on the body of the gasifier. At these ports, the temperature is lower compared to the overall temperature of the gasifier. Fuel with high moisture content causes the temperature to drop inside the gasifier. At this lower temperature deposition of tar happens. A significant amount of slagging was observed inside the oxidizer, as demonstrated in Figure 1.2(b). The new slagging inside the oxidizer is affected by the syngas quality. Figure 1.2(c) shows an example of slagging inside the gasifier caused by ash content. Figure 1.2(d-g) illustrate how the boiler has been physically affected by the fuel at UBC-BRDF. The hot flue gas flows through the bigger tube in the first pass before running through the smaller tubes in subsequent passes. During the maintenance period, the boilers are cleaned to make sure there is no fouling or slagging inside the tubes. A change in fuel quality affects the amount of fouling and slagging that are two main types of deposits in boilers. Slagging usually takes place at a higher temperature. Fouling occurs as flue gas and suspended fly ash cool down. Because of fouling and slagging, heat transfer from flue gas to water decreases and the flue gas temperature starts to increase. The rate of deposition will also increase. The particle size and distribution of the mineral species inside the flue gas determine the degree of slagging and fouling. 5 (a) (b) (c) 6 (d) (e) (f) (g) Figure 1.2 Examples of the effect of fuel quality on the gasifier plant and the equipment: (a) tar generated from gasifier, (b) slagging inside oxidizer, (c) slagging inside gasifier, (d) heat exchanger, (e) main body of the boiler, (f) A broken small tube in the boiler, (g) main tube in the boiler. Pictures were taken by the author. 1.2.2 Description of fuel preparation CFL collects the urban and industrial wood wastes from sources that are within 100 km radius of the CFL facility in Langley BC. CFL has distributed a number of collecting bins at the wood working industrial sites. When a collecting bin is full, CFL is informed to send a truck to pick up the filled bin and replace it with an empty one. Once at the depot, the wood residue are sorted with a frontend loader and piled temporarily in the recycling yard. Depending the requirement of the end user the material is ground, further sorted to produce wood chip biofuel. 7 The biofuel is then stored in a shed to be protected from precipitation. Eventually, the sorted materials are ground, screened and blended to meet the requirements of end users. CFL accepts the woody biomass in a large number of forms, such as stumps, branches, trees, logs, brush, lumber, pallets, crates, fences, timbers, decking, leaves, grass, molding, millworks, and wood chips. The materials are primarily unsalable and unusable scrap woods, and they come from a variety of sources, as illustrated in Figure 1.3. These sources include cedar fencing planks, lumber and veneer manufacturing, window and door makers, hardwood floor manufacturers, custom log processing plants, land-clearing, pallet manufacturers, shake and shingle plants, and municipalities with wood recycling programs in the cities such as Surrey, Maple Ridge, Langley, Vancouver, and Coquitlam. Wood wastes from tree species that grow on the coast as well as interior BC are processed at CFL. SPF is the major type of wood species. Other species include hemlock, western red cedar (WRC), alder, maple, birch, and balsam. CFL processes the materials into various forms, including fine bark mulch (fir), bark (fir, hemlock), sawdust (fir, hemlock, western red cedar), chips (alder, cedar), hog fuel (hemlock, western red cedar), kiln-dried SPF shavings, and yellow cedar shavings. Clean wood in this research represents lumber-grade low moisture wood with low variability in their properties. In contrast, green wood is the wood that comes to the CFL primarily from green trimmings and reclamation or demolition sites. The green and unclean wood is not usually destined for biofuel applications. 8 Figure 1.3 Sources of biomass, products after pre-processing, and the end users. The source providers are within 100 km from the CFL recycling yard. After the materials are processed at CFL, the woody biomass is shipped to the BRDF which is 50 km away. The materials used by BRDF are the product from blending wood chips and hog fuel. Figure 1.4 shows piles of the materials stored before processing into biofuel. As can be seen in the picture the material is exposed to weather elements like rain. A pool of water next to the pile is visible. Figure A.1 shows the delivered clean wood materials, which are small pieces of lumber, indicating that there are no size reduction operations at the sources. About 20 suppliers provided 80% of the clean wood, and about 90 suppliers provided the remaining 20% of the clean wood. Sitka SprucePineDouglas FirHemlockWest red cedarMapleYellow CedarBalsamCedar fencingSawmill (lumber)Veneer mfgWindow and door mfgLand clearing operationsPallet mfgShake and shingleMunicipal wood recyclingIn-TestCloverdale fuelSorting, Grinding, BlendingBark MulchFirewoodWoodchipsHog fuelShavingsSaw dustUBCGreenhouseGardenworksPulpmillLivestockResidential heatSpecial Blend100 km 50 km 9 (a) (b) Figure 1.4 As received raw materials are piled before processing in the CFL recycling yard.(a) Green wood: The woody parts of the tree trimmings collected from Metro Vancouver’s curbsides and landfills; (b) Clean wood: The lumber grade wood with low variability in their properties. The green wood materials have high average moisture content of 40.2% as compared to the average moisture content of 16.7% for the drier clean wood materials. Pictures were taken by the author. Western red cedar (WRC) is one of the major species of feedstock that CFL handles. In addition, for combustion and gasification facilities, the pre-processed WRC chips from CFL are sent to other main applications, which include pulp and paper manufacturing, landscaping, and agricultural bedding. Fuel price constitutes one motivation for CFL to include the lower-price WRC along with other wood species in the blended fuel. The higher chlorine content of WRC is undesirable characteristics for the gasification plant, but WRC is still used as part of fuel because it has the advantages of lower price and lower moisture content than other species. WRC has a strong aroma, higher chlorine concentration, and higher content of extractives. The moisture content of WRC received at the recycling yard can be as low as 15% depending on the season. 10 The percentage of WRC in the blended solid biofuel for UBC is not constant. It can vary depending on the time of the year. Figure 1.5 Distribution of suppliers that provide waste wood to CFL. All the suppliers are located around the recycling yard. UBC-BRDF is located at the northwest corner in the map. Google Earth is used for computing the distance between CFL and its source providers and the BRDF. Figure 1.6 Histogram of distance between the suppliers and CFL. Over 40% of the suppliers are located at a distance of 20-40 km from CFL. Another 40% of the suppliers are within 20 km away from CFL. Only approximately 20% of suppliers are farther than 40 km from CFL. 11 Information regarding the distance between the depot and each supplier, tonnage of supplied woody biomass, the type of wood and frequency of collection are collected for our study. Google Earth (Figure 1.5) was used to calculate the distance traveled by CFL trucks to load and unload the materials from the residue beans spread out throughout the lower mainland. Figure 1.6 shows the distribution of the distance of suppliers from CFL: 35% for less than 20 km, 40% for less than 40 km, and 25% for more than 40 km. CFL receives the biomass in a loose, bulky, unprocessed form that is not suitable for the gasification system. It shall be noted that in general, feedstocks with higher moisture contents would have disadvantages in regard to transportation, handling, and gasification. These disadvantages include higher transportation cost per tonne of dry matter, greater chance to clog the gasifier feeding system, lower calorific value, and difficulty in the ignition. Mishra et al. (2010) studied the response of gasifiers to fluctuations in the biomass moisture content and concluded that higher moisture content would bring down the gasifier temperature and the syngas heating value. Additional energy is required for evaporating the excess water in the gasifier, thus impairing the gasifier heat budget. Rajvanshi (1986) suggested some pretreatment of fuel is necessary to achieve a desired moisture content of less than 20%. According to Demirbas (2009), when biomass is dried, the stored moisture equilibrates with the ambient relative humidity. The equilibrium moisture content (EMC) is usually about 20% for air-dried fuel. Thus, for biomass with moisture content exceeding the EMC, making moisture adjustment before storage is critical to the operation of the gasification reactor. Quaak et al. (1999) pointed out that the ash content of biomass should also be limited as it will cause handling and shipping problems. Pre-processing operations are needed before the fuel is delivered to UBC and they comprise of grinding, screening, and blending. Grinding and screening will also make biofuel financially attractive because it will be more suitable for utilization in bioenergy systems. For size reduction, CFL uses an industrial electric tub grinder (Model Hogzilla 1664) equipped with two side-by-side grates (Figures A.2 and A.3). The grates could be the screens with either 76 mm or 127 mm square-hole openings. The ground biomass is transferred to an industrial screener (Figure A.4) for fractionation purposes. The screener is 1.83 m x 3.35 m in size. It has two screens and a pan. One screen with smaller holes has 7.1 mm diameter round openings. The other screen with larger holes is rectangular (sides 38.1 mm, diagonal 53.8 mm). 12 Both screens are 3.5 mm thick. The biomass is separated into three fractions and categorized as oversize, midsize, and undersize. The oversize biomass is recycled to the grinder for re-grinding. The undersize biomass is used for other applications such as landscaping. The accepted size (midsize) is used as the solid biofuel for the UBC-BRDF. 1.2.3 Description of transporting biofuel from CFL to gasifier After grinding and screening, the fuel is transferred to the storage area - a covered shed for temporary storage (Figure A.5). One major operation for fuel preparation is blending of several types of materials to obtain a desirable fuel quality and to control the fuel cost. At CFL, the percentage of western red cedar (WRC) in the blend is controlled due to its high chlorine content. Blending does not only occur at CFL but also at UBC-BRDF. Mixing occurs when the biomass is loaded from the storage to the truck (Figure A.6a). The materials loaded into the truck consist of a blend of ground and chipped wood wastes. Upon delivery of the materials at UBC-BRDF, blending occurs again when a moving floor in the truck dumps the biomass into the front storage bins (Figure A.6b). Further blending occurs when the material is transferred from the storage bins to the gasifier with moving floor and cross movement of a chain conveyor. The effect of grinding, screening, storage and blending on fuel properties is investigated in detail in Chapter 2. After preprocessing is done, CFL delivers chipped and ground biomass to UBC-BRDF. “Chipped wood waste” refers to the woody biomass generated from a chipper. Wood chips have more uniform (narrower range) particle sizes and geometry with less dust. In the chipper, size reduction can be achieved with more control over the size and shape of biofuel. Wood chip is of rectangular shape with a typical dimension of 5x5 mm to 50x50 mm. The thickness of the wood chip can be adjusted by machine. “Ground wood waste” refers to the woody biomass (generally called hog fuel) generated from a hammer mill grinder. Figure 1.7 shows a side-by-side comparison between the wood chip and hog fuel. The hog wood is passed through a screen to control the particle sizes. However, hog fuel still has a wider particle size distribution; it contains large pieces of woods as well as dust and small particles. The shape of wood particles in hog fuel is typically long and narrow. The daily supply to the gasifier is a mix of wood chips and hog fuel in varying proportions. 13 Figure 1.7 Side-by-side comparison of wood chips (left) and hog fuel (right) before they are mixed at CFL. “Chipped wood waste” refers to the woody biomass generated from a chipper. Wood chips have more uniform (narrower range) particle sizes and geometry with less dust. “Ground wood waste” refers to the woody biomass (generally called hog fuel) generated from a hammer mill grinder. Picture was taken by the author. Two sizes of trucks (50 m3 and 110 m3) are available for biofuels transport, and most of the time the larger truck is used for delivery. These trucks are equipped with a moving floor. The single driver is also responsible for weighing the truck at a weighing station and unloading the truck at BRDF. For larger capacity truck, loading takes 15 minutes using frontend bucket loaders. Unloading the truck at BRDF takes about 30 minutes. Figure A.7 shows the route of delivery from CFL to UBC. The distance is 50 km, and round-trip traveling time is typically about two hours. Sampling was conducted at the time of unloading. The fuel properties of the samples were measured daily for over two years. 1.3 Solid biofuel qualification The annual supply from CFL is sufficient for the gasifier, so BRDF does not need to source biomass from other recycling yards. Figure 1.8 shows the annual quantity of waste wood supplied by the 114 residue bins spread out within 100 km of the CFL. The tonnage of biomass available from sources differs depending on the size of wood working operations. Roughly 90% of the sources can provide each less than 200 tonnes. The remaining 10 % each can provide up to 14 2,000 tonnes of woody materials per year. The quantity of available biomass depends on the size of the operation, as well as the distance from CFL and the type of wood residues. It is not clear that CFL charges a tipping fee for removing the material. Figure 1.8 Annual quantity of waste wood supplied by the 114 source providers to CFL. Most suppliers deliver less than 200 tonnes of woody waste materials to CFL. There are six main suppliers that provide more than 2,000 tonnes of woody materials per year. The fact that a small number of providers contribute most of the wood results in a high consistency in biomass properties. Initially four of major sources of wood residue were selected for analysis, with an aim to determine the extent of spatial variation of the sources of fuel supply. Prior to start-up of the BRDF in 2012, the biomass samples were collected from these four companies for conducting analysis on the fuel properties. The measurements are summarized in Error! Reference source not found.. It can be seen that the biomass moisture content was relatively low (10-20% wb) at the time. Moreover, the variation of moisture content, ash content and higher heating value (HHV) among the primary suppliers are quite small. Yet, as CFL started blending these materials with city tree trimmings the solid biofuel properties exhibited large variability because of high 15 and variable moisture content of green trimmings. This aspect will be further discussed in Chapter 2. Table 1.1 Variation of fuel properties among the four providers. They provide over 400 tonnes of biomass annually to UBC BRDF. The variation of moisture content (9% -18%), ash content (0.8% – 1.3%) and higher heating value, HHV, (18.9 – 19.3 MJ/kg) among the primary suppliers are quite small. Supplier Annual quantity (tonne) Moisture content (% wb) Ash content (% db) HHV (MJ/kg) 1 2190 18 1.3 18.9 2 1095 10 0.8 19.1 3 936 9 0.9 19.3 4 468 10 1.1 19.0 1 Quadra Wood Products Ltd; 2 D&D Pallets Ltd; 3 Absolute Lumber; 4 Bmw Shake Ltd. CFL provide us with four bags of wood chips each weighing about 40 kg. The materials in each bag consisted of Hogzilla produced hog (HGZ) (Hogzilla is the model name of a grinder available at CFL), spruce-pine-fir (SPF), UBC’s Malcolm Knapp Research Forest (UBCRF), and western red cedar (WRC), respectively. HGZ was prepared by grinding clean wood that contains no adulterants from manufactured sources. SPF, UBCRF, and WRC had been ground or chipped at the sawmill site rather than the CFL site. All of the samples did not contain any paint or treated wood. The bags were placed in cold storage to avoid the development of mold. Three batches were prepared from these materials for the analysis of fuel properties. Batch 1 was an equal blend of HGZ, SPF, and URF. Batch 2 comprised of solely WRC. Batch 3 was an equal blend of HGZ, SPF, URF and WRC. Table 1.2 lists the measured physical and compositional properties of the fuel with respect to the initial specifications by Nexterra for fuel qualification purposes. The three batches of samples are mostly in compliance with the Nexterra specifications in terms of the presence of greens, metals, chemicals, glues/paint, moisture content, ash content, volatiles, and fixed carbon Table 1.2 Measurements of fuel properties with respect to specs by Nexterra Systems Corp. for fuel qualification purposes. The three batches of samples are mostly in compliance with the Nexterra specifications, with only a few exceptions. For example, batches 1, 2 and 3 had mass 16 contents, calorific value (HHV), elemental contents (H, O, N, S), and ash fusion temperature. The specifications (Table 1.2) stipulate no fuel particles should be greater than 76 mm; yet fraction of particles (10.7%, 10% and 8.7%) that exceed the maximum length allowed. Physical & compositional properties Batch 1 (Blend of HGZ, SPF and URF) Batch 2 (WRC) Batch 3 (Blend of HGZ, SPF, URF and WRC) Length (none > 3” or 76 mm) 10.7% (N) 8.2% (N) 10.0% (N) Fines (< 25%, 1/4” or 6.3 mm) 11.5% 0% 8.6% Greens (trace amount) None None None Metals (none) None None None Chemicals (none) None None None Glues/paint (none) None None None Moisture content (10–55% wb) 47.2 37.3 42.5 Volatiles (70-85%) 83.5 82.0 83.5 Fixed carbon (15-25%) 16.0 17.6 16.0 Ash (<10% db) 0.45 0.38 0.48 HHV (>19.8 MJ/kg) 19.06 19.12 19.03 Carbon (48-52%) 54.4 (N) 55.6 (N) 53.8 (N) Hydrogen (5-6%) 5.9 5.7 5.8 Oxygen (36-44%) 39.1 38.2 39.8 Nitrogen (< 0.2 %) 0.08 0.10 0.10 Sulphur (< 0.025%) 0.02 0.02 0.02 Chlorine (< 0.025%) 0.018 0.048 (N) 0.041 (N) Ash fusion temperature (> 2100oF) Initial deformation (F) 2152 2599 2327 Softening (F) 2183 2620 2352 Hemispherical (F) 2193 2634 2358 Fluid (F) 2255 2651 2377 (N): Not in compliance with the Nexterra specifications 17 batches 1, 2 and 3 had a mass fraction of particles (10.7%, 10%, and 8.7%) that exceed this length. All three batches had carbon content that slightly deviates from the specifications. Nexterra requires the fuel to contain less than 0.025% chlorine content. Batch 1 meets the requirement at 0.018%; but batches 2 and 3 are not in compliance, as they contain 0.048% and 0.041% chlorine content, respectively. WRC logs have a higher level of chlorine due to transport of the logs along the Fraser River and into the ocean in the form of log booms. Reducing the percentage of WRC in batch 3 blended fuel may be one way to achieve compliance in this aspect. Methods of characterizing wood fuel properties are outlined in European Standard EN 14961-1 (CEN, European Committee for Standardization 2005f). According to the standard, wood fuels specifications should include the following: origin and source of fuel; trade form (wood chips or hog fuels); chemical treatment statement (yes or no); specifications of fuel properties including both informative and normative properties like heating value, bulk density, and chemical treatment. Particle size, moisture content, and ash content are also included normative properties. The most significant characteristics are moisture content, ash content, calorific value, and particle size. Measurements of moisture content, ash content, and bulk density of fuel samples were measured according to CEN/TS 14774, 14775 and 15103 standards (CEN, 2004, 2005a), respectively. The samples were burned at 575oC in a muffle furnace to produce ash. The samples were kept in the furnace for 24 hours to make sure all carbon is burned out. The higher heating value HHV (also known as gross calorific value, GCV) of biofuel is measured according to CEN/TS 14918 (CEN 2005e) standard using a bomb calorimeter (Model 6300, Parr Instrument Co., Moline, Ill.). Gilson shaker and wire mesh screen were used for particle size distribution analysis (European Comitte for Standardisation, 2006a) and to separate the particles that are smaller than 6.3 mm and bigger than 76 mm. Each measurement was repeated three times. For fuel qualification purposes, samples of the “clean wood” (originated from the suppliers) and “green wood” (originated from the City of Vancouver tree trimmings) have also been obtained from CFL for conducting a preliminary analysis of the various biomass properties. Table 1.3 lists the typical data taken from the clean wood and city green wood as these materials 18 were unloaded at the depot yard (Figure 1.4). The blending ratio (green wood: clean wood) ranges from 1:1 to 3:2. Moisture content has the largest variation among the measured feedstock properties. The green wood materials have high average moisture content of 40.2±3.6% as compared to the average moisture content of 16.7±3.8% for the drier clean wood materials. Moisture contents reported in this research are on wet mass basis. For clean wood, the average ash content, calorific value (higher heating value HHV) and bulk density were 0.85±0.3%, 19.1±0.6 MJ/kg and 142±28 kg/m3, respectively. For green biomass, ash content, calorific value and bulk density were 0.91±0.27%, 18.9±0.6 MJ/kg and 206±37 kg/m3, respectively. Thus, the coefficient of variation (CV) for moisture content, ash content, calorific value and bulk density was 23%, 35%, 3% and 20%, respectively for the clean wood, and the corresponding CV values were 9%, 29%, 3% and 18%, respectively for the green wood. These results indicate that the green wood materials also met the Nexterra fuel specifications. Table 1.3 Feedstock properties of clean wood from working mills and green wood from tree trimmings. Number of samples and standard deviations are shown. The blending ratio (green wood: clean wood) ranges from 1:1 to 3:2. Moisture content creates the largest variation when the drier clean wood and wetter green wood (trimmings) are mixed. Category Moisture Content (% wb) Ash Content (% db) Calorific Value (MJ/kg) Bulk Density (kg/m3) Processed clean wood n=10 16.7±3.8 0.85±0.30 19.1±0.6 142±28 Processed green wood n=8 40.2±3.6 0.91±0.27 18.9±0.6 206±27 Blended materials measured at the gasification plant, n = 10 31.0±7.2 0.83±0.17 19.2±0.32 187±38 1.4 Thesis objectives More detailed literature review that is pertinent to the biomass feedstock (fuel) properties and the effects of fuel properties on the performance of gasification (and in particular, updraft gasifier) was performed and summarized in the various chapters in this thesis. 19 Research work on improving biomass collection, storage, pre-processing, and reducing material losses have been carried out to different extents for more than two decades. Studies on gasification including optimization have been done mostly either through modeling work or lab-scale experiments under highly controlled conditions with limited applications to commercial operations. Published data from lab-scale experiments on biomass gasification indicates that there exists either a direct relation or some correlation between biomass fuel properties (such as moisture content, ash content, particle size) and syngas yield and tar formation (Dai et al., 2012; McKendry, 2002a, 2002b). However, there is a lack of similar detailed biomass fuel property data relevant to performance of full-scale gasification plants that operate on variable quality of biomass feedstock (especially those originating from urban sources) in published literature. Given the complexity of supplying a blend of biomass derived from urban/industrial waste wood to a gasification plant, the major driving force for conducting the thesis research is to gain a deep understanding of the supply system, which can lead to reducing the uncertainties in the quality and hence the utilization of urban biomass feedstock for bioenergy production. The specific scenario for analysis represents a unique opportunity to study the commercial gasification system (UBC-BRDF) that started operation since 2012, following the “Campus as a Living Lab” concept initiated by UBC Sustainability. The central theme of the thesis is to reduce the uncertainties in utilizing urban biomass feedstock for a full-scale bioenergy system by analyzing the system complexity. The specific objectives of this study are as follows: 1. To characterize spatial and temporal variations in the physical and compositional properties of ground and chipped wood waste supplied to the gasification system; 2. To evaluate the gasifier performance in terms of steam production, syngas quality, and tar formation; 3. To establish industrial correlations between the wood chip properties and the performance of the full-size gasifier in terms of steam output, syngas quality and tar formation. The outcomes of this thesis are expected to have the following impacts and contributions to the field of biomass and bioenergy: • Quantifying daily, monthly, and annually the potential variability of biomass feedstock gathered from a wide range of urban and industrial sites. 20 • In-depth understanding of inherent variability of industrial biofuel supply logistics and their effect on the performance of a full-size gasification system; • Quantifying deviations of an industrial scale gasification system from highly controlled lab-scale data published in open literature. 1.5 Structure of the thesis Based on the objectives of the research, the thesis is organized as shown in Figure 1.9. Chapter 1 provides a detailed description of the bioenergy system from the source of biomass feedstock to the utilization of wood chips at the UBC-BRDF. The objective of Chapter 2 is to quantify seasonal variations in the feedstock characteristics and plausible causes of the variations in feedstock quality. For Chapter 3, the objective is to investigate the impact of variability in feedstock quality on system reliability and steam output over time in an industrial gasifier that is operating routinely under commercial obligations. The objective of Chapter 4 is to assess the effects of fuel properties on syngas quality and tar formation in the industrial gasifier. Chapter 5 concerns the modeling and analysis of the efficiency of the industrial gasifier based on data that include fuel properties, gas profiles and operational conditions. Finally, Chapter 6 summarizes the key results and findings; also, recommendations for future studies are suggested. Figure 1.9 Structure of this thesis based on the research objectives Chapter 1 System description and Statement of Objectives Chapter 2 In-depot upgrading quality of fuel chips for a commercial gasification plant Chapter 3 Effect of particle size and moisture content of fuel wood on the gasifier performance Chapter 4 Characterization of recycled wood chips, syngas yield, and tar formation Chapter 5 Modeling and analysis of the gasifier efficiency based on plant data Chapter 6 Overall conclusions and recommendations 21 In-depot Upgrading the Quality of Fuel Chips for a Commercial Gasification Plant 2.1 Introduction Gasification is a thermal process in which material such as biomass is exposed to an oxygen deficit environment undergoes a sequence of two conversions. The biomass breaks down into volatiles and char during the first conversion at around 600 oC. The resulting gas contain hydrocarbon gases, methane, hydrogen, carbon monoxide, carbon dioxide, tar, water vapor and nitrogen. The char is mostly made up of fixed carbon and ash. The char is burned to produce heat during the second conversion at temperature that exceeds 1000 oC. The combustible gases are eventually burned in an oxidizer to generate process heat. Gasification reaction, operating conditions, and gas composition depend on the physical characteristics and composition of the biomass (IEA task 33, 2016). According to (Kirsanovs et al., 2014), the gasifier’s operating temperature tends to decrease as the feedstock moisture content increases. The equivalence ratio ER (oxygen/fuel) must be increased to increase the operating temperature and in order to maintain a stable bed-zone operating condition. In general, the types of biomass gasifiers are classified as fixed bed, fluidized bed, and entrained bed (Warnecke, 2000). The maximum size of particles for fixed/moving bed is specified at less than 51 mm, for fluidized bed at less than 6 mm and for entrained bed at less than 0.15 mm. The 51 mm maximum particle size specified in reference (Warnecke, 2000) is probably related to the maximum size a screw auger could handle. The maximum size of 75 mm for the fixed bed gasifier used in the present research (to be discussed later in this chapter) is based on the ability of the screw feeder to negotiate the delivery of particles from a horizontal auger to a vertical auger. Fixed bed gasifiers are simpler than the other types but their thermal output is typically lower than 10 MWth (Basu, 2013). Fixed bed gasifiers are further classified as updraft, downdraft and cross flow depending upon the flow direction of gas and biomass particles. The updraft gasifiers can operate with high moisture biomass up to 60% moisture content (wb) whereas the downdraft gasifiers operate with biomass less than 25% moisture content (wb). Updraft gasifiers are also suitable for high ash but the volatiles contain large concentration of tar that can make the gas more difficult to be used as a fuel for internal combustion engines (Chopra and Jain, 2007). The higher amount of tar in updraft gasifiers is 22 due to the released tar not passing through hotter zones of the gasifier where it would be destroyed (e.g. as in downdraft units) (Ahmad et al., 2016). The work by (Dai et al., 2012) showed that the analysis of biomass physical properties and chemical compositions was important for feeding, reactor performance, downstream operations and equipment for all biomass-related processes. For quality control, the most important fuel parameters for wood chips for end-users are: species, moisture content, calorific value, particle dimensions (including low amount of fine and coarse fractions), and absence of impurities like sand, stones, plastics and so on (Kaltschmitt et al., 2009). Other quality influencing factors that are important and relevant for biofuels (such as wood chips, hog fuel, and wood pellets) include weather conditions, storage time, selection of machinery for pre-processing operations (such as debarking, chipping, grinding, drying), the number of handlings, and microbial activities (BioNorm II, 2007). Biomass is a widely spread resource with low energy density. Thus, significant volumes of biomass must be collected across over large areas, which can lead to complicated logistics and expensive transportation (Richard, 2010). The work by (Anon, 2017) showed that feedstock cost represented as high as 40-50% of the total cost of a heat or power generation unit. The consistency of biofuel properties will affect the cost of supply logistics. Using covered storage can help to control the fuel moisture content. Consistent fuel moisture content is essential for the smooth operation of a gasifier (Sokhansanj and Fenton, 2006; US Department of Energy, 2010). The mass of water in living trees, newly cut logs, and freshly sawn lumber can be greater than the mass of the wood. In such cases, moisture content often reaches up to 67 wt% (Reeb and Reeb, 2009). In general, feedstock with higher moisture contents would have disadvantages in regard to transportation, handling and gasification. These disadvantages include higher transportation cost per tonne of dry matter, greater chance to clog the gasifier feeding system, lower calorific value, and difficulty in ignition. Mishra et al. (2010) studied the response of gasifiers to fluctuations in the biomass moisture content, and concluded that higher moisture content would bring down the gasifier temperature and the syngas heating value. Additional energy is required for evaporating the excess water in the gasifier, thus impairing the gasifier heat budget. Rajvanshi (1986) suggested it is necessary to have pretreatment of fuel for reaching a desirable moisture content of less than 20%. A lower 23 fuel moisture content at 15% was suggested by Motta et al. (2018) as high moisture content reduces the reactor temperature which leads to slower endothermic reactions. Watson et al. (2018) stressed that bed temperature is the most important parameter in gasification, and they showed a similar relationship between bed temperature and fuel moisture content. According to Demirbas (2009), when biomass is dried the stored moisture equilibrates with the ambient relative humidity. The equilibrium moisture content (EMC) is usually about 20% for air-dried fuel. Thus, for biomass with moisture content exceeding the EMC, making moisture adjustment before storage is critical to the operation of the gasification reactor. Alfonso et al. (2009) considered ash content as one of the major fuel properties that play important roles in process of converting biomass to energy. The quantity and quality of ash depend on factors such as the type of tree, the part of the tree (wood, bark, leaves), and the amount and type of soil contaminants. The work by (Melin, 2008) found that the overall ash content of the hard wood was noticeably higher than that of the softwood. Uniformity in feedstock size is important both for feeding and stable gasification. According to (Kaupp and Goss, 1981) longer residence time is required to avoid the incomplete utilization of oversize feedstock. Smaller particles have larger surface area-to-volume ratio per unit mass, which leads to faster heat transfer and gasification process (Inayat et al., 2016; Mishra et al., 2010). The works by (Lv et al., 2004) and (Feng et al., 2011) have also shown that smaller particles will increase the yield of CO and CH4. Kumar et al. showed the high gas yield and energy efficiency are dependent on the particle heat transfer characteristics (Kumar et al., 2009). The authors found that smaller particles (0.075 mm) increase the efficiency compared to larger particle (1.2 mm). On the other hand, particulate matter in gas streams contains a significant portion of ash and dust. These small particles cause plugging in downstream equipment and engine components (Stevens, 2001). In addition, to control particulate emission levels, the particulate from producer gas needed to be removed. The work by (Rapagnà and Latif, 1997) found a strong relationship between particle size (0.287-1.90 mm) and the syngas concentration. This study is a detailed analysis of the feedstock characteristics during two operating years 2012-2014. Feedstock quality data as well as the data relevant to gasifier operation were taken on a regular basis at the gasification plant. The objective was to quantify seasonal variations in the feedstock characteristics and plausible causes of the variations in feedstock 24 quality. The UBC gasifier with its fuel supply system provides a real case study and an example of ways to improve the design and reliability of future biomass supply and gasification systems. 2.2 Overview of fuel supply and gasification system Given the goal of reducing GHG emissions, UBC installed an updraft biomass gasification system at the Point Grey Campus in Vancouver in 2012. The gasification facility is called Bioenergy Research & Demonstration Facility (BRDF). It consists of a woodchip receiving area, feeding system, silo for temporary biomass storage, gasifier, thermal oxidizer, boiler for steam production and an internal combustion engine (Figure 2.1). The gasifier was designed to provide up to 5% of 40 MW campus power and 15% of 40 MW thermal based heat (Staley, 2012). The engine that was originally designed to use syngas from biomass has been operating with renewable natural gas purchased from a local gas utility since 2016. Figure 2.1 Diagram of the updraft gasifier plant at the University of British Columbia. Note the mechanism of feeding feedstock that flows from underneath into the gasification chamber. Within gasifier, the fuel is piled up in a cone shape, and the air comes through nuzzles from outside in order to provide oxygen for partial combustion. The syngas is directed from the top of gasifier to oxidizer. (Adopted from https://nexterra.ca/files/environmental-advantages.php) 25 UBC has a limited term contract with a waste wood recycling yard (depot) to deliver chipped and ground wood waste fuels to the gasification plant based on a set of schedule and feedstock specifications. The depot is located in Langley, BC, a township 50 km from the gasification plant. The sources of feedstock are urban wood waste generally obtained from within a 100 km radius of depot. The urban wood wastes consist of two major types of materials: woody scraps from construction and demolition (C&D) waste, and green wood (which is the woody parts of the tree trimmings) collected from Metro Vancouver’s curbsides and landfills. In Metro Vancouver 54% of the C&D materials (595,000 tonnes) are woody wastes (Metro Vancouver, 2015). Table 2.1 List of physical and compositional properties and their values specified for the biomass gasification plant. The important parameters include length, fines and moisture content. These parameters are determined by the characteristics of the gasification system. Parameter Value length (> 76 mm or 3”) none fines (6.3 mm or 1/4”) < 25% moisture content 10-55% volatiles content 70-85% fixed carbon content 15-25% ash content <10% Calorific value >19.1 MJ/kg Carbon 48-52% Hydrogen 5-6% Oxygen 36-44% Nitrogen < 0.5% Sulphur < 0.025% Chlorine < 0.025% green wood trace amount metals none chemicals none glues, paint none 26 Table 2.1 lists the initial (2012) specifications of the gasifier for the physical and compositional make-up of the biomass fuel. The plant specifies 6.3 mm -76 mm particle sizes, and 10-55% moisture content (wb). The manufacturer states that the gasifier can tolerate an ash content of 10%. A preliminary analysis showed that the feedstock from recycled clean wood in Metro Vancouver met these specifications. As shown in Figure 1.1, the depot is a privately-owned commercial operation that handles more than 200,000 tonnes of recycled wood annually. The supply of wood to UBC is less than 7% of their total annual volume of wood being handled. The depot collects the urban biomass from 114 sites using collection bins placed at each site. When these bins are full, the depot dispatches a truck to pick up the full bin and replace it with an empty bin. The City of Vancouver also sends green wood to the depot. The recorded data for each supplier consists of the distance between the depot and the supplier, tonnage of supplied woody biomass per year, per month, and per week when any or all of these data are available. Besides, the type of wood and the frequency of collection are recorded. Upon arrival at the depot, a truck unloads the materials in a pile. The material is sorted visually using a frontend loader. The sorted material is ground and blended for specific applications and customer requirements. Figure (a) shows the shed for temporary storing the processed materials at the depot before delivery, and Figure (b) shows the storage area at UBC. About 35% of suppliers are less than 20 km away; 40% of the suppliers are located at a distance 20-40 km from the depot. A few of the suppliers are farther than 40 km. About 20 suppliers provided 80% of the clean wood, and about 90 suppliers provided the remaining 20% of the clean wood. Clean waste wood come from pallet manufacturing operations, scrap lumber from saw mills, and scrap pallets from various local companies that use wood pallets to handle packaged goods. Most of the clean wood originated from lumber grade wood and hence had low variability in their properties. 2.3 Size reduction at depot An industrial tub grinder for wood, Hogzilla Model WC-1354P (CW Mill Equipment Co., Inc. Asbetha, KS) comminuted the biomass to small pieces. The grinder was equipped with a rotating drum on which a set of swinging hammers (9.4 cm × 16.5 cm) was installed. The curved housing around the rotor consisted of two removable grates. The square openings on one of the 27 grates were 76 mm and on the other were 127 mm. The ground wood that passed through the grates was conveyed onto a set of three 3.5 mm thick oscillating screens that measured 1.8 m × 3.5 m. The top screen had 38 mm × 54 mm rectangular holes; the bottom screen had 7 mm round holes. The bottom tray was a solid steel plate that separates fines from the mixture. The large pieces constitute the “oversize” coming off the top screen. The leftover materials in the middle screen were the “midsize” destined for the UBC gasification plant. Grinding tests were conducted with the same size grates or two grates with different sizes. For each grate configuration, samples were collected from 4 different sampling points. One sample was taken from the ground materials immediately after the grinder (S11) but before the screeners. The other three samples (S12, S13, S14) were taken as undersize, midsize and oversize streams after screening. Three replicate samples were collected during each sampling event. The sampling was repeated for grate configuration 2 (S21, S22, S23, S24) and for grate configuration 3 (S31, S32, S33, S34). With this scheme, 12 unique operating conditions were evaluated altogether. Each batch of screened samples was further analyzed in the lab using a stack of sieves. The moisture content, ash content, and calorific value for each batch were measured following standard methods (European Comitte for Standardisation, 2004b, 2005e, 2006a). 2.4 Measurements at the depot The data shown in Table 2.2 indicates that the ash content of the undersize feedstock fraction was several times higher than the ash content in the midsize and oversize fractions. The high ash content of 4.7-10.6% in the smaller feedstock fraction must have originated from soil as contamination. The ash content of the midsize and oversize fractions was less than 1%. This observation reinforces the premise that the source of ash is from contamination with soil. Figure 2.2 shows the relationship between particle size vs. ash content and calorific value. The samples were fractionated into several sizes (from 0.08 to 2 mm) using a stack of sieves. Figure 2.2(a) shows that average ash content decreases as the size of particle increases, ranging from about 35% for 0.08 mm to 4% for 2 mm particles for the biomass samples collected from the undersize sampling port. Figure 2.2(b) shows that average higher heating value HHV increases with the size of particles, and a wider range of values (13.0-18.5 MJ/kg) was observed. The variation of HHV is caused by the amount of ash contained in the biomass. Figure 2.3 shows that HHV 28 increases from 10 to 20 MJ/kg as ash content decreases from 45 to 2%. Two sources contribute towards ash content of biomass: a) the wood itself, inherent ash content and b) contamination with soil. The inherent ash content is relatively constant, and it will not vary with particle size. At the recycling yard, the biomass is in contact with the ground. Part of the biomass can be readily mixed with dust, sand and mud that blend with small particles. Soil contamination is known to be a source of variability for ash content which reduces preprocessing efficiency and increases maintenance cost of feeding systems as well as disposal cost. Table 2.2 Measurements of feedstock properties from the grinder and after the screens. The properties measured include ash content, calorific value and moisture content. The ash content of the undersize feedstock fraction was several times higher than the ash content in the midsize and oversize fractions Screening configuration and sampling points Designation Average ash content (%) Average calorific value (MJ/kg) Average moisture content (%) Two 76 mm grates Ground not screened S11 0.9 19.1 44.5 Screened undersize S12 6.6 19.0 61.3 Screened midsize S13 0.6 18.6 43.5 Screened oversize S14 0.8 18.6 39.9 Two 127 mm grates Ground not screened S21 0.9 18.8 42.2 Screened undersize S22 10.6 17.7 61.3 Screened midsize S23 0.9 18.2 41.9 Screened oversize S24 0.8 18.5 35.9 76 mm grate + 127 mm grates Ground not screened S31 0.8 18.8 49.9 Screened undersize S32 4.7 18.5 51.4 Screened midsize S33 0.4 18.8 41.9 Screened oversize S34 0.8 18.4 39.7 29 (a) (b) Figure 2.2 The relationship between: (a) ash content and particle size; (b) calorific value (or higher heat value) and particle size. This plot shows a high amount of the ash in the feedstock which could belong to the dirt content (soil dust) mixed with biomass. Figure 2.3 Relationship between the higher heating value and ash content of the biomass. The data for this plot is extracted from the data obtained from Figure 2.2. The high ash content does not represent the biogenic (inherent) ash in biomass but rather in the fraction of debris mixed with biomass. 2.001.701.200.800.550.230.200.0850403020100Seive Size (mm)Ash Content (%)Boxplot of Ash Content2.001.701.200.800.550.230.200.08201816141210Seive Size (mm)Higher Heating Value (MJ/kg)Boxplot of Higher Heating Value50403020100201816141210Ash Content (%)Higher Heating Value (MJ/kg)Higher Heating Value vs Ash Content 30 Figure 2.4 Size distribution for the particles that fell through the screen configurations (S12, S22, S32) installed in the Hogzilla grinder. The graph shows that the three screen configurations had a similar performance in terms of removing small particles of less than 2 mm from the feedstock. S12: Two 76 mm grates; S22: Two 127 mm grates; S32: One 76 mm and one 127 mm. As shown in Figure 2.2, higher ash content does reduce HHV. In Table 2.2, the values of ash content and HHV are the weighted sum of each particle size category. Figure 2.4 shows the weight percent for each setup. S32 has the highest weight percent for the largest particle size (> 2 mm). In this particle size category, ash content is lower and HHV is higher, compared to other particle size categories. As a result, the weighted ash content for S32 is the lowest with a higher HHV value. In summary, if two batches of fuels are studied, particle size distribution affects the total ash content and HHV for each batch. Figure 2.3 shows the relationship between ash content and HHV. In order to fully demonstrate such relationship, particle size distribution should be incorporated. Moisture content of the particles varied with the various grinder configurations and screening streams. The results show that the undersize fraction had biomass particles with the highest moisture content (55-60%), compared to other output streams with moisture content ranging from 35-45%. This could be attributed to the greater surface area of the smaller particles that favor water absorption. This implies removing the undersize materials will lead to lower average moisture content of the resulting biomass. Figure 2.5 demonstrates the size analysis for the unscreened and midsize particles after 05101520253035Mass fraction (%)Particle sizeS12 S22 S32 31 the screeners. The gasifier specification requires the percentage of particles smaller than 6.7 mm to be less than 25% (Table 2.1). Data from sieve analysis showed that screening ground biomass to three fractions (resulted in under-, mid- and over-size streams) improved the size distribution in the midsize stream. The midsize stream still contains particles of small, medium and large sizes. The unscreened stream contains 25% of particles smaller than 6.7 mm while the midsize stream only had about 17%. The distribution of particles between 6.7 mm and 25 mm is also improved when feedstock is screened. The unscreened stream had 64% of particles between 6.7 mm and 25 mm while the midsize stream contained 76% of particles in this size range. The percentage of large size particles (>25 mm) also decreased in the screened fraction. Within the undersize stream, about 15% of the materials was smaller than 0.5 mm (data is not given). Figure 2.5 The distribution of particle size for unscreened output and midsize output. The bar graphs show that fractionation using three sets of screens (two screens 76 mm and 127 mm perforations and that pan) was successful in removing small pieces (<6.7 mm) and large pieces greater than 25 mm. About 75% of the midsize fraction met the gasifier’s size specifications. 0102030405060708090< 6.7mm 6.7mm - 25mm > 25mmMass fraction (%)Particle sizeUnscreenedMidsize output 32 2.5 Measurements at the gasification plant The feedstock was sampled daily at UBC. The measured properties of fuel samples include moisture content, ash content, wet bulk density, calorific value, percentage of undersize materials, and percentage of oversize materials (see Table 2.3). Three replicates were made for each measurement. 871 loads of feedstock were tested. Methods of characterizing woody feedstock characteristics and quality are based on the European CEN methods. The standard CEN/TS 14778 Part 1 and Part 2 (CEN, 2005b, 2005c) were used for sampling solid biofuels. The method is applicable to a variety of materials including the types of biomass used in our study, the coarse or irregularly-shaped particulate materials with particle sizes up to about 200 mm. Sample preparation was done according to the CEN/TS 14780 (CEN, 2005d). Measurements of the various properties were done according to CEN/TS 14774-1, 14775, 15103, and 15149 for moisture content, ash content, bulk density and particle size distribution, respectively. Higher heating value HHV of biofuel was measured following CEN/TS 14918 using a bomb calorimeter (Model 6300, Parr Instrument Co., Moline, Ill.). Figure 1.7 shows the biomass feedstock upon delivery. The average moisture content is 31% with a standard deviation of 7.2%. It is noted that undersize material ranged from 4% to 26% and oversize material ranged from 4% to 11%. Bulk density has an average value of 187.4 kg/m3 and a rather large standard deviation of 37.8 kg/m3. This large variation is expected as bulk density is highly correlated with moisture content (Moni et al., 2016). Ash content is consistently low at 0.8% (db) on average with a standard deviation of 0.2% db. As for particle size, on average 9.1% particles are larger than 76 mm while 17.6% particles are smaller than 6.4 mm. These numbers indicate that after preprocessing at CFL, the moisture content of the biomass supplied to UBC is noticeably higher because of mixing the drier clean wood with the wetter green wood materials. The latter is cheaper, but the moisture is 40-50% wb. If the blended biomass contains less green wood, the final delivered biomass would have a lower moisture content. An area which needs further study is to determine the maximum variation in the properties of the blended materials that a gasifier can tolerate (Sulaiman et al., 2016; 2017). 33 Table 2.3 Characterization of feedstock supplied from CFL to UBC. Long term properties with 871 samples collected over two operating years 2012-2014. Coefficient of variation (CV) indicates the percent variation in a single property among the samples. Fraction of particles <6.4 mm particles had the largest calorific value and smallest CV. Measured biomass moisture content ranged from 10% to 57.4% during this period. MC (% wb) Bulk density (kg/m3) Calorific value (MJ/kg) Ash content (% db) Fraction of particles >76 mm (%) Fraction of particles <6.4 mm (%) Average 31.0 187 19.4 0.8 9.1 17.6 STD 9.6 38 0.5 0.2 2.9 7.1 CV (%) 31 19 3 25 32 40 Minimum 10.0 92 18.1 0.2 0.0 0.9 Maximum 57.4 328 20.9 1.5 18.4 47.6 The Vancouver temperature and precipitation data were collected over the three-year time period (July 2012 to June 2015) [National Climate Data and Information, 2016]. Figure 2.6(a) shows that precipitation varied from 4 mm to 222 mm per month. An overall downward trend of biomass moisture content between 2012 and 2014 was observed. The monthly average biomass moisture content depends on precipitation. For instance, there was noticeably less precipitation during November 2013 - January 2014 when the feedstock moisture content was also lower (18-28%). Yet during November 2014 – January 2015 the feedstock moisture content was high with a peak value of 46%. A shed with a semi-closed structure was available for short-term storage of biomass at the Cloverdale recycling yard, but it was not always possible to supply drier biomass to the UBC plant during high rainfall season. Figure 2.6(b) shows the humidity and temperature from the same period. Biomass moisture content appears to have a positive correlation with humidity and a negative correlation with temperature. The correlation coefficients between biomass moisture content and 1) temperature, 2) precipitation and 3) humidity are -0.63, 0.51 and 0.45, respectivel 34 (a) (b) Figure 2.6 The record of precipitation, humidity, temperature and biomass moisture content (MC) for the period from July 2012 to May 2015. Biomass moisture content had similar trend with precipitation and humidity. Moisture of the biomass delivered to the BRDF increased during rainy periods and dropped during no-rain periods. Temperature has a negative correlation with MC. It is not clear whether the slight shift between maximum precipitation and moisture content is due to a lag between when material got wet due to rain at the depot and when the moisture was measured at the point of delivery at the gasification plant. (Weather data from Environment Canada, https://weather.gc.ca/, August 1, 2017) 0.05.010.015.020.025.0Jul-12Sep-12Nov-12Jan-13Mar-13May-13Jul-13Sep-13Nov-13Jan-14Mar-14May-14Jul-14Sep-14Nov-14Jan-15Mar-15May-15405060708090100Temperature (C)Humidity (%)Average humidity Average temperature0%5%10%15%20%25%30%35%40%45%50%Jul-12Sep-12Nov-12Jan-13Mar-13May-13Jul-13Sep-13Nov-13Jan-14Mar-14May-14Jul-14Sep-14Nov-14Jan-15Mar-15May-15050100150200250Fuel MCPrecipitation (mm)Total precipitation MC 35 Further examination of the data reveals that the monthly average temperature (not shown) ranged from 2oC to 19oC, and feedstock moisture content was lower during warm periods. For example, August 2014 had the highest temperature at 19oC while the feedstock moisture content was the lowest at 18%. Although moisture content has a mild correlation with precipitation, it is desirable to minimize feedstock exposure to precipitation in order to obtain a more consistent feedstock with reduced variations in moisture content. The UBC gasification plant keeps daily record of the operational parameters (biomass feeding rate to the gasifier, bed temperature, operating hours, and the amount of steam produced over time). Generated steam was recorded in a database. The relationship between feedstock moisture content and steam production is analyzed and discussed in Chapter 3. 2.6 Conclusions The analysis of feedstock properties and operating conditions of the commercial gasifier showed that, in the absence of a dryer, blending of feedstock with differing moisture contents would increase the variability in the feedstock delivered to the gasifier plant. Protecting the biomass from precipitation (rain) reduced the level and variation in fuel moisture content. Soil contamination during handling in the depot was reduced by providing a paved surface. Comminuting and fractionating the fuel using three screens were effective measures in producing a more uniform feedstock size. The pairing of the two grinder screens 76 mm perforations and 126 mm perforations did not have a major effect on fractionating the fuel to three size groups of large (>75 mm), small (<6 mm) and medium (6-75 mm) particles. Next chapter will show that large variation in steam production during the earlier years of operation after startup in 2012 may be attributed to the variations in feedstock moisture content and particle size. 36 The Influence of Particle Size and Moisture Content of Wood Fuel on the Performance of a Commercial Updraft Gasifier 3.1 Introduction Combustion and gasification are two methods of producing heat from biomass. Gasification is touted as a preferred mode of producing heat than direct combustion when considering environmental consequences of burning biomass. Moreover, gasification is more efficient than direct combustion in extracting heat from the solid materials. The syngas produced during gasification can be further processed into liquid biofuels and chemical products (Heidenreich and Foscolo, 2015). In this chapter, a case study of collecting and preprocessing waste wood from a variety of urban and industrial sources to feed a commercial updraft gasifier is investigated. The collected woody biomass included the ends of dimensional lumber, pallets, and pulp wood chips. These seemingly unusable wood pieces were collected from construction sites and wood working mills and transported to the Cloverdale Fuel (CFL) recycling yard located in Langley, BC, Canada. The materials were then ground, screened and trucked 50 km to the commercial gasification plant on the Point Grey campus of University of British Columbia (UBC), Vancouver, BC. Data taken on the delivered biomass and from the gasification plant operation since 2012 are presented in this chapter. The research question was to investigate the factors that are responsible for gradual improvement in the performance of the system in terms of downtimes and steam production. Published data from lab-scale experiments on biomass gasification indicates that there exists either a direct relation or some correlation between biomass fuel properties (such as moisture content, ash content, and particle size) and syngas yield and tar formation (Dai et al., 2012; McKendry, 2002a, 2002b). Studies on gasification including optimization have been done mostly either through modeling work or lab-scale experiments under highly controlled conditions with limited applications to commercial operations (Bronson et al., 2016). There is a lack of similar data relevant to full-scale gasification plants that operate on variable quality of biomass feedstocks (especially those originating from urban sources) in published literature. Our recent work started to investigate biomass properties and their impact on industrial gasification systems (Oveisi et al., 2018). 37 The UBC biomass gasification system (Bioenergy Research and Demonstration Facility, BRDF) was used in this study. The facility was designed as a combined heat and power system, with one line feeding the syngas to an internal combustion (IC) engine to produce power (2 MWe) and another branch feeding syngas to a thermal oxidizer to generate heat for a steam boiler (6 MWth). The line providing syngas to IC engine was disconnected shortly after startup because of the inability of the system to clean up the syngas. Hence, the produced syngas was solely used to generate steam for the campus (Figure 3.1). Figure 3.1 The components of the UBC gasification plant (BRDF) with the boiler and emission control system. The syngas produced in the gasifier is supplied to a thermal oxidizer. Heat recovery is in the form of steam generation by the boiler. Emissions control is used for removing fine particles from the flowing gas. (Adopted from http://www.nexterra.ca/files/media-resources.php) UBC established a limited term contract with CFL to deliver wood chips to the gasification plant according to a set of schedule and fuel specifications. The sources of biomass are from hundreds of construction sites and wood working facilities along the Fraser River. CFL places collecting bins at these sites. The amount of biomass available, location, and distance vary from site to site. When a bin is full, CFL is notified to sends a truck to drop off an empty bin and pick up the filled bin. The maximum travel distance from these sites to CFL is about 100 km. In addition to delivering wood chips to UBC, CFL provides ground biomass to other users including greenhouses, trails, and erosion control applications. Upon arrival at the recycling yard, the truck unloads the residuals in a pile. A frontend loader or a knuckle boom crawler does the initial manual/visual sorting of the material. The materials that appear clean are grouped into a 38 separate pile designated for the BRDF. The materials are then ground, screened and placed under a shed to be protected from the rain before being trucked to UBC-BRDF. Literature review of the characteristics of sampled biomass and their effect on the industrial gasifier performance is presented below. Wood fiber has a highly heterogeneous structure and this variability makes planning and production control of a bioenergy system a difficult task (D’Amours et al., 2008; Frayret et al., 2007). Vallios et al. (2009) stressed the importance of low logistics cost when designing a biomass supply chain. Ekşioǧlu et al. (2009) identified biomass availability and transportation cost as two significant factors affecting the planning of a sustained supply chain. Ruiz et al. (2013) considered moisture content, heating value, and bulk density major factors that influence the logistics costs. Hess et al. (2009) proposed the conversion of raw biomass to a uniform flowable format to reduce the uncertainty of feedstock quality and to lower the cost of feedstock. The uniform format required a network of depots where the biomass is converted from its initial hard-to-handle bulky and wet form to a granulated dry form like pellets (Eranki et al., 2011). Pelletization improves the stability of biomass so that feedstock can be stored for a longer time. However, traditionally wood chips have been thought of a lower cost option for feeding to gasifiers. Wood chips do not flow as smoothly as pellets do, but wood chips provide a convenient way of storing biomass particles. Some depots actively tailor feedstock quality to generate on-spec biomass required by biomass power plants (Lamers, Roni, et al., 2015; Lamers, Tan, et al., 2015). In a depot, biomass should be protected from rain and insects by storing the biomass under a shed or in a building before delivery to the power generation plant (Sansaniwal, Rosen, et al., 2017). Castillo-Villar and Webb (2014) pointed out that obstacles arise in bioenergy projects due to the lack of understanding of the cause and effect relationship between the fuel quality and energy outcome. Although updraft gasifiers can handle a higher fuel moisture content (up to 60% wb) compared to <25% wb for downdraft gasifiers (Basu, 2013), a consistent fuel moisture content was deemed to be essential for the smooth operation of a gasifier (Sokhansanj and Fenton, 2006; US Department of Energy, 2010) as gasifiers have high sensitivity to moisture content (Chopra and Jain, 2007). In general, feedstocks with higher moisture contents would 39 have disadvantages in regard to transportation, handling and gasification. These disadvantages include higher transportation cost per tonne of dry matter, greater chance to clog the gasifier feeding system, lower calorific value, and difficulty in ignition. Mishra et al. (2010)studied the response of gasifiers to fluctuations in the biomass moisture content, and concluded that higher moisture content would bring down the gasifier temperature and the syngas heating value. With a higher gasifier operating temperature, a larger volume of combustible syngas with less tar is produced (Sansaniwal, Pal, et al., 2017). Additional energy is required for evaporating the excess water in the gasifier, thus impairing the gasifier heat budget. Hence earlier, Rajvanshi (1986) suggested some pretreatment of fuel is necessary to achieve a desirable moisture content of less than 20%. Demirbas (2009) considered moisture adjustment critical to the operation of a gasification system. Uniformity in feedstock size is essential for the feeding of biomass and stable gasification. According to Kaupp and Goss (Kaupp and Goss, 1981) longer residence time is required to avoid the incomplete utilization of oversize feedstock because of slower heat transfer. Grinding biomass increases the particles surface area to volume ratio (Luo et al., 2011). A larger surface area leads to faster heat transfer and gasification process (Inayat et al., 2016; Mishra et al., 2010). Grinding makes the feeding of biomass to the gasifier more uniform. The reviewed literature highlights the importance of feedstock moisture content, particle size, and uniformity in particle size as the three major properties for improving the performance of a gasifier. The objective of this research was to investigate the effects of variability in biomass quality on the performance of a commercial gasifier. The design of experiments consisted of sampling biomass along the supply chain and evaluating the gasifier performance in terms of steam production and plant utilization rate. The analysis took place over 3-4 years of plant operation since the system startup in 2012. The investigated field data demonstrated gradual improvement in gasification performance due to a few adjustments in moisture content and the size of biomass particles. 3.2 Observations 3.2.1 Overall annual performance Table 3.1 lists the overall annual performance of the gasifier system from 2012 to 2016. The total yearly working hours increased from 6,060 hrs in 2012 to 7602 hrs in 2016. Similarly, 40 the plant utilization rate increased from 69% in the first year of operation to 88% in 2015 and dropped slightly to 86.5% in 2016. The annual biomass supplied to the plant increased from 7523 to 7,924 tonnes (metric ton) during this time period. However, the largest increase was in steam production that almost doubled to reach 119,721 tonnes in 2014-2015. The ratio of steam-to-dry biomass (i.e. tonne of steam: dry tonne of biomass) increased from 8.2 to 15.6 over this period. The ash content of biomass increased somewhat from 0.55% in 2012 to 1.17% in 2015. Table 3.1 Overall annual performance of the gasifier from 2012 to 2016. The annual is from June to next July. The total yearly working hours increased from 6,060 hours to 7602 hours in 2016. The plant utilization rate increased from 69% in the first year of operation to 87.9% in 2015 and 86.5% in 2016. Year Working hours Plant utilization rate (%) Biomass (dry tonnes) Steam (tonnes) Ash (tonnes) 2012-2013 6,060 69.0 7,523 61,359 41.5 2013-2014 7,408 84.7 7,629 102.435 77.2 2014-2015 7,709 87.9 7,691 119,721 92.6 2015-2016 7,602 86.5 7,924 - 18.2 The regular annual preventive maintenance shut-down is in May-June when the operating hours are at a minimum. The bar graph in Figure 3.2 and the trend line show the monthly utilization rate (percent working hour) of the gasification plant. Aside from regular maintenance, the additional plant shutdowns were due to frequent breakdowns. Feeding low-quality fuel, excessively larger or smaller biomass particles outside the optimum 6.3-76 mm range were responsible for the disruptions. The system performance gradually improved through the fall of 2014. The improvement could be attributed, in part, to the reduction of feedstock moisture content and feeding more uniform size particles to the gasifier. Figure 3.3 shows the frequency of the fuel moisture content over a period of three years. Percentage of the fuel lower than 30% MC was improved from 47.7% in the first half of this period to 53.1% in the second half. For fuel lower than 25% MC, the percentage of the fuel is improved by 7% in this period. 41 Figure 3.2 The bar graph and the trend line showing monthly utilization rate (percent operational hours) of the gasification plant. The utilization rate increased from an average of 70% in 2012 to over 90% in 2015. The regular annual preventive maintenance shut down is in May to June. Other low utilization rates are due to breakdowns. Figure 3.3 The frequency of fuel moisture content over a period of three years. Percentage of the fuel lower than 30% MC was improved from 47.7% in the first half of this period to 53.1% in the second half. For fuel lower than 25% MC, the percentage of the fuel is improved by 7% in this period. 2030405060708090100110Utilization rate (%)Months00.10.20.30.40.50.6>40% >35% <30% <25%FrequencyFuel moisture contentHalf-1 Half-2 42 Table 3.2 lists the different types of materials supplied to CFL and their fuel moisture content. The percentage received from primary mills is 19% ends, 31% scrap, 34% pallets, and 16% chips. The corresponding fuel moisture content is (15%, 15%, 15%, 35%) for ends, scrap, pallets and chips, respectively. Table 3.2 Characterization of major feedstock and suppliers to CFL. The capacity of transport container is 41.3 m3. The average delivered wet biomass was 3 tonnes per truckloads. Assuming each container was full to 75% capacity, the bulk density of wood in a load was estimated to be 96 kg/m3. Material types collected from primary mills Percent received at CFL recycling yard (%) Moisture content (% wb) Ends 19 15 Scrap 31 15 Pallets 34 15 Chips 16 35 3.2.2 Wood fuel supply and physical properties The properties of the woody materials supplied to CFL and the properties of the biomass delivered from CFL to UBC were investigated. Scrap lumber and scrap pallets were the unused lumber and pallets collected from manufacturers. White wood consisted of spruce, pine, and fir. Other than the above-mentioned sources, the remaining was mixed wood. All these types of materials originated from lumber-grade wood. Figure 3.4 shows the frequency and tonnage of wood fuel delivery to the gasification plant during the month of January 2015 and January 2016. Each data point represents a truckload. The delivered tonnage at 710 tonnes in January 2015 increased to 834 tonnes in January 2016. The corresponding average dry truckloads were 11 tonnes in 2015 vs. 13 tonnes in 2016. On average, two truckloads were delivered daily. 43 Figure 3.4 Frequency and tonnage of wood fuel delivery to the gasification plant during January 2015 and January 2016. Each data point represents a truckload. 2-3 truckloads were delivered daily. The delivered amount (710 tonnes, January 2015) was increased to (834 tonnes, January 2016). The corresponding average dry truckloads were 11 tonnes in 2015 and 13 tonnes in 2016. It appears in a few instances large truck loads followed by a smaller truckload and vice versa. As for the physical properties, Figure 3.5 shows the daily moisture content of wood chips received at the BRDF over two months (February and March 2014), which ranged from 11% to 54% (wb). The data were taken upon unloading the truck. Similar to the data shown in Figure 3.4, two sequential values of moisture content may have a difference greater than 20 percentage points. Figure 3.6 shows the measured bulk density of wood fuel received at the gasification plant over two years (July 2012 - June 2014). As expected, the bulk density increased with the fuel moisture content. However, this increase varied widely, probably due to variations in the particle size of the wood chips. Overall, the average bulk density increased from 105 kg/m3 at 10% moisture content to 210 kg/m3 at 50% moisture content. Considering that the average volumetric capacity of the truck is 110 m3, the estimated biomass bulk density shall be 180 - 220 kg/m3. The difference between the measured bulk density and the estimated bulk density provides an indication that the dry truckloads actually varied with fuel moisture contents. 891011121314151617185 10 15 20 25 30 35 40 45 50 55 60 65Wood fuel (Dry tonnes)Daily deliveriesDaily delivery 2015 Daily delivery 2016 44 Figure 3.5 The daily moisture content of wood chips received at the BRDF. The data is taken when unloading the truck during two months (February and March 2014). The set of data shown in this graph represents a sample of temporal variations in moisture content. Similar to the data in Figure 3.3, two sequential values of moisture content may have a difference greater than 20 percentage points. Figure 3.6 Measured bulk density of wood fuel received at the gasification plant over two years (July 2012 - June 2014). As expected, the bulk density increased with the fuel moisture content. However, this increase varied widely, probably due to variations in the particle size of the wood chips. Overall, the average bulk density increased from 105 kg/m3 at 10% moisture content to 210 kg/m3 at 50% moisture content. 01020304050600 5 10 15 20 25 30 35 40 45 50 55 60Moisture content (%)Days in February and March 2014y = 2.6209x + 78.076R² = 0.60241001201401601802002202402602803000 10 20 30 40 50 60Bulk density (kg/m3)Fuel moisture content (%) 45 Particle size of the biomass is another factor that can influence the logistics associated with the system. The gasifier manufacturer’s specifications require particle sizes between 6.3 mm (1/4”) and 76 mm (3”). Results of the tests performed at the Cloverdale Fuel (CFL) recycling yard indicate that fractionation using three sets of screens (two screens 76 mm and 127 mm perforations) was successful in removing both the small pieces (<6.7 mm) and the larger pieces (>25 mm). In addition, the installation of vibratory screens after the grinder at the CFL led to less variations in the particle sizes. About 70% of the midsize fraction between 6.3 and 25 mm met the gasifier’s size specifications. Please refer to Figure 2.5 for more details. 3.2.3 Gasifier performance in terms of steam production The amount of wood fuel delivered to the plant was based on the estimated demand for steam. Figure 3.7 shows that the overall monthly steam production increased substantially from roughly 1,500 tonnes to almost 6,000 tonnes per month during the time period April 2012 to July 2015. It shall be noted that the average supply of biomass did not change from month to month. The monthly consumption of dry biomass and production of steam during the sub-interval April 2013 to December 2014 are plotted in Figure 3.8. Both biomass supply and steam production varied over time, with steam production following closely the ups and downs in the amount of biomass fed to the gasifier. Initially, the quantity of steam produced was less than the expected output from the quantities of biomass. Occasional sharp reductions in biomass supply and steam production were due to plant shutdowns. As seen in Figure 3.8, the system performance gradually improved towards and through the fall of 2014. This was due, in part, to the reduction in feedstock moisture content over time. Besides, the percent of operating hours (plant utilization rate) increased from 83% in April 2013 to almost 100% in December 2014. 46 Figure 3.7 Monthly steam generation at the UBC gasification plant from April 2012 to July 2015. The overall monthly steam production increased substantially. The three low points in 2012, 2013 and 2014 are indicative of the annual shutdown/maintenance services during May-June. Figure 3.8 Monthly steam production and monthly total dry biomass during the sub-interval April 2013 to December 2014. Steam generation increased from 3400 to 4350 tonnes/month while the consumption of biomass stayed constant, indicating gradual improvement of the gasifier performance during this time period. y = 4.7494x - 193699R² = 0.667901,0002,0003,0004,0005,0006,0007,000Apr-12 Oct-12 May-13 Nov-13 Jun-14 Dec-14 Jul-15Steam production (MT)Months 01,0002,0003,0004,0005,0006,00002004006008001,0001,200Thermal steam (tonnes/month)Monthly dry biomass (tonnes)Dry biomass Thermal steamTrend line (Dry biomass) Trend line (Steam) 47 While the amount of delivered feedstock and its moisture content fluctuated from day to day, an attempt was made to deduce the relationship between the steam production per tonne of wet biomass and fuel moisture content. Results are illustrated in Figure 3.9(a)-(d). Figure 3.9a is pertinent to daily recorded steam production that ranged from 3 to 5 tonnes per tonne wet biomass and daily measured moisture content of the fuel that ranged from 30% to 53% (wb). The data are highly scattered as the wet biomass tonnage fluctuated from load to load, so as the moisture content of the fuel in each load. Apparently, the spread of data suggests that fuel moisture content is not the only factor that affects the thermal output of the gasifier, and other operational parameters including the oxygen content in the combustion air, gasifier bed temperature, biomass pile height, firing rate, bin speed and ID fan speed might play a role. Steam production was recorded by the UBC-BRDF facility once a day, whereas fuel samples were obtained once or twice a day from the point of delivery. Typically 2-3 truckloads of materials are delivered to the BRDF every day. After unloading, the wood chips are placed in a vertical storage vessel before they are conveyed to the gasifier in accordance with the projected steam demand. Hence, there is a time lag between the recorded steam production and the measured moisture content of the fuel after sampling took place. In order to determine if a stronger correlation can be derived from the time-average values, the steam production as well as the amount of delivered biomass over 3-day, 5-day and 10-day time period were summed up. The “steam production per tonne of wet biomass” vs. the “average fuel moisture content” was plotted over each of the time periods. Results for the 3-month analysis period January-March 2015 are shown in Figure 3.9(b, c, d). The regression coefficient, R2, for the linear relationship between steam production per tonne wet biomass and fuel moisture content, is seen to improve from 0.47 (3d cumulative data) through 0.82 (5d cumulative data) to 0.98 (10d cumulative data). The higher R2 values for 5d and 10d cumulative data imply that it is more appropriate to use the time-average values to develop correlations, and thus, the fuel moisture content would be the main factor that affects steam production by the gasification system. 48 Figure 3.9(a) Steam production per tonne wet biomass vs. fuel moisture content based on daily values of steam production, wet biomass tonnage and fuel moisture content during the time period January-March 2015 Figure 3.9(b) Steam production per tonne wet biomass vs. fuel moisture content based on time-average values over 3-day period during January-March 2015 y = -5.136x + 6.5778R² = 0.11363.003.504.004.505.0025% 35% 45% 55%Steam production, tonne/tonne wet biomassFuel moisture content (wb)y = -5.3886x + 6.498R² = 0.46893.003.504.004.505.0020% 30% 40% 50% 60%Steam production, tonne/tonne wet biomassFuel moisture content (wb) 49 Figure 3.9(c) Steam production per tonne wet biomass vs. fuel moisture content based on time-average values over 5-day period during January-March 2015 Figure 3.9(d) Steam production per tonne wet biomass vs. fuel moisture content based on time-average values over 10-day period during January-March 2015. For each tonne of fuel, and using the linear relationship based on 10d cumulative data (Figure 3.9d), the predicted steam production would be 4.58 and 3.85 tonnes/tonne wet biomass when the fuel moisture content is 35% and 50% (wb), respectively. If the linear relationship is extrapolated to include 20% moisture content (wb), the steam production would become 5.35 y = -5.2545x + 6.4277R² = 0.82183.003.504.004.505.0020% 30% 40% 50% 60%Steam production, tonne/tonne wet biomassFuel moisture content (wb)y = -5.152x + 6.3798R² = 0.97993.003.504.004.505.0020% 30% 40% 50% 60%Steam production, tonne/tonne wet biomassFuel moisture content (wb) 50 tonnes/tonne wet biomass. Therefore, if the fuel moisture content can be reduced from 35-50% to 20% (wb), the corresponding steam generation by the gasifier would increase by 17-41%. 3.3 Conclusions The operation of an industrial gasifier fed by wood chips collected from municipal sources and industrial operations was monitored during various time periods since 2012, which ranged from several months to several years. The data indicate that fuel moisture content varied between 10% (wb) and more than 50% (wb), whereas the ash content had little variation. Particle size depends on the screening operations at the recycling yard. By proper selection of screens, the percent rejects can be reduced from 50% to less than 25%. The overall performance of the gasifier regarding monthly steam generation improved while the unscheduled shutdowns dropped steadily over a time period of three years. Most improvements were in line with increasing the infeed of particles with size between 6 and 25 mm as well as protecting the wood fuel from rain and contamination with dirt. While the amount of delivered feedstock and its moisture content fluctuated from day to day along with fluctuations in steam generation, strong correlations could be derived from the time-average values by summing the steam production and the quantity of delivered biomass over 5 days and 10 days during the three-month time period January-March 2015. Fuel moisture content was found to be the main factor that affects the production of steam in tonnes per tonne of wet biomass, amidst other factors that include the gasifier temperature and oxygen content in the air fed to the gasifier. Applying the correlation thus developed, reduction of the fuel moisture content from 35-50% to the equilibrium moisture content of 20% (wb) would lead to an increase in steam production by 17-41 percent from (3.81-4.58) to 5.35 tonnes per tonne of wet biomass, respectively. In this regard, protecting the biomass from precipitation (rain) has been an effective measure adopted by CFL to reduce the level and variation in fuel moisture content. It is desirable for UBC to further tighten the allowable moisture content for the fuel wood chips. 51 Characterization of Recycled Wood Chips, Syngas Yield, and Tar Formation in an Industrial Updraft Gasifier 4.1 Introduction Combustion and gasification are key thermal/thermochemical conversion technologies. In terms of climate change mitigation, gasification processes have the advantage of lesser greenhouse gas emissions compared to full oxidative combustion processes. Biofuels and chemical products can be produced using syngas (Heidenreich and Foscolo, 2015). In this work, two commercial gasification systems are studied. The first gasifier is a fixed-bed updraft biomass gasification system at the University of British Columbia (UBC). In addition to the gasifier at UBC, a gasification system in a tissue plant is investigated in our study. This plant operates two gasifiers identical to UBC’s system and produces household paper products in Metro Vancouver. The urban wood wastes are preprocessed by sorting, grinding, screening and blending for utilization in these plants. At the plant, the fuel is sampled for measuring moisture, size distribution, ash content and calorific value. Following the fuel specifications, particles should not be greater than 76 mm. The proportion of particles with a size below 6.3 mm shall have a mass fraction less than 25%. Our recent work on the gasifier (Ehsan Oveisi et al., 2018) showed that moisture content has the largest variability over time compared to other fuel properties. Nevertheless, steam production in tonnes per tonne wet biomass was found to have a strong linear relationship with fuel moisture content using time-average values over a 10-day cumulative period (chapter 3). If the fuel moisture content can be reduced from (35-50%) to 20% wb, an increase in steam production by 17-41 percent from (3.81-4.58) to 5.35 tonnes per tonne of wet biomass, respectively would occur. Syngas composition depends on the fuel composition and amount of oxygen in the reactor; variation in syngas composition can disrupt the usage of syngas (Bridgwater, 1995; Monteiro et al., 2011). Rajvanshi (1986) suggested that fuel moisture, ash content and its elements, particle size, heating value, and bulk density are major fuel properties that affect biomass gasification. Molino et al. (2018) summarized the effect of these biomass feedstock characteristics on syngas composition. They also discussed the effect of operating conditions, including gasifying agent and equivalence ratio (ER, the ratio of actual A/F value to 52 stoichiometric A/F value). Van der Drift et al. (2001) suggested that the ER should be raised with increasing fuel moisture content in order to keep the gasifier temperature constant. With an increase in ER, more feedstock is combusted to produce heat and promote carbon conversion (Huang et al., 2017). Therefore, it is important to have an appropriate ER level to ensure the balance between reactor temperature and syngas production. In contrast, without adjusting the ER, high moisture biomass would have a negative impact on the internal heating rate of a particle since latent heat is needed for evaporating water. The resulting slow-down of biomass devolatilization leads to a lower gasification temperature and less carbon conversion and syngas yield (Bharadwaj et al., 2004; Carpenter et al., 2010; Chan et al., 1985; Chopra and Jain, 2007). Tar may be defined as the hydrocarbon and oxygenated compounds in the combustible gas stream with a molecular weight greater than benzene (Qin et al., 2010). Lignin is a main precursor of tar due to its aromatic nature (Palma, 2013). The tar in the gas stream is an undesired by-product of biomass gasification. It may cause fouling of heat transfer surfaces and engine parts when the gas is used as a combustion fuel, resulting in the malfunctioning of engines, mechanical, and handling equipment. Besides, catalysts cannot survive for long in the presence of tar (Abu El-Rub et al., 2004). The concentration and composition of tar depend on the gasifier type, operating conditions, and fuel properties/quality (Milne and Evans, 1998). Kiel et al. (2004) studied the impact of three important fuel properties on tar formation and composition - moisture content, ash content, and lignocellulose composition. Ash contains a considerable amount of alkali metals. These metals act as catalyst both in the decomposition of biomass and in tar formation. Their results showed that changes in ash concentration had a low impact on tar formation. Although updraft gasifiers can produce syngas with the most tar (Milne and Evans, 1998), they have certain advantages over other types of gasifiers. Fixed bed gasifiers (updraft or downdraft) are simpler than the fluidized bed gasifiers. Updraft gasifiers can operate using wetter biomass with up to 60% MC (moisture content) (wb), yet downdraft gasifiers must operate using biomass with less than 25% MC (wb) (Basu, 2013). Chianese et al. (2016) investigated H2 production from catalytic conversion of syngas generated in a fixed-bed biomass gasification pilot plant, using steam as gasifying agent. They showed that tar concentration decreased as operating temperature increased. Bronson et al. 53 (2016) evaluated the effect of moisture content and particle size of forest residues (hog fuel) on tar formation at three gasifier temperatures (725, 800 and 875 °C) in a small (0.16 m diameter) pilot-scale bubbling fluidized bed gasifier. At each temperature, ER was adjusted to compensate for the effect of moisture content in order to maintain a constant temperature. They found that moisture content ranging from 25–31% (wb) had a negligible effect on tar concentration. Rather, the smallest particles (with sizes less than 3.15 mm) resulted in the highest tar concentration, as compared to the larger particles with sizes ranging from 3.18 to 6.35 and 19 mm. Published data from lab-scale experiments on biomass gasification indicates that there exists either a direct relation or some correlation between biomass fuel properties (such as moisture content, ash content, and particle size) and syngas yield and tar formation (Dai et al., 2012; McKendry, 2002a, 2002b). However, there is a lack of similar data relevant to full-scale gasification plants that operate on variable quality of biomass feedstocks (especially those originating from urban sources) in published literature. The main objective of this chapter is to investigate the effects of fuel properties on syngas quality and tar formation in an industrial gasifier that is operating routinely under commercial obligations. No attempt is made to influence the fuel properties or operating conditions of the gasifier for the sake of this research. A related objective is to perform multi-variable regression analysis on syngas heating value and tar formation. 4.2 Materials and methods 4.2.1 Gasifiers Two commercial fixed-bed updraft gasifiers were used in the study. As previously mentioned, these gasifiers were identical models made by the same manufacturer, and the only difference between them was the utilization of their output energy. The gasifier installed at UBC used the energy for steam generation while the other gasification system provided energy for a tissue plant. Each of the gasifier reactors was made of a cylindrical chamber 4.9 m in diameter and 4.9 m in height. The fuel was vertically fed to the reactor by a feeder screw located at the center of and underneath the reactor. The fuel then passed through several stages before it was converted to syngas. The major stages were drying, partial oxidation (combustion), pyrolysis, gasification, 54 and reduction to ash. Air was introduced to the fuel pile at the combustion stage. The gasifier temperature was kept constant by controlling the fuel feed rate, equivalent ratio, and fuel pile height. The bed temperature generally ranges from 815–980 °C when combustion occurs. The fuel gets converted into three major components: syngas, tar, and non-combustible ash. The tar-rich syngas is directed to the thermal oxidizer where it is burned to generate heat. Ash is removed by an automated system. An electrostatic precipitator is in place to treat the particulate matter to ensure the emission standard specified in the air quality permit is met. 4.2.2 Gas and tar sampling Various tar sampling methods have been developed over recent decades, each with their own advantages and disadvantages (Tourigny et al., 2013). The most common method for tar sampling is condensing the tar in a cooled liquid solvent, such as isopropanol (Kiel et al., 2004). The contents of the solution are analyzed to determine the tar concentration via gravimetric evaporation or gas chromatography (GC). The tar measurement and CEN/TS 15439 standard is an impinger-based sampling protocol developed by the European Union (CEN, European Comitte for Standardisation 2006; Van de Kamp et al., 2005) following the work done by Kiel et al. (2004). CanmetENERGY-Ottawa (CE-O) has designed, tested and fabricated an alternative syngas and tar sampling device for in-situ industrial applications. The unit comprises of a sampling probe, chiller, vacuum pump, and gas flow meter. Robinson (2015) conducted a series of experiments using a lab-scale bubbling fluidized bed gasifier and hardwood pellets as fuel, and showed that the CE-O was able to sample syngas with tar concentrations less than 10 g/m3. Moreover, Robinson (2015) found the samples taken by the CE-O and the CEN/TS 15439 standards provided a similar syngas profile and tar production. In this study, the CE-O and the CEN/TS 15439 standards were used side-by-side to sample syngas and collect tar from the gasification reactor. The gas chromatography-thermal conductivity detector (GC-TCD) technique was used to identify and quantify syngas composition. The amount of solvent, or isopropanol, for each sampling event was about 600 g. Approximately 170 L of syngas was pulled through the sampling device. One round of an experiment takes over a period of 90 min. The concentration of the tar samples was quantified using gas chromatography – flame ionization detector (GC-FID) equipped with a column packed with 55 DPDM-siloxane (5% diphenyl + 95% dimethyl). The heating program applied to the sample in GC was as follows: initial temperature 50 °C for 5 min, ramp at 8 °C/min to 300 °C and hold at 300 °C time for 5 min. GC-FID identified the major compounds in the sample. Other unknown peaks in the GC-FID results were added to the major compounds to calculate the total tar concentration in the solvent Cs. Tar concentration in syngas, Cg was calculated as follows: 𝐶𝑔 =𝑀𝑉𝑔=𝐶𝑠. 𝑉𝑠𝑉𝑔 where M is the mass of tar [g], Vs is the volume of solvent [m3], Vg is the volume of syngas [m3], and Cs is the concentration of tar in the solvent [g/ m3] as measured by GC. 4.2.3 Biomass fuel sampling Biomass fuel samples were taken from the port near the feeding auger for the gasifier. Approximately 5 kg of fuel was collected during each syngas sampling event for subsequent measurements of moisture content, ash content, and calorific value. Fuel samples were taken before, during, and after tar collection following the CEN/TS 14778-1 standard (CEN, 2005b), and fuel moisture content was measured in accordance with the CEN/TS 14774 standard (CEN, 2005a). 4.2.4 Test procedure Initially, a test was conducted using the CE-O and compared the results with those derived from the impingers. This test occurred at the tissue plant. Samplings using the two pieces of equipment were done in parallel and from the same port over two days (Figure 4.1). For the impingers technique, a sampling probe was connected to an in-line particulate matter filter, which was then attached to the first impinger bottle in the ice bath. The last impinger bottle was in the hot bath and connected to a pump that sent the gas through a flow meter to exhaust. Overall cleaning was carried out at the end of each syngas sampling, whereby the probes were washed with isopropyl alcohol in order to collect the heavy tar that had not been absorbed by the impingers. The cleaning procedure took an additional 10 min to complete. 56 Figure 4.1 Side-by-side syngas and tar sampling arrangement using impingers and the CE-O (CanmetENERGY-Ottawa) device. The sampling port is located about 1/3 distance from the bottom of the reactor. The goal of this setup is to ensure the same syngas sample is collected by both sampling device. The testing procedure using the CE-O approach was in accordance with the specifications in the CanmetENERGY report, which included instructions regarding device operation, assembly guidelines, sampling procedures, and use of solvent. Setup of the CE-O device consisted of three main modules. The first one includes hot gas particulate filtration and heated lines. The second module contains tar collection flask, chiller, and pump, and the third one is gas flow rate measurements. A follow-up test was performed at the UBC gasifier over a period of two months. The tests involved 40 sampling events using the CE-O device. The device was checked for oxygen leakage. After the test, the concentration of oxygen in the syngas was evaluated. Those samples with high oxygen levels (greater than 0.5%) were deleted from the dataset to ensure that there was no gas dilution due to outside air. 57 4.2.5 Multi-variable regression analysis Factors that influence gasification process are categorized into three groups: gasifier design, operational conditions and fuel properties (Jayah et al., 2003). The key operational parameters are gasifier bed temperature, oxygen content of the injected air, and fuel feeding rate. Here, the gasifier plant’s operating data and multi-variable regression were used to analyze the relationship between the fuel properties, operational conditions and the gasifier performance indicators - syngas heating value and tar formation. It will be shown that the oxygen content and the mass fraction of small particles were neglected as they were correlated with fuel moisture content. In the following analysis, the independent variables are fuel moisture content, gasifier temperature and fuel feeding rate. The dependent variables are syngas heating value and tar concentration. Linear regression will be performed as the relationship between the dependent and independent variables can be described linearly. In regression, all the measured data need to be normalized or standardized. Standardizing the data will provide a better scaling for the variables with respect to each other. The following equation shows how the measured data are normalized for the regression, 𝒅𝒔 =𝒅−𝝁𝒅𝝈𝒅 (Eq. 4.1) where 𝑑 denotes the original data, 𝜇𝑑 is the mean of the data, 𝜎𝑑 is the standard deviation of the data, and 𝑑𝑠 is the normalized or standardized data. The assumption for use of Eq. 4.1 is that the data are distributed normally around the mean value. The mean and standard deviation of the variables are summarized in Table 4.1. The normalization is done as follows: Feeding rate: 𝒎𝒇′ =𝒎𝒇−𝟎.𝟕𝟕𝟎.𝟏𝟏 (Eq. 4.2) Gasifier bed temperature: 𝑻′ =𝑻−𝟗𝟓𝟖.𝟎𝟔𝟎.𝟎 (Eq. 4.3) Fuel moisture content: 𝒙′ =𝒙−𝟎.𝟐𝟔𝟎.𝟎𝟒 (Eq. 4.4) Lower heating value: 𝑳𝑯𝑽𝒈′ =𝑳𝑯𝑽𝒈−𝟒.𝟒𝟑𝟎.𝟕𝟎 (Eq. 4.5) Tar concentration: 𝒄𝒕𝒂𝒓′ =𝒄𝒕𝒂𝒓−𝟖𝟏.𝟐𝟐𝟖.𝟗 (Eq. 4.6) The instantaneous values for 𝑚𝑓, 𝑥, 𝑇, 𝐿𝐻𝑉𝑔, 𝑐𝑡𝑎𝑟 were obtained from the measured values in this 58 research. The F-test for “overall significance in regression analysis” was then performed, which compares the regression model to the intercept-only model that has no independent variables but the constant term. The intercept-only model uses the mean of response (dependent variable) for prediction. Thus, if the critical F-value is significant in F-test, the regression model predicts the dependent variable better than the mean of the response. Table 4.1 Mean and standard deviation of the variables used in the analysis. Cold gas efficiency will be discussed in detail in Chapter 5. Moisture content (%) Bed temperature (oC) Feeding rate (%) Syngas heating value (MJ/m3) Tar (g/m3) Cold gas efficiency (%) Mean 25.5 958 77.1 4.43 81.2 55.9 Std Dev 4.3 60 11.3 0.70 28.9 13.7 4.3 Results and discussion 4.3.1 Fuel properties The biomass fuel samples obtained from the gasifier at the tissue plant were fractionated into five sizes (Figure 4.2) using round-hole screens with diameters of 25, 12.5, 6.3, and 3.15 mm, and a pan. Figure 4.3 shows the size distribution of the biomass samples. Almost half of the materials were large pieces (greater than 25 mm) on day 2, whereas 40% of the mass was between 12.5 and 25 mm on day 1. The mass fraction of small pieces (below 3.15 mm) was less than 2% on both sampling days. 59 Figure 4.2 Fuel samples collected during tar sampling. These fuel samples were separated into five sizes using round-hole screens. Particle size (x) from the top left to the bottom: x > 25 mm, 12.5 < x < 25 mm, 6.3 < x < 12.5 mm, 3.15 < x < 6.3 mm, and x < 3.15 mm. Pictures were taken by the author. Figure 4.3 Mass fraction of the biomass particles and their size distribution for each day of the test conducted at the tissue plant. Almost half of the materials were large pieces (greater than 25 mm) on day 2, whereas 40% of the mass was between 12.5 and 25 mm on day 1. The mass fraction of small pieces (below 3.15 mm) was less than 2% on both sampling days. 01020304050> 25 mm > 12.5 mm > 6.3 mm > 3.15 mm < 3.15 mmParticle mass fraction, %Sieve sizeDay 1 Day 2 60 For the UBC gasifier, particle size distribution analysis of the fuel samples shows that about 70% of particles falls in between 3.15 and 25 mm. A somewhat smaller fraction of particles is greater than 25 mm. The samples “as received” at UBC were slightly wetter than the samples at the tissue plant. Table 4.2 lists the properties of fuel. Fuel MC ranged from 20.6 to 33.3% wb with an average of 26.8% and standard deviation of 4.3%. Bulk density and calorific value were 150.7±17.3 kg/m3 and 19.2±0.65 MJ/kg, respectively. The physical properties of the biomass between the two sites were not too different, except that the samples at the tissue plant had a somewhat higher ash content (1.8±0.5% db vs. 1.06±0.3% db at the UBC site). Table 4.3 shows the ultimate analysis and proximate analysis results, indicating that the fuel’s specifications were within the expected values. 4.3.2 Syngas composition Visual observations and lab results show that no char or unburnt biomass remained in the ash collected from the UBC gasifier. Thus, results only relevant to syngas and tar production are presented in this section and the following section. Table 4.4 shows the average, minimum, and maximum concentrations of the syngas components (H2, CH4, CO, CO2) for the two sampling techniques used at the tissue plant. During each sampling event, syngas was collected using sampling bags. Insignificant differences in the syngas composition were observed for the two methods. Moreover, the syngas calorific value of 3.7 MJ/m3 on day 1 was in the neighborhood of 4.1 MJ/m3 measured on day 2. The UBC gasification system was routinely managed to ensure the gasifier bed temperature remained between 875 and 1050 °C as fuel moisture content varied from 20% to 35% wb, and this was achieved by raising the ER from 0.11 to 0.22 in order to increase the O2 level and hence promote the combustion reaction. 61 Table 4.2 Properties of fuel based on samples taken at University of British Columbia. Fuel MC ranged from 20.6 to 33.3% wb with an average of 26.8% and standard deviation of 4.3%. Bulk density and calorific value were 150.7 ± 17.3 kg/m3 and 19.2 ± 0.65 MJ/kg, respectively. Parameter Moisture Content (% wb) Bulk Density (kg/m3) Ash Content (% db) Calorific Value (MJ/kg) Average 26.8 150.7 1.06 19.20 Standard deviation 4.3 17.3 0.28 0.65 Coefficient of variation 0.15 0.11 0.27 0.03 wb: wet basis; db: dry basis. Table 4.3 Ultimate and proximate analysis of fuel taken at UBC. The fuel’s specifications are compared with the expected values that the gasifier specifies. Having impact on the higher heating value, volatiles have a concentration of 83% which is well within the expected range between 70% to 85%. Parameter Measured Value (%) Expected Range from Gasifier Specs (%) Volatiles 83.0 70–85 Fixed carbon 16.5 15–25 Carbon 54.6 48–52 Hydrogen 5.80 5–6 Oxygen 39.0 36–44 Nitrogen 0.09 <0.2 Sulphur 0.02 <0.025 Chlorine 0.04 <0.025 62 Table 4.4 Comparison of gas composition using the impingers and the CE-O sampling techniques at the tissue plant. During each sampling event, syngas was collected using sampling bags. Insignificant differences in the syngas composition were observed for the two methods. Species CEN/TS 15439 method CE-O device Ave Std Dev Min Max Ave Std Dev Min Max H2 9.1 0.5 8.4 9.9 8.0 1.2 6.0 9.8 O2 0.7 0.6 0.3 2.1 0.7 0.4 0.3 1.3 N2 57.3 1.9 54.6 61.3 57.2 2.3 53.7 61.7 CH4 1.3 0.3 0.8 1.9 2.0 0.5 1.1 2.7 CO 19.8 2.0 16.7 21.9 20.1 1.6 18.0 22.7 CO2 11.9 0.7 10.8 12.7 12.0 0.8 10.7 13.2 4.3.3 Syngas composition and fuel properties Figure 4.4 highlights the variability in syngas composition from the UBC gasifier. The plot of syngas component concentrations vs. MC shows that as MC increases from 20% to 35%, CO concentration drops from about 30% to 20% (v/v). CO2 concentration increases mildly. H2 and CH4 show a mild decrease. The H2/CO ratio ranging from 0.26 to 0.54 is consistently less than 1.0. The correlation coefficients pertinent to (CO, CH4, H2) vs. syngas calorific value are 0.91, 0.85, 0.76, respectively, indicating that CO has the strongest correlation with MC. Figure 4.5 shows the effect of moisture content on syngas calorific value. Despite a wide variation in the calorific values vs. MC, the calorific value shows a mild overall decrease from about 5.5 to 3.8 MJ/m3 as MC increases from 20% to 35%. 63 (a) (b) (c) (d) Figure 4.4 Variation of gas contents with fuel moisture content: (a) CO, (b) H2, (c) CH4, (d) CO2. The gas content is on a volume (v/v) basis. Tests were done at the UBC gasifier. As MC increases from 20% to 35%, CO concentration drops from about 30% to 20% (v/v). CO2 concentration increases mildly. H2 and CH4 show a mild decrease. 162024283220% 24% 28% 32% 36%CO content, %Fuel moisture content67891020% 24% 28% 32% 36%H2content, %Fuel moisture content01234520% 24% 28% 32% 36%CH4content, %Fuel moisture content369121520% 24% 28% 32% 36%CO2content, %Fuel moisture content 64 Figure 4.5 Syngas calorific value vs. fuel moisture content. Despite a wide variation in the calorific values vs. fuel moisture content, the calorific value shows a mild overall decrease from about 5.5 to 3.8 MJ/m3 as MC increases from 20% to 35%. 4.3.4 Tar concentration Tissue plant gasifier The measured tar concentration varied from 23.5 to 100 g/m3. Lower tar was observed on day 2 along with a higher gasifier temperature. On day 2, a lesser quantity of fuel was fed to the gasifier; hence the O2 content in the gasifier was higher, resulting in a greater combustion. The average tar concentration derived from the impingers was 90.6 and 11.5 g/m3 on day 1 and day 2, respectively. By comparison, the corresponding average tar concentration for the CE-O device was 82.1 and 23.4 g/m3, respectively. Tar concentration has been estimated to be of the order of 10-100 g tar/m3 of gas for updraft gasifiers, 10 g/m3 for fluidized beds, and 1 g/m3 for downdraft gasifiers (Milne and Evans, 1998; Sun et al., 2011). Downdraft gasifiers generate the lowest tar concentration because the produced tar has to pass through the combustion zone (Maniatis, 2008; L. Zhang et al., 2010; W. Zhang, 2010). UBC gasifier A wider range of tar concentration (24–170 g/m3) with an average value of 78 g/m3 was observed during the 40 sampling events. The gasifier bed temperature remained within the range of 875–1050 °C. Since the system attempts to keep the bed temperature relatively constant, there are more data points around 900 °C (Figure 4.6). Particle size distribution analysis of the fuel samples indicated that the mass fraction of particles in the four size ranges (3.15–25 mm) varied from 17.8±4.3%, to 28.5±5.6%. Roughly 27% of particles had a size greater than 25 mm, and roughly 6% of particles were below 3.15 mm. Figure 4.7 345620% 24% 28% 32% 36%Calorific value, MJ/m3Fuel moisture content 65 illustrates that particle percentages in the small size range (3.15–6.3 mm) had a weak correlation with tar concentration. Overall, as the particle percentages in this size range increased from 10% to over 25%, the tar concentration also increased from around 40 g/m3 to more than 90 g/m3. Figure 4.6 Tar concentration vs. fuel moisture content. Tests done at the UBC gasifier. Overall, tar concentration increased slightly with fuel MC, but the correlation coefficient was low at around 0.50. Figure 4.7 Tar concentration vs. percent particles with a smaller size range. Tests done at the UBC gasifier. The mass fraction of particles in the small size range (3.15–6.3 mm) had a weak correlation with tar concentration. 030609012015018015 20 25 30 35Tar concentration, g/m3Fuel moisture content, % wb03060901201501805% 10% 15% 20% 25% 30%Tar concentration, g/m3Percent particles in the size range 3.15 - 6.3 mm 66 4.3.5 Correlation coefficients Table 4.5 Correlations between syngas heating value, tar concentration and cold gas efficiency and the parameters that contribute to them. Moisture content Bed tempera ture Oxygen content Feeding rate Small particles Syngas heating value Tar formation Cold gas efficiency Moisture content 1.00 Bed temperature -0.12 1.00 Oxygen content 0.69 -0.50 1.00 Feeding rate -0.02 0.03 0.10 1.00 Particles <6.3 mm 0.58 -0.08 0.45 -0.01 1.00 Syngas heating value -0.53 0.14 -0.27 -0.35 -0.42 1.00 Tar formation 0.51 0.07 0.04 -0.12 0.17 -0.26 1.00 Cold gas efficiency -0.17 -0.27 0.00 -0.37 -0.28 0.63 0.32 1.00 As shown in Table 4.5, the correlations between syngas heating value and (fuel moisture content, mass fraction of small particles, feeding rate, oxygen content of the injected air, and gasifier bed temperature) are (-0.53, -0.42, -0.35, -0.27, and 0.14), respectively. The correlations between tar concentration and fuel moisture content, fraction of small particles, feeding rate, oxygen content of the injected air, and gasifier bed temperature are (0.51, 0.17, -0.12, 0.04 and 0.07), respectively. The mild degrees of correlation imply that tar formation generally increases with moisture content, whereas syngas heating value generally decreases with moisture content. The correlations between cold gas efficiency (defined in Chapter 5) and fuel moisture content, mass fraction of small particles, feeding rate, and gasifier bed temperature are (-0.17, -0.28, -0.37, and -0.27), respectively. Moreover, cold gas efficiency is positively correlated with syngas heating value, but it has no relationship with oxygen content of the injected air. Tar 67 concentration and syngas heating value show opposite trends, and similar findings have been reported by Katsaros et al. (2019). Fuel moisture content has noticeable correlation with oxygen content and the fraction of small particles, with corresponding correlations of 0.69 and 0.58, respectively. This means, with a higher fuel moisture content, the gasifier required more air and hence oxygen supply in order to keep the gasifier temperature constant and provide enough heat for the gasification reactions. Smaller particles have a larger surface area-to-volume ratio, and this larger surface area leads to more water adsorbed on the surface of the fuel and in between particles. Gasifier bed temperature and oxygen content are negatively correlated at -0.50. This negative relation is partially compensated by the increase in the injected airflow. The correlation between the mass fraction of small particles and oxygen content is 0.45. A larger percentage of smaller particles would result in higher fuel moisture content that in turn lowers the bed temperature. Our findings are aligned with Bronson et al. (2016), in that biomass fuel with higher mass fraction of small particles caused higher tar concentration. 4.3.6 Multi-linear regression analysis Syngas heating value Eq. 4.7 represents the regression model for syngas heating value. The regression utility function as supported by Data Analysis in Excel was used to obtain the regression model. Least squares optimization was performed, whereby the sum of squares of the residuals between the measured and predicted syngas heating values was minimized. 𝐿𝐻𝑉𝑔′ = −0.50𝑥′ − 0.31𝑚𝑓′ + 0.19𝑇′ + 0.22𝑥′𝑇′ + 0.12𝑚𝑓′ 𝑇′ (Eq. 4.7) Results of the F-test for the regression are shown in Table 4.6. These include the goodness of fit (R2 value), the mean squared error (MS), and the critical F-value of the regression. Expressing the results of F-test in terms of the critical F-value is analogous to the p-value in ANOVA (Analysis of Variance) results. In this case, the R2 is 0.48 and the critical F-value is 0.012. For a statistical significance level α = 0.05, the critical F-value is less than the significance level. This means, the data has provided sufficient evidence to conclude that the null hypothesis can be rejected, and hence the regression model fits the data better than the model with no independent variables (as previously explained). There is a statistically significant difference in syngas heating value among the examined variables. Based on the coefficients in 68 Eq. 4.7, fuel moisture content is the most sensitive variable, followed by fuel feeding rate. Thus, the variability of these two parameters had the highest impact on the syngas heating value. Figure 4.8 shows the measured and the predicted syngas heating values. Table 4.6 Results of the F-test for regression, which indicates the goodness of fit and degree of significance Syngas calorific value Degrees of Freedom Sum of Squares Mean Squared Error F-value Critical F-value R2 Regression 7 15.0 2.2 3.4 0.012 0.48 Residual 25 16.0 0.6 Total 32 31 Tar concentration Degrees of Freedom Sum of Squares Mean Squared Error F-value Critical F-value R2 Regression 7 17.3 2.5 3.8 0.004 0.44 Residual 33 21.7 0.7 Total 40 39 Cold gas efficiency Degrees of Freedom Sum of Squares Mean Squared Error F-value Critical F-value R2 Regression 7 20.0 2.9 6.5 0.0002 0.65 Residual 25 11.0 0.4 Total 32 31 Our findings of the opposite trends between syngas heating value and fuel moisture content agree with those of James et al. (2016). They investigated the effect of biomass physical properties on updraft gasification of pine woodchips. In their test, particle size was fixed at 7 mm along with four levels of moisture content: 10%, 14%, 18% and 22%. In their work, H2 content 69 was found to have no significant changes but CO content was reduced from 15.2% to 11.1% and the syngas LHV decreased from 6.67 to 2.65 MJ/m3 as the moisture content increased from 10% to 18%. Besides, Li et al. (2004) studied the gasification of western hemlock sawdust in a pilot-scale circulating fluidized bed at two temperatures (700 and 850 °C) and with moisture content ranging from 6.6% to 22% wb. They observed a decrease in both CO and H2 concentrations from (16.6%, 5.5%) to (9.6%, 3.0%), respectively as ER increased from 0.20 to 0.54. In general, ER is positively correlated with the reciprocal of fuel feeding rate. According to James et al. (2016), when ER was increased from 0.21 to 0.29, tar formation was raised from 1.6 to 3.1 g/m3 for all types of biomass (wood waste, agricultural residue, perennial) in their experiment, though there was no influence on syngas composition. Atnaw et al. (2014) investigated the effect of gasifier temperature and moisture content of biomass fuel (oil palm fronds) on syngas composition and calorific value. Lower CO concentration was observed at higher fuel moisture content, but the changes in H2 and CH4 concentrations were only marginal. Syngas calorific value almost doubled within the range of 2.5–6.0 MJ/m3 as biomass moisture content was reduced from 29% to 22% wb. In our case, fuel particles were mostly in the range 3.15 to 25 mm. CO content dropped from 30% to 20% and the syngas LHV dropped from 5.5 to 3.8 MJ/m3 as fuel moisture content increased from 20% to 35%. James et al. (2016) suggested that the reduction of CO was due to higher fuel moisture that is favorable for the exothermic water-gas-shift reaction: CO + H2O ⇌ CO2 + H2 "Water-gas-shift reaction" (-41 MJ/kmol) The relationship between syngas heating value and gasifier temperature (Eq. 4.7) aligns with the work by Couto et al. (2015). They studied the gasification of municipal solid waste in a semi-industrial gasifier. They noted an increase in H2 and CO contents due to a higher gasifier temperature, which in turn led to an increase in the syngas lower heating value (LHV) from 1.6 to 4.1 MJ/m3. For the UBC gasifier, the same increase in temperature led to a rise in syngas LHV from 3.4 to 5.4 MJ/m3. Abuadala et al. (2010) conducted a parametric study for a steam gasification system using sawdust fuel and operating at a constant gasifier temperature of 726 oC. They observed that, when the biomass feeding rate increased from 10 to 32 kg/s, H2 content decreased slightly from 59% to 54% but CO content increased substantially from 3% to 35%, resulting in an overall increase in syngas LHV. 70 (a) (b) (c) Figure 4.8 Predicted vs. measured values for (a) syngas heating value, (b) tar concentration and (c) cold gas efficiency using multi-variable regression analysis. The R2 values of these fittings are 0.48, 0.44, and 0.65. The Critical F-values of the regression are 0.012, 0.004 and 0.0002. The average absolute residual errors of the predicted values are 9.8%, 20.9% and 12.2%, respectively. Cold gas efficiency will be discussed in detail in Chapter 5. 33.544.555.563 3.5 4 4.5 5 5.5 6Predicted syngas heating value (MJ/m3) Measured syngas heating value (MJ/m3)2040608010012020 40 60 80 100 120Predicted tar concentration(g/m3) Measured tar concentration (g/m3)30%40%50%60%70%80%90%100%30% 40% 50% 60% 70% 80% 90% 100%Predicted cold gas efficiencyMeasured cold gas efficiency 71 Tar formation Eq. 4.8 represents the regression model for tar concentration 𝐶𝑡𝑎𝑟′ = 0.59𝑥′ − 0.28𝑚𝑓′ − 0.12𝑇′ − 0.35𝑥′𝑇′ − 0.16𝑚𝑓′ 𝑇′ (Eq. 4.8) As shown in Table 4.6, the R2 and the critical F-value associated with the regression are 0.44 and 0.004, respectively. For a statistical significance level α = 0.05, the critical F-value of F-test is less than the significance level. There is a statistically significant difference in tar concentration among the examined variables. Fuel moisture content is again the most sensitive variable, followed by the interaction (product) of fuel moisture content and gasifier temperature and the interaction of fuel feeding rate, moisture content, and temperature. Ismail and El-Salam (2017) found that increasing ER resulted in reduction of tar, CO and H2 concentration. Kiel et al. (2004) conducted a lab-scale experiment with constant ER (0.25) and three levels of moisture content, 10%, 26% and 43% wb. They reported a modest increase in H2 and a decrease in CO, along with a decrease in tar concentration from 14.2 g/m3 to 11.4 g/m3 and 8.5 g/m3 as fuel (willow) moisture content increased from 10% to 43% wb. In their experiment, the bubbling fluidized bed gasifier was electrically heated to eliminate the effect of ER on the bed temperature maintained at a narrow range of 808-826 oC. James et al. (2016) investigated the effect of fuel (pine woodchips) moisture content and bed temperature on tar formation. According to their results, when moisture content increased from 10% to (14%, 18% and 22%) while bed temperature was at 840-860 oC, a corresponding decrease in tar concentration also occurred from 13 to 7.5 and 6.2 g/m3. The impact of bed temperature on tar formation was discussed in depth in (Park et al., 2016). The authors explained that higher bed temperature can promote the tar cracking and reforming reactions, which result in lower tar concentration. The modeling study made by Chaurasia (2016) also shows that tar yield decreases from 2.41 to 0.56 vol% as the temperature increases from 827 oC to 1027 oC. Ren et al. (2019) also pointed out that biomass is decomposed into condensable gases, oxygenated gases and primary tar at the pyrolysis stage. As temperature increases, primary and heavy tar will crack into secondary tar and non-condensable gases, and tar completely changes to tertiary tar at higher temperature. A simple sensitivity analysis was performed, with an aim to discern the combined effect of temperature and fuel moisture content on tar formation. In our study with the industrial 72 gasifier at UBC, fuel moisture content ranged from 20-35% wb and bed temperature varied from 875 to 1050 oC, whereas tar concentration ranged from 24-170 g/m3. The procedure of sensitivity analysis started by keeping the feeding rate constant at normalized value of -1.5 (i.e. 60%). The next step was to apply the regression model (Eq. 4.8) to calculate and predict the change in tar concentration as the bed temperature is raised from normalized value of -1.5 to 1.5 (870 to 1050 oC) while fuel moisture content is held at a lower normalized value of -1.5 (19% wb); the calculations were repeated with fuel moisture content held at a higher normalized value of 1.5 (32% wb). Finally, similar calculations were repeated twice for normalized values of feeding rate being 0 and 1.5 (77% and 90%), respectively. Results of the sensitivity analysis show that when the gasifier bed temperature increases from -1.5 to 1.5, the change in tar concentration would depend on the magnitudes of fuel moisture content and feeding rate, as illustrated in Table 4.7. Specifically, at lower feeding rates (normalized values -1.5 and 0), tar concentration decreases with an increase in bed temperature when the fuel has a higher MC (normalized value 1.5 or 35% wb); yet the tar concentration increases at lower fuel MC (normalized value -1.5 or 22% wb). In addition, for a higher feeding rate with normalized value 1.5, regardless of whether fuel MC is higher or lower (normalized value 1.5 or -1.5), tar concentration decreases with an increase in bed temperature. Thus, the trends exhibited by our tar formation results (with respect to fuel moisture content and gasifier bed temperature) are partially in line with those observed by other researchers (such as Kiel et al., 2004 and James et al., 2016) that are based on laboratory-scale experiments as described above. It shall be noted that the trends displayed by their data are based on a limited number of 3-4 data points associated with several levels of moisture content and gasifier temperature in each study, whereas the present study is based on 40 sampling events with a range of moisture content and gasifier temperature. 73 Table 4.7 Sensitivity analysis on tar concentration using regression model Eq. 4.8. Normalized values Case Bed temperature Fuel moisture content Feeding rate Tar concentration Change in tar concentration* Normalized feeding rate: -1.5 1 -1.5 1.5 -1.5 2.35 2 1.5 1.5 -1.5 0.26 -2.09 3 -1.5 -1.5 -1.5 -1.87 4 1.5 -1.5 -1.5 0.94 2.81 Normalized feeding rate: 0 5 -1.5 1.5 0 1.85 6 1.5 1.5 0 -0.08 -1.94 7 -1.5 -1.5 0 -1.49 8 1.5 -1.5 0 -0.28 1.22 Normalized feeding rate: 1.5 9 -1.5 1.5 1.5 1.35 10 1.5 1.5 1.5 -0.42 -1.78 11 -1.5 -1.5 1.5 -1.11 12 1.5 -1.5 1.5 -1.50 -0.38 * Change in tar concentration when normalized gasifier bed temperature is increased from -1.5 to 1.5 Among the three factors (fuel moisture content, gasifier temperature, feeding rate), moisture content would have the largest influence on tar formation. This is evidenced by an average change in the normalized tar concentration by 2.11 units when gasifier temperature and feeding rate were held constant but moisture content was allowed to increase from -1.5 to 1.5. Whereas, the average change in tar concentration was 1.77 and 1.22 units, respectively when gasifier temperature and feeding rate were, in turn, allowed to increase from -1.5 to 1.5 while the 74 other two factors were held constant in each case. By extending the sensitivity analysis onto syngas heating value (LHV), moisture content was again found to exert the largest influence among the three factors. The advances in biomass gasification were summarized by (Sikarwar et al., 2016). They emphasized the modeling study made by Schuster et al. (2001), and reported that biomass with greater than 30% moisture content had negative impact on gasifier temperature and tar formation. It was concluded the same with the commercial gasifier at UBC albeit with almost uncontrolled moisture content. According to a recent literature review on the modeling of biomass gasification by La Villetta et al. (2017), syngas heating value (LHV) ranges from 5.0-6.0 MJ/m3, and tar formation varies from 20 to 100 g/m3. These values are in line with our experimental results. A comparison of experimental conditions and results between this study and previous research studies is quantitatively summarized in Table 4.8. . 4.3.7 Verification of the regression models To verify the regression models, the predicted values were compared with the measured values of syngas LHV and tar concentration for four samples collected from the tissue plant. These samples were taken under similar conditions as in the UBC-BRDF gasifier. The verification results are summarized in Table 4.9. The errors in syngas LHV for three out of the four samples lie in between 10-20.1%. The error for the first sample is much greater at 45.5%. This can be explained by the higher N2 content in the syngas as a result of more air being injected into the gasifier, thus reducing the CH4, H2 and CO contents. Considering all four samples, the average absolute errors for the predicted LHV and tar are 21.8% and 8.5%, respectively, suggesting that the regression equations obtained using data collected at the UBC-BRDF are also applicable to the data collected at the tissue plant. This means, the regression equations are generally good for the updraft gasifier developed by Nexterra Systems Corp. for biomass fuel. 75 Table 4.8 Comparison of experimental conditions and results between this study and previous research works. Conditions Responses Moisture content (% wb) Particle size (mm) Gasifier temperature (oC) Gas composition (%) Syngas LHV (MJ/m^3) Cold gas efficiency (%) Tar concentration (g/m3) This study 20 to 35 * 3.15 - 25 1,075 to 875 * CO: 30.0 to 20.0 H2: 7.0 - 8.0 5.5 to 3.5 55.5 30 -167 This study 27 to 32 3.15 - 25 (925–1050) to (875–925) -- -- -- (100–160) to (60–100) (James et al., 2016) 10 2 - 30 840 to 614 CO: 15.0 to 11.8 H2: 6.6 to 2.9 3.67 to 2.71 -- -- (James et al., 2016) 10 7 840 to 860 CO: 15.0 H2: 6.6 3.67 -- 13 to 6.24 14 CO: 12.9 H2: 5.7 3.13 18 CO: 10.5 H2: 5.6 2.65 22 CO: 11.2 H2: 6.0 2.84 (Couto et al., 2015) -- -- 500 to 700 CO: 4.0 to 26.0 H2: 3.0 to 15.0 1.6 to 4.1 43 to 62 -- (Abuadala et al., 2010) -- -- 727 to 1,227 CO: 28.0 to 39.0 H2: 55.0 to 53.0 -- -- -- (Kiel et al., 2004) 10 to 45 -- -- -- -- -- 14 to 8 (Chaurasia, 2016) -- 827 to 1027 -- -- -- 2.41 to 0.56 vol% (Schuster et al., 2001) 10 to 65 -- -- 9.5 to 4.0 -- -- (Y. Wang et al., 2007) -- 940 to 1100 -- -- 57% to 74% -- * (value of a parameter to another value of a parameter): This denotes an increase from a lower value to a higher value, or, a decrease from a higher value to a lower value (applicable to the whole table) 76 Table 4.9 Results of verification of the regression equations using data collected at the tissue plant Syngas heating value Tar concentration Samples Measured (MJ/m3) Predicted (MJ/m3) Absolute error Measured (g/m3) Predicted (g/m3) Absolute error 1 3.5 5.1 45.5% 86.8 87.7 1.0% 2 4.4 4.9 11.7% 96.7 85.0 12.1% 3 4.0 4.9 20.1% 88.0 76.7 12.9% 4 4.4 4.8 10.1% 90.9 98.1 7.9% Average 4.1 4.9 21.8% 90.6 86.9 8.5% 4.4 Conclusions Two industrial updraft gasifiers were used in this study. Field data on the variability of urban woody biomass fuel vs. syngas composition and tar concentration were analyzed. No efforts were made to adjust the properties of biomass and operational conditions of the gasifier. The measured data is characterized by its wide variations from one test to the next. This shows that actual operating conditions produce data that have much more variation than the highly controlled laboratory tests. For the industrial gasifier at UBC with a longer period of gasifier run and sampling events, the gasifier temperature ranged between 875 and 1075 °C during the testing period. Results indicated that as the fuel moisture content increases from 20% to 35%, CO concentration drops from about 30% to 20% and CO2 concentration has a mild increase, H2 and CH4 concentrations decrease slightly. The H2/CO ratio of 0.26–0.54 is consistently less than 1.0 since air was used as the gasifying agent for the industrial gasifier. Tar concentration varied from 24 to 170 g/m3 with an average value of 78 g/m3. Statistical analysis shows that fuel moisture content is mildly and negatively correlated with syngas heating value (-0.53) but positively correlated with tar concentration (0.51). Furthermore, fuel moisture content is noticeably correlated with oxygen content (0.69) and the fraction of small particles (0.58). 77 Results of the multi-variable regression analysis on syngas heating value showed that the there was a statistically significant difference in syngas heating value among the examined variables. Fuel moisture content was the most sensitive variable, followed by the fuel feeding rate. Regression analysis on tar concentration showed that there was a statistically significant difference in tar concentration among the studied variables. Fuel moisture content was again the most sensitive variable, followed by the product of fuel moisture content and gasifier bed temperature. The regression equations based on data collected at the UBC-BRDF were verified using data collected at the tissue plant. Results of the sensitivity analysis show that when the normalized gasifier bed temperature increases from -1.5 to 1.5, the change in tar concentration would depend on the magnitudes of fuel moisture content and feeding rate. The trends exhibited by tar formation results with respect to fuel moisture content and gasifier bed temperature are partially in line with the limited data collected from lab-scale experiments by other researchers. It may be concluded that, among the three factors (fuel moisture content, gasifier temperature, feeding rate), moisture content would have the largest influence on tar formation and syngas heating value. Unlike the published data from controlled laboratory experiments, the data collected from a commercial unit indicate a wider variation. This may be due to conditions under which the use of automation for process control is difficult. Despite, the results derived from this study are in line with the trends reported in the literature for lab-scale and pilot-scale studies that have different operating conditions and the types of gasifiers installed. Furthermore, our findings suggested that even for an updraft gasifier operating under commercial conditions, syngas quality would be enhanced if the biomass fuel moisture content could be maintained at around 20%. The CO concentration (~30%) and calorific value (>4 MJ/m3) will be high while tar concentration is kept below 60–70 g/m3 for the commercial gasifier. 78 Modeling and Analysis of the Efficiency of a Commercial Updraft Gasifier Based on Plant Data 5.1 Introduction Biomass gasification consists of combusting biomass in an oxygen deficit reactor. The reduced oxygen causes the biomass to break down to a combustible vapor. The gaseous product is called producer gas or synthesis gas (syngas). Syngas consists multiple compounds, and the major ones are CO, CO2, H2, H2O and CH4. Syngas also contains heavier hydrocarbons molecules that are referred to as tar (Stevens, 2001). Tar is generated from a series of reactions mainly during the pyrolysis stage under oxygen deficiency conditions (Pirc et al., 2012). Syngas and tar are burned inside the oxidizer to produce hot flue gas (Figure 5.1). Figure 5.1 Inputs and outputs of the gasification system. Syngas is combusted in the oxidizer, and hot flue gas is fed into a boiler for steam generation. Gasifier efficiency is measured in terms of the purity of gases and its total calorific content (Schapfer and Tobler, 1937). Fluidized bed gasifiers achieve higher efficiency compared to fixed bed gasifiers. Among fixed bed gasifiers, updraft gasifiers can operate with biomass at moisture content up to 60% (wb) whereas downdraft gasifiers operate with biomass less than 79 25% moisture content (wb) (Basu, 2013). Updraft gasifiers are also suitable for high ash but the volatiles contain large concentration of tar. Tar can make syngas more difficult to be used as fuel for internal combustion engines (Mamphweli and Meyer, 2010). The heat content of syngas is divided into sensible heat and latent heat. Sensible heat comes from the gas temperature. Latent heat refers to the heat trapped in water vapor/moisture. A portion of the sensible heat can be used for applications such as biomass drying and steam generation (Sansaniwal, Pal, et al., 2017). In the present case study, the gasifier reactor is a cylindrical chamber 4.9 m in diameter and 4.9 m in height. Fuel wood chips enter the reactor by a feeder at the center section of the gasifier floor. The continuous upward flow of fuel forms a hot pile. The ash drops into slots that surrounds fuel incoming port. Air is introduced into the gasifier from the underneath of the pile through a series of inlets. To manage the gasifier, the gasifier temperature needs to be kept constant as wood chips are converted to syngas. The injected air comes from two sources: ambient air and recirculated hot flue gas before it exits the plant, with an aim to control the oxygen level and increase the temperature of the injected air. Ahn et al. (2011) discussed the effect of flue gas recirculation on the peak flame temperature and NO production in an air gasification system. The oxygen content is reduced by mixing the hot flue gas with fresh air, thus leading to a lower flame temperature and lower nitric oxide emission. Dall Energy (www.dallenergy.com) has patented a biomass gasification furnace, in which the gas flow, temperature and emission in an updraft gasifier are controlled by recirculating the flue gas. Surjosatyo et al. (2014) analyzed the effect of external recirculation of hot flue gas on tar and syngas composition in an updraft gasifier. They showed that tar formation and CO concentration were reduced by increasing the ratio of recirculated hot flue gas. Subsequently, Vidian et al. (2016) developed a thermodynamic model to investigate the effect of recirculating hot flue gas on tar formation and syngas quality in the updraft gasifier. Their model showed that as the ratio of recirculation is increased, the H2 content increases while the tar formation decreases. They also found that the CO and CH4 contents depend on the equivalence ratio. Efficiency of gasification can typically be expressed as cold gas efficiency, hot gas efficiency and net gasification efficiency (Basu, 2013). 80 Figure 5.2 illustrates the concept of these efficiency terms. Efficiency is typically defined as the ratio of output to input energy. Figure 5.2 Energy balance around a gasifier and the definitions for cold, hot and net energy efficiencies. Two major factors determine the efficiency of the gasifier: air/fuel ratio and the moisture content of the fuel. The term equivalent ratio (ER) represents the ratio of actual A/F value to stoichiometric A/F value (Gil et al., 1999; Mathieu and Dubuisson, 2002; Van Der Drift et al., 2001). An ER in the range of 0.2–0.4 represents an effective gasification process (Bhavanam and Sastry, 2011; Narvaez et al., 1996). The Food and Agriculture Organization of United Nations (Wood and Branch, 1986) reported that the typical cold gas efficiencies (CGE) of biomass gasifier systems is between 65% and 75%. Some gasification systems achieve CGE as high as 90%. In (Mamphweli and Meyer, 2010), a fixed-bed downdraft is studied to estimate the cold-gas efficiency. In their simulation, the gasifier achieved an efficiency of 75% with an average gas heating value of 6 MJ/m3. Mamphweli and Meyer (2009) evaluated a downdraft gasification system, and reported the cold-gas efficiency and hot-gas efficiency to be 73% and 83%, respectively. Ojolo et al. (2012) developed an 11 kW lab-scale updraft gasifier, and they used sawdust and palm kernel shell as fuel. The net gasifier efficiency was reported to be 93–97% for both types of fuel. Beohar et al. (2012) conducted a CFD modeling study of updraft biomass gasifier; they found that the optimal biomass feed rate and moisture content were 8.8 kg/hr and 25% (wb), respectively. 81 The major objective of this chapter is to model the performance of gasifier based on data collected from an industrial scale gasification system. This is achieved through analysis of fuel properties, gas profiles and operational conditions at the gasifier. This work focuses on the performance of the gasifier without considering the downstream processes. A related objective is to perform multi-variable regression analysis on cold gas efficiency. This is similar to the statistical analysis conducted in Chapter 4 for syngas heating value and tar formation as the performance indicators. 5.2 Methodology 5.2.1 Modeling As mentioned in Chapter 4, the fuel was sampled during three months of testing in 2015 where 40 samples were collected. For each test the tar samples were kept in a sealed jar. Syngas samples were taken with gas sampling bags. In the meantime, wood chips were sampled from the infeed screw conveyor and brought to the lab at UBC for analysis. Operational data are also recorded, including fuel feeding rate, fuel composition, airflow rate, steam production rate, input and output gasifier temperatures, and volumetric fractions of syngas components. These data are used for estimating and verifying the efficiencies of gasifier as described in the literatures (Basu, 2013; Mamphweli and Meyer, 2009). Denote the fuel mass flow rate in mass to be mf (kg/h) and the mass flow rate of air to be mA (kg/h). For the BRDF gasifier, ambient air is mixed with recirculated flue gas and used as injected air to the gasifier. The recirculation ratio can vary slightly. The volumetric fraction of oxygen, Av,O (all fractions are decimal values) in the injected air is recorded by the plant. The mass fraction of oxygen in the air is calculated as follows, noting that volume fraction equals molar fraction in a gas mixture. XO2 =AO2 WO2AO2 ∙ WO2 + ൫1 − AO2൯ ∙ WN2 (Eq. 5.1) XN2 = 1 − XO2 (Eq. 5.2) where XO2 and XN2 are the mass fraction of O2 and N2, respectively. 𝑊𝑂2 (kg/kmol) and 𝑊𝑁2 82 (kg/kmol) are the molecular weight of O2 and N2, respectively. AO2 is the volumetric fraction of oxygen in injected air. The mass flow rate of nitrogen (supply from air) is mNA = XN2 ∙ mA (Eq. 5.3) where mNA (kg/h) is the mass flow of N2 supplied from air. mA (kg/h) is the air mass flow rate. The mass flow rate of nitrogen (supply from fuel) is mNF = YN ∙ mf (Eq. 5.4) where mNF (kg/h) denotes the mass of N2 supplied from fuel. mf (kg/h) is the fuel mass flow rate. YN is the mass fraction of N in the fuel, which is obtained from our measurements. The molar flow rate of nitrogen, MN2 (kmol/h) is calculated as MN2 =mNA + mNF28 (Eq. 5.5) The constant 28 is molecular weight of N2. Volumetric fractions of syngas composition (components) were measured in the lab. Hence, the molar flow rate of syngas, Mg (kmol/h) is Mg =MN2SN2 (Eq. 5.6) where SN2 is the volumetric fraction of N2 in syngas. Below the mass of water vapor in syngas based on hydrogen mass balance is derived: the input mass of hydrogen equals the output mass of hydrogen. The input hydrogen includes the hydrogen supplied from fuel moisture content and fuel constituents hydrogen. The output mass of hydrogen is found in three parts, CH4 and H2 in syngas and water vapor in syngas. CH4 and H2 are measured in lab. Subtracting the mass of hydrogen in CH4 and H2 from the hydrogen input, The remaining hydrogen in the form of water vapor is obtained. The total mass flow rate of hydrogen from fuel, mH,in (kg/h), is mH,in = YH ∙ mf +2 ∙ x ∙ mf18 (Eq. 5.7) where 18 is the molecular weight of water (H2O). YH is the mass composition of H2 in the fuel. The fuel moisture content is x (Fraction wb.). 83 The molar flow rate of hydrogen, MH2 (kmol/h), in exit syngas is MH2 = (SCH4 ∙ 2 + SH2) ∙ Mg (Eq. 5.8) where SCH4 and SH2 are the volumetric composition of CH4 and H2 in syngas, respectively. The mass flow rate of hydrogen, mH,g (kg/h), is mH,g = MH2 ∙ 2 (Eq. 5.9) where 2 is the molecular weight of H2. The mass flow rate of moisture in syngas, mw (kg/h), is mw = ൫mH,in − mH,g൯ ∙182 (Eq. 5.10) 5.2.2 Gasifier efficiencies The lower heating value of syngas is calculated by Eqn (11) (Motta et al., 2018; H. Wang et al., 2016), LHVg = ෍ LHVi ∙ Si3i=1 (Eq. 5.11) where 𝐿𝐻𝑉𝑔 (MJ/m3) denotes the lower heating value of syngas. The symbol i represents the major syngas species CO, H2 and CH4. The heating value of CO2 is considered zero since it doesn’t react with O2. The lower heating values of CO, H2 and CH4 are 12.63, 10.78 and 35.88 MJ/m3, respectively (Basu, 2013). 𝐿𝐻𝑉𝑖 (MJ/m3) is the lower heating value of the gas component 𝑖, and 𝑆𝑖 is the volumetric composition (decimal fraction) of the gas component in the syngas. The input rate of energy Ef (MJ) is calculated as Ef = LHVf ∙ mf = (19.5(1 − x) − 2.44x) ∙ mf (Eq. 5.12) where x is the fuel moisture content, fraction wb. 𝐿𝐻𝑉𝑓 (MJ/kg) is the lower heating value of fuel. The constant 19.5 is the higher heat value of the dry mass and 2.44 is the heat of vaporization of water) The cold-gas efficiency, ηcg (fraction decimal), is defined as the ratio of the syngas output energy rate (due to its calorific value) to the fuel input power (due to its calorific value) 84 ηcg =LHVg ∙ 𝑉𝑔𝐸𝑓 (Eq. 5.13) Vg (m3/h), is the volumetric flow rate of syngas, which is calculated as: Vg =R ∙ TP∙ Mg (Eq. 5.14) where R is the Regnault (universal gas) constant. Since syngas composition is measured in lab, it is reasonable to assume that the volume of 1 kmol of the gas is 22.4 m3. Where the standard temperature and pressure are in K and atm respectively. Next, the hot-gas efficiency, ηhg (fraction decimal), is defined as: ηhg =Eg + QEf=LHVg ∙ 𝑉g + QLHVf ∙ mf (Eq. 5.15) where Eg (MJ) is due to the calorific value of syngas, and Q (MJ) is the sensible heat of syngas. The sensible heat of syngas is the total enthalpy of the syngas components and the water vapor in syngas. Q = ෍ Qi6i=1 (Eq. 5.16) where 𝑖 represents the syngas species, which include CO, CO2, H2, N2, CH4 and H2O. For CO, CO2, H2, N2 and CH4, the equation for calculating the enthalpy of each species is Qi = Cpi ∙ Mg ∙ Si ∙ ൫Tg − T൯ (Eq. 5.17) where 𝐶𝑝𝑖 (kJ/kmol-K) is the specific heat of gas species 𝑖. Specific heat is a function of temperature. 𝑇𝑔 (K) is gas temperature at the gasifier exit, and T (K) is gas temperature of the air entering the gasifier. The enthalpy of water in syngas, Q w (MJ), is Q w = mw ∙ (HTg − HT) (Eq. 5.18) where mw (kg/h) is the mass flow of moisture in syngas previously calculated. HTg (MJ/kg) and HT (MJ/kg) are the enthalpy of water at Tg and T, respectively. 85 Finally, the gasifier net efficiency, ηnet (fraction decimal), is defined as the ratio of the net energy output over the energy input. To calculate the net energy output, the energy recovered through burning tar is added to the syngas heating content and the sensible heat. ηnet =Eg + Er + QEf (Eq. 5.19) where Er (MJ) is the energy recovered through burning tar, calculated as Er = Ctar ∗ ൫Mg ∗ 22.4൯ ∙ Htar (Eq. 5.20) where Ctar (𝑘g/m3 ) is the concentration of tar. Htar is the heating content of tar and its value is 14.7 MJ/kg. The heating value of tar was provided by Nexterra. The volume of 1 kmol of any gas is 22.4 m3. 5.2.3 Multi-variable regression analysis on cold gas efficiency The normalization for cold gas efficiency was done as follows: Cold gas efficiency: 𝜂𝑐𝑔′ =ηcg−0.560.14 (Eq. 5.21) The instantaneous value for ηcg was obtained from Eqn 13. The constant 0.56 and 0.14 are the mean value and standard deviation of cold gas efficiency respectively. The F-test for “overall significance in regression analysis” was performed, which compares the regression model to the model with no independent variables. 5.3 Results and discussion Table 5.1 lists the properties of the fuel utilized in the gasifier at the time when the syngas and tar were sampled. The average fuel moisture content and ash content are 25.5% (wb.) and 1.1% (db.), respectively. The HHV of the fuel is 19.2 MJ/kg. The standard deviations for both HHV and ash content are low. Because the amount of ash content is low, the heat loss from dumping ash is negligible in the efficiency calculations. Table 5.2 lists the measured compositions of the fuel samples. The mass proportions are carbon 54.6%, hydrogen 5.8%, oxygen 39.0% and nitrogen 0.1%. Overall the compositions of feedstock are compatible with the UBC-Nexterra specifications. Table 5.3 lists the syngas compositions. For moisture content ranging from 20% to 35% (wb), the gas compositions were found to be 1.7% CH4, 7.6% H2, 86 9.0% CO2, 24.0% CO and 56.7% N2. The nitrogen content was high as air is used as the gasifying agent. Table 5.1 Properties of the fuel samples utilized in the gasifier at the time when the syngas and tar were sampled. Moisture content (% wb) HHV (MJ/kg) Ash content (% db) Mean 25.5 19.2 1.1 Std Dev 4.3 0.7 0.3 CV 16.9% 3.6% 27.3% Table 5.2 Composition of the fuel during the sampling of syngas and tar. The numbers in the brackets (first column) are the ranges of values required by UBC-Nexterra specifications. Fuel sample 1 (%) Fuel sample 2 (%) Fuel sample 3 (%) Average (%) Ash (<10%) 0.5 0.4 0.5 0.4 Carbon (48-52%) 54.4 55.6 53.8 54.6 Hydrogen (5-6%) 5.9 5.7 5.8 5.8 Oxygen (36-44%) 39.1 38.2 39.8 39.0 Nitrogen (<0.2%) 0.08 0.10 0.10 0.09 Sulphur (<0.025%) 0.02 0.02 0.02 0.02 Chlorine (<0.025%) 0.02 0.05 0.04 0.04 Table 5.3 Syngas composition for fuel moisture content from 20% to 35% (wb). Nitrogen content was high as the gasifier is air blown Moisture Content Hydrogen in syngas Nitrogen in syngas Methane in syngas CO in syngas CO2 in syngas Mean 27.1% (wb) 7.6% 56.7% 1.7% 24.0% 9.0% Std Dev 4.4% (wb) 0.8% 3.3% 1.1% 3.6% 1.7% 87 Table 5.4 shows the gasifier performance calculated from the percentage contribution of gases including N2, CO, CO2, H2, and CH4. The lower heating values of the combustible gases were obtained from the standard table. They are 12.6 MJ/m3 for carbon monoxide, 10.8 MJ/m3 for hydrogen, and 36.0 MJ/m3 for methane. Methane has the highest heating value but its concentration in syngas is low, thus its contribution to syngas heating value is less significant compared to that of hydrogen and carbon monoxide. The calorific value shows a mild overall decrease from about 5.5 to 3.8 MJ/m3 as MC increases from 20% to 35%. These syngas heating values are similar to those reported by Ren et al. (2019), which range from 4 to 7 MJ/m3. Table 5.4 Gasifier performance. The cold-gas, hot-gas and net gasifier efficiencies were calculated using field data. Fuel Moisture Content (%) Cold-Gas Efficiency Hot-Gas Efficiency Net Gasifier Efficiency Syngas flow rate (kmol/hr) Syngas moisture flow rate (kg/hr) Mean 27.1 55.5 79.6 92.8 114 1203 Std Dev 4.4 14.3 14.1 14.3 19.5 213.6 CV 16.1 25.8 17.7 15.4 17.1 17.8 The cold gas efficiency, hot gas efficiency, and net gasifier efficiency were calculated using the field data. For fuel moisture content ranging from 20% to 35%, cold-gas efficiency is 55.5±14.3%, hot-gas efficiency is 79.6±14.1%, and net gasifier efficiency is 92.8±14.3%. Calculations were also done to estimate the effect of recirculating flue gas to the gasifier. As a result of a change in the sensible heat of syngas, hot-gas efficiency was found to decrease from 82% (at injected air temperature of ~ 40 oC without flue gas recirculation) to 78.5% (at injected air temperature of ~ 200 oC with flue gas recirculation). For comparison purposes, La Villetta et al. (2017) reported that cold gas efficiency ranges from 40-60% based on review of modeling studies. According to a recent review paper, the cold gas efficiency was up to 60% with gasifier temperature varying between 950–1150 oC (Molino et al., 2018). By comparison, Yahaya et al. (2019) studied a downdraft gasifier and reported a cold gas efficiency of 51%, and they showed that CGE increased at higher CO concentration. Ernie et al. (2010) studied the performance of an 88 updraft gasifier by changing fuel (wood) moisture content from 17% to 40%. The highest net gasifier efficiency was found to be 92.7% at the low end of fuel moisture content at 16–17%. By comparison, our calculated net gasifier efficiency is 92.8% at fuel moisture content of 27%. The net output energy was about 12.1 MJ per kg feed of fuel. The net output energy includes the syngas heat content, sensible heat and tar heat content. The LHV of fuel was about 12.86 MJ/kg. The rate of tar production was from 63 kg/h to 332 kg/h. On average, tar contributed to 14.6% of the net output energy. The total sensible heat contributed about 25% towards the net output energy. The sensible heat of syngas, water vapor and tar was 1481, 3529, and 2884 MJ per hour, respectively. There are four main sources of water vapor in the syngas - moisture in the fuel, fuel combustion, methanation reaction and injected wet air. Total sensible heat increased from 3844 to 6898 MJ per hour when syngas exit temperature changed from 350 to 440 oC. The fuel feeding rate was between 1200 to more than 2400 kg per hour. Since the gasifier reactor was well insulated, heat loss through the body of reactor is expected to be minimal, at about 1%. Another 1% of heat loss comes from ash discharge. Multi-linear regression analysis for cold gas efficiency Eqn. 22 represents the regression model for cold gas efficiency. 𝜂𝑐𝑔′ = −0.30𝑚𝑓′ − 0.14𝑥′ + 0.49𝑥′𝑇′ + 0.26𝑚𝑓′ 𝑇′ (22) The R2 and the critical F-value associated with the regression are 0.65 and 0.0002, respectively. For a statistical significance level α = 0.01, the critical F-value is less than the significance level. Thus, there is a statistically significant difference in cold gas efficiency among the studied variables. In contrast to syngas heating value or tar formation, cold gas efficiency is most sensitive to the product of fuel moisture content and gasifier bed temperature, followed by the fuel feeding rate. According to Eqn. 22, cold gas efficiency decreases with increasing fuel moisture content. Reduction in the cold gas efficiency is due to the decrease in the heating value of the fuel and energy loss when biomass moisture evaporates. This observation agrees with the model developed by (Schuster et al., 2001). The positive correlation between gasifier temperature and cold gas efficiency is well established (Kumar et al., 2009; Sansaniwal, Pal, et al., 2017). The sensible heat of gases and 89 heat from the gasifier combustion zone are provided to the reduction zone. At a higher temperature, more energy is available for the endothermic reactions to produce syngas. As a result, more energy is stored in the syngas leading to greater cold gas efficiency. Couto et al. (2015) confirmed the positive effect of gasifier temperature on cold gas efficiency at various air flow rates of 40-100 m3/h. For instance, at an airflow rate of 70 m3/h, cold gas efficiency increased from 43% to 62% as gasifier temperature increased from 500 oC to 700 oC. Wang et al. (2007) reported that an increase in gasifier bed temperature led to a rise in cold gas efficiency from 57% to 74%. The change in gasifier temperature was measured in two locations in their experiment, from 940 to 1100 oC and from 730 to 900 oC, respectively. In our study, as fuel moisture content changed from 20% to 35% and gasifier temperature varied between 875 °C and 1050 °C, a cold gas efficiency of 55.5±14.3% was attained. 5.4 Conclusions The gasifier efficiency was evaluated based on data collected from a commercial updraft gasification system. For fuel moisture content ranging from 20% to 35% (wb), the gasifier performance was found to be 55.0%, 79.6%, and 92.8%, in terms of cold gas efficiency, hot gas efficiency, and net gasifier efficiency, respectively. The results show that 14.6% of the net output energy came from tar, and 25% came from sensible heat. The sensible heat of syngas, water vapor and tar were 1481, 3529, and 2884 MJ per hour, respectively. According to the regression for cold gas efficiency, the critical F-value of the F-test was less than the significance level of 0.01, hence there was a statistically significant difference in cold gas efficiency among the examined variables. In contrast to syngas heating value or tar formation, cold gas efficiency is most sensitive to the interaction of fuel moisture content and gasifier bed temperature, followed by the fuel feeding rate. 90 Overall Conclusions and Future Work 6.1 Overall conclusions This thesis begins with a description of the end-to-end a gasification system that starts from the source of fuel to its final utilization in the form of steam production. The raw woody biomass materials are collected by the aggregator Cloverdale Fuel Ltd. (CFL). The materials are preprocessed primarily by grinding, screening, and blending clean wood that is originated from unsalable scrap woods from a variety of wood products manufacturing facilities with green wood (originated from city tree trimmings) in the CFL recycling yard to produce the biomass feedstock (fuel) required by the UBC-BRDF gasification facility. The thesis research is based on the “Living Lab Concept” (a UBC Sustainability initiative), whereby data were collected from the BRDF facility over extended time periods since its start-up in 2012. The gasifier is operating routinely under commercial obligations. No attempt was made to adjust or influence the fuel properties or operational conditions of the gasifier for the sake of this research. In Chapter 2, the temporal variability in the biomass feedstock properties was characterized, which corresponds to objective #1 of the thesis study. Initially, over a time period of three years (2012-2014), samples of the fuel were obtained from the point of delivery at the BRDF, and sampling frequency was every day on every load of delivered biomass. The measured properties include moisture content, ash content, wet bulk density, Calorific (higher heating) value, and the percentage of undersize and oversize materials. Objective #2 of the thesis was fulfilled by investigating the effects of variability in biomass quality on the performance of the industrial gasifier in terms of system reliability and steam production in Chapter 3. Investigation was extended to assess the effects of fuel properties on syngas quality and tar formation in the industrial gasifier in Chapter 4. Furthermore, objective #3 of the thesis was met by performing multi-variable regression analysis on syngas heating value, tar formation and cold gas efficiency. Two industrial updraft gasifiers were used in this study, the one at UBC and the other at Scott Paper in New Westminster BC. Field data were collected from tests performed at the UBC gasifier and a tissue plant. The tests took place over a period of two months (November-December 2015) that involved 40 sampling events. Chapter 5 concerns the modeling and analysis of the efficiency of the industrial gasifier based on data that include fuel properties, 91 gas profiles and operational conditions. The key findings from the various chapters of this study may be summarized below. 1. Statistical analysis on the biomass feedstock properties was performed and the measurements were examined against the UBC/Nexterra fuel specifications. Over the three-year period 2012-2014, moisture content was found to be 31±9.6 % wb, though it varied substantially from 10% to 57.4% wb. Blending of feedstock with different moisture contents (the drier clean wood and the wetter green wood) would increase the variability in the feedstock delivered to the gasifier plant despite the fact that most of the clean wood had low variability in their properties. Wet bulk density was 187±38 kg/m3. Ash content was always low with an average of 0.8% db and standard deviation of 0.2% db, and this resulted from discarding the undersize fraction with an order of magnitude higher ash content compared to the desirable midsize fraction. Calorific value being 19.4 MJ/kg was observed to have the smallest variation. Field trials on size fractionation that were conducted at the CFL recycling yard with industrial grinders and screens showed that on average 75% of the particles were in the midsize range 6.3-25 mm while 9% and 16% of the particles were undersize (<6.3 mm) and oversize (>25 mm), respectively. Therefore, in terms of compatibility with the fuel specifications, moisture content was mostly in compliance, whereas ash content completely followed the specifications. System performance in terms of the frequency of breakdowns and hence the plant utilization rate gradually improved through the fall of 2014. This could be attributed, in part, to the reduction of feedstock moisture content and feeding more uniform size particles to the gasifier. 2. While the amount of delivered feedstock and its moisture content fluctuated from day to day along with fluctuations in steam generation, this research derived strong correlations from the time-average values by summing the steam production and the quantity of delivered biomass over 5 days and 10 days during the three-month time period January-March 2015. 92 Fuel moisture content was found to be the main factor that affects the production of steam in tonnes per tonne of wet biomass, amidst other factors that include the gasifier temperature and oxygen content in the air fed to the gasifier. Applying the correlation thus developed, reduction of the fuel moisture content from 35-50% to the equilibrium moisture content of 20% (wb) would lead to an increase in steam production by 19-39 percent from (3.81-4.58) to 5.35 tonnes per tonne of wet biomass, respectively. In this regard, protecting the biomass from precipitation (rain) has been an effective measure adopted by CFL to reduce the level and variation in fuel moisture content. It is desirable for UBC to further tighten the allowable moisture content for the fuel wood chips. 3. Correlation coefficients were obtained through statistical analysis to enable the identification of key factors that affect the gasifier performance in terms of syngas quality and tar formation. Fuel moisture content, mass fraction of small particles, gasifier bed temperature, fuel feeding rate, and oxygen content of the injected air were identified to be the key factors. Multi-variable regression equations were then developed using data collected from the UBC gasifier. Results of the multi-variable regression analysis showed that, for syngas heating value, fuel moisture content was the most sensitive variable, followed by the fuel feeding rate. Regression analysis on tar concentration showed that fuel moisture content was again the most sensitive variable, followed by the interaction of fuel moisture content and gasifier bed temperature. The regression equations were verified using data collected at the tissue plant (that is, the other industrial gasifier). Comparison was made between the predicted values with the measured values of syngas heating value and tar concentration, and results demonstrated that the regression equations are generally good for the specific type of updraft gasifier installed at UBC-BRDF. A simple sensitivity analysis was performed, with an aim to discern the combined effect of gasifier temperature and fuel moisture content on tar formation. Results of the sensitivity analysis show that, among the three factors (fuel moisture content, gasifier temperature, feeding rate), moisture content would have the largest influence on tar formation and syngas heating value. The trends exhibited by our tar formation results are partially in line with those 93 observed by other researchers that are based on lab-scale experiments with a limited number of data points associated with moisture content and gasifier temperature in their studies. By comparison, this study is based on more sampling events from an industrial gasifier with a larger range of fuel moisture content and operating gasifier temperature. 4. Results obtained from the modeling and analysis of the gasifier performance indicate that the cold gas efficiency, hot gas efficiency, and net gasifier efficiency are, on average, 55.0, 79.6, and 92.8, respectively, as fuel moisture content varies from 20-35% (wb). The net output energy is about 12.1 MJ/kg fuel, 15% of which coming from tar, and 25% coming from sensible heat. Multi-variable regression analysis shows that, unlike syngas heating value and tar formation, cold gas efficiency is most sensitive to the interaction of fuel moisture content and gasifier bed temperature, followed by the fuel feeding rate. Overall, the data that were collected from the commercial gasification unit indicate a wider variation when compared to the published data from highly controlled laboratory experiments. This may be due to conditions under which the use of automation for process control is difficult. Despite, the results derived from this study are in line with the trends reported in the literature for lab-scale and pilot-scale studies that have different operating conditions and the types of gasifiers installed. Research findings indicated that even for an updraft gasifier operating routinely under commercial obligations, syngas quality and steam production would be enhanced if the fuel moisture content could be maintained around 20% (wb). With air as the gasifying agent, the carbon monoxide (CO) concentration (~30%) and calorific value (>4 MJ/m3) will be higher, whereas tar concentration (<60–70 g/m3) will be lower. The near-optimal level of fuel moisture content (20% wb) is recommended for the future operation of the BRDF. It also coincides with the typical equilibrium moisture content of air-dried wood fuel under ambient air conditions, thus high temperature drying of the wood chips to attain even lower fuel moisture contents can be avoided in order to save energy. The higher CO concentration would also be beneficial in case it is desired to utilize the syngas from gasification as a platform to produce other industrial and chemical products. 94 6.2 Recommendations for future work 1. The number of tar and syngas samples can be improved in future studies. More frequent sampling events may be carried out with in-line measurements of tar concentration and syngas composition along with continuous measurements of fuel properties and gasifier operating parameters. The amount of data collected will allow for more comprehensive analysis, thus demonstrating the effects of fuel properties and plant operational conditions on tar formation and syngas quality with higher degree of accuracy. 2. Based on the research findings, blending may cause a wide variation in fuel properties. Further studies can be conducted to determine the optimum range of blended fuel properties that a biomass gasifier can tolerate. This optimum range can be based on a proper balance among syngas quality (syngas heating value or CO and H2 contents, depending on how the syngas is utilized), steam production, and tar concentration. 3. Robust cost-benefits analysis can be done to determine whether it is worth drying fuel either on-site at the BRDF or in the CFL recycling yard. This would involve estimating the benefits as well as the capital and operating costs with various target fuel moisture contents in mind. Optimal fuel moisture content can then be derived, whereby the fuel drying cost is justified by the improvements in steam generation, tar formation and syngas quality. 4. This research showed how fuel particle size and moisture content affects the gasification plant operation. In general, solid biofuel in the form of wood pellets (processed and densified wood waste) have higher and uniform quality including better particle size, lower moisture content and higher energy density. 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Pictures were taken by the author. 106 Figure A.3 Blade (left) and sieve (right) of the grinder. The opening of the sieve is either 76.2 mm or 12.7 mm. Also, the combination of sieves can be used, i.e. one 76.2 mm and one 12.7 mm. Pictures were taken by the author. Figure A.4 Industrial screener used at Cloverdale Fuel Ltd. in Langley. Double sieves utilized in the industrial screener. There are two screens and one pan with the size of 152.4 mm x 292.1 mm. Pictures were taken by the author. 107 (a) (b) Figure A.5 Storage shed at CFL: (a) With open ends at depot was effective in protected the feedstock from rain. The cement floor minimizes contamination with dirt. (b) Unloading the biomass using a truck with walking floor at the UBC-BRDF. The storage capacity is approximately 40 tonnes, adequate for three days of gasifier operation. Pictures were taken by the author. (a) (b) Figure A.6 The first two places of mixing the woody materials: a) loading of biomass into the truck at Cloverdale Fuel Ltd. in Langley; b) walking floor in the truck used to ship the biofuel to BRDF in UBC. Pictures were taken by the author. 108 Figure A.7 The route from CFL to UBC--‐BRDF. The distance is 50 km and a round-trip travelling time is typically about two hours. There are usually two or three trucks per day, and they go through the city. Scheduling of the trucks is important in order to minimize the impact from traffic.
Thesis/Dissertation
eng
Chemical and Biological Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Logistics of wood chips supply and performance analysis of an industrial updraft gasifier
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