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Development of an alternative biofilter system for odor treatment Lee, Dal-Hoon 1998

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DEVELOPMENT OF AN ALTERNATIVE BIOFILTER SYSTEM FOR ODOR TREATMENT By Dal - Hoon Lee B. Eng., Hanyang University, Seoul, Korea, 1971 M. Eng., University of Alberta, Edmonton, 1979 M. A. Sci., University of British Columbia, Vancouver, 1994 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE STUDIES CHEMICAL/BIO-RESOURCES ENGINEERING We accept this thesis as conforming to the required standard THE UNiVERSrHY OF BRITISH COLUMBIA Dec. 1998 ©Dal-HoonLee, 1998 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my writ ten permission. Department The University of British Columbia Vancouver, Canada DE-6 (2/88) ABSTRACT Biofiltration has been used successfully to control odors, both organic and inorganic in nature. The most common and unsubstantiated assumption for packed bed operation is the plug flow behavior, which is impossible to duplicate in practice. The objectives of this study are: a) to investigate the residence time distribution (RTD) of a tracer gas passing through the biofilter; b) to determine a model to describe the hydraulic characteristics of the biofilter; c) to measure the ammonia removal efficiency and elimination capacity; and d) to determine the order of reaction and the related kinetic parameters. The standard biofilter with vertical gas upflow and the modified biofilter with horizontal gas flow were studied. The latter system design was novel. First, step tracer tests were conducted on these lab-scale biofilters to determine the RTD of CO2 passing through the filter. Both biofilters consisted of two layers. Matured compost materials were used as the filter medium. Next, the kinetic parameters were calculated from the break-through results and the RTD's as previously determined. The hydraulic characteristics can be modeled in terms of the number of continuous stirred tank reactors (CSTR) in series. The second bed had a better removal efficiency than the first bed. The merits of the modified system lie with a greater ammonia removal efficiency compared to the standard biofilter system. Given an improved removal efficiency, the size and therefore the capital cost of biofilter could be ii reduced. Porosity differences in the beds may explain the larger microbial population in the second bed. The modified biofilter was better than the standard biofilter, in terms of elimination capacity. The biodegradation of ammonia was found to be first-order at low substrate concentration. Each of the beds in the two-bed biofilter had significantly different reaction rate constants. These differences were explained by measurements of bacterial concentrations and surface area. These findings have helped in the development of a quantitative understanding of the principle and operation of a biofilter. This study is a first attempt in testing the RTD of non-column type reactors with practical measurements to model the flow as the number of CSTR's in series. iii TABLE OF CONTENTS Abstract ii List of Tables viii List of Figures ix Acknowledgement xi Chapter 1 INTRODUCTION 1 1.1 General 1 1.2 History of Biofiltration 2 1.3 Comparison with Traditional Air Pollution Control Methods 5 1.4 Comparison with Other Biotechnological Methods 6 1.4.1 Bioscrubbers 6 1.4.2 Biotrickling Filters 7 1.5 Theory and Principles of Biofiltration 8 1.6 Basic Design and Operation of Biofilters 11 2 RESEARCH OBJECTIVES AND SCOPE 13 2.1 Justification of the Study 13 2.1.1 Modeling the Reactor Operation 14 2.2 Scope of the Study and Research Objectives 16 3 LITERATURE REVIEW 17 3.1 Ammonia Degradation 18 iv 3.2 Design and Operational Parameters 19 3.2.1 Microorganisms 22 3.2.2 Biofilter Medium 24 3.2.3 Moisture Effects 25 3.2.4 Temperature Effects 26 3.2.5 Filter Bed pH 26 3.2.6 Flowrate Effects 27 3.2.7 Loading Rates and Elimination Capacities 27 3.2.8 Removal Efficiency 29 3.3 Kinetics and Modeling of Biofilter Operation 30 4 THEORETICAL ASPECTS RELATED TO RESEARCH 34 4.1 Residence Time Distribution (RTD) 34 4.1.1 RTD Measurement 3 5 4.1.2 Analysis of RTD 36 4.2 Removal Efficiency and Elimination Capacity 39 4.2.1 Removal Efficiency 39 4.2.2 Elimination Capacity 40 4.3 Kinetics 40 5 MATERIALS AND METHODS 44 5.1 Experimental System for the Residential Time Distribution (RTD) Tests 44 5.1.1 Experimental Apparatus for the RTD 44 V 5.1.2 Experimental Parameters for the RTD 5 0 5.1.3 Experimental Measurement Procedure for the RTD 5 0 5.2 Experimental System for the Kinetics Determination 51 5.2.1 Experimental Apparatus for the Kinetics Experiments 51 5.2.2 Experimental Parameters for the Kinetics Study 52 5.2.3 Experimental Measurement Procedures for the Kinetics 55 5.3 Biofilter Medium Sampling 58 5.3.1 B acterial Colony Count 5 8 5.3.2 pH Measurement of the Biofilter Medium 59 5.3.3 Moisture Content of the Biofilter Medium 59 5.3.4 Specific Area of Particles 59 6 RESULTS AND DISCUSSION 61 6.1 Residence Time Distribution (RTD) 61 6.1.1 Measured RTD Values 61 6.2 Ammonia Removal Tests 64 6.2.1 Modified Biofilter System 68 6.2.2 Standard Biofilter System 81 6.3 Kinetics of Ammonia Biodegradation 82 6.4 Microbial Activity in Biofilters 89 6.5 Porosity and Fluid Flow Properties of Biofilters 97 6.6 Oxygen Limitation 100 6.7 Practical Design Example 104 vi 7 CONCLUSIONS 109 8 RECOMMENDATIONS 113 BIBLIOGRAPHY 114 APPENDICES: A Experimental Procedure 121 B Medium Preparation and Bacterial Count 123 C Measurement Procedure for Specific Surface Area of Particles and Porosity 127 D Tabulated Data and Calculations 130 E Sample Calculations 143 F Determination for First Order 145 G Calculation To Determine Reaction, n, from Experimental Data 148 H Application of Designing (sizing) for a Biofilter System 153 vii LIST OF TABLES 1.1 Relative capital and operational costs for off-gas purification 5 1.2 Advantage & disadvantages of basic biotechniques for off-gas treatment (adapted from Kok, 1992) 9 6.1 Results of RTD and hydraulic flow (N) 62 6.2 Ammonia removal efficiency and elimination capacities 70 6.3 pH and moisture contents for modified and standard systems 81 6.4 Microbiological assays for total bacteria counts and nitrifiers 93 6.5 Pressure drop and specific surface area 98 viii LIST OF FIGURES 1.1 Biophysical model for the biofilm 11 3.1 Sorption of odors 31 4.1 Typical exit age distribution, the E-curve 37 4.2 Typical downstream signal, the F-curve 37 5.1 Schematic of standard biofilter system 45 5.2 Schematic of modified bio fdter system 46 5.3 Apparatus set-up for RTD 48 5.4 Detailed specification of baffle system 49 5.5 Schematic diagram of lab-scale biofilter system set-up 53 5.6 The humidification chamber 54 5.7 Acclimation of the biofilters 57 6.1 Internal age distribution of tracer gas in the first bed of the modified system 65 6.2 Internal age distribution of tracer gas in the second bed of the modified system 66 6.3 Internal age distribution of tracer gas of the standard system 67 6.4 Flow model of compost packed-biofilters 68 6.5a Effect of ammonia loading rate on the removal efficiency of the first bed (modified) 71 6.6a Effect of ammonia loading rate on the removal efficiency of the second bed (modified) 72 6.7a Effect of ammonia loading rate on the overall removal efficiency of the modified system 73 6.5b Effect of ammonia loading rate on the elimination capacity for the first bed 74 ix 6.6b Effect of ammonia loading rate on the elimination capacity of the second bed 75 6.7b Effect of ammonia loading rate on the elimination capacity of the overall beds 76 6.8a Effect of ammonia loading rate on the removal efficiency for standard system 79 6.8b Effect of ammonia loading rate on the elimination capacity for standard system 80 6.9a Dynamics of ammonia removal at an airflow rate of 3 cffn 83 6.9b Dynamics of ammonia removal at an airflow rate of 4 cfm 84 6.9c Dynamics of ammonia removal at an airflow rate of 5 cfm 85 6.10 Normalized vertical concentration distribution on height for standard system 87 6.11 Variation of first-order reaction rate constant with the loading rate of the first Bed 90 6.12 Variation of first-order reaction rate constant with the loading rate of the second bed 91 6.13 Variation of first-order reaction rate constant with the loading rate of the standard system 92 6.14a No. of nitrifying bacteria at first and second bed 95 6.14b No. of total bacteria at first and second bed 96 6.15a Pressure drop on first & second bed and standard system 101 6.15b Permeability (K) on first & second bed and standard system 102 6.15c Surface area of porosity on first & second bed and standard system 103 6.16 Comparison of modified vs. standard system (removal efficiency based on measured data) 106 6.17 Predicted vs. measured elimination capacity for standard system 107 6.18 Predicted vs. measured elimination capacity for modified system 108 Acknowledgement The author wishes to express his gratitude to Dr. Kenneth L. Pinder for receiving me in his research group and assuming the responsibility of examiner. His ideas, supports, suggestions and experience in the field of biofiltration ensured the success of this report. The author also wishes to thank to Dr. Anthony A.K. Lau for valuable advice and critical discussions, particularly in biological modeling and for acting as co-examiner. Further, the author would like to express his thanks Dr. Richard M.R. Branion and Dr. K.V. Lo for reviewing the initial manuscript, providing me with the necessary advice and sitting on my Committee. Last, but by no means least, my wife Hae-kyu for her encouragement, supports and understanding during the course of this doctoral study. The financial support of the National Science and Engineering Research Council (NSERC) of Canada is also gratefully acknowledged. x i 1 C H A P T E R 1 I N T R O D U C T I O N 1.1 General Odor generation by various industries is considered to be a big problem in North America because neighbours have become less tolerant of low-level releases that were previously acceptable. The closure of three large Municipal Solid Waste composting plants in the United States since 1990 is a good example. Another example is the continual odor problems experienced by a mushroom composting facility in the Fraser Valley of British Columbia in recent years. Increasingly stringent federal and provincial regulations have made it necessary to apply air pollution control measures at publicly owned treatment works. Traditional odor control technologies such as thermal oxidation or carbon adsorption may not be cost-effective for treating the low and variable contaminant concentrations found in large volumes of off-gases, and new technologies are required to ensure compliance with government regulations. The alternative to these traditional control methods is biological treatment of odorous waste gases. 2 Biological off-gas treatment is based on the absorption of volatile contaminants in an aqueous phase or biofilm followed by oxidation through the action of microorganisms. Biofdters, bioscrubbers and biotrickling fdters, which are used for the elimination of odor and bioconvertable volatile organic and inorganic compounds, are enjoying increasing popularity. In Europe, biofiltration has been used successfully to control odors, organic and inorganic air pollutants that are toxic to humans, as well as volatile organic compounds (VOC) from a variety of industrial and public sector sources. The experiences in Europe (in particular, Germany and Holland) have demonstrated that biofdtration has economic and other advantages over existing air pollution control technologies, more so if applied to off-gas streams that contain only low concentrations of air pollutants that are readily biodegradable (Koch, 1990). 1.2 History of Biofiltration Biofiltration is among the oldest biotechnological odor control methods. The concept of using microorganisms for the removal of odorous gases by biodegradation can be found in the literature as early as 1923 when Bach (1923) introduced the basic concept for the control of H2S emissions from sewage treatment plants. Reports on the application of this concept dating back to the 1950s were published in the U.S. and in Germany. Up to 1980 biofdtration had mainly been used to reduce odor in off-gas from sewage plants, but in 3 the early 1980s the field of application was extended to the removal of many other volatile compounds that are easily biodegraded. Pomerey (1957) described a successful soil bed installation in California. In Europe the first attempts, also with soil filters, were made for the treatment of exhaust air from composting works (Dupont, 1964). The first systematic research on the biofiltration of H2S in the U.S. was conducted by Carlson and Leiser (1966), who stated that the soil bed should be optimal for growth of the bacteria (e.g. temperature, moisture content, pH, etc.). Frechen (1972) described the removal of odorous components from the composting works of the city of Duisburg-Huchingen, which was realized by water scrubbing followed by filtration through a compost layer. Helmer (1972) conducted laboratory scale experiments with an industrial organic components filter, and concluded that the elimination of odorous organic components from air can be described by an absorption mechanism followed by simultaneous microbial degradation and regeneration of the microorganisms. Thistlethwayte et al. (1973) discussed a trickling filter column packed with river gravel or with glass balls in which waste air flows counter current with a nutrient solution. The column was seeded with activated sludge from sewage treatment works. In another article Helmer (1974) described the regeneration processes of the filter bed by microbial, thermal and chemical methods, and discussed various types of filter beds, dry and wet. Based on experimental results with biological filters in sewage treatment works in Nuremberg, Germany, Hartmann (1976; 1977) recommended that 4 every two or three years part of the compost must be replaced to preserve the structure of the filter. The moisture content of the compost proved to be of great importance. Jsger and Jager (1978) compared the cost of the biofiltration process with other modes of operation as summarized in Table 1.1, describing several methods of purifying waste gases from a composting plant at Heidelberg. Gust et al.(1979) suggested that humus-like substances could be used as filter materials; in practice peat or compost filters were often used and the choice of the filter bed material was determined by the structure, the void fraction, the area per unit volume, the specific flow resistance and the ability of the filter material to hold water. Biological filters have also been tested in Dutch sewage treatment works. An example was the air purification system of the waste water treatment plant described by Visscher et al.(1979). The authors described how a pH regulating substance (Dolokal, mainly consisting of CaCC^) had been added to enhance the oxidation of organic components. Several researchers in the U.S. have studied the soil bed concept further and demonstrated its usefulness in full-scale applications. In this aspect, Bohn (1975; 1986) has researched the theory and applications of soil beds for more than 15 years. Winer and Leson (1991) estimated that the total number of biofilter and soil bed installations in the U.S. and Canada was less than 50 at the time. These were predominantly used for odor control. 5 Reports on experiences with biofiltration in other countries, including Switzerland, Japan and Austria, can also be found (Devinny et al., 1998). Table 1.1: Relative capital and operational costs for off-gas purification Reference Maurer(1979) Jager & Jager (1978) Process Investment costs Operational costs Total costs DM/(m 3/h) DM/1000m 3 DM/1000m 3 (Application not specified) (composting works) price level 1974 Thermal incineration 12-14 1.4-1.7 9.10 (fuel costs only) Catalytic incineration 14-16 1.3-1.5 Adsorption 5-20 0.5-1.5 1.5 (include. Regeneration incineration) Absorption 8-10 0.8-1.0 4.20 (chlorine) Ozone oxidation 6-8 0.4-0.6 4.2 Biofilter open 3-10 0.3-0.5 0.6 1.3 Comparison with Traditional Air Pollution Control Methods The traditional odor control technologies can be segregated into either physical or chemical treatment processes. The physical treatment processes involve sorption of the odor compound to another media (e.g. activated carbon). Chemical treatment is accomplished by oxidation of the odor compounds (e.g. chemical oxidation, thermal and catalytic oxidation, i.e. incineration, and chlorination). Some treatment process, e.g. wet scrubbing, involve both a physical and chemical treatment phase. These traditional odor control methods can produce the desired result but they also create secondary wastes which require treatment. Additionally, they are expensive to construct and operate. While incinerators are appropriate for off-gases with higher concentrations of organics, energy costs will become prohibitive if large volumes of dilute off-gas must be treated. Carbon adsorption systems are appropriate if on-site regeneration of the carbon is feasible if a valuable raw material is to be recovered, or if only small amounts of organics need to be removed from the off-gas. Otherwise the cost for off-site carbon regeneration can become significant. Furthermore, carbon systems will achieve low removal rates for poorly sorbed compounds, such as methylene chloride. Condensation by refrigeration is an efficient method of material recovery if used on a highly concentrated and relatively pure off-gas stream. The removal of highly volatile compounds from dilute off-gas streams, however, requires large compressors involving high energy costs. 1.4 Comparison with Other Biotechnological Methods 1.4.1 Bioscrubbers Like wet scrubbing, the off-gas is contacted generally with water in a spray tower with inert packing, resulting in absorption of off-gas components in the water phase. The water 7 with the dissolved target compounds is subsequently treated and reused or discharged. In the case of bioscrubbing, treatment takes place by biological degradation of the compounds. Thus, a bioscrubber consists of a scrubber and a bioreactor with activated sludge. The aqueous phase (often with suspended microorganisms) is continuously recirculated over the two separate units. In contrast to biofilters the liquid phase in bioscrubbers is mobile, which allows a better control of the reaction conditions. Nutrients and buffers can be added and the liquid can be refreshed and discharged in order to remove undesired products. In addition, temperature, pH and ionic strength can be monitored and controlled more easily. A drawback, compared to biofilters, is the lower specific gas/liquid surface area. Scippert (1989b) developed a specialized model for the elimination of poorly water-soluble VOCs in a bioscrubber with added organic solvent as second liquid phase. 1.4.2 Biotrickling Filters Biotrickling filters can be regarded as an intermediate between biofilters and bioscrubbers. In biotrickling filters the off-gas is forced through a packed bed with inert material covered with an active biofilm. Like bioscrubbers, liquid is sprayed on the packed bed and continuously recirculated, which makes control of the reaction conditions easy. In contrast to bioscrubbers, absorption and biodegradation of the target compounds are combined in one column. A separate bioreactor for regeneration is not necessary, unless different reaction conditions are needed for conversion of intermediates or other 8 off-gas components. Biotrickling filters have a lower specific surface area (100-300 m2/m3) than biofilters (Ottengraf, 1987), which makes them unfit for treatment of poorly water-soluble compounds. A model for biotrickling filters was presented by Wolff (1992) in which the elimination efficiency is a function of the maximum reaction rate, concentration in the water phase, height of the filter, Michaelis-Menten constant, Henry's law coefficient, superficial gas velocity, specific surface area and concentration in the influent gas. A similar model for biotrickling filters and biofilters was described by Windsperger(1992). The advantages and disadvantages of the three biotechnological techniques are summarized in Table 1.2. In compost production plants, sewage plants and in agricultural facilities, there is a preference for biofilters and biotrickling filters, while biofilters and bioscrubbers are preferred in chemical and food processing industries (Ottengraf, 1987). 1.5 Theory and Principles of Biofiltration Biofiltration is a process which utilizes microorganisms immobilized in a biofilm on the surface of the filter material. As odorous or contaminated off-gases from the emitting source are forced through this medium, two basic removal mechanisms occur simultaneously, they are absoiption/adsorption and bio-oxidation. Given sufficient residence time, the air contaminants will diffuse into a wet, biologically active layer (the so called, "biofilm") which surrounds the fdter particles. The biofilm concept is frequently used to describe degradation processes in aqueous systems. Table 1.2: Advantage & disadvantages of basic biotechniques for off-gas treatment (adapted from Kok, 1992) 1. Biofiltration Advantages Disadvantages - High gas/liquid surface area Easy operation and start-up Low operation costs - Poor control of reaction conditions Slow adaptation to fluctuating concentrations in gas - Large area required 2. Bioscrubbing Advantages Disadvantages Better control of reaction conditions (pH, nutrients) Possibilities to avoid accumulation of products Compact equipment - Low pressure drop Low surface area for mass transfer Wash out of slow growing microorga-nisms Stagnation periods of a few days detrimental - Disposal of excess sludge Complicated start-up procedure Extra air supply needed at high degradation rates - High investment, maintenance and operational costs 3. Biotrickling Filtration Advantages Disadvantages - Comparable to bioscrubbing Better retention of slow growing microorganisms - Single reactor Low surface area for mass transfer - Disposal of excess sludge Complicated start-up procedure - Higher operational costs 1 0 Microorganisms (principally bacteria and actinomycetes) are attached to the filtering medium. This medium acts as an inorganic nutrient supply and/or organic substrate for the microorganisms, thereby supplementing these nutrients which may or may not be present in the gas stream being treated (Ottengraf, 1986). As odorous compounds are oxidized (aerobic degradation), adsorptive sites on the biofilters become available for additional odorous compounds in the gas stream, thereby self-regenerating the fdter's odour removal capacity. End products from the complete bio-degradation of many air contaminants are C O 2 , water and microbial biomass. The oxidation of reduced sulfur compounds and chlorinated organic compounds also generates inorganic acidic compounds such as chlorides and sulfates. In steady-state operation, the rate of microbial degradation of the sorbed odorous compounds must equal or exceed the absorption/adsorption rate in order to maximize odor removal rates. If fdters are fully saturated (Figure 1.1, Case 1), pollutant elimination is limited by the biological activity in the film, assuming that the gas film resistance is negligible. When the biofilm is no longer fully penetrated (Figure 1.1, Case 2), then pollutant removal is limited by diffusion in the biofilm. If fdters are overloaded, absorption sites are filled faster than they are regenerated by bio-oxidation, causing break-through of odorous gases into the atmosphere (Bonn and Bonn, 1988; Kuter, 1990). 11 Figure 1.1: Biophysical model for the biofilm 1.6 Basic Design and Operation of Biofilters The optimal design and operation of a biofilter system is very important and requires a number of technical considerations: These include microorganisms, filter medium, moisture effects, temperature effects, filter bed pH, gas flowrate effects, loading rate and elimination capacities, pressure drop and permeability, and filter construction and sizing. 12 In Chapter 3, the above parameters will be discussed in detail. In adition, more detailed discussions of these topics can be found elsewhere (Ottengraf, 1986; Van Lith, 1989; Fischer et al., 1990). The basic contribution of this thesis is providing an approach to the analysis of the operation of biofilters of any shape or size with respect to the kinetics of odor removal, kinetics being the study of the rate of biodegradation in a biofilter system. 13 CHAPTER 2 RESEARCH OBJECTIVES AND SCOPE 2.1 Justification of the Study The biofiltration process is a microbial system incorporating microorganisms in a biofilm which are growing on a porous solid medium like compost, peat, soil or a mixture of these materials. Due to its major advantages over other gas cleaning systems, simplicity of operation and low cost, biofiltration has become an alternative industrial air purification technique. Although the process has been proven to be an efficient, practical and simple biological cleaning technology, the operational and design parameters as well as the microbiological processes involved have not been well explained. In particular, little research has been carried out on the details of how biofiltration controls odorous gases and volatile organic compounds (VOC). Models of biofilter operation have been developed, but without an understanding of the non-ideal flow of reactants in a biofilter design techniques would be limited. More research is needed to demonstrate the effectiveness of these systems, to help further illustrate the utilization of biofiltration as well as to develop better biofilters based on an understanding of the fundamental physical, chemical and biological processes involved. 14 2.1.1 Modeling the Reactor Operation Most text books on chemical reaction engineering (Fogler, 1986; Levenspiel, 1972; Denbigh & Turner, 1971) cover the residence time distribution (RTD) approach to reactor design very fully, using the nomenclature introduced by Dankwerts (1953). Non-ideal flow, as this topic is called, has shown that the flow reactors do not behave like either type of ideal reactor: the perfectly stirred reactor, often called a completely stirred tank reactor (CSTR) or the plug flow reactor. In the perfectly stirred reactor the reactant is instantly distributed evenly throughout the reactor volume; and some of it may leave immediately, that is at time zero. Also in this type of reactor the reactant concentration is immediately diluted to the outlet concentration and thus the reaction rate is always relatively low compared to plug flow reactors, except for zero order reactions. In plug flow reactors the reactant is assumed to move through the reactor as a plug with perfect radial mixing and to leave the reactor as a plug after a time, t = V/u (where t: residence time, V: reactor volume and u: flow rate). Thus the reactant concentration remains at a maximum value and the conversion is a maximum. Though real reactor flow never fully follows an idealized flow pattern, a large number of designs assume that they do, often with only small errors. In other cases deviation from ideality can be considerable. Differences are noticed between observed and predicted results because of back-mixing, the presence of stagnant regions within the reactor, bypassing or short-circuiting of portions of a reactor and other non-idealities. 15 In the design of biofilters it is important to have a model of the rate at which the toxic or odorous gases will be oxidized. Residence times for biofdm reactors must take into account the flow characteristics in the bed to clearly define the distribution of times that each molecule remains in the reaction zone. Only then can the actual extent of reaction be calculated from the intrinsic reaction rate constant, or inversely the actual intrinsic reaction rate constants can be determined from operating biofilters. The most common, unsubstantiated, assumption for packed bed operation is plug flow (Hardes et al, 1992; Kirchner et al, 1992; Shareefdeen et al, 1994). Plug flow assumes that all the gas enters the reactor together and one residence time later leaves together. This is an ideal behavior, impossible to duplicate in practice in biofilters. The performance of a reactor of known free volume (V) treating a feed supplied at a rate of u (volume/unit time) is not solely determined by V, u, the feed composition and the reaction rate constant. To understand why a specific reactor can appear to have longer or shorter reaction times than predicted by t = V/u, it is necessary to look at the RTD of the reactants in the biofilter. If what is happening to the flow of gas within the reactor can be precisely known, the behavior of a reactor can be predicted. Thus far, very few studies have been made on the flow patterns and residence time distribution measurements in biofilters to characterize the actual residence times of the gas phase in a wet porous media (Kiared et al ,1996) and the flow characteristics in the beds. 16 Therefore, one of the main contribution of this research will be the use of RTD (Residence Time Distribution) in the determination of reaction kinetics in two different types of biofilters. 2.2 Scope of the Study and Research Objectives The major objectives of this research are to develop a quantitative understanding of the principles and operation of a biofdter using the removal of ammonia gas from air as a model system. To achieve these goals, the study was focused on the following topics: 1. To determine the residence time distribution of a tracer gas passing through the biofilters; 2. To determine a model to best describe the hydraulic characteristics of the biofdter; 3. To measure the ammonia removal efficiency and elimination capacity; and 4. To determine the order of reaction and the related kinetic parameters for ammonia removal. 17 CHAPTER 3 LITERATURE REVIEW Amrnonia has been found to be the most significant odor of many odorous waste gas streams, e.g., in the ventilation of intensive cattle breeding facilities, manure handling and waste composting. The removal of ammonia is also important because of its contribution to soil acidification by its deposition from air (Don, 1986). As reported by Williams and Miller (1992), ammonia gas generated from composting facilities generally is released in large amounts. Terasawa et al. (1986) compiled data for the concentrations of ammonia (0.2-50 ppm), hydrogen sulfide (0.01-0.1 ppm), mercaptans (0.16-1 ppm) and trimethylamine (< 0.1 ppm) emanating from composting facilities for garbage, fish waste and sludge. Ammonia has a odor detection threshold concentration at approximately 17 ppm (Aiha, 1989) and is commonly present in concentrations greater than 35 ppm in composting facilities (Williams, 1993). It is this intensity that causes it to mask other offensive and more pervasive odors. Ammonia, as the major nitrogen-containing constituent in composting off-gases, can impede biological activity and is toxic at high concentration. Pinnette et al. (1994) identified unusually high ammonia levels in the inlet air to the biofilter at a biosolids composting facility as a possible cause of the poor performance of the biofilter. 18 3.1 Ammonia Degradation High temperature and alkaline pH levels encountered during aerobic composting processes are conducive to ammonia release. In general, less ammonia volatilization will occur in a compost mixture having a proper bulking agent and carbon-to-nitrogen ratio (Mahimairaja et al., 1994). A high rate of respiration and thus carbon dioxide production can lower the pH of the material, yet ammonia in excess of biological requirements would accumulate during the rapid degradation period and is degassed when the respiration rate declines. Several scientists have tried to develop better biofilter systems for ammonia removal with different biofilter media. Demiriz (1992) removed ammonia exhaust gas from foundry areas and used compost packing materials. With ammonia gas from a mineral wool plant, Lehtomaki et al., (1992) compared operation of three biofilters in parallel; the packing in each was different. They were peat mixed with porous clay, composted bark and sieved compost and clay. Mackowiak (1992) researched ammonia gas removal from a plywood plant using compost mixed with sawdust and Pearson et al. (1992) studied ammonia off-gas from chicken houses with 100% mature heather to keep the pressure drop low. Hartikainen et al., (1996) have also studied the removal efficiency with peat as packing in a biofilter and Williams (1996) did research on the degradation of ammonia gas produced by composting facilities with matured compost materials. 19 3.2 Design and Operational Parameters Biofiltration is a biological process whereby odorous gases and volatile organic compounds are broken down by bacteria, actinomycetes and fungi into carbon dioxide, water, minerals, non-odorous air and increased biomass. The actual breakdown of the odors can be separated into two distinct phases: sorption and biological oxidation. As the waste gas stream passes through the fdter medium, odorous compounds diffuse and absorb into the moist biofdm surrounding the solid phase particles. Microorganisms then proceed to metabolize the sorbed gases using dissolved oxygen from the air to form carbon dioxide (if carbon is present), water and mineral byproducts such as nitrates and sulfates. Consequently, the driving forces in the breakdown process of a given substance are its solubility in water and its biodegradability. Biofiltration involves mainly the activity of mesophilic microorganisms. Optimizing the environmental conditions for microbial growth results in the optimization of odor treatment. The growth and metabolism of microorganisms need sufficient oxygen in the biofilm, the absence of toxic compounds such as sulfur dioxide, adequate nutrients and moisture, and suitable ranges of temperature and pH. Highly efficient biofiltration can only be attained with a strict control over the filter medium temperature, air flowrate and relative humidity, as well as other essential parameters, although unfavorable biochemical factors could also lead to inefficient biofiltration. 20 The filter medium must first provide an environment in which microorganisms can thrive. It must also provide sufficient quantities of nutrients to support the microbial population in the event of a system shutdown (Ottengraf, 1986). Generally, these nutrients are present in biofilter media, especially in compost rich media. Aging of the medium may also result in the loss of nitrogen binding sites and acidification, resulting in the need for additional nutrients. The medium particles should have a large specific surface for the attachment of micro-organisms and the availability of sorption sites, while maintaining a porosity of at least 60% to minimize system headloss and channelling (Ernst and Eitner, 1987). In order to achieve these two mutually exclusive conditions, blends of porous materials such as bark, woodchips and coarse sand, and materials with high specific surfaces such as compost are common. Volumetric loading rates have been reported to range between 80 to 120 m /m .h (Naylor and Gormsen, 1992). Low values give an adequate residence time for odor removal and a low pressure-drop, at the expense of a larger filter area requirement. High values result in inadequate residence times, thus not all odor components are removed; the filter material also becomes more susceptible to drying and heat losses. Overloading of absorption sites will cause breakthrough of odorous constituents in the gas stream. Several authors (Neff, 1989; Leson and Winer, 1991; Williams, 1993) quoted residence times between 30 and 60 seconds for common composting odor substances, although retention times is up to 400 seconds, are required to remove less readily degradable organic substances such as chlorinated hydrocarbons, ethers and aromatic hydrocarbons from other industrial emissions. The filter's large mass can provide sufficient buffer capacity to prevent 21 breakthroughs during peak loadings and allow sizing based on hourly average loading rather than instantaneous peak loads. Degradation rates for known air pollutants typically range from 10 to 100 g/m3.h (Ottengraf, 1986). Proper control of temperature and moisture of the fdter is particularly important at high loading rates. While degradation rates generally increase with temperature, the increase is counterbalanced by a decreasing solubility of the target pollutants. For optimum results, it is recommended that off-gas temperatures be maintained between 20 and 40°C (the mesophilic range). The limiting step in the nitrification process is the growth rate of Nitrobacter which is found to be highly temperature dependent. The maintenance of adequate moisture content within the filter bed is critical for optimum performance. The main problem with existing biofilters, regardless of medium used, is the evaporation of bound water in the porous medium; bacteria exposed to the desiccation effects of the flowing air will die. Other bacteria suspended in solution will be drawn away from the contaminants being deposited in the filter medium by the air stream. Low moisture content results in reduced biological activity and the volatilization of adsorbed pollutants and may cause compaction. Excessive moisture levels will lead to increased headloss and the formation of anaerobic zones which emit foul smelling compounds. High pressure heads in turn lead to the development of preferred pathways, channeling the raw gas through the filter untreated. An optimal moisture content range 22 from 40 to 60% (by weight) has been recommended by Leson and Winer (1991), while a range from 50 to 70% was suggested by both Mildenberger (1992) and Neff (1989). Maximum microbial activity occurs at or near neutral pH. Variations can result in a loss of diversity in the microbial population and even destroy the resident population with subsequently marked reduction in removal rates of some odorous compounds. 3.2.1 Microorganisms Several groups of microorganisms are known to be involved in the degradation of air pollutants in biofdters, including bacteria, actinomycetes, and fungi which are necessary for effective biofiltration of odorous gases. A wide range of microorganisms has been observed in operating biofdters (Eitner, 1984; Mueller, 1988). Several researchers have also investigated the spatial distribution of microorganisms within biofdters, and have observed that the density of microorganisms is greatest where VOC removal is highest (Ottengraf and van Oever, 1983; Eitner, 1984; Kampbell et al, 1987; Ergas et al., 1994). Compost-based filter materials typically show significantly higher population densities of these organisms than does soil (Eitner, 1984; Hartmann, 1976). Biofiltration relies predominantly on heterotrophic organisms that use organic off-gas constituents as carbon and energy sources. As a result, introduction of these compounds into the filter material upon start-up will generally shift the distribution of existing microbial populations towards strains that metabolize the target pollutants (Leson and Winer, 1991). The 23 degradation of poorly biodegradable compounds may require inoculation of the bed with microorganisms that have been previously exposed to a contaminant. Activated sludge suspensions, soil from a petroleum landfarm and specially cultivated microorganisms have been used as inoculum to enhance the degradation of resistant compounds (Ottengraf, 1986; Hodge et al., 1991). Acclimation time can be defined as the time it takes for a biofilter to reach a pseudo-steady-state or maximum removal efficiency after start-up. Acclimation time depends on a number of factors, including the complexity of a gas stream (number of chemicals), properties of specific contaminants, and characteristics of the bed material. However, steady- state biofilter operation of greater than 10 days is generally required to allow microbial acclimation to the specific waste gas streams being treated (Ottengraf, 1986; Shoda, 1991). Peters et al., (1993) reported a wide range of start-up times for a range of bed materials and substrates as did Ottengraf and van den Oever (1983), Peter et al. (1993), Barshter et al. (1993), and Dhamavaram et al. (1993). Growth and metabolic activity of microorganisms in a filter depend primarily on the presence of dissolved oxygen in the biofilm, the absence of compounds that are toxic to microorganisms, the availability of nutrients, sufficient moisture, and suitable ranges for temperature and pH. Therefore, the control of these parameters, as discussed below, is essential for the efficient operation of a biofilter. 24 3.2.2 Biofilter Medium In order for a biofdter to operate efficiently, the filter material must meet several requirements. First, it must provide optimum environmental conditions for the resident microbial population to achieve and maintain high degradation rates. Second, the filter particle size distribution and pore structure should provide a large reactive surface and a low pressure drop. Third, compaction should be kept to a minimum, reducing the need for maintenance and replacement of the filter material. Compost derived from municipal waste, bark, tree trimmings and leaves is widely used as basic filter material (Demiriz, 1992; Williams, 1996). Usually, compost recycled from waste material is relatively inexpensive and provides sufficient inorganic nutrients for microorganisms and the addition of nutrients will not be required (Bardtke, 1990). Other materials, such as porous clay or polystyrene spheres are sometimes added to increase reactive surface and durability, reduce back pressure and extend the filter material's useful life. Activated carbon can be used to increase the filter's buffer capacity for emissions from sources that operate only intermittently. This can reduce the required filter volume significantly (Ottengraf, 1986). The packing material can be a mixture of a natural fibrous substance with a large specific surface area and a coarse fraction (Mackowiak, 1992). Widely used materials are compost and peat. The coarse fraction serves as support material, prevents high pressure drops in the filter and may consist of inert materials like polystyrene or lava particles, or partially 25 active natural material like wood bark, wood chips and heather (Ottengraf and Diks, 1992; Pearson et al., 1992). Many mixtures have been tested to develop optimal filter materials with high activities and a low flow resistance (Don and Feenstra, 1983; Don, 1986; Ottengraf and Diks, 1992). Eitner (1989) has developed a set of easily measured parameters to assess the suitability of a given filter material. For fresh material he recommends a pH between 7 and 8, a pore volume of greater than 80 percent, a fdter material size of greater than 4 mm and a total organic matter content, measured as loss on ignition, of more than 55 percent. 3.2.3 Moisture Effects Moisture control is crucial for maintaining optimal biofilter performance. Without providing additional moisture, the raw gas (usually unsaturated) would quickly dry out the fdter bed. Optimal moisture content within a fdter range between 40 and 60 percent by weight depending on the media used (Eitner, 1984; Ottengraf, 1986; Mueller, 1988). However, Bohn (1975) suggested, in his studies, that 20 to 40 percent water content by dry weight basis is optimal for compost filters and 10 to 20 percent by dry weight basis is optimal for sandy soil and somewhat higher for finer-textured soils. A biofilter bed with a moisture content of less than 30% is generally not useful for VOC control (Sabo et al, 1993). 26 Humidification of incoming waste gas is the preferred method to prevent bed drying. Williams and Miller (1992) recommended a degree of saturation of greater than 95% in the waste gas inlet stream. 3.2.4 Temperature Effects The temperature of a biofdter bed is affected by the exothermic biological reactions that occur within the bed, as well as the temperature of the gas stream that is treated. Temperature itself affects the rate of microbial activity, and can play an important role in evaporation of water from the biofdter bed. Optimal temperatures for aerobic compost biofdters have been noted to be between 25 and 35°C (Mueller, 1988), i.e., the mesophilic temperature range. Bohn (1976) suggested that it is critical that biofilter beds should not exceed 60°C. Thus, for hot waste gas streams, it may be necessary to reduce gas temperature using a heat exchanger upstream of the filter bed. Hartikainen (1996) reported that temperatures as low as 5°C didn't affect the operation. 3.2.5 Filter Bed pH Changes in the pH of the filter material will strongly affect their activity because most microorganisms prefer a specific pH range. The pH in compost filters is typically between 7 and 8, a range preferred by bacteria and actinomycete. Mueller (1988) suggested that 27 optimal biofilter performance occur with a bed pH of 6 to 8. Leson and Winer (1993) reported the optimal conditions of pH 7 to 8 for compost biofilters. The oxidation of nitrogen, sulfur, and chlorine-containing compounds can lead to the formation of acid intermediates which lower bed pH. The resulting drop in the pH can destroy the resident population and reduce the filter's degradation capacity. In such cases chemical buffers, such as lime, are added (Bardtke, 1990; Van Lith, 1990; Eitner, 1990; Williams, 1996). Chalk, marl and oyster shells have also been used to buffer such acid production (Ottengraf, 1983; Ergas et at, 1994). 3.2.6 Flowrate Effects Increases in gas flowrate can have some negative impacts on removal efficiency; i.e., without changes in concentration, an increase in gas flowrate equates to an increase in input mass loading, and possibly exceeding of the elimination capacity of the biofilter. Bonn (1976) suggested that bed drying and cracking can occur at high gas flowrate. Similarly, Barshter et al, (1993) observed that bed drying was due to locally high air flowrate in full-scale biofilters. These kinds of effects were observed to be most frequent near comers and along the walls of the vessel containing the bed material. 3.2.7 Loading Rates and Elimination Capacities Elimination capacity relates the amount of pollutant removed to the flow and to the volume of the filter. Hartikainen et al. (1996) found the removal of ammonia to be 28 effective, with a mean removal rate of 95%, when inlet N H 3 concentrations were less than 14 mg /m3 (or 1.8 g NHVm3 peat per hour). The biofilter became overloaded at a concentration of 45 mg/m3 (or 7.9 g N H 3 /m3 peat per hour), which caused an accumulation of ammonium and nitrite, as the high concentrations of ammonium ion inhibited oxidation of the nitrite ion, thus lowering the removal efficiency. The load of ammonia, being 0.17 g N/kg dry peat per day, was rather similar to the maximum capacity of a peat biofilter to oxidize ammonium (0.18 g N/kg dry peat per day). This was reported by Togashi et al. (1986) who tested loads of 0.70, 1.56, 3.12 and 7.01 g N/kg dry peat per day. The maximum removal rate of ammonia was found to be about 70% of the nitrifying capacity of the peat biofilter. Although almost complete removal of ammonia was initially achieved in all runs, breakthroughs depending on ammonia loadings were observed. This suggests that ammonia was removed by adsorption onto the peat. On the contrary, when soil was used as the filter medium, at an N H 3 loading of 0.15 g N/kg dry soil per day no breakthrough was seen, but at a higher loading of 0.4 g N/kg dry soil per day, breakthrough did occur. Leson et al. (1991) suggested that elimination capacities from 50 to 100 g/m3h are typical for easily degradable compounds. Large fluctuations in inlet mass loading can reduce the performance of a biofilter by temporarily exceeding the elimination capacity, and/or causing toxic shock to microorganisms. A sudden but sustained increase in mass loading may require an intermediate acclimation period for microorganisms to "adapt" to the increased loading. 29 3.2.8 Removal Efficiency Several researchers have studied ammonia removal by biofiltration. Some discrepancies have been found between these scientists. Shoda (1991) reported that ammonia was removed mainly by adsorption to peat or absorption into water in the biofdter but Terasawa et al. (1986) found that removal was achieved through nitrification. Apparently the microbial contribution to the removal of ammonia is very small unless a significant acclimation period is provided or the media is seeded with nitrifying bacteria. Sweeten et al. (1991) reported ammonia removals from a poultry manure composting facility's off-gases of between 87% and 99% through a soil biofilter. Don (1985) suggested that ammonia removal efficiencies dropped when ammonia concentration in the gas stream being treated exceeded 35 ppm because of toxification from the build-up of ammonia in the filter and Hartikainen et al. (1996) reported that the filter became overloaded at 50 ppm. Weckhuysen et al. (1994) conducted lab-scale biofiltration experiments with wood bark as filter medium at a pH of 7.2 and 58% moisture content for a waste stream containing 4 to 16.5 ppm (v/v) of ammonia. The size of the experimental biofilter column was 0.10 m diameter and 0.33 m in height, in each of the three sections. The volumetric load was 100 3 2 m /m h. Ammonia concentrations were measured during a 7-week operation. The results, expressed as removal efficiency, showed a mean elimination efficiency of 83%. The first section of the biofilter removed only about 20% of the ammonia. Under optimal 30 conditions, efficiencies of 95% and more were achieved. In comparison, Eggels (1986) reported elimination efficiencies of 90% and more with an optimal moisture content, and ammonia input concentration of less than 24 ppm. 3.3 Kinetics and Modeling of Biofilter Operation In a biofilm, nutrients are transported by diffusion, which is described by an effective diffusion coefficient, D e (Yu and Pinder, 1994). Theoretical descriptions of the processes involved in the operation of a biofilter published by several researchers (Ottengraf, 1986; Van Lith, 1989; Sabo, 1990; Dirks and Ottengraf, 1992). In particular, Ottengraf (1986) provided a comprehensive analysis of the overall process, presented experimental data to support his model, and discussed its implications for the design and operation of a biofilter. The major assumptions underlying the overall model are that microkinetics of the biodegradation occurring in the biofilm follow the Michaelis-Menten relationship and that off-gas flow through the filter can be described as plug flow. A conceptual, biophysical model of the biofilm is shown in Figure 3-1. The gas- and liquid-phase concentrations of each pollutant are also assumed to be always in equilibrium at the phase boundary and related by Henry's Law. 31 Figure 3.1: Sorption of odors Bacterial cell mass Ottengraf and van den Oever (1983) were perhaps the first to suggest a theoretical approach to biofiltration and to develop a solution to a steady state mass transfer equation. Mass transfer in the gas phase and in the biofilm was solved for first order and zero order biodegradation. With zero order biodegradation, the Michaelis-Menten kinetic coefficients are such that the half - velocity constant or substrate concentration at one-half the maximum growth rate is much less than the concentration of growth-limiting substrate, i.e. Ks « S. Two situations are distinguished: one in which diffusion does not limit the reaction, meaning that the biofilm is fully active and hence the bioconversion rate is only controlled by the reaction rate; and one in which diffusion limitation occurs, i.e. the biofilm is not fully active, and the depth of penetration in the biofilm is smaller than its thickness, hence the bioconversion rate is controlled by the rate of diffusion. One of the 32 key assumptions of the model is that a water film of uniform-thickness surrounds solid spherical particles; other assumptions include plug-flow behavior and no consideration of substrate inhibition effects. Otten and Janes (1994) presented a model for the biofdtration of odorous compounds, and dimethyl disulfide was chosen as a representative of these odorous compounds due to its predominance in compost odors. The mass transfer model incorporates Michaelis-Menten (or Monod) kinetics parameters. Leson et al. (1991) reported that the removal of ethanol in a lab-scale biofilter was zero order with respect to ethanol. Shareefdeen et al. (1993) derived and validated a kinetic model of biofiltration for the removal of methanol vapor. They took into account the effects of substrate inhibition and the diffusion of both the substrate and oxygen. Ergas et al. (1993) proposed that the removal of dichloromethane, trichloroethene and toluene followed first order kinetics. They characterized a compost biofiltration system degrading dichloromethane. As predicted by biofilm models which incorporate first order biodegradation kinetics, the removal efficiency, r\, is given by: n = 1-C/C 0= l-exp(-Kzt) where C is the bulk gas phase concentration at a position (z) in the biofilter column, C 0 is the inlet substrate (contaminant) concentration, Kz is a function of a first-order biodegradation rate constant (1/s) and t is a time. 33 However, naturally bioactive materials consist of porous particles that contain a distribution of active microorganisms inside the porous particle. Wang and Govind (1997) suggested that a shrinking -core model, commonly used for porous catalysts, should be employed to describe this process because diffusion-bioreaction is more likely to take place inside the particle rather than in a biofilm outside the particle. Deshusses (1994) developed a dynamic model of a biofdter for the biodegradation of mixture of ketone vapors in waste air. The biofilter height is divided into 10 layers in the finite-difference model which involves the gas phase system of the biofilm and the sorption volume. Pulse tracer tests showed that the bed behaved in a manner close to plug flow. The dispersion number was converted to 26 tanks in series for his model. 34 CHAPTER4 THEORETICAL ASPECTS RELATED TO RESEARCH In the design of biofdters, it is important to obtain accurate kinetic expressions which describe the biodegradation rate at which odorous gases will be oxidized in the biofilm. Residence time distribution for biofilter reactors must take into consideration the flow characteristics in the bed to clearly define the distribution of times that each molecule remains in the reaction zone. Very few studies have thus far been directed toward the residence time distribution measurement in biofilters which characterize the actual flow times of the gas phase through a wet porous media (Deshusses, 1994; Kiared et al., 1996). To understand why a specific reactor can appear to have longer or shorter reaction time than predicted times, it is necessary to look at the residence time distribution (RTD) of the reactants in the biofilter. 4.1 Residence Time Distribution (RTD) MacMullin and Weber (1935) first studied residence time distribution (RTD) in the analysis of chemical reactor performance. Most later text books on chemical reaction engineering (Fogler, 1986; Levenspiel, 1972; Denbigh and Turner, 1971) cover the RTD approach to reactor design very fully, using the nomenclature introduced by Dankwerts (1953). 35 The residence time distribution (RTD) of a reactor is a physical characterization of the mixing that occurs in the reactor. The time, which the reactant has spent in the reactor, is called the residence time in the reactor. Nonideal flow, as this topic is called, indicates that the flow reactors do not behave like either type of so-called ideal reactors: the perfectly stirred reactor, often called a completely stirred tank reactor (CSTR), or the plug flow reactor. In the perfectly stirred reactor the reactant is instantly distributed evenly throughout the reactor volume; and some of it may leave immediately, that is at time zero. Also in this type of reactor the reactant concentration is immediately diluted to the outlet concentration and thus the reaction rate is always low, except for zero order reactions. In plug flow reactors, the reactant is assumed to move through the reactor as a plug with perfect radial mixing and to leave the reactor as a plug after a time, V/u. Thus the reactant concentration remains at a maximum value and the conversion is a maximum. In biofilters, neither of these ideals may be attained in practice. To determine the true residence time distribution of reactants in a reactor, tracer tests are made. 4.1.1 RTD Measurement The RTD is determined by either injecting a pulse of an inert gas of concentration, c0, called a tracer, into the reactor at time, t=0, and then measuring the tracer concentration, 36 c, in the effluent stream as function of time or by using a step change in tracer concentration. Any material which can be easily measured, is of the same phase as the reactant, does not react with the packing and which does not disturb the flow pattern in the vessel can be used as a tracer. The time record of tracer in the exit stream from the biofilter is measured as c/c0 to give the C-function for pulse inputs or the F-function, F(t), the fraction for step inputs as a function of time. Typical E-curve and F-curve are shown in Figures 4.1 and 4.2, respectively. The E-curve of a step input and the C-curve of a pulse input are same. 4.1.2 Analysis of RTD The distribution of times for the stream of tracer leaving the biofilter is called the exit age distribution, E, or the residence time distribution (RTD) of tracer. With no tracer initially present anywhere, impose a step input of tracer of concentration, C 0 , on the tracer stream entering the biofilter. Then a time record of tracer in the exit stream from the biofilter, measured as c/c0, is called the F-curve. The residence time distribution function, E(t) can be derived from the cumulative residence-time distribution function, F(t) as follows: Eft) = - dF(t)/dt (1) 37 The residence-time distribution function is also known as the exit age distribution of fluid leaving a vessel. Here, the term "age" for an element of the exit stream refers to the time Figure 4.1: Typical exit age distribution, the E-curve Figure 4.2: Typical downstream signal, the F-curve 38 spent by that element in the vessel. Another useful piece of information is the internal-age distribution, I(t)*dt, which represents the fraction of material inside the reactor that has been inside the reactor for times between t and t + dt. I(t) characterizes the time the tracer gas has been (and still is) in the reactor at a particular point in time. The function E(t) is viewed outside the reactor and I(t) is viewed inside the reactor. In unsteady-state problems, it can be important to know what the particular state of a reactor mixture is, and I(t) gives this information. I(t) may be computed from F(t), as E(t) is called the exit-age distribution function or the residence time distribution of tracer gas. This is defined by saying that the fraction of tracer gas in the outlet stream which has been in the system for times between t and t +At is equal to E dt. E is a function of t, hence, Tracer information is used either directly or in conjunction with flow models to predict performance of real flow reactors. The performance of nonideal flow reactors can be determined precisely given the RTD and the rate constant for say a first-order biochemical reaction (reaction rate, r = k*c). I(t) = l-F(t) (2) Jo C °E(t)dt=l (3) 39 In most plug-flow like reactors, the flow is nonideal because of entrance and exit flow disturbances and axial dispersion caused by the velocity profile. Because it is difficult to model these effects, the plug-flow configuration is usually simulated as a series of completely-mixed reactors or as a dispersed flow. The hydraulic characteristics of the unconventional biofilter can be modeled in terms of the number of equal continuous stirred tank reactors (CSTR) in series, N . To obtain N , the following equation is used (Levenspiel, 1972). a 2 / V 2 = 1/N (4) where V= £ ti Ej At, and a 2 =[ Z tj2*Ej*At] - V 2 (5) i i 4.2 Removal Efficiency and Elimination Capacity System performance may be represented by considering both ammonia removal efficiency and elimination capacity. These measurements must be taken after sufficient time has occurred so that both initial ammonia loading and the step increase in ammonia concentration, have reached steady-state or stabilized biofilter response. 4.2.1 Removal Efficiency Generally the removal efficiency can be expressed as follows; 40 R = [ ( c „ - c ) / c 0 ] *100% (6) Where R is the removal efficiency, [%], c0 is the inlet concentration, [ppm], and c is the outlet concentration, [ppm]. 4.2.2 Elimination Capacity The most important parameter used for designing /sizing a biofilter is its elimination capacity, since it relates the inlet load of pollutant to be treated to its removal efficiency. The elimination capacity of a filter bed is defined as the mass of contaminant compounds degraded per unit volume of bed material per unit time. The general equation for elimination capacity is given as; where E c is the elimination capacity, [g NH3/m 3.h], u is the gas flow, [cfm], V is the biofilter volume, [m ] and f is a units conversion factor. 4.3 Kinetics E c = f * ( C „ - C ) * U / V (7) A number of theoretical and empirical models have been found which describe the kinetics of the biodegradation of organic compounds and ammonia. Ottengraf and his co-41 works (1986) have reported studies describing the processes involved in biofdter operation. Starting with Michaelis-Menten kinetics, the substrate utilization rate is given as; r = dc/dt = -(pmX/Y)* [c/(Ks + c)] (8) where p m is the maximum specific microbial growth rate, Y is the cell yield coefficient, Ks is the half-saturation constant and X is the active microorganism concentration. The rate expression (eqn. 8) approaches first order kinetics in the substrate concentration w h e n K s » c , hence, r = dc/dt = -c(pmX/YK s) = -kc (9) where k is the first-order reaction rate constant = | a m X / Y K s . On the other hand, the rate expression would approach zero-order kinetics in the substrate concentration when K s « c : r = dc/dt = - p m X / Y = ko (10) where ko is the zero-order reaction rate constant. 42 With zero-order kinetics, if there is no diffusion limitation in the biofilm, the situation is referred to as "reaction limiting". Otherwise, if the biofilm is not fully active, the situation will be referred to as "diffusion-limiting". The Michaelis-Menten kinetics equation (eqn. 8) has been used by many researchers in analyzing the biofiltration process, especially for volatile organic compounds, such as acetone, ethanol, methanol and reduced sulfur compounds Very few scientists studied the reaction mechanism and rate constant in a biofilter system. Based on the measurements of inlet and outlet ammonia concentration and assuming a tank in series flow model, the first order reaction rate constant, k, may be calculated as follows (Levenspiel, 1972): k = l / x * [ ( c „ / c ) I / N - l ] (11) where x is the mean residence time distribution (RTD), in [s], N is the number of tanks in series for a mixed reactor system, c0 is the inlet concentration [ppm], and c is the outlet concentration from the Nth tank [ppm]. It is also useful to compute the loading rate, L, in [g NH3/m3.h], thus; L = f * c0 * u/V (12) 43 where u is the air flow rate [cfm], V is the biofilter volume [m3], c0 is the inlet concentration [ppm ] and f is the conversion factor. 44 CHAPTER 5 MATERIALS AND METHODS In order to develop a model for a biofilter system and to determine as an example the kinetics of ammonia degradation using two types of biofilter flow patterns, the research was organized to first study the residence time distribution (RTD) in a vertical bed (standard or conventional system) and in a horizontal bed biofilter (modified system). Next, the reaction kinetics were studied in these beds, using ammonia as the reactant to be oxidized. From the break-through results and the RTD's, the kinetic parameters were calculated. 5.1 Experimental System for the Residence Time Distribution (RTD) Tests Step tracer tests were carried out on lab-scale biofilters to determine the residence time distribution of gases passing through the two types of biofilters; the standard biofilter with vertical gas flow and the modified biofilter with horizontal gas flow. Figures 5.1 and 5.2 show the detailed specification for these two biofilter systems. 5.1.1 Experimental Apparatus for the RTD The standard biofilter (60 cm in width x 30 cm in depth x 90 cm in height) was designed to have similar flow characteristic to conventional biofilters. It was made with 13 mm 45 thick plywood sides and ends, and 6.5 mm thick inert Plexiglas plate front and back. The system was packed with 25 kg (wet weight) of coarse compost materials (screened to <5 mm) for the bottom layer and 35 kg (wet weight) of matured compost materials (screened to < 2 mm) for the top layer. The carbon-to-nitrogen ratio of the media was measured to be in the range 25 to 30. Figure 5.1: Schematic of standard biofilter system Outlet sample port Inlet sample port Air Distributor 46 Figure 5.2: Schematic of the modified biofilter system Off-Gas Sample) port Opening w/metal mesh o CD Air Distributor / £nd BedU o O - > - ^ - " - a O — / / ?Po/.,;> /o U/Q(bt>/ /^ o 4-' O n AIT .-v' n ....--'"0 A | / O o ! o 0 ° o / O Itt Bed 60 cm r-iO-Q-Opening w/metal mesh •Sample port 47 For measurement of the outlet tracer concentration and pressure drop, the standard biofilter had two sampling ports as seen in Figure 5.1. A water-filled manometer was used to measure pressure drops. The gas flow was controlled by a 6.5 mm needle valve connected to a gas rotameter (Figure 5.3, VI; Cole-Palmer, range 0-10 cfm). Another 6.5 mm needle valve (V2) was used to make a step change in CO2 flow. The air was from the laboratory supply, while tracer gas (C02) was obtained from a gas cylinder (PRAMAX, T-type). Both the CO2 gas line and the air line had pressure regulators. The outlet gas stream was connected to an infrared gas dispersion type analyzer (HORIBA Model No. APBA-210, Irvine, CA). This C0 2 analyzer provided a 0-100 mV D.C output signal for continuous recording with a Multi-recorder (Model MC640). A more detailed experimental procedure may be found in Appendix A. For the modified biofilter system (60 cm in length x 30 cm in depth x 90 cm in height), the physical size is the same as the standard system; the dimensions of each bed are 60 cm in length x 30 cm in depth x 30 cm in height. The modified system was unconventional, since it consisted of two beds (There was space for a third bed at the top of the unit but it was not used), and gas flow through each bed was horizontal. Baffles having greater flow area at the base than at the top were also present to prevent channeling in each bed and enhance back-mixing. Figure 5.4 shows the specifications of the baffle system. A recent study has been made by Wright et al, (1998) using a reversing flow biofilter to distribute the biomass evenly in the bed; the baffles in the modified system would have a similar effect. The same characteristic materials as used in the 48 standard biofilter medium were supplied to each bed for this reactor; 25 kg (wet weight) of coarse compost materials for the first bed, and 35 kg (wet weight) of matured compost materials for the second bed. Figure 5.3: Apparatus set-up for RTD Gas flow meter vi Gas analvzer Itiofilter Recorder V2 c o 2 Tank 49 For measurement of the outlet tracer gas concentration (C0) and pressure drops, each bed of this system was fitted with sampling ports (Figure 5.2). A water-filled manometer was also used to measure the pressure drop for each bed. The same measurement equipment was used in these studies. Figure 5.4: Detailed specification of baffle system 4 mm-typical 30 c d o o o o o o o o o o o o o o o o o o o oo oo o ooooooooo ooooooooo ooooooooo 30 cm cm 50 5.1.2 Experimental Parameters for the RTD The gas flow rate is the main parameter in this RTD measurement. Four different gas flowrates (2 cfm, 3 cfm, 4 cfm and 5 cfm) were tested for each biofdter system. 5.1.3 Experimental Measurement Procedures for the RTD Carbon dioxide ( C O 2 ) was used as the tracer gas in this step, also known as purge-step-response experiment. The biofdter beds were dried with air for 14 days before the test to prevent absorption of the tracer gas in the presence of water and/or bacteria. No gas could be found which would not adsorb into wet packing and then be released slowly at the end of the test, causing tailing of the data. The flow pattern of gas in the biofilter bed was assumed to change only very little as a result of drying even though there was a reduction in pressure drops. The tracer, C O 2 , was mixed into the air stream prior to it entering the biofilter. With no tracer initially present, a step input of tracer (at concentration C0) was imposed on the fluid stream entering the biofilter and the outlet concentration, C, was measured. The time record of tracer in the exit stream from the biofilter was measured at each bed sampling port and calculated as C/Co to give the F-function, F(t), defined as the fraction of the inlet C O 2 which had left after a given time. Triplicate runs were conducted with each combination of gas flow rate and C O 2 concentration. Flow rates of tracer gas (air plus C O 2 ) were 2, 3, 4 and 5 cfm, while the entering C O 2 concentration was maintained around 2000-3000 ppmv. These gas flow 51 rates corresponded to loading rates of 36, 54, 72 and 90 m3/m2.h, respectively. The procedure used for the analysis of the RTD data has been outlined in section 4.1.2. 5.2 Experimental System for the Kinetics Determination A schematic diagram of this lab-scale biofdter system is shown in Figure 5.5. The standard and the modified, lab-scale biofilter systems were tested to determine their ability to remove ammonia gas from air. The experimental system was an extension of the previously described set-up, and it was composed of the following three components; 1. Biofilter Column 2. Pre-humidification System 3. Ammonia Gas Sample Injection System 5.2.1 Experimental Apparatus for the Kinetics Experiments Each bed of the modified system was fitted with a sampling port for measurement of the outlet ammonia concentration (Q) and the pressure drop (Figure 5.5). Ammonia gas used in the experiment was supplied from a gas cylinder (PRAMAX, T-type), and its flow was controlled by a microflow controller and a pressure regulator. Ammonia gas was diluted with compressed air which had passed through 3-5 micrometer filters to remove any oil aerosols, and through the humidification chamber tank (Figure 5.6) into the air mixture plenum of the biofilter. The humidification chamber was made of plastic tubing (120 cm 52 in length and 15 cm i.d.) and contained a controlled water heater (10 watts) to give a temperature which produced high air humidity (95 to 100%). Water was manually and periodically added through the water make-up system. A plastic tube header (38 mm i.d.) was used to distribute the flow to the air mixing plenum. An air-drag pump (GASTEC SENSIDYNE Model No. 800) and ammonia gas detector tubes (LEVITT-SAFETY LTD.) were used to measure concentrations of ammonia in air samples taken at the inlet and outlet sampling ports. Gas detector Model No. 3L was used for the low range (0.5 to 60 ppm) and Model No. 3La for the medium ranges (10 to 250 ppm). For the standard system, the same apparatus was used. Besides the sampling ports for measurement of the inlet and outlet ammonia concentrations and pressure drop, four additional ports for gas sampling were drilled at the backside of the biofilter to obtain the vertical distribution of ammonia concentration in the filter media. 5.2.2 Experimental Parameters for the Kinetics Study The parameters measured in the experiment were ammonia inlet and outlet concentrations, pressure drop and gas flow rates. Five different inlet concentrations of 25, 50, 75, 100, and 130 ppm were tested for each gas loading rate of 3 cfm, 4 cfm, and 5 cfm. The flow rates tested were equivalent to specific flow rates of 55 to 90 m3/m2.h. Triplicate runs were conducted with each combination of gas flow rate and NH 3 concentration. 53 Figure 5.5: Schematic diagram of lab-scale biofilter system Outlet and Gas Sampling Ports for Gas Sampling Standard biofilter Mixing Chamber Draining Water Gas Regulators Draining Water Gas Moisturizing Air Flow Meter Pressure Air 54 Figure 5.6: Humidification chamber 55 These combinations of ammonia concentrations and air flow rates also represent a sequence of increasing ammonia loading rates, ranging from 0.4 to 8.8 g NH3/m3.h. 5.2.3 Experimental Measurement Procedures for the Kinetics Study The kinetic experiments were carried out in the same standard biofilter and the modified biofilter as used in the RTD study. For the acclimatization of bacterial activities, the biofilter media of both reactors were mixed with activated sludge materials and repacked before testing. Specifically, nitrifying activated sludge is most appropriate for treating ammonia. The sludge was obtained from a U.B.C. pilot-scale study of a municipal wastewater treatment plant. For biofiltration of easily biodegradable compounds, activated sludge from wastewater treatment plants has been reported as a good source of microorganisms (van Groenestijn and Hesselink, 1993). For the modified system, 25 kg (wet weight) of coarse compost and 3000 ml activated sludge materials were added to the first bed, and 35 kg (wet weight) of matured compost and 4000 ml activated sludge materials were supplied to the second bed. The modified biofilter column consisted of two beds and an air distribution section (air mixing plenum). The top bed area was left empty. The gas flow through each bed was horizontal, and one baffle was located in each bed to prevent channeling. 56 Generally, acclimation times were one to three weeks depending on the complexity of the gas stream, properties of specific contaminants and characteristics of the bed material and substrates (Peter et al., 1993). As shown on Figure 5.7, the removal efficiency was initially very high (95%) mainly due to sorption of the contaminant on biofilter media. After that, real biodegradation started with low removal efficiency (12%) and the removal efficiency increased as the microorganisms started to grow and acclimatize. The efficiency was stabilized within 10 days. Therefore, ten days were applied for incubation of this study. During the initial ten-day acclimation period, the average inlet concentration of ammonia gas was 75 ppm, and the average air flowrate delivered to the biofilter was 3 cfm. To determine the kinetic data, a breakthrough was necessary to assure that the total bed was being used. Therefore, measurements were continued until the exit concentration remained constant. The efficiency of and kinetic study of the biofilter for removing ammonia were tested at steady state by measuring the concentrations of ammonia from the inlet and outlet sampling ports. Gas samples were analyzed twice using GASTEC detector tubes (LEVITT-SAFETY LTD.). For the standard system, 3000 ml and 4000 ml of activated sludge were mixed and packed with 25 kg (wet weight) of coarse compost for the bottom layer and with 35 kg (wet weight) of matured compost for the top layer. Thus, the two biofilter systems had in total identical packings. 57 Figure 5.7: Acclimation of the biofilters 100 10 11 T i m e , days 58 The measurements of the ammonia concentration were repeated as described for the modified system, using two sampling ports for measuring the outlet ammonia concentration (C) and pressure drops 5.3 Biofilter Medium Sampling Following the series of runs using ammonia and air, the characteristics of the filter media were studied. For the modified system, biofilter medium samples (approx. 200g) were taken at the middle point of each bed. For the standard system, samples (approx. 200g) were selected at the middle point of each layer. These samples were used to measure bacterial colony count, pH and moisture content and specific surface area of the particles. 5.3.1 Bacterial Colony Count For the counting of the number of total bacteria and nitrifying bacteria, 20 g of each biofilter medium sample were suspended in 200 mL of sterile water for 15 minutes. 0.1 mL of the sample suspensions were diluted serially with sterile water to different dilutions (10 2 , 10 3 , 10 4 and 10 7), transferred to nutrient agar plates (for total bacteria) or ammonium medium plates (for nitrifying bacteria), and gently dispersed on the surface of the agar medium in the plate using a flame sterilized glass rod. Both plates were incubated at 30 °C for 3-7 days, and the number of bacterial colonies was counted with a colony counter (Gerhardt et al, 1981). Duplicate runs were conducted as a standard method and Appendix B included the experimental procedure details. 5 9 5.3.2 pH Measurement of the Biofilter Medium Biofilter medium sample (30 g) was suspended in 200 mL distilled water (pH=7), stirred for 15 minutes and the pH was measured by a pH meter (Good Digital pH/mV/Temp. meter, Model No. 201 ATC). 5.3.3 Moisture Content of the Biofilter Medium Samples of the biofilter medium were dried for 24 hours at 105°C in an oven (BLUE M ELECTRIC Co. Model No. OV 18A), and the moisture content of each sample was determined. 5.3.4 Specific Surface Area of Particles and Porosity Biofilter medium sample (2 mL) was put into an enclosed chamber of N 2 / H e mixture gases and dried over night to remove other gases. Micrometrics BET equipment was used to measure the surface area of the sample (see Appendix C for procedure). The average surface area of the first bed had 4.7 m2/g and the second bed had a larger particle surface area of 10.7 m /g due to smaller-size particles (diameter of <2 mm). The porosity was determined with a Beckman Air Comparison Picnometer (Model 930). The picnometer operates on the principle of pressure differential in two connected 60 chambers, one containing a sample of known bulk volume and one empty chamber. The difference in pressure is then calibrated to a linear scale, from which the solids volume can be read directly. The sample measurement procedure is also shown in Appendix C. 61 CHAPTER 6 RESULTS AND DISCUSSION 6.1 Residence Time Distribution (RTD) Tracer gas (carbon dioxide) was used in these step input tests (also known as purge-step-response experiments). Since all gases are to some extent soluble in water, the biofdter systems were dried with air for 14 days before the experiments. Effectively the assumption is that the flow patterns of air through wet and dry biofilter medium are similar, since the main definers of the RTD are baffles and packing porosity which changed only slightly. 6.1.1 Measured RTD Values Table 6.1 shows the residence time distribution of the tracer gas in the various layers of biofilters in the modified and the standard systems. Airflow rate had an effect on the residence time in the two biofilter systems. As airflow rates increased, the residence times of both filter systems decreased. The CSTR results showed that the modified system behaved almost like a complete-mixed reactor, whereas the standard system behaves more like plug-flow reactor. The highest difference in residence time (Equation 5) between the two biofilter systems occurred when the airflow rate was 2 cfm. Table 6.1: Results of RTD and hydraulic flow (N) A. Modified System Air Flow Rate Bed y 5i N (cfm) (second) 2 First 46 0.64 1.56 3 a 28 0.64 1.55 4 it 22 0.54 1.82 5 it 19 0.61 1.64 2 Second 60 0.31 3.25 3 a 56 0.49 2.04 4 a 37 0.60 1.68 5 a 21 0.36 2.76 B. Standard System Air Flow Rate y Si N (cfm) (second) 2 76 0.51 1.96 3 60 0.25 4.02 4 45 0.15 6.56 5 19 0.11 8.89 63 In the modified system, the residence times at all the different airflow rates were longer in the second bed than in the first bed because of differences in packing. The airflow rate producing the highest difference in residence time between the two beds was 3 cfm. For a given depth of the biofilter and a fixed airflow rate, a wet biofilter would have a larger pressure drop because of its greater bulk density, compared to a dry bed used for the tracer gas experiment. This would likely lead to shorter residence times than those measured. Figures 6.1 and 6.2 show the I-functions calculated from the F-curves obtained in the first and second beds for the experimental runs with air flow rates of 2, 3, 4 and 5 cfm, respectively. Figure 6.3 is for the standard system. The I-function was plotted against time, t. After a rapid initial drop, it exhibits a gradual decline when t = 0.5*V. Polynomial regression analyses were performed on these I-curves, and the equations thus obtained were used to derive the exit age (E(t)) functions which is used to calculate conversions. Finally, together with the measured residence time, the number of completely mixed reactors in series, N, was computed. Results are given in Table 6.1 and also in Figure 6.4 for the two layers and the standard system. Appendix D contains the detailed data used for the analysis of RTD, and sample calculations are given in Appendix E. Table 6.1 shows the computed N-values for each bed and all airflow rates involved. In the modified system, there were some differences in the N values between the first and the second bed, but the N values were not much changed as airflow rates increased. The 64 hydraulic characteristics of the two beds in the modified system can be deduced from the N values: the first bed behaved like two CSTR reactors in series (N=1.55 tol.82), while the second bed may be represented by two or three CSTR reactors in series (N=1.68 to 3.25). These results indicate that the biofilter system tested in these experiments could not be modeled as a plug-flow reactor since a large N-value (>20) (Levenspiel, 1972) is essential for such an assumption to become valid. Rather, their performances were more similar to completely mixed systems. The layered biofilter system was therefore further from being complete-mix reactor than the standard biofilter. This phenomenon is likely due to the presence of baffles in the flow path, which cause back mixing. In the standard system higher gas flow rates lead to blunter velocity distributions and larger values of N. In the standard system, the N values increased from 1.96 to 8.89 as airflow rates increased from 2 to 5 cfm. These N values are higher than for the altered system. These results can be explained because the standard system had no baffles to cause back-mixing, therefore, higher airflow gave a behavior closer to plug flow. A dispersion model of the flow-behavior could have been used for high flow rate cases, but it was decided to use only one model, the tanks in series, for both reactors. 6.2 Ammonia Removal Tests The biofiltering results, including removal efficiency and elimination capacity (Ec), for ammonia, were evaluated for the two types of biofilter systems, operated in parallel. 65 Figure 6.1: Internal age distribution of tracer gas (the first bed of the modified system) 1.1 H 0 20 40 60 80 100 120 140 T ime , s e c o n d 66 Figure 6.2: Internal age distribution of tracer gas (the second bed for the modified system) 1.1 0 50 100 150 200 T ime , s e c o n d 67 Figure 6.3: Internal age distribution of tracer gas (the Standard System) 0 100 200 300 400 500 600 700 800 T i m e , second 68 Figure 6.4: Flow model of compost packed-biofi l ters 1 0 (A 0) Ui in c re 8 H 6 H 4 H 1.5 S T A N D A R D FIRST BED S E C O N D BED 2.0 T T" 2.5 3.0 3.5 4.0 A i r F l o w , c f m 4.5 5.0 5.5 The results represent the mean values of the three (triplicate) runs done in random order; any change in organics in the packing medium will be minor even though it is not inert, replicates were carried out randomly. 6.2.1 The Modified System Table 6.2 shows the removal efficiencies at different loading rates of ammonia in air for both beds. For the first bed, the removal efficiency was very high (>90%) below a loading rate of 2.76 g NH3/m3.h. It can also be seen that the removal efficiency decreased with the inlet ammonia level throughout the biofilter. This phenomenon is similar to observations made by 69 Shareefdeen et al. (1993), Deshusses et al. (1993) and Tang et al. (1996) in their studies of the removal of other contaminants (methanol vapor, ketones and triethylamine, respectively). The removal efficiency was as low as 47 % at a medium loading rate of 8.25 g NH3/m3.h, while a maximum removal efficiency of 98 % was achieved at the lowest loading rate of 1.65 g NH3/m3.h (Figure 6.5a). The second bed had a better removal efficiency than the first bed, due primarily to the lower ammonia loading in this bed (Figure 6.6a). Ammonia is less likely to have a toxic effect on the microorganisms when the ammonia loading is low. After the second bed, the overall removal efficiency was greater than 90 % at all loadings (Figure 6.7a ); As loading rate increased beyond 8 g NH3/m3.h, the removal efficiency remained high. Therefore, it may be possible to use smaller biofilter units without jeopardizing the ammonia removal efficiency. The effect of the loading rate on elimination capacity was also analyzed, as demonstrated in Figures 6.5b to 6.7b. Elimination capacities were found to have a strong correlation with loading rates, for the first bed, the second bed, and the combined bed performance. As biofilter load was increased, the elimination rate for the biofilter also increased. In general, the elimination capacity for a filter medium depends on the nature of the material and the operating conditions of the system. Changes in operating conditions, such as acidification (lowered pH), dry-out of compost media, accumulation of ammonium or nitrite in the bed material, etc. can significantly decrease the ammonia elimination capacity of the filter medium. The opposite trend of 70 elimination capacity versus removal efficiency had been observed in an open-field standard biofilter for composting off-gases in Aldergrove, B.C. (Lau et al., 1996). Table 6.2: Ammonia removal efficiency and elimination capacities 1. Modified System 1st Bed 2nd Bed Overall Co CFM CI L % Ec C2 L % Ec C2 L % Ec 25 3 0.5 1.65 98 1.62 0 0.03 100 0.03 0 1.65 100 0.82 25 4 1 2.20 96 2.11 0 0.09 100 0.08 0 2.20 100 1.10 25 5 2 2.75 92 2.53 0 0.22 100 0.21 0 2.75 100 1.37 50 3 11.5 3.30 77 2.54 0.50 0.76 96 0.73 0.50 3.30 99 1.63 50 4 14.5 4.40 71 3.12 2.0 1.28 86 1.10 2.0 4.40 96 2.11 50 5 17 5.50 66 3.63 3.0 1.87 82 1.54 3.0 5.50 94 2.59 75 3 30 4.95 60 2.97 3.0 1.98 90 1.78 3.0 4.95 96 2.38 75 4 35 6.60 53 3.52 5 3.08 86 2.64 5.0 6.60 93 3.08 75 5 40 8.25 47 3.85 8 4.40 80 3.52 8.0 8.25 89 3.69 100 3 34 6.60 66 4.36 6 2.24 82 1.85 6.0 6.60 94 3.10 100 4 38 8.80 62 5.46 7 3.34 82 2.73 7.0 8.80 93 4.09 100 5 42 11.0 58 6.38 9 4.62 79 3.63 9.0 11.0 91 5.01 130 3 40 8.58 69 5.94 8 2.64 80 2.11 8.0 8.58 94 4.03 130 4 42.5 11.4 67 7.70 9 3.74 79 2.95 9.0 11.4 93 5.32 130 5 45 14.3 65 9.35 10 4.95 78 3.85 10.0 14.3 92 6.60 2. Standard System (July 20) C3 L % Ec Where, Co; Inlet Concentration, ppm 30 2.5 1 0.83 97 0.80 Cl,2,3; Outlet concentration of 1st, 2 n d bed and 33 2.5 1.5 0.91 95 0.87 standard, respectively, ppm 33 3 3 1.09 91 0.99 L; Loading Rate [g NH 3/m 3.h] 30 4 4 1.32 87 1.14 %; Removal efficiency Ec; Removal capacities [g NH 3/m 3.h] C F M ; Air flow rate 71 Figure 6.5a: Effect of ammonia loading rate on the removal efficiency of first bed (modified) 100 -n 90 -• 80 - • iency, % 7 0 -6 0 -• • • • • • • • /a\ effici 5 0 -• • Remo\ 4 0 -30 -2 0 -10 -0 - i i i i 0 3 6 9 12 15 Load ing rate, g N H 3 / m3 . h 72 Figure 6.6a: Effect of ammonia loading rate on the removal efficiency for second bed (modified) 100 95 90 H< 85 80 H 75 0 2 3 4 Load ing rate, g NhL /m .h 73 Figure 6.7a: Effect of ammonia loading rate on the overall removal efficiency of the modified system 80 -75 -i 1 1 1 — i 1 1 — r 0 2 4 6 8 10 12 14 16 Load ing rate, g N H 3 / m3 . h 74 Figure 6.5b: Effect of ammonia loading rate on the elimination capacity for first bed 75 Figure 6.6b: Effect of ammonia loading rate on the elimination capacity of second bed 76 Figure 6.7b: Effect Of ammonia loading rate on the elimination capacity for overall bed Unlike previous reports in the literature, the elimination capacity neither reached a constant (saturated) value, nor did it attain a peak value followed by a subsequent decline with even the highest ammonia loadings. These results indicate that substrate inhibition did not appear to pose a problem, nor was the biofiltration process inhibited by the accumulation of ammonium or nitrite ions. By using baffles in the beds, the ammonia concentration was reduced to a similar value throughout the bed because of back-mixing resulting in better bed use. No additional carbon source was required during the experimental period. Nutrient supplements were not necessary for biofiltration using compost media that contains timed-release sources of organic nitrogen, as compared to biotrickling filters and bioscrubbers. The pH of the first bed decreased where the maximum reaction rate occurred (Table 6.3, in which 0.5 & 1 indicate sample positions in bed 1 and 1.5 & 2 are in bed 2). Since the pHs of the biofilter in the modified system ranged from 5 to 7, the amount of ammonia loading that the biofilter medium was able to sustain indicated a reasonably good neutralizing capacity. After long use, some adjustment may be needed or the bed should contain limestone. Moisture content varied between the first and the second beds in the modified system (Table 6.3). In this system, the first bed contained twice the moisture of the second bed. However, there were no significant differences between the first and the second section in each bed. Proper air pretreatment before entering the biofilter and air distribution within the biofilter are also important for maintaining the bed moisture distribution, especially in large beds. The good performance shown by these tests might also be attributed to the large size of the pilot-scale biofilters as compared to lab-scale biofilters used in most studies. For instance, Weckhuysen et al. obtained a mean elimination capacity of 83% in their experiment involving three biofilter columns in series, with the first section of the biofilter removing only about 20% of the ammonia. Eggels (1986) reported removal efficiencies of 90% and more with an optimal humidity and an input ammonia loading of less than 2.4 g NH3/m3.h. Hartikainen et al. (1997) found effective ammonia removal when the loading rate was less than 1.8 g NH3/m3.h. They further observed that breakthrough occurred (filtration efficiency approaching zero) for a loading rate of 8 g NH3/m3.h or with an inlet ammonia concentration of 45 mg/m3. Toxification of the filter did not occur in the present work even though ammonia concentration was well beyond 35 ppm, a threshold suggested by Don (1983). However, it is interesting to note that when ammonia loading is expressed in terms of the mass of the filter media, as 0.17 to 0.85 g NH3/kg dry matter per day (corresponding to 1.7 to 8.8 g NH3/m3.h), the range of values of overall elimination capacity obtained (0.16 to 0.66 g NH3/kg dry matter per day) for the two beds was closer to those reported in the literature (Shoda, 1991); with a maximum of 0.19 g NH3/kg dry matter per day. 79 Figure 6.8a: Effect of ammonia loading rate on the removal efficiency for standard system 80 H 0.6 0.9 1.2 Load ing rate, g N H 3 / m .h 1.5 80 Figure 6.8b: Effect of ammonia loading rate on the elimination capacity for standard system 0.6 0.9 1.2 1.5 Load ing rate, g N H 3 / m .h 81 Table 6.3: pH and moisture contents for modified and standard systems 1. Modified System Section PH Moisture Content % <1st Bed> first section. 0.5 4.85 + 0.25 50.12 ± 2.15 second section. 1.0 7.23 + 0.37 46.94 ± 1.86 <2 n d Bed> first section. 1.5 5.95 ± 0.30 23.25 ± 1.25 second section. 2.0 6.03 ± 0.35 21.89 ± 1.20 2. Standard System 7.02 6.64 ± + 0.36 0.35 25.00 ± 1.35 30.69 ± 1.71 6.2.2 The Standard System Removal efficiency and elimination capacity are illustrated in Table 6-2 as well as Figures 6-8a and 6-8b. The removal efficiency of the standard biofilter was not as good as the modified system's at comparable loading rates. In terms of elimination capacity, the modified biofilter system is better than the standard biofilter system. E c ranged from 0.16 to 0.66 g NH3/kg dry matter per day for the former, whereas the latter's E c was 0.08 to 0.14 g NH3/kg dry matter per day. 82 The pH of biofilter material in the standard system was close to neutral in both layers (Table 6.3). Moisture content was a little higher in the second layer than in the first layer (Table6.3). As in the case of the modified system, the values of pressure drop also increased as airflow rates increased (Table 6.5). Compared to the modified system, the pressure drops in the standard system were lower at all levels of airflow rates tested. 6.3 Kinetics of Ammonia Biodegradation The dynamics of ammonia removal in the reactors after a concentration change are illustrated in Figures 6.9a, 6.9b, and 6.9c, respectively, for airflow rates of 3 cfm, 4 cfm and 5 cfm. Steady state was reached after 90 to 120 minutes of an ammonia concentration change in the air entering two-bed biofilter system. The linear dependence of elimination capacity on contaminant loading in tests in the horizontal beds suggests a first-order biodegradation rate constant (n=l). In fact, based on a log-log plot of the measured reaction rates (rate of disappearance of ammonia) and the outlet ammonia concentrations, the order of reaction was computed to be one (1). These calculations are shown in Appendix F and G. Medina et al. (1995) cited Hodge et al.'s modeling (1991) of such a behavior as having first order biological kinetics. It should be noted that zero-order biodegradation kinetics can also result in such a relationship under conditions in which diffusion limitation is occurring, and it is expected that elimination capacity will eventually become saturated. Thus, if zero-order kinetics should have been the case, the bed would have been pushed to its maximum removal capabilities. 83 Figure 6.9a: Dynamics of ammonia removal at an airflow rate of 3 cfm 80 o H i i i i i r~ 0 30 60 90 120 150 T i m e , m in . Figure 6-9b: Dynamics of ammonia removal at an airflow 4 cfm Figure 6-9c: Dynamics of ammonia removal at an airflow rate of 5 cfm 86 Furthermore, Atkinson et al. (1967) put forward the argument that a first-order biological rate equation at low substrate concentration is in agreement with Monod kinetics as deduced from experimental results. Atkinson and Daoud (1970) showed that when the inlet substrate concentration was sufficiently large, the biological rate equation would reduce to one of zero-order. The profile of ammonia levels measured at various heights of the standard biofilter are shown in Figure 6.10 in the form of normalized values with respect to the inlet ammonia level. For the two inlet concentrations tested (50 ppm and 100 ppm), the decrease in ammonia with height in the biofilter appears to suggest first-order kinetics behavior. Specifically, at a C 0 of 50 ppm, about 60% of ammonia was removed in the first quarter (lower portion) of the filter, and as the inlet ammonia increased to 100 ppm, 75% of ammonia was removed in the same region. In the upper portion of the filter, the concentration gradient between gas phase and dissolved phase is much reduced and sorption does not appear to take place at the same rate. Studies have also shown that the majority of the microbial activity occurs at the lower part of the filter because the odor concentration (i.e. food supply) is higher. Results obtained for the modified biofilter also showed similar overall behavior. Figures 6.11 to 6.13 show the variation of the reaction rate constant with loading rate. As seen in Figures 6.11 and 6.12, the 2-beds in the modified biofilter had significantly different reaction rate constants. 87 Figure 6.10: Normalized vertical concentration distribution on height for standard system 1.2 0 10 20 30 40 50 60 70 Height , c m The rate constant was actually greater for the second bed. Its k-value varied from 0.03 to 0.09 sec , with a L ranging from 0.76 to 4.95 g Nldym h. In comparison, the k-value of the first bed varied from 0.02 to 0.04 sec"1, with a L ranging from 3.30 to 14.30 g NH3/m3h. The magnitudes of the reaction rate constants are in agreement with ammonia removal efficiencies; although the loading rate for the second bed was always lower than the first bed's. The first bed's removal efficiency was consistently less than the second bed's. Using a statistical method (/-test), the reaction rate constants of each of the beds of the modified biofilters could be proved to be significantly different. The sample mean and standard deviation of the first bed were 0.02583 and 0.0095, respectively. The sample mean and standard deviation of the second bed were 0.06583 and 0.091, respectively. We tested : hypothesis H0: = 0.02583, Ha: * 0.02583. The t values were 1.5226 and the P-valve for a two-sided test was found from statistical Table E which showed that 1.5226 was beyond the 0.05 critical value (at degrees of freedom =11). When the 95% confidence interval was used for the 0.05 critical value of the t (11) distribution, it was in shown that the second bed's rate constant was not the same as the first bed's. Therefore the difference was significant at the 95%o confidence interval. Since 0.02583 lay outside the 95% confidence interval, H0 could be rejected at the 5% level. The loading rate to the second bed has a linear relationship with the loading rate to the first bed, assuming the total biofilter bed was fully used for deodorizing. Therefore other factors such as microorganisms (Table 6.4) would be accountable for the higher reaction rate constant of the second bed. 89 In contrast, in the standard biofdter, L (loading rate) did not increase beyond 1.4 g NH3 /m 3 .h, because breakthough occurred before this loading rate. The reaction rate constant was found to be relatively constant with a value of 0.025 second _ 1 based on residence time as determined previously and was smaller than the modified system's because the standard biofilter medium was not fully utilized for the deodorizing process. 6.4 Microbial Activity in Biofilters Since microbial activity is one of components affecting the efficiency of a biofilter, the populations of living microorganisms were examined in the two biofilter systems. The results are presented in Table 6.4. In the modified system, the microbial density of total and nitrifying bacteria was higher in the second bed than in the first bed. The total and nitrifying bacterial density in the second bed was two to eight times greater than bed 1. No significant difference in both the total and nitrifying bacterial density was found between the first and the second section of the first bed. In contrast, some differences in those bacterial densities were observed between the two sections of the second bed. 90 Figure 6.11: Variation of first-order reaction rate constant with the loading rate of the first bed 0.10 Load ing rate, g N H 3 / m .h 91 Figure 6.12: Variation of first-order reaction rate constant with the loading rate of the second bed 0 . 1 0 Load ing rate, g N H 3 / m .h 92 Figure 6.13: Variation of first-order reaction rate constant with the loading rate of the standard system 93 Table 6.4: Microbiological assays for total bacteria counts and nitrifiers Section No. of Bacteria Total (x10 A7)/g No. of Bacteria Nitrifying (x10 A5)/g 1. Modified system <1st bed> first section 0.5 6.6 ±0.95 1.14 ±0.54 second section 1.0 5.9 ±0.83 0.88 ±0.46 <2nd bed> first section 1.5 10.4 ±1.42 7.5 ±0.77 second section 2.0 15.5 ±1.78 10.25 ±1.34 2. Standard system 1 st layer 10.8 ± 1.45 5.49 + 0.62 2nd layer 18.6 ±2.04 11.43 ± 1.50 In the standard system, the second layer had a higher density of both the total and nitrifying bacteria. When the modified and the standard systems were compared, both the total and nitrifying bacterial densities were higher in the modified system, suggesting that more microbial activity was available in the modified biofilter system. In terms of the bacterial population, the total bacterial counts of the two biofilter systems were 19.2x 107 (for the modified 94 system) and 14.7 x 107 (for the standard system). These numbers are compatible with the numbers reported by Pearson (1992). He reported that the total bacterial count in biofilter packed with heather ranged between 5 x 107 and 3 x 10'° Colony Forming Unit (CFU)/g heather dry weight. Upon checking the bacterial density in the two beds (see Table 6.4 and Figure 6.14a & 6.14b), it was realized that the first bed had a density of 1.01 x 105/g for nitrifying bacteria, while the density of nitrifying bacteria for the second bed was 8.87 x 105/g. Total bacterial density was also greater in the second bed (average 12.95 x 107/g compared to an average of 6.25 x 10 /g in the first bed). The higher level of microbial activity in the second bed could have also contributed to a faster bio-oxidation rate of ammonia in the biofilter medium of the second bed. The operating conditions were not very different between the two beds except the moisture contents; bed temperature was around 22 °C for both beds, and the moisture content was 48% for the first bed as compared to 22.5% for the second bed. There may be other reasons for the differences in microbial activity. For instance, previous measurements by the BET instrument on a biofilter bed with similar materials indicated that the second bed had a larger particle surface area of 10.7 m /g, due to smaller-size particles (diameter of <2 mm). In comparison, the larger-size particles (2 to 5 mm in diameter) in the first bed gave rise to a smaller particle surface area of4.7m2/g. 95 Figure 6.14a: Number of nitrifying bacteria at 1st and 2nd beds 12 10 8 •8 6 £ 4 0 0.0 0.5 1.0 1.5 Sect ion (in bed) 2.0 2.5 96 Figure 6.14b: Number of total bacteria at 1st and 2nd beds 16 14 12 E 10 8 6 H 0.0 0.5 1.0 1.5 Sect ion (in bed) 2.0 2.5 97 It is thus suggested that the modified system could provide a better microbial activity than the standard system. 6.5 Porosity and Fluid Flow Properties of Biofilters Porosity differences in the beds may also explain the larger surface area and hence the larger bacteria population in the second bed. This may be demonstrated by one form of Darcy's Law which governs gas flow through a porous medium (Haug, 1993): where AP is the pressure drop [m H2O]; v is the superficial flow or discharge velocity (u/A, where u is air flowrate and A is area of biofilter) [m/s], L is the length of the flow path [m] and k is the inverse of coefficient of permeability of the porous media [s/m]. Therefore k changes with pressure drop (AP) and air flowrate (u) at the same time. The Carman-Kozeny equation (Mohsenin, 1986) considered the structure of a porous medium as a bundle of capillary tubes which are not necessarily of circular cross section. Based on classical hydrodynamic equations for slow, steady state flow through such a system, the permeability, K , varies nonlinearly with porosity, n, and inversely as the specific surface of the pores, S [m /m ] as follows: AP = k v L or k = AP/ v L = (AP*A) / (u*L) (13) K = 0.2n3/S2 (1- n)2 (14) Based on the experimental data of pressure drops and airflow rates (Figure 6.15a), the pressure drops always rose as the airflow rate increased for both biofilter systems, The second bed was the highest (from 125 to 375 pa.) and standard system (from 38 to 112 pa.) was the lowest. Figure 6.15b shows the intrinsic permeability, K, on first, second bed and standard system. K was calculated to be (average 2.87 and 1.17) x 10 "9 [m2], for the first bed and second, respectively, meaning that the second bed is less permeable than the first bed and hence less porous bed. Although the standard bed had the smallest pressure drops, it had a greater cross sectional area (0.2 m2), and hence its permeability was not the least, but intermediate between the first and the second beds. The porosity was determined with the Beckman Air Comparison pycnometer (Model 930): 72% for the first bed and 63% for the second bed. Table 6.5: Pressure drop & specific surface area air flow u, m/s (cfm) pressure drop pa. H 20 permeability K , m 2 porosity n (average) spec, surface area, S* m2/m3(cm2/cm3) 1. Modified First bed 0.018(2.5) 37 4.35E-09 0.72 14802 (148) 0.0142 (3) 63 3.10E-09 0.72 17536(175) 0.0188 (4) 112 2.32E-09 0.72 20268 (203) 0.0236 (5) 188 1.73E-09 0.72 23465 (235) Second bed 0.018(2.5) 125 1.30E-09 0.63 16761 (168) 0.0142 (3) 175 1.11E-09 0.63 18114(181) 0.0188 (4) 188 1.38E-09 0.63 16250(163) 0.0236 (5) 375 8.67E-10 0.63 20528 (205) 2. Standard 0.0059 (2.5) 37 9.75E-10 0.675 24434 (244) 0.0071 (3) 63 6.94E-10 0.675 28947 (289) 0.0094 (4) 69 8.47E-10 0.675 26218 (262) > 0.0118 (5) 112 6.50E-10 0.675 29926 (299) * from Equation (14) 99 The specific surface area (S) of the pores (Figure 6.15c) exposed to fluid flow per unit volume of the particles was calculated according to the Carman-Kozeny equation. Results as shown in Table 6.5 confirmed an increase in specific surface of the pores with porosity, under the operating conditions in the experiment. As flowrate increased, pressure drop increased and hence the specific surface of the pores increased as well and bacteria may not access these pores. These results indicated that reaction rate constant was inversely proportional to the porosity of the filter media, or proportional to the specific surface area of the packing or particles. Hence, the larger bacterial population was due to a larger surface area in the second bed. A Micrometrics BET particle surface analyzer was used to measure the surface area of the mixed dry packing. The relationship between pressure drop and surface loading rate is an important parameter for energy consumption considerations. The effect of air flow rate on pressure drop across the biofilters was tested. Results are presented in Table 6.5. The standard biofilter system exhibited a pressure drop between 0.0038 to 0.0114 m H 20, whereas the modified biofilter system had a higher pressure drop (0.0188 to 0.0563 m H20) for a combined filter depth of 0.9 m. As expected, the pressure drop values were close to data obtained by Kiared et al. (1996) and Otten and Gibson (1994). The former researchers measured a pressure drop of less than 0.06 m H 2 0 per meter of biofilter depth, while the latter observed a pressure drop of 0.032 m water for particle size 1.7-3.2 mm, and 0.0065 m water for particle size of 3.2-6.5 mm. Also Deshusses and Hamer (1993) cited typical values of 0.025 to 0.0375 m H 2 0 per 100 meter of filter bed. Hodge and Devinny (1995) reported that the pressure drop increased from 0.01 m of water at a surface load of 7 m3/m2.h to 7 cm of water at a load of 27 m3/m2h. 6.6 Oxygen Limitation One possible concern is whether oxygen was a limiting substrate. Studies by Shareefdeen et al. (1992) and Hwang et al. (1997) showed that the methanol and acetone biofiltration process was limited by oxygen diffusion. This limitation occurred at high loading rates even though the binary liquid-phase mass diffusivity of oxygen in water is twice as large as that of methanol or acetone in water. On the other hand, Deshusses (1994) investigated the kinetics of the biofiltration of mixtures of ketone vapors in compost-based biofilters, and concluded that neither diffusion nor oxygen limitation occurred and the process was controlled by the microbial growth rates. Even though the diffusivity of oxygen in water (1.8 x 10"5 cmVsec) is very close to the diffusivity of ammonia in water (1.76 x 10"5 cnvVsec), the concentration of oxygen is several orders of magnitude greater than that of ammonia in these biofiltration experiments, hence its diffusion rate should be much higher, and oxygen would not likely be a limiting factor. Rather, ammonia flow is most likely the controlling factor. 101 Figure 6.15a: Pressure drop on first & second bed and standard system 400 1 2 3 4 5 6 Air flow rate, cfm 102 Figure 6.15b: Permeability (K) on first & second bed and standard system 5e-9 -, 1 1 2 3 4 5 6 Ai r f low rate, c fm 103 Figure 6.15c: Specific surface area on first & second bed and standard system 32000 30000 H 28000 E 26000 H CO CD 24000 22000 g 20000 CO 18000 -I 16000 14000 first bed second bed standard 2 3 4 5 Air flow rate, cfm 104 Considering the heterogeneous fluid-fluid reaction of ammonia with oxygen in the biofdm, and the special case of high oxygen concentration relative to ammonia, it can be shown that if the condition (Levenspiel, 1972) applies (where PA is the partial pressure of gas phase ammonia, C B i is the concentraiton of ammonia in the liquid film, R is universal gas constant and T is temperature), b is stoichiometric coefficient, then the reaction rate can be expressed as (Levenspiel, 1972) where kA g is the mass transfer coefficient of ammonia in gas phase. The above equation is further proof of a first-order rate reaction, since the partial pressure of the ammonia is directly proportional to its concentration at the same temperature. Details of these calculations are shown in Appendix F. 6.7 Practical Design Example PA/RT < 104 CB i/b (15) r A kAg P A (16) From the parameters developed as a function of the loading rate in this thesis, a biofilter for a practical plant may be designed. Since the kinetics measured in this research were for ammonia oxidation, the off-gases from a composting plant were chosen as odorous input to 105 two types of biofilter; the modified design and the standard design. The composting plant is described in the paper by Bruce et al. (1995). The air flow rate from the composting area was 10.92 m3/s with a typical ammonia concentration of 50 ppm. A target removal efficiency of 95 % was chosen. By assuming that the modified bed would scale up to give a RTD model of two tanks in series, the loading rate for 95 % removal efficiency can be found from Figure 6.7a. The calculations given in Appendix H show that for a 5 meter long bed, 7 meter a side in cross-section the air would be stripped of 95 % of its ammonia. The pressure drop would be 26.5 cm of water. For this large volume of gas, this pressure drop is reasonable. The standard (vertical) biofilter was also designed for the same service. It needed seven times the packing volume. For comparison purposes the length of the column in the longitudinal direction was kept the same as used in the calculation of the modified bed. The result is a large cross sectional area and a pressure drop comparable to that for the modified system. The overall removal efficiency comparison between the modified system and standard system is presented in Figure 6.16. The removal efficiency (96 to 99 %) of the modified system was better than that of the standard system (87 to 97 %) in a similar range of loading rates. The predicted and measured values of elimination capacity (Ec) based on RTD were also compared as shown in Figure 6.17 and 6.18. The predicted and measured elimination capacity (Ec) for standard and modified systems are in very good agreement. 106 Figure 6.16: Comparison of modified vs. standard system (removal efficiency based on measured data) 100 98 96 94 H 92 90 88 -\ 86 o o o • • • • • o o O Modified • Standard 0.0 0.5 1.0 1.5 2.0 2.5 Loading rate, g NH 3/m .h 107 Figure 6.17: Predicted vs. measured elimination capacity for standard system 2.0 o Predicted • Measured 1.5 O ~ 1.0 • o • o • • 0.5 o 0.0 0.0 0.5 1.0 Loading rate, g NH 3/m3.h 1.5 2.0 108 Figure 6.18: Predicted vs. measured elimination capacity for modified system 3 A 0 Loading rate, g NhL/m .h 109 CHAPTER 7 CONCLUSIONS The objectives of this study were: a) to determine the residence time distribution of a tracer gas passing through the biofilter and using this data, b) to determine a model to describe the hydraulic characteristics of the biofilter; c) to measure the ammonia removal efficiency and elimination capacity; and d) to determine the order of reaction and the related kinetic parameters. Two types of biofilters were investigated, the standard biofilter system with vertical gas upflow and the modified biofilter system with horizontal gas flow and baffles to increase backmixing. Matured compost materials were used as the filter medium. The following conclusions may be made with respect to the above objectives: 1. Residence time distributions (RTD) of reactant flow in the biofilters were derived from step tests using carbon dioxide as the tracer gas. The horizontal biofilter with backmixing can accommodate a shorter residence time without the usual requirement of greater biofilter surface area, for increased biofiltration efficiency. 2. The hydraulic characteristics of the biofilter can be modeled in terms of number of continuous stirred tank reactors (CSTR) in series. Experimental results indicated that the first bed of the modified biofilter behaved like two CSTR in series while the second bed may be represented by two or three CSTR in series. Hence, the biofilter performance because of flow baffles used in the horizontal reactors was more similar to completely mixed systems and it could not be modeled as a plug-flow reactor. For the standard biofilter, the number of CSTR was found to be 2 to 9 for various air flow rates. 3. Removal efficiency of ammonia decreased as the inlet ammonia level was increased. It was as low as 47% at a loading rate of 8.25 g NH3 /m 3 h, whereas a maximum removal efficiency of 98% was achieved at the lowest loading rate of 1.65 g NH3/m3h. The second bed had a better removal efficiency than the first bed. The removal efficiency of the standard biofilter was not as good as the modified system's. 4. Porosity differences in the beds may explain the larger bacteria population in the second bed of the modified reactor. The second bed was found to be less permeable than the first bed and hence less porous. The reaction rate constant was inversely proportional to the specific surface area on the filter media. 5. The elimination rate increased as biofilter load increased, which exhibited an opposite trend with respect to ammonia removal efficiency. The modified biofilter system was better than the standard biofilter system, in terms of elimination capacity. E c ranged from 0.16 to 0.66 g NH3/kg dry matter per day for the former, and 0.08 to 0.14 g NFL/kg dry matter per day for the latter. I l l Because the flow model for the baffled reactor was similar to that of a CSTR, the total volume of the bed was used for bacterial growth. This design is a simple method for increasing the capacity of a given volume of biofilter. 6. The biodegradation of ammonia was found to be first-order at low substrate concentrations. The inlet ammonia concentration in these experiments was not high enough for the beds to demonstrate their maximum removal efficiencies and hence whether a saturation kinetics phenomenon should prevail. The normalized vertical ammonia concentration profile in the filter bed also suggested first-order kinetics behavior. 7. The 2-bed biofilter had significantly different reaction rate constants for the first bed versus the second bed. The rate constant for the second bed varied from 0.03 to 0.09 sec"1, with a loading rate of 0.76 to 4.95 g NH3/m3.h. In comparison, the reaction rate constant for the first bed ranged from 0.02 to 0.04 sec"1, with a loading rate ranging from 3.30 to 14.3 g NH3/m3.h. For the standard biofilter, the reaction rate constant had a value of 0.025 sec"1. 8. The predicted and measured values of reaction rate constant based on RTD were y compared and had very good agreements. 112 9. The modified biofilter system needs 265.6 m3 compared to 1682 m3 required of the standard biofilter system, meaning a saving of about seven times in capital cost. In terms of power cost, the modified system sustains a pressure drop of 26.5 cm water compared to 23 cm water for the standard system. This implies power cost as part of operating cost will be similar. This study is a first attempt in testing the residence time distribution in non-standard biofilters with practical measurements and to model the flow as the number of continuous stirred tank reactors in series. For instance, the performance of non-ideal flow reactors (biofilters) can be determined more precisely, knowing the mean residence time, as derived from the E-curve (or RTD, residence time distribution), and the intrinsic first-order biochemical reaction rate constant. The set of relevant equations that were used in the analysis of experimental results in the thesis constitute a model (incorporating the RTD, reactor flow sub-model, reactor kinetics sub-model and relevant physical properties of the filter media) that can be followed to obtaining a quantitative understanding of the principle and operation of modified biofilter systems in future studies. 113 CHAPTER 8 RECOMMENDATIONS The following activities are recommended for future research work on this subject. 1. Other pollutants such as reduced sulfur compounds and volatile organic compounds may be investigated by a similar procedure adopted in the current study. 2. The ammonia loading rate may be further increased to the point where saturation would occur with respect to elimination capacity. This would enable the determination of other possible reaction kinetics behavior such as Michaelis-Menten or zero-order. 3. Better baffle systems may be installed. 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Vriens and H. Verachtert, 1994, Biotreatment of ammonia- and butanal-containing waste gases. Appl Microbiol Biotechnol 42: 147-152. Werner, W., H.G. Liebe and B. Stiefler, 1986, Emission control with biofilters; Application, design and operational experience. In: Proc. 7th World Clean Air Congress. Sydne Australia, 1986:537-544 Williams and Miller, 1992, Biofilters and Facility Operations, Biocycle, pp. 75-79 Winer, A.M.; Leson, Gero., 1991, Biofiltration; An Innovative Air Pollution Control Technology for VOC Emissions, J. Air. Waste, Manage. Asso. Vol.41, No.8. pp.1045-1054, U.S.A Williams, T.O., 1993, Biofiltration for control of odorous emissions from composting facilities. In: Proc. 3rd Annual Meeting, Composting Council of Canada. September, 1993. Montreal, PQ. pp. 425-442. Windsperger, A., 1992, Optmierung von biologischen Abluftreinigungsanlagen an praktischen Beispielen. In: Dragt AJ & Hamj van (Eds). Biotechniques for Air Pollution Abatement and Odor Control Policies, Proceedings of an International Symposium, Massstricht, the Netherlans,. 27-29 Oct. 1991, p.p. 107-117, Elsevier, Amsterdam. Wolff, F., 1992, Biologische Abluftreinigung mit einen intermitatierend befeuchteten Tr pfkorper, In: Dragt AJ & Hamj van (Eds). Biotechniques for Air Pollution Abatement and Odor Control Policies, Proceedings of an International Symposium, Massstricht, the Netherlans,. 27-29 Oct. 1991, p.p. 49-62, Elsevier, Amsterdam. 120 Wright, W.F., E.D. Schroeder and D.P.Y Chang, 1998, Characterization of regular transient loading response of a flow-direction-switching vapor-phase biofilter, In: 1998 USC-TRG conference on biofiltration, Los Angeles, California, USA, Oct. 22-23, 1998, p.p. 143-152, Yu, J. and K.L. Pinder, 1994, Effective diffusivities of volatile fatty acids in methanogenic biofilms, Bioresource Technology. 48: 155-161 A P P E N D I X A Experimental Procedure 122 Experimental Procedure 1. Turn on the Horiba APB A-210 C O 2 analyzer and the Multirecorder Model MC-640 series. 2. Perform calibrations for both C O 2 analyzer and recorder according to their manuals. 3. Connect the line from the reactor outlet to the gas analyzer. Also, connect the signal output from the gas analyzer to the recorder. 4. Open the pressure regulator for air. 5. Open the C O 2 cylinder tank and its pressure regulator. 6. Flush the reactor with air until the concentration reading on the C O 2 analyzer is constant. 7. Adjust the air flowrate by using valve VI to the desired value. 8. Get the recorder ready and set the paper speed on the recorder to at least 500 mm/min. 9. Record the initial C O 2 concentration. 10. Make a step change on the C O 2 flow by opening valve V2. 11. Mark the paper at the point when the valve V2 is opened. 12. Record the final C O 2 concentration once the concentration reading is constant, and 13. Close valve V2 and flush the reactor with air for new run. APPENDIX B Medium Preparation and Bacterial Count 124 Medium Components for Total & Nitrifying Bacteria Total Ammonium medium (Nitrifying) Nutrient Aga 25 g Ammonium sulfate (NH4)2S04 2.0 g Water (dH20) 1000 ml Dibasic sulfate (K2HP04) 1.0 g Magnesium sulfate (MgS04) 0.5 g Ferrous sulfate (FeS04) 0.4 g Sodium chloride (NaCl) 0.4 g Magnesium carbonate (MgCC»3) 5.0 g Water (dH20) 1000 ml A. Medium Preparations The following procedure is recommended: Prepare suitable agar medium in ordinary glass or plastic 9-cm petri plates. 1. Place the covered container with molten agar, after removal from the autoclave and after any addition of thermolabile substance (sugar, dyes, antibiotics, chromogenic substance), into a dishpan containing hot tap water (45 to 50 °C). The agar cools quickly but uniformly throughout the vessel and reaches a temperature plateau above the solidification point of the agar. For 1,000 ml of agar, 30 min is sufficient. With this procedure there is no gelling around the edges of the container. The agar is now sufficiently cooled that little condensation forms on the undersurface of the petri dish lid. 2. Pour the petri plates with a lighted Bunsen burner nearby. If bubbles occur on the agar surface, the flame can be momentarily directed downward on them until they burst. With 9-cm petri plates anywhere from 15 to 20 ml of agar is satisfactory. This corresponds to a thickness ranging from 0.24 to 0.32 cm. The thicker the plates are poured, the less contrast is made by the colonies against their background. Too thin plates may result in small colonies, in a reduced number of colonies over the plate, or in thin spots, because some nutrient is limiting or the plate becomes locally dried. . 3. Dry the plates at room temperature overnight or for 24 hr, depending on the relative humidity. 4. Store indefinitely at 4 °C in closed plastic containers. 125 B. Bacterial Counts The spreading procedure is as follows: 1. Prepare a suitable dilution of the culture based on all information and hunches at your disposal. Calculate to get 100 to 200 colonies, but use the results of plates containing between 30 and 300. If the organism forms only small colonies, up to 500 may be counted. Make the dilutions with a solution that does not favor adsorption to glass, if ordinary glassware and pipette are to be used. High ionic strength, pH between 4 and 5, and the presence of small amounts of anionic detergents (if not toxic) are helpful in this regard, Alternatively, deal with presterilized plastic vessels and pipette tips for the Pipetteman-type device. 2. The actual dilutions can be carried out in many ways. Historically, large volumes of diluent (99 ml) and 1-ml samples were used. As pipette and volumetric apparatuses have been improved, smaller volumes have been employed, economizing on reagents and mixing time. Modem plastic-tipped semiautomaic pipettes allow very small samples of bacterial culture to be used, but then the problem is proper sterilization. For many purposes, it is suggested that only 0.1-ml volume of culture to 0.9 or 4.9 ml of diluent contained in 13 by 100 mm tubes or 9.9 ml of diluent in 16 by 150 mm tubes. The dilution factors are about optimum, for ease of making an accurate dilution and ease of mixing adequately. 3. Pipette 0.1 ml of the final dilution on the agar surface of the petri plate. Form tow or three free-falling drops on the surface; then blow the remaining fluid on the surface. If using a pipettor. The cells may have a tendency to become immediately attached in situ, so do not delay. 4. Sterilize a spreader by dipping it in alcohol, shaking off the excess alcohol, and flaming. A spreader prepared from a Giant Gem paper clip (Nestling) is recommended. Bend it out, being careful to keep the longest straight part of the wire unbent. Then place a length of no. 16 shrinkable Teflon tubing (Small Parts, Inc., Miami, Fla.) over the straight part, and gently heat to shrink it onto the wire. The part serving as handle can be bent to your convenience. This spreader has the advantage of low heat capacity, cools quickly compared with solid glass rods, and has much less affinity for bacterial cells. Also recommended is one that can be quickly made from a Pasteur pipette. 5. Spread the plate. Try to achieve a uniform coverage as close to the edges as possible. The major difficulty with this method is learning the technique for uniform distribution of the culture. Therefore, after the plates have been incubated, examine the distribution of colonies on all of the pates to learn how uniform your spreading technique has become. Those that have a larger number of colonies are especially useful in this regard, even if they have too many colonies to count. Also note whether the number of colonies is higher in the 126 vicinity of the original droplets. If so, then the technique must be improved. If drips of water remain on the lid of the petri dish, a vigorous shake can remove the water (to the floor). The plates should be dry enough so that the 0.1 ml delivered from the pipette is absorbed in 15 to 20 sec. by the agar. 6. Incubate the inverted plates in a constant-temperature room or chamber whose temperature regulation is good. Incubating in closed containers is advantageous, but do not overfill the containers. Of so done, an increased time is needed for the temperature of the plates to equilibrate to the temperature of the incubator. This will be especially important when utilizing temperature-sensitive mutants. Storing in closed containers also avoids the effects of any noxious gases which may be present in general-purpose constant-temperature room (e,g., acetic acid fumes from gel destining). Opaque containers protect against inactivation of colored drugs and dyes by keeping our light. Finally, in closed containers the plates do not dry out and can be used to look for slow-or late-developing colonies. Another point of concern has to do with CO2. CO2 atmosphere may have a C0 2 requirement. This may be particularly evident when single cells are spread at high dilution and incubated in normal laboratory air. Some of these considerations may be of small importance in any particular case, but should be kept in mind. 7. Observe plates before they have fully developed, mature colonies. Many times, one can see that too many colonies are developing. It may be possible to make fresh dilutions, or to count the very small colonies under a dissecting scope, to obtain reliable information with less coincidence correction. 8. Count the colonies. Depending on the circumstance, various types of illumination are advantageous and may not be obtainable with the commercial colony counters. Experiment with various types of magnifying glasses that can be worn or clipped onto your own glasses. Also try various lamps that have a magnifying lens as on integral part. The colonies may be enumerated by marking the bottom of the petri plate with a pen, or thy may be counted by hand with an electronic counter, had tally, or television-based scanning equipment. One technique that clearly marks the individual colonies is to stick the point of a colored pencil into the colony. Not all brands of colored pencils transfer color to the agar; Eberhard Faber Mongol colored pencils do this well, but some colors work better than others (e.g., French Green N. 898). Several colors can be used for differential counting. One can speed the counting process by suing a had tally to record the tens of colonies and mentally keeping track of the units. If using the electronic scanning counter, careful attention must be made to by sure that false-positive counts are not registered due to dirt and imperfections in the plastic or lass petri dished (which would not cause difficulty to a human eye). Dyes can be incorporated into the agar to increase the contract needed to avoid these errors during the electronic scan. APPENDIX C Measurement Procedures for Specific Surface Area of Particles & Porosity The Measurement Procedure for Specific Surface Area Calculations 1. dry sample (2 cc) into N 2 / H e gas to remove other gases from sample. 2. keep sample in sealed chamber and place it in liquid N 2 temperature. 3. pass N2/He mixture gas through sample. 4. measure thermal conductivity of both sources of N2 /He gas through cold sample; a. N2/He entering sample b. N2 /He leaving sample 5. measure the differences in thermal conductivity of the two sources by equipment (Miacrometrics BET). 6. calculate total quantity of N 2 liquified onto the surface of sample. 7. assume a mono layer of N 2 is formed onto sample surface. 8. calculate area of N 2 layer/mass N 2 . 9. calculate area of N 2 layer. Results: Average surface area : Modified biofilter, first bed = 4.7 m /g second bed = 10.7 m /g 2 2 Standard biofilter was 7.7 m /g (standard deviation = 0.6 m /g), Desorb machine time = 48.5 hours Flowsorb machine time = 27.5 hours 129 IL Measurement Procedure for Porosity Before beginning sample measurements, make a zero measurement check. 1. Close purge valve, Open coupling valve. 2. Rotate handwheels to COUNTERCLOCKWISE extreme. 3. Turn measuring handwheeel CLOCKWISE until starting number is set on the counter. Starting number is stamped on case above measuring handwheel. 4. Place sample in cup. Insert cup in compartment. Lock sample cup in place by pressing clamp handle down FIRMLY. 5. Wait 15 seconds, then close the coupling valve. 6. Turn both handwheels simultaneously or alternately until reference handwheel rests against stop. Keep pointer on scale during this process. 7. Wait 10 seconds. Bring pointer to null(zero) with measuring handwheel. 8. Open coupling valve. Read sample volume on counter directly in cc. 9. Turn both handwheels COUNTERCLOCKWISE to rest against stops. 10. Remove sample cup. 11. Pycnometer is now ready for the next determination, starting at Step 3. APPENDIX D Tabulated Data and Calculations 131 L O 8 C M C O E C L C L c - T3 03 * _ -9 LU +-> * r -* CM O CD CO C M LU I -LLJ 0 0 I I CO Q o O OJ I f ) N L O o L O o) rg a> r-~ co C M i n C D r~- m oo 0 0 > C D t - ~ C O C O O O T r v -o o C O O f » - C O C O C O O l 0 0 C O C M f -0 0 CO T — o i— 0 0 T t " C O C M C O o O CM CM L O O ) C O CO 0 0 0 0 0 0 I S - 0 0 CO LO T - C O T j " CD 0 0 CM o L O C O T f 1 0 0 L O ^ t - oo o C O L O 0 0 C M C M 0 0 T — CO o co o o O O O O O O O O O L O L O o o C M C D L O L O _^ _ - C O L O L O L O L O C O " T i D c c c D u i i n n t o i M * CO CD r o • • - . • ^ - o c o o o c f t C M c n . o o m i o o) 0) C M C M CD o 0 0 C O E CO L O L O C O ( O O ) N N O) . C » O ) i j - N ( O 0 0 C 0 c O ( O ( D n W O O ) i t N ( O O s o f - ' - o o ^ m m i f • ^ • o o o o o o o O Q O O O O O O C M C D •<*-o o cu E _ E S C L L L S o o ^ C O s C O 0 0 T -O J <S> r? 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CO d CO O CO CO II CD CM d T — E L O CN o C N c - C D CO r - T ~ CO CO CO CO CD" E DU UJ _ E CO-CO CD —' U_ T D c C N °a •a CD J D . TD C C N O O O O O O l O C D c n T - C N 0 0 c O , _ C O - < l -( D C O M C O r - O O T - ^ N i r ) O O C N T - r ^ C N C M C O j ^ T - c D m c o o i o N i n a S o o c o O O O O O g o o o d d d o o o o d d o o o o T i -e n . co co co co CM m oo T -m TJ-r - C O in o d ••a- m c o m L O o o C N x- C O C O CT) T - c o i n T -C M o o c o i o S N ( O O ) d d o" d £ „ „ „ r o r ^ " ^ " O C N o c o c o o o c o o C L O O O C N I ^ I O O O T O C N C N O C O O ^ ^ ^ S ^ £ ^ ° ^ ° 0 1 • * C I «i o • 5 t O O O T - T f T - C D - < - L 0 t - c 0 C M 0 0 C M c • ^ c M c o c o T » - - < a - i n L O c o c o r - - -o E o O c\i E CD to > N CO T D CD CO O C D C L O O C M E o o o - > f c o c N o C X J C 2 c n ' ^ C O L n o Q . o o o T - T f T - ( o ^ ! 5 T " ! 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CM CO CO ^ ° CO 0 0 CD CD CD CD CO CD O LO 0 0 CD T - LO O x- r- -r-CO CO CO 0 0 0 0 C N T— x f O CD . CO CM CO CO o o f -x f CO CO CD CO CO x f c o cQ CO CO CO CN CN N CO O) CO i f CM x - x f CD x f O LO CD C D CO CO CO x - r -co o m to s CO CD CO CD x - O O r- oo CD x f CO o x f o C M C M C M C M C M C N C N C N ^ C ^ K K c ^ S ^ c o S E o O O O CD C L C O 0 0 0 0 0 0 c o a. L O io io in CM $i LO CD O ^ CN CO CO L O 0 0 CD x - C O O x - r-. x -c o c o o o o o o o x f L O c n c o c o c o c M — C N " ' CD x f CO OO C D x f x f r~-o> m co oo C M m co CM x f o CM f - 0 0 C D x f C D x f C O C O C O C O C O x f CM CO CD CD CD CD O l^r C D i d co ai x f CD" rC 0 0 CM co CD (D N CO CD CO 0 0 CD CO CO O O CN CN CN CO x f CN x f CN x - CD in M C D ^ O l f f l C O S O l r - r - r -j t i o n c o n ^ r - i j N ^ c q i o o o r - r - c N n o N o o N co x f i n m r--' oo' oo" oo oo oo" c d od d oo ai ai ^ x f co oo u ! 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CD r»- CD o CM O T f CD m co co Tf Tf Tf Tf T - O ) O ) N T f 0 0 CD O m co . ^ CO-CO CD Tf T f T f T f CM CT) T-0 0 0 0 T f CO r--o CD CM CD CD CO 0 0 1--CD C O I O T f CO 0 0 CD CD i n 0 0 r-) O O O O o o 0 0 0 o* o 0 o o CM N CO CO CD CM CO CM T— CO CO CD CO CD 0 0 CD CD 0 0 CD CD T f CM O O T - T f T f CM i n i n co 0 0 CM ^ 0 0  CV) U J /v* T f 0 0 CD 0 0 c r i CD CD T f r -co CO CM CM O T-CM r— ^ T f CO CO CM CO 1- CM CD CD 'I CO CM O O CM CO C D C O m co E c n to r-- co T -CD T-O § CO o o o 0 d r -T f CD" E CC co 3 c CM _ E CM CM CM 0 0 CT) O <N T- T f CM CM O CM T f CO o d d E C L Q- o "cu c o o o T -o co T f 0 0 CT) CO Tf CT) l O 0 0 CM I O CO CD CD CM 0 0 f-. CD CM Is— O CD CM CM CD m m CO CO CM CM 10 CO . T - C D O O C O C M C O C O O O r— O O O O C D C D C D O C D O O O O O CD CO CO CD T— CD CD CO CO CD CD r - CD Tf CO T f m CM T- O co m co T-m co in CO co CD CM CM CM s co CO T f T f T f co in r--o 0 0 m E t> o T f E o o o o Q . CM CM CM CM d m m in in 0 0 T - C M C O N T - C O t O f l l T - n C D O l O T f c D T - C D C D C O T f T f o o c o c D c o r - c D T f m c M O co' i n T d T f CM* CM' CM r—- r—- 2 C O N I O C O T - C B C O O O I N T - T f c D C D C D C D C D O T - O T - T - T - T - T - T - T - C M C M C M CM E Q> CO >i CO cc r -T T "T CD CD i n CD i n ^ T- N N . s . ^ N N N N C O ^ O N t N O J C N C O ^ i n CM CM CM CM* co T t 10 co r—' r-'r^r--'r^r~~f^ TO "O I— 5 o CD CO c C M T f C D 0 0 O O J ' * C D O O O C N J " * C D O O ' - T - T - T - T - C M C M C M C M C M C M O T f 6 z C L E cc CO CD E o CM 0 0 0 g o o o o o o o ^ ^ T - T - , - , - , - C N C N C M O T - C M C 0 T f l 0 C D f ^ 0 0 C D O 142 CN CN CO CO o c o o o r - c D T i - c N c N i O T -co co CN • CO CO o CO CO CN T t CN CD CN CO TJ- J > o> CO CO CO CO CO — T t CN c o i n w T f r o r - T - T - O ddddddddd CN o CD CO " D Lu i -n *_ LU * CN <_ (-LU $ CD o o o o o o o o o o o o T CD CO 0 0 OO 0 0 O CO T f T - CO CO oo N r o O C O C N C O CD 0 0 o o o o o o o CN T f CD o 0 0 0 0 CN CO CO CO CO N N CO O 0 OO 0 0 T ~ f CN CN CN CT) CD _ 0 0 CO T -CN N CO CN O CD T t 0 0 CD CO T — LO — • , r— C D C O C D O O C N C D C D T - O C O CO C O C O c O N T f O l O N CN CO 0 0 CN CO O CO CO T— T - CO 0 0 CN CO O o o o T - CO T f T - T - CN 0 0 CN CO O CO T f T - T - CN o o o CO T -o o r~- CD CN O o o o o o o o o o o o o CN LO Tf C O L O CO CO CD CO CO o CD CD CO CD CO CO C O CO CD oo C O L O C O C O c O C N C O C O T f T f r- CD CO CDi 00i COi od id C D C O OJ T— C N C O L O C D C D C D T - O O C N C N T — T t T f C N T t O O C D T - C O C O T -8 0* CD CN T f T f CD E : CO C O o CN o o 0 0 CO i>" E LT Io CD •o c CN E C L C L E C L C L ID c: C N C N C O T f c O O O O O L O C D c o i O N u i s o N r o m T - T - C D O C D T - C O T - T f O O O O O O O C N C N C N T f L O C O O O t O C D m N co s co m CD T ~ T f d d o o o o o o o T f 0 0 CO 0 0 oo ^-CO CD 0 0 0 0 0 0 CD d d d d d d d cn T t T t CN CD CN CD C O T f T - T - T f CO CN T — CN CN CN CO T f T f T f CO O) CD CD CO CD CO CO CO CO CD CO 0 0 CO 0 0 CO CN o ^ o 0 0 o T f E t5 o eh E C L C L o o o o o o p o o o o o CO CD CO CO CO CD CO CO 0 0 0 0 CO 0 0 0 0 co r~-r-CN o CN 0 0 co co CN CN m T— m T f . CO CO T f CO CM CD T f CN oo co oo in m T f CO CN OO CD o o T f O CN CN CO o o CN o d °o r-- o o E CD co CO CD o co C D O O C D C D O O O O C D C O C D O 0 O 0 C D C D C D C D C N CN C N C N C N C N C N C M C O C D T— CO CO OO CD 0 0 OO C N CM CO CO C O T f T f T " T f T t T f T f T f T f T f CD T3 cz CD co CN I o I CO 6 2 0) C L E CD CO t-$ CD c CD E in i n „ m m m m i n c o „ i n CN CO LO CD 00 T - C N C O T f L O C D I ^ O O C D 0 APPENDIX E Sample Calculations 144 Mean Residence Time : t = 2ti*Ei*At Variance: o 2 = 2ti2*Ei*At - t 2 No. of Tanks in series: N = t 2 / o 2 Sample for 2 cfm at first bed of the modified system; Mean Residence Time : t = Eti*Ei*At = 45.57 Variance: o 2 = Eti2*Ei*At - t 2 = 1332.1 No. of Tanks in series: N = t 2 / o 2 = (45.57)2 / 1332.1 - 1.559 APPENDIX F Determination for First Order 146 For heterogeneous fluid-fluid reactions that may be expressed as follows (Levenspiel, 1972, pp.413-415): A (from gas) + b B (liquid) —-> product (Al) the rate of disappearance of A and B are given by: -rA"=-rB"/b=kAG(PA-PAi)=kA1(CAi-0)*xo/x=kB^(CB - 0)*xo/xo-x (A2) A in gas film A in liquid film B in liquid film where kAc and kA]> kBi are the mass transfer coefficients in gas and liquid phases. The liquid side coefficients are for straight mass transfer without chemical reaction and are therefore based on flow through the whole film of thickness x0. At the interface the relationship between P A and C A is given by the distribution coefficient, called Henry's law constant for gas-liquid systems. In the special case of high concentration of B, then based on the two-film theory for an infinitely fast irreversible reaction of any order (eqn Al), the resistance of the gas phase controls, and the reaction rate is not affected by any further increase in the concentration ofB. It can be shown that if k A g P A <k B i CB/b or PA/RT< 10-4CBi/b (A3) then the reaction rate is given by rA - k A g P A Substituting the values of the coefficients and parameters in equation A3, k A g RT = Dg/xo = 0.236/0.01 = 23.6 cm/sec kBi = DBI/XO = 1.76 x 10"5/0.01 = 1.76 x 10"3 cm/sec thus k A g = 23.6/RT now, compare "23.6 PA/RT" with "1.76 x IO"3 CB/b" thus, PA/RT = 7.45 x If/5 < IO"4 Therefore, the simple form of rate expression, -rA" = -l/S*dNA/dt = k A g *P A Equation A4, may be used. 147 (A4) APPENDIX G Calculations to determine reaction, n, from experimental data 149 C A i v = C A 2 + k C A 2 V C A 1 - C A 2 = k C A 2 n V / v = k C A 2 n (CAI - C A 2 ) / = k C A 2 " log (C Ai - CA2 ) / = log k + n log C A 2 Plot, log (CAi - C A 2 ) / vs. log C A 2 Then, n : slope and log k : intercept, Example : for the first bed of the modified system at 3 cfm, 1st tank; C A i =75.0 NH 3 ppm, CA2=50.5 N H 3 ppm, = 20 sec. 2 n d tank; C A i =50.5 NH 3 ppm, C A 2 =34.0 N H 3 ppm, = 20 sec. l o g ( (QUZCAZIL 1°-B-CA2 First tank 0.2030 3.9220 Second tank - 0.1925 3.5263 Linear Regression for Data : Y = A + B * X Parameter Value A(logk) 3.7177 B (n) 0.9960 B (n) is almost 1.0 (=0.9960), therefore, it is first order. Following pages are for 75, 100,130 ppm at each 3, 4, 5 cfm 150 For first bed : 4 & 5 cfm and 75 ppm Parameter Value A 3.9917 B (n) 0.9997 For first bed : 3 &4 cfm and 100 ppm Parameter Value A 3.1874 B (n) 0.9996 For first bed : 5 cfm and 100 ppm Parameter Value A 3.5409 B (n) 0.9845 For first bed : 3 &4 cfm and 130 ppm Parameter Value A B(n) 3.2156 0.9997 151 For first bed : 5 cfm and 130 ppm Parameter Value A 3.3532 B(n) 0.9996 For second bed : 3 &4 cfm and 75 ppm Parameter Value A 2.1339 B (n) 0.9996 For second bed : 5 cfm and 75 ppm Parameter Value A 2.7838 B(n) 1.0001 For second bed : 3 & 4 cfm and 100 ppm Parameter Value A 2.7831 B (n) 0.9991 For second bed : 5 cfm and 100 ppm Parameter Value A 2.8930 B(n) 1.0002 For second bed : 3 & 4 cfm and 130 ppm Parameter Value A 2.7836 B(n) 1.0001 For second bed : 5 cfm and 130 ppm Parameter Value A 2.8815 B(n) 1.0005 APPENDIX H Application of Designing (sizing) for a Biofilter System 154 Sample calculation of designing (sizing) for a biofilter system A ) Steps for sizing of a modified biofilter system: For 95 % removal efficiency; 1. To find a loading rate from design curve (Fig. 6.7a): Loading rate, L [g-NH3/m3h] = 5.7 Air flow rate, u = 10.92 m3/s or 39317 m3/h 2. To contain 50 ppm of ammonia by volume Density of ammonia = 0.77 g/L NH 3 rate = 0.77 * 10 3 * (39317*50)/ 10 6 = 1513.8 g NH3/h 3. To size a biofilter volume needed: Volume, V [m3] = (g NH3/h)/L = 1513.8/5.7 = 265.6 m3 4. To size a biofilter cross-section area: Assume; bed is 5 m long, Area, A [m2] == V/ length = 265.6 / 5.0 = 53.12 m 2 or 7.3 m x 7.3 m x 5 m Steps for calculating of a power cost based on pressure drop: 5. To find an airflow velocity; v [m/s] = u / A = 10.92 / 53.12 = 0.206 [m/s] 6. To calculate k (1/ coefficiency of permeability); k = viscosity / K (permeability from Table 6.5) = 1.02 * 10 "5 [Pa.s] / 4.0 * 10 "9 (assumed, [m2[) = 2550 [Pa.s/m2] 7. To get an pressure drop, dp [Pa.]; dp = k * v * L (length) = 2550 * 0.206 * 5.0 = 2621 Pa. or 26.5 cm water 155 B) Steps for sizing of a standard biofilter system: For 95 % removal efficiency; 1. To find a loading rate from design curve (Fig. 6.8a): Loading rate, L [g-NH3/m3h] = 0.9 Air flow rate, u = 10.92 m3/s or 39317 m3/h 2. To contain 50 ppm of ammonia by volume Density of ammonia = 0.77 g/L NH 3 rate = 0.77 * 10 3 * (39317*50)/ 10 6 = 1513.8 g NH3/h 3. To size a biofilter volume needed: Volume, V [m3] = (g NH3/h)/L = 1513.8/ 0.9 = 1682 m3 4. To size a biofilter cross-section area: Assume; bed is 5 m long, Area, A [m2] = V/ length = 1682 / 5.0 = 336.4 m 2 Steps for calculating of a power cost based on pressure drop: 5. To find an airflow velocity; v [m/s] = u / A = 10.92 / 336.4 = 0.0325 [m/s] 6. To calculate k (1/ coefficiency of permeability); k = viscosity / K (permeability from Table 6.5) = 1.02 * 10 - 5 [Pa.s] / 7.0 * 10 " 1 0 = 14600 [Pa.s/m2] 7. To get an pressure drop, dp [Pa.]; dp = k * v * L (length) = 14600 * 0.0325 * 5.0 = 2372 Pa. or 23 cm water Comments: For removal of ammonia at characteristics residence time of the biofilter, and at a inlet N H 3 concentration of 50 ppm, air flow rate of 10.92 m3/s, the modified 156 biofilter system needs 265.6 m3 compared to 1682 m3 required of the standard biofilter system. This is implies a saving of about seven times in capital cost. In terms of power cost, the modified system sustains a pressure drop of 26.5 cm water compared to 23 cm water for the standard system. This implies power cost as part of operating cost will be similar. 

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