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Diauxic Lag of Denitrifying Bacteria in Oxic/Anoxic Cycling under Continuous Flow Conditions

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DIAUXIC LAG OF DENITRIFYING BA CTERIA IN OXIC/ANOXIC CYCLING UNDER CONTINUOUS FLOW CONDITIONS By DONG-UK LEE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by Dong-Uk Lee

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To God, MJ and my family.

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iv ACKNOWLEDGMENTS I would like to truly thank my co-advi sors, Dr. Ben Koopman and Dr. Spyros Svoronos, for their guidance and advice thr oughout my graduate study. Their passion and sincerity toward academic research and teaching will be strong guiding lights for the rest of my life. I also have to acknowl edge the other members of my committee, Dr. Angela Lindner, Dr. Atul Narang, and Dr. Samu el Farrah for, their advice and help on my research since I asked them to be on my committee. I would like to appreciate the Alumni Fellowship from the University of Florida for my entire doctoral study. I thank Mr. Chuck Fender and the fellows in the Physical Plant Division of University of Florida at the Wa ter Reclamation Facility for their help and friendliness. I have to thank the fellows in our resear ch group, Anna I. Casasus-Zambrana, Ryan K. Hamilton, Dr. Sung-Hoon Woo, Kiran Durvas ula, and Adrian Vega, for their help, support and friendship. Also, I thank Jao Ju e, Gautam Kini, Vijay Krishna and other fellows in the Academic Interface La b for being good friends of mine. Finally, I would like to thank my family and MJ for their endless love, prayer, and, most of all, for being my family. I trul y thank my God for preparing everything and leading me here.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES.............................................................................................................ix LIST OF FIGURES...........................................................................................................xi ABSTRACT.....................................................................................................................xvi CHAPTER 1 INTRODUCTION........................................................................................................1 2 REVIEW OF LITERATURE.......................................................................................4 Dynamics of Heterotrophic, Denitrif ying Bacteria Switching between Electron Acceptors..................................................................................................................4 Effects of Alternating Electron Acceptors............................................................5 Diauxic Lag of Bacteria Switc hing between Electron Acceptors.........................8 Factors Affecting the Diauxic Lag of Heterotrophic, Denitrifying Bacteria.......12 Bacterial species...........................................................................................12 Length of aerobic phase...............................................................................12 Dissolved oxygen concentra tion in aerobic phase.......................................13 Nitrate exposure history of preceding culture..............................................14 Nitrate concentration in anoxic phase..........................................................14 Modeling of Denitrification in Activated Sludge.......................................................15 Activated Sludge Model No. 1 (ASM1)..............................................................15 Modeling of Denitrification with a C ybernetic Approach for Denitrifying Enzyme Kinetics..............................................................................................17 Modeling of Denitrification with Me chanistic Approach for Denitrifying Enzyme Kinetics..............................................................................................23 3 EXTENSION OF ACTIVATED SLUDGE MODEL NO. 1 TO INCORPORATE DENITRIFYING ENZYME KINETICS...................................................................26 Extension with Cybernetic Approach (eASM1c).......................................................26 Extension with Mechanistic Approach (eASM1m)....................................................29 Comparison of Extended Versions of ASM1 to the Original Version of ASM1.......29

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vi Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by ASM1..........................................................................................................33 Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by eASM1c......................................................................................................36 Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by eASM1m.....................................................................................................48 Re-examination of Results from a Previous Study..............................................55 4 SIGNIFICANCE OF DENITRIFYING ENZYME DYNAMICS IN BIOLOGICAL NITROGEN REMOVAL PROCESSES: A SIMULATION STUDY.......................................................................................................................57 Experimental Methods................................................................................................58 Process Configurations and Modeling.................................................................58 Wastewater Composition and Model Parameters................................................61 Diurnally Varying Flow and Compon ent Concentrations in Influent Wastewater.......................................................................................................62 Results and Discussion...............................................................................................64 Simulations of Fed-Batch Process.......................................................................64 Simulations of BDP Process................................................................................65 Optimum Cycle Length as a Function of UVF...................................................67 Conclusions.................................................................................................................68 5 OBJECTIVES.............................................................................................................69 6 GENERAL MATERIALS AND METHODS............................................................70 Bacterial Cultivation...................................................................................................70 Reviving Freeze-Dried Bacteria and Deep -Freezing of Bacterial Cultures........70 Reviving of Frozen Bacteria................................................................................71 Preculture Procedure...........................................................................................71 Reactors......................................................................................................................71 Overall Layout.....................................................................................................72 Fermentor Assembly...........................................................................................72 Feed Reservoir Assembly....................................................................................75 Autoclaving Procedure and Aseptic Conn ection of Feed Reservoir Assembly to Fermentor Assembly....................................................................................75 Inoculation of Fermentor and Initiation of Startup Phase...................................76 Initiation of Continuous Flow Phase...................................................................77 Sampling from Fermentor...................................................................................77 Monitoring of Contamination of Pure Culture....................................................78 Analytical Measurements...........................................................................................78 Biomass Absorbance...........................................................................................78 Chemical Oxygen Demand..................................................................................79 Nitrate and Nitrite................................................................................................79 Nitrate Reductase Activity..................................................................................79

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vii 7 METHOD FOR ACHIEVING REPRODUCIBLE INITIAL CULTURE STATES IN STUDY OF BACTERIAL DE NITRIFICATION KINETICS.............................81 Introduction.................................................................................................................81 Materials and Methods...............................................................................................82 Results........................................................................................................................ .85 Paracoccus pantotrophus ....................................................................................85 Pseudomonas denitrificans ..................................................................................95 Discussion.................................................................................................................101 8 DIAUXIC LAG OF DENITRIFYING BAC TERIA IN A CONTINUOUS FLOW REACTORI. SINGLE SWITCH FROM OXIC TO ANOXIC CONDITIONS....110 Introduction...............................................................................................................110 Materials and Methods.............................................................................................111 Experimental Procedures...................................................................................111 Modeling............................................................................................................114 Results.......................................................................................................................115 Determination of Diauxic Lag under Continuous Flow Conditions.................115 Experimental Results.........................................................................................121 Modeling Results...............................................................................................131 Discussion.................................................................................................................136 9 DIAUXIC LAG OF DENITRIFYING BAC TERIA IN A CONTINUOUS FLOW REACTORII. ALTERNATING OXIC/ANOXIC CONDITIONS........................147 Introduction...............................................................................................................147 Materials and Methods.............................................................................................148 Alternating Oxic/Anoxic Cycling un der Continuous Flow Conditions............148 Modeling............................................................................................................149 Results.......................................................................................................................151 Preliminary Simulations....................................................................................151 Experiments.......................................................................................................154 Short cycle length (12 hours).....................................................................157 Long cycle length (24 hours).....................................................................160 Simulations to Predict Experimental Results using eASM1m..........................166 Discussion.................................................................................................................170 Effect of Alternating Cycling on Growth of P. denitrificans Predicted by eASM1m........................................................................................................170 Growth Dynamics of P. denitrificans in Alternating Oxic/Anoxic Cycling under Continuous Flow Condition-Experiment.............................................171 Suggestion of a Preliminary Modeling Con cept to Show the Effect of Growth Patterns during Oxic Phase on Diauxic Lag of P. denitrificans ....................175 Fast Decrease of Biomass Absorbance during Lag Period...............................176 10 SUMMARY AND CONCLUSIONS.......................................................................179

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viii 11 FUTURE WORK......................................................................................................182 APPENDIX A SIMULATIONS OF RESULTS FROM A PREVIOUS STUDY............................185 B MEASUREMENTS USING HACH TEST TUBES................................................186 LIST OF REFERENCES.................................................................................................187 BIOGRAPHICAL SKETCH...........................................................................................191

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ix LIST OF TABLES Table page 2-1. Factors affecting the di auxic lag of denitrifiers.........................................................13 3-1. Process rates and stoichiometric coefficients of eASM1c.........................................27 3-2. Process rates and stoichiome tric coefficients of eASM1m.......................................30 3-3. Characteristics of growth medium in batch simulations...........................................34 3-4. Parameters of ASM1 for the batch simulation..........................................................34 4-1. Design of UF BDP water reclamation facility (train 1 of two parallel trains)..........60 4-2. Sequence of phases in the fed-batch and BDP processes..........................................61 4-3. 24-hour flow-weighted aver age wastewater composition.........................................62 4-4. Stoichiometric and ki netic parameters in the ASM1 and eASM1 models................63 7-1. Composition of nutrient solution for P. pantotrophus ..............................................83 7-2. Composition of nutrient solution for P. denitrificans. ..............................................83 7-3. Amount of carbon substrate in feed solutions...........................................................83 7-4. Nutrients in two feed solutions..................................................................................84 7-5. Summary of experimental results of anoxic batch phases.......................................103 7-6. Summary of experimental resu lts of oxic continuous flow phases.........................104 7-7. Comparison of experimental data............................................................................109 8-1. Experimental conditions..........................................................................................113 8-2. Composition of nutrient solution for P. denitrificans. ............................................113 8-3. Amount of carbon substrate, ammoni a in nutrient solution of each stage..............113 8-4. Nutrients in two feed solutions................................................................................113

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x 8-5. Calculation procedures of virtual batch curve method............................................119 8-6. Parameters of eASM1m for simulation...................................................................134 8-7. Initial conditions for eASM1m simulations............................................................134 8-8. Parameters of eASM1m after calibration................................................................137 8-9. Summary of experimental results............................................................................142 9-1. Parameters of eASM1m for simulations.................................................................169

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xi LIST OF FIGURES Figure page 2-1. Schematics of Mechanis tic Denitrification Model....................................................24 3-1. Simulation of experimentally observed diauxic lag of Pseudomonas denitrificans predicted by eASM1c...............................................................................................32 3-2. Simulation of experimentally observed diauxic lag of Pseudomonas denitrificans predicted by eASM1m.............................................................................................32 3-3. Growth of heterotrophic biomass duri ng cyclic simulations with 8 mg/L of DO during oxic phase, predicted by ASM1....................................................................35 3-4. Mass specific and volumetric denitrif ication rate during cyclic simulation, predicted by ASM1..................................................................................................36 3-5. Growth of heterotrophic biomass duri ng cyclic simulations with 4 mg/L of DO during oxic phase, predicted by ASM1....................................................................37 3-6. Growth of heterotrophic biomass duri ng cyclic simulations with 2 mg/L of DO during oxic phase, predicted by ASM1....................................................................38 3-7. Growth of heterotrophic biomass duri ng cyclic simulations with 1 mg/L of DO during oxic phase, predicted by ASM1....................................................................39 3-8. Growth of heterotrophic biomass under oxic/anoxic switch.....................................41 3-9. Specific nitrate reductase level a nd activity of heterotrophic biomass under oxic/anoxic switch, predicted by eASM1c...............................................................42 3-10. Growth of heterotrophic biomass duri ng cyclic simulations with 8 mg/L of DO during oxic phase, predicted by eASM1c................................................................43 3-11. Mass specific and volumetric denitrif ication rate during cyclic simulation, predicted by eASM1c...............................................................................................44 3-12. Growth of heterotrophic biomass duri ng cyclic simulations with 4 mg/L of DO during oxic phase, predicted by eASM1c................................................................45 3-13. Growth of heterotrophic biomass duri ng cyclic simulations with 2 mg/L of DO during oxic phase, predicted by eASM1c................................................................46

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xii 3-14. Growth of heterotrophic biomass duri ng cyclic simulations with 1 mg/L of DO during oxic phase, predicted by eASM1c................................................................47 3-15. Growth of heterotrophic biomass duri ng cyclic simulations with 8 mg/L of DO during oxic phase, predicted by eASM1c................................................................49 3-16. Specific nitrate reducta se level and specific intr acellular nitrate level of heterotrophic biomass under oxic/anoxi c switch, predicted by eASM1c................50 3-17. Growth of heterotrophic biomass duri ng cyclic simulations with 1 mg/L of DO during oxic phase, predicted by eASM1m...............................................................52 3-18. Growth of heterotrophic biomass duri ng cyclic simulations with 0.5 mg/L of DO during oxic phase, predicted by eASM1m........................................................53 3-19. Growth of heterotrophic biomass duri ng cyclic simulations with 0.1 mg/L of DO during oxic phase, predicted by eASM1m........................................................54 3-20. Simulation of experimental results from a previous study......................................56 4-1. Process schematics of fed-batch pr ocess (top) and BDP process (bottom) showing the fraction of the cycle length or hydraulic residence time occupied by each phase or part of the processes..........................................................................59 4-2. Sequence of phases in the BDP oxidation ditches.....................................................59 4-3. Effects of anoxic volume fraction and cycle length on performance of fed-batch process predicted by ASM1 and eASM1c...............................................................65 4-4. Effect of unaerated volume fraction (UVF) and cycle length on performance of BDP process.............................................................................................................66 4-5. Optimum cycle lengths of fed-batc h and BDP processes as a function of unaerated volume fraction (UVF)............................................................................67 6-1. Overall layout of expe rimental configuration...........................................................73 6-2. Side view of New Brunswick Bioflo 2000 Fermentor..............................................73 6-3. Fermentor assembly...................................................................................................74 6-4. Feed reservoir assembly............................................................................................76 7-1. Biomass absorbance profile of Experimental 1.........................................................86 7-2. Biomass absorbance during anoxic ba tch phase (Trial 1, Experimental 1)...............88 7-3. Biomass absorbance during anoxic ba tch phase (Trial 2, Experimental 1)...............89

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xiii 7-4. Biomass absorbance profile of Experimental 2.........................................................90 7-5. Biomass absorbance during anoxic ba tch phase (Trial 1, Experimental 2):.............92 7-6. Biomass absorbance during anoxic ba tch phase (Trial 2, Experimental 2):.............93 7-7. Biomass absorbance profile of Experimental 3.........................................................94 7-8. Biomass absorbance during anoxic ba tch phase (Trial 1, Experimental 3)...............96 7-9. Biomass absorbance during anoxic ba tch phase (Trial 2, Experimental 3)...............97 7-10. Biomass absorbance profile of Experimental 4.......................................................98 7-11. Biomass absorbance duri ng anoxic batch phase (Trial 1, Experimental 4).............99 7-12. Biomass absorbance duri ng anoxic batch phase (Trial 2, Experimental 4)...........100 8-1. Feed inlet configurations.........................................................................................114 8-2. Flow and components around CSTR in simulation.................................................116 8-3. Determination of diauxic lag under c ontinuous flow conditi on using virtual batch curve method..........................................................................................................118 8-4. Biomass absorbance profile (Trial 1)......................................................................122 8-5. Biomass absorbance profile during a noxic continuous flow phase (Trial 1)..........122 8-6. Determination of diauxic lag (Trial 1).....................................................................123 8-7. Biomass absorbance profile (Trial 2)......................................................................124 8-8. Biomass absorbance profile during a noxic continuous flow phase (Trial 2)..........124 8-9. Biomass absorbance profile (Trial 3)......................................................................125 8-10. Biomass absorbance prof ile during anoxic continuous flow phase (Trial 3)........125 8-11. Determination of di auxic lag (Trial 3)...................................................................127 8-12. Biomass absorbance profile (Trial 4)....................................................................127 8-13. Biomass absorbance prof ile during anoxic continuous flow phase (Trial 4)........128 8-14. Determination of di auxic lag (Trial 4)...................................................................128 8-15. Biomass absorbance profile (Trial 5)....................................................................129 8-16. Biomass absorbance prof ile during anoxic continuous flow phase (Trial 5)........130

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xiv 8-17. Determination of di auxic lag (Trial 5)...................................................................130 8-18. Biomass absorbance profile (Trial 6)....................................................................131 8-19. Biomass absorbance prof ile during anoxic continuous flow phase (Trial 6)........132 8-20. Determination of di auxic lag (Trial 6)...................................................................132 8-21. Simulation of experimental result (Trial 6)...........................................................135 8-22. Simulation of experimental result w ith calibrated parameters (Trial 6)................137 8-23. Simulation of experimental result w ith calibrated parameters (Trial 5)................138 8-24. Simulation of experimental result (short oxic continuous flow phase).................138 8-25. Change of biomass absorbance a nd carbon substrate concentration during diauxic lag and recovery of growth, predicted by eASM1m.................................140 9-1. Schematic view of gas supply system.....................................................................150 9-2. Biomass absorbance profile from a typical simulation...........................................152 9-3. Biomass absorbance profile during ul timate state in alternating oxic/anoxic cycling (6-hour cycle length).................................................................................153 9-4. Biomass absorbance profile during ul timate state in alternating oxic/anoxic cycling (12-hour cycle length)...............................................................................153 9-5. Biomass absorbance profile during ul timate state in alternating oxic/anoxic cycling (24-hour cycle length)...............................................................................155 9-6. Biomass absorbance profile during ul timate state in alternating oxic/anoxic cycling (48-hour cycle length)...............................................................................155 9-7. Biomass absorbance prof ile in alternating oxic/anox ic cycling (180-hour cycle length).....................................................................................................................156 9-8. Overall biomass absorbance profile of short cycle length experiment (12 hourcycle length)...........................................................................................................158 9-9. Biomass absorbance profile in alte rnating cycling (12 ho ur-cycle length).............159 9-10. Overall biomass absorbance profile of long cycle length experiment (24-hour cycle length)...........................................................................................................161 9-11. Biomass absorbance prof ile in alternating cycling (24-hour cycle length)...........162 9-12. Component concentrations during ul timate state (24-hour cycle length)..............163

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xv 9-13. Component concentrations during the final anoxic phase ....................................165 9-14. Simulation of experimental results (12 hour cycle length)....................................168 9-15. Simulation of experimental results (24 hour cycle length)....................................168 9-16. Simulation of experimental results (final anoxic phase, 24 hour cycle length)....169

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xvi Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DIAUXIC LAG OF DENITRIFYING BA CTERIA IN OXIC/ANOXIC CYCLING UNDER CONTINUOUS FLOW CONDITIONS By Dong-Uk Lee August 2005 Chair: Ben L. Koopman Cochair: Spyros A. Svoronos Major Department: Environmental Engineering Sciences The present study was conducted to investigate diauxic lag of denitrifying bacteria under an ultimate state of oxic/anoxic cyc ling under continuous flow conditions. As preliminary steps, the industry standard Ac tivated Sludge Model No. 1 was extended with denitrification models and a simulation st udy was conducted to compare predictions of the conventional and an extended version. An experimental system was developed to implement bacterial pure culture growth under continuous flow conditions. The performance of the system was verified by determining the reproducibility of experimental results. Us ing the experimental system, diauxic lag of denitrifying b acteria was then studied under oxic/anoxic cycling conditions. The experimental system developed in th e present study was capable of achieving pure culture of denitrifying b acteria without contamination up to the desirable length of time for experiments. The reproducibility of the length of diauxic lag and the highest

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xvii anoxic observed specific growth rates were significantly improved by achieving steady state growth of bacteria as a preliminary stage. Diauxic lag of Pseudomonas denitrificans under continuous flow conditions could be characterized by the virtual batch curve method developed in the present study. The eASM1m was able to predic t the observed diauxic lag under the continuous flow conditions with slight modifi cation of parameters. The experimental results were significantly influenced by the magnitude of biomass accumulation at the feed inlet. Growth patterns in preceding oxic phase were likely to have an effect on length of diauxic lag during consecutive anoxic phase, which could no t be predicted by eASM1m. Predictions of eASM 1m on growth of Pseudomonas denitrificans in alternating oxic/anoxic cycling under continuous flow cond itions were consistent with results from a previous study. It has been found that Pseudomonas denitrificans could not establish significant anoxic growth duri ng alternating oxic/anoxic cy cling under continuous flow conditions, with up to 24 hour cycle length. The eASM1m could not fit the growth behaviors with the previous parameters. Fu rthermore, the diauxic lag after the cycling was significantly longer than the initial la g, which was additional evidence explaining that growth patterns of bacter ia in the preceding oxic phases may influence diauxic lag of bacteria in the following anoxic phases.

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1 CHAPTER 1 INTRODUCTION Nitrogen removal from wastewater has b ecome more and more important because of a number of reasons, including pollution, eutrophication of rece iving water bodies and increasing needs for reuse of reclaimed wast ewater. Biological nitrogen removal using activated sludge is a popular method to remove nitrogen from wastewater. Ammonia nitrogen is first oxi dized to nitrite and nitr ate nitrogen by nitrifying bacteria (nitrification) in activated sludge under oxic conditi ons in a typical wastewater treatment process utilizing bi ological nitrogen removal. N itrite and nitrate nitrogen are then reduced to dinitrogen or other gaseous nitrogen com pounds by denitrifying bacteria (denitrification) under anoxic condition. Since the two major reactions take place under different growth conditions in a single sludge biological nitrogen re moval process, it is inevitable that bacteria in activated sludge are exposed to cycling oxic/anoxic conditions. Growth dynamics of bacteria can occur in such conditions if bacteria cannot adjust their growth capabilities to repetitiv e change of growth conditions Therefore, it is very important to study growth dynamics of bacteria that have important roles in biological nitrogen removal from wastewater. The phenomenon of diauxic lag for bacteria switching between electron donors was discovered at least 60 years ago (M onod, 1942). Subsequently, Kodama et al. (1969) observed a similar lag for bacteria switching between electron acceptors. Experiments with activated sludge and pure culture of Pseudomonas denitrificans have established that the diauxic lag of ba cteria switching between oxyge n and nitrate as electron

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2 acceptors can last for several hours and depe nd on the preculture environment, length of aeration period, and dissolved oxygen concen tration during the aeration period that precedes anoxic conditions (Liu et al. 1998a, b; Gouw et al. 2001; Lisbon et al. 2002). Effects of aeration period length and dissolv ed oxygen concentration on diauxic lag of bacteria switching between oxygen and nitr ate were successfully modeled (Liu et al. 1998a, b; Casass-Zambrana, 2001). However, the popular Activated Sludge Models No 1, 2, 2d, and 3 (Henze et al. 2000) cannot portray the diauxic lag phenomenon. This deficiency could result in sub-optimal opera tional strategies or designs and lead to needless environmental impact on receiving wate rs or waste of economic resources. Recently, Lee et al. (2004) compared the predicti ons of an extended version of ASM1 (eASM1c) with enzyme kinetics to th e predictions of ASM1 for periodically operated nitrogen removing processes (fed-bat ch and BioDenipho). ASM1 and eASM1c gave similar predictions of optimal unaerated volume fraction (UVF) that were consistent with operation of the BioDeni pho process at the University of Florida. However, the eASM1c predicted substantially longer optimal cycle lengths which are more consistent with the BioDenipho process operation at the Un iversity of Florida than those predicted by ASM1. Furthermore, eASM1c predic ted a critical cycle length, below which denitrification would cease. The growth dynamics of denitrifying bacter ia under alternating oxic/anoxic cycling found by Lee et al. (2004) has never been investigated by experimental work. In early studies of diauxic lag of deni trifying bacteria, growth respons es of those bacteria were investigated within few switches be tween oxic and anoxic conditions (Liu et al. 1998a, b; Gouw et al. 2001; Lisbon et al. 2002). Moreover, the expe riments were performed

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3 under batch conditions where carbon substrates were provided with non-limiting amounts. In such conditions, growth dynamics of denitr ifiers could be different from those taking place in real wastewater treatment plants, wher e concentrations of or ganic substrates are relatively low. With these needs, the present study was c onducted to investigat e growth dynamics of denitrifying bacteria with better understandings. Efforts were made to develop a proper experimental setup to implement a bact erial pure culture syst em under continuous flow conditions. Diauxic lag of Pseudomonas denitrificans in single switch and alternating cycling between oxic and anoxi c conditions was studied under continuous flow conditions. The capabilities of an ex tended version of ASM1 were evaluated with respect to prediction of experimental results.

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4 CHAPTER 2 REVIEW OF LITERATURE The phenomenon of diauxic bacterial gr owth was extensively studied by Monod (1942, 1949). He mentioned that diauxie is ch aracterized by a dual growth cycle which consists of two exponential growth phases separated by a period during which the growth rate reaches a minimum, or becomes negative. He reported that a diauxie could occur when bacteria grew on media where the orga nic substrate is limiting and consists of mixtures of two carbohydrates. He reported th at this phenomenon indicated that each of two exponential growth cycles corresponded to the exclusive utilization of one of the substrates, due to an inhibitory effect of one of the substrates on formation of the enzyme for the other substrate. It has also been f ound that bacteria may experience lag when they switch between electron acceptors (Kodama et al. 1964, Liu et al. 1998a, b). In the following sections, the diauxic lag of bacter ia switching between electron acceptors will be reviewed. The discussion will be focuse d on diauxic lag of denitrifying bacteria switching their electron acceptors from nitr ate to oxygen. Furthermore, several mathematical models to predict the denitrification and related enzyme dynamics will be reviewed. Dynamics of Heterotrophic, Denitrifying Bacteria Switching between Electron Acceptors A number of studies have been perfor med to investigate the dynamics of denitrification under conditions in which the electron acceptors switch. A general discussion of the effect of a lternating electron acceptor on de nitrification a nd growth of

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5 bacteria will be given and the diauxic lag of denitrifying bacter ia switching between electron acceptors between oxygen and nitrate will be discussed. Effects of Alternating Electron Acceptors Several investigators have examined the e ffect of alternating electron acceptors on the dynamics of bacteria. Simpkin and Boyle (1985) investigated variations of nitrate and nitrite reductase activities of activated sludge exposed to alternating aerobic/anoxic conditions. In laboratory sequencing batch r eactors (SBRs), anoxic phases were provided during part of the reaction phase or during settling. The highes t nitrate reductase activity was found when the feed had a high level of nitrate (> 30 mg/L) and the anoxic period included 4 hours out of a total 6-hour reac tion phase, plus onehalf hour of anoxic conditions during settling. An intermediate n itrate reductase level was found in a reactor with high nitrate (16 mg/L) but only one-hal f hour of anoxic conditions. The lowest nitrate reductase activit ies were found in SBRs with low nitrate and only one-half hour of anoxic conditions (during settling). ONeil and Horan (1995) investigated th e effect of oxic/anoxic cycling on nitrification and denitrifica tion in a chemostat that was inoculated with nitrifying activated sludge. They first cycled the growth conditions between 4 hours of aerobic phase and 20 hours of anaerobic phase for 15 days. They performed two experiments involving the same length of oxic and anoxic periods and different feeding patterns. There was no indication of a growth lag in de nitrification after the switch from oxic to anoxic conditions. In the third experiment they provided 14 hours of oxic growth conditions. After aeration st opped and dissolved oxygen droppe d to zero, denitrification activity remained very low for the remainder of the time monitored, which was 20 hours.

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6 Baumann et al. (1996) studied the response of Paracoccus denitrificans to changes between aerobic and anaerobic growth conditions in a continuous culture. They first ran the reactor until an aerobic steady state was reached. They then stopped aeration and ran the reactor until an anaerobic steady state wa s reached. Finally, they restarted aeration and ran the reactor to another aerobic steady state. When the growth conditions were changed from aerobic to anaerobic, the culture did not immediately establish complete denitrification. Nitrite started accumulating i mmediately after the switch and nitric oxide production began somewhat later. Dinitrogen became the major denitrification product after the intermediates disapp eared and the culture established a new steady state. The mRNA levels for nitrate reductase and n itrous oxide reductase started increasing immediately after the switch whereas the mRNA level for nitrite reductase started increasing somewhat later. Biosynthesis of nitrite reductase was st arted about 30 minutes after the increase of the mRNA level of th e enzyme and gradually built up over a period of 30 hours. Baumann et al. (1997a) investigated the effect of repeated alternating aerobicanaerobic conditions on denitrificat ion in continuously-fed cultures of Paracoccus denitrificans and activated sludge. In the case of a Paracoccus denitrificans growth reactor with alternating aer obic (24 h) and anoxic (24 h) phases, the authors grew the bacteria for three cycles of aerobic-anoxic pha ses. The authors showed measurements of nitrate versus time over a span of 120 hours. Vertical lines in the graph indicated switches between external electron acceptors. In the first cycle, it is apparent that nitrate levels began to decrease immediately afte r oxygen supply was stopped. In the second cycle, decrease of nitrate le vels lagged exhaustion of dissolved oxygen by one hour. In

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7 the third cycle, the lag lasted two hours. According to the authors, nitrate consumption began immediately after dissolved oxygen was depleted. However, it seems doubtful that one or two hours would be required for oxygen depletion in cycles 2 and 3. These data thus suggest the occurrence of a diauxic lag w ith the long phase lengths. Denitrification intermediates (nitrite, nitric oxide, nitrous oxide) accumulated duri ng the anoxic phase of the first cycle but not during the anoxic pha ses of the second or third cycles. The authors performed another experiment with shorter phase lengths, 1.5 h anaerobic and 2.5 h aerobic, th rough a total of four cycles With the shorter phase lengths, nitrite accumulated during the anoxic phases and there was negligible nitrous oxide production. The nitrite reductase level incr eased throughout the experiment, during aerobic as well as anoxic phases. The au thors attributed nitrite accumulation to insufficient time for the bacteria to adjust their enzyme synthesis system. The authors also measured mRNA for nitrate reductase during the second experiment. The mRNA for nitrate reductase decreased during the aerobic phases and increased in the anoxic phases after approximately 0.5 hour of lag peri od. The characteristics of behaviors of activated sludge under longer alternating aer obic-anaerobic conditions (24 h aerobic and 24 h anaerobic) were similar to those of Paracoccus denitrificans (i.e., denitrification intermediates (e.g., nitrite and nitrous oxide ) were accumulated during the first anoxic phase but later disappeared). Upon change to anoxic conditions, nitrate consumption lagged for about 4 hours. Baumann et al. (1997b) studied the effect of change from aerobic to anaerobic growth conditions on the denitrific ation of a continuous culture of Paracoccus denitrificans at a suboptimal pH. The bioma ss concentration started decreasing

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8 immediately after the switc h and continued decreasing for 50 hours. This trend approximately followed a dilution and decay curv e with a near-zero specific growth rate (simulation not shown). Nitrite and nitric oxide started accumulati ng almost immediately after the switch. Accumulation of nitrous oxide and dinitrogen starte d somewhat later. Increases in levels of the mRNAs for nitrat e reductase and nitric oxide reductase were observed immediately after the switch, whereas levels of the mRNA for nitrite reductase began increasing one hour later. Howeve r, even though mRNA levels for nitrite reductase increased, the amount of nitrite re ductase synthesized was low. The authors suggested that biosynthesis of nitrite reductase was inhib ited by higher free nitrous acid concentration due to lower pH. Cult ures grown under cyc ling aerobic/anaerobic conditions or strictly anaer obic conditions were less affect ed by the low pH, indicating that they may have accumulated ni trite reductase over time. Oh and Silverstein (1999) studied the eff ect of feeding pattern on the mass specific denitrification rate of activated sludge in sequencing batch reactors. They found that the mass specific denitrification rate during a noxic phases decreased and the oxygen uptake rate of the sludge increased as the length of time that the substrate was present during aerobic phases was increased. The lengths of aeration period in the absence of substrate did not influence the mass specific denitrific ation rate. They c oncluded that feeding during the aerobic phase led to growth of aerobic (non-denitrifying) bacteria. Diauxic Lag of Bacteria Switching between Electron Acceptors Diauxic lag of bacteria switching between electron acceptors was first reported by Kodama et al. (1969). The authors examined the growth of Pseudomonas stutzeri in the presence of various concentrations of nitrate. The authors reported that the initial growth continued until all nitrate in the culture wa s consumed. Nitrite was accumulated while

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9 nitrate was consumed. The lag of growth bega n after exhaustion of th e nitrate. After a period of time, bacterial growth resumed, along with consumption of nitrite. The length of the lag period depended on the initial nitrate concentration (i.e., the higher the concentration, the longer the lag). The author s gave two possible mechanisms to explain this effect: (1) repression of development of the nitrite reducing machinery by nitrate, and (2) competition between nitrate and nitrite for electrons. Since the original description of a lag experienced by bacteria switching between electron acceptors, a number of other inves tigators have studied this phenomenon. Waki et al. (1980) investigated the e ffect of aerobic-anaerobic c ondition change on the growth, carbon source and nitrate consumption, and ni trate and nitrite reductase activity of Paracoccus denitrificans They reported that the car bon source consumption and the growth of the bacteria stopped for a few hours when the condition was changed from aerobic to anoxic (oxygen absent, nitrate pres ent). During this lag period, incomplete denitrification occurred (i.e., a rapid nitr ate consumption was observed, but with a high level of nitrite accumulation). A careful glance at the profile of the bacterial growth in the reference reveals a second lag period that begins after the bacteria stop accumulating nitrite. However, there was stil l nitrate available at the beginning of this lag period. The specific nitrate reductase activity began to in crease after the transition from aerobic to anoxic conditions. About 6 hours were require d for the bacteria to reach the maximum nitrate reductase concentrati on. In comparison, the nitrite reductase activity remained constant for 2.5 h after the transition from aer obic to anoxic conditions and then started to increase as the bacteria started reducing nitrite.

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10 Robertson and Kuenen (1984) tested denitrification of Thiosphaera pantotropha and Thiobacillus A2 switching their electron accep tor from oxygen to nitrate. Aerobically grown bacteria were exposed to anaerobic conditions with nitrate present as the electron acceptor. Acetate and thiosulpha te or a mixture of both was provided as electron donor. Thiosphaera pantotropha produced a gaseous pr oduct immediately after the switch, regardless of electron donor. Thiobacillus A2 began to produce gas 3 hours after the switch when the electron donor wa s acetate, 4 hours after the switch when the electron donor was thiosulphate, and 2 hours in the presence of mixed electron donor. Bonin et al. (1989) examined growth and nitrat e and nitrite reductase activity of bacteria exposed to alternating aerobic and a noxic environments. They found that nitrate reductase activity declined under aerobic conditions but was regained under anoxic conditions, once bacteria ended the la g phase and began to grow again. Liu et al. (1998a) exposed samples of activated sludge from a wastewater treatment plant to aerobic and anoxic conditions. They were the first investigators to observe diauxic lag of activated sludge and poin ted out that this phenomenon could have significant engineering and economic implica tions for nitrogen-removing, single-sludge activated sludge processes. The authors repo rted that both activated sludge and nitrate enrichment denitrifying culture did not grow or grew very slowly for a while during anoxic conditions that followed oxic conditi ons. The authors modeled the phenomenon using a cybernetic approach. They noted that conventional models of single-sludge wastewater treatment process (e.g., Activated Sludge Model 1; Henze et al. 2000) could not depict the phenomenon of diauxic lag wh en bacteria switched between electron acceptors. Liu et al. (1998a) suggested that the reason for the onset of diauxic lag during

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11 denitrification was the lack of enzyme that was required for reduction of nitrate. They hypothesized that the lack of enzyme was due to decay and dilution of the enzyme when the bacteria grew exponentially under aerobi c conditions without the synthesis of the enzyme. They suggested that more than one hour of average lengt h of the diauxic lags under conditions of their experiment was quite surprising, because the length of the lags was similar to the length of anoxic phase in a typical BioDenipho pr ocess, which is the nutrient removal process utilized at the facility where the samples were obtained. Liu et al. (1998b) studied the grow th characteristics of a facultative denitrifying bacterium, Pseudomonas denitrificans The authors observed that length of aerobic period and presence of nitrate during aerobic pe riods could affect the length of diauxic lag under subsequent anoxic conditions and su ccessfully modeled these effects using a modified cybernetic approach. In the study of Baumann et al. (1997b), the authors observe d that the biosynthesis of nitrite reductase was less inhibited by the low pH when the cultures were grown under cycling aerobic/anaerobic condi tions or strictly anaerobic co nditions, indicating that they may have accumulated nitrite reductase over time Hence, it would be interesting to see whether alternating oxic/anoxic conditions results in development of a stable denitrifying continuous culture due to building up of den itrifying abilities over time or failure of denitrification in continuous cultu re due to diauxic lag. In this point of view, the cycle length of alternating oxic/anoxic conditions will be very important to the continuous denitrifying culture because insufficient le ngth of anoxic condition would result in difficulties in developing denitrifying abilities, such as reductase enzymes.

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12 Factors Affecting the Diauxic Lag of He terotrophic, Denitrifying Bacteria Investigations of the dynamics of bact eria switching betw een oxygen and nitrate have identified several factors that can affect the length of the diauxic lag. These include bacterial species, length of the aerobic pha se, dissolved oxygen concentration in the aerobic phase, and nitrate exposure history of the preceding culture. These factors are summarized in Table 2-1. Bacterial species There is some evidence in the literature that diauxic lag of denitrifiers under cyclic oxic/anoxic conditions differs according to bacterial species. For example, Pseudomonas denitrificans have relatively long diauxic lag when they experience an oxic/anoxic switch (Liu et al. 1998b; Lisbon et al. 2001; Casass-Zambrana, 2002) whereas Paracoccus denitrificans exhibit little or no lag follo wing oxic/anoxic switches (Baumann et al. 1996, 1997a, b). Length of aerobic phase Bonin et al (1989) examined the effect of a lternating changes from aerobic to anoxic conditions on an enzyme le vel of denitrifying bacteria, Pseudomonas nautica 617. The authors reported that, in case of a s hort aerobic phase, both nitrate and nitrite reduction activities, which were depleted unde r aerobic conditions, recovered quickly in the following anoxic phase. However, after a long aerobic phase, the start of nitrate reduction activity was delayed for four hours, and the nitrite reduction rate reached only 20% of the original rate before aerobic conditions. Liu et al. (1998b) reported that a pure culture of Pseudomonas denitrificans aerated for a longer period experienced a longer lag th an the same culture aerated for a shorter time. This result can be explained using diluti on and decay of a denitrifying enzyme, as

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13 Table 2-1. Factors affecting th e diauxic lag of denitrifiers. Factors Effects on length of lag Length of aerobic phase Positive effect on length of lag Dissolved oxygen concentration in aerobic phase Positive effect on length of lag Nitrate exposure history of preceding conditions Presence of nitrate in preceding aerobic phase has negative effect on length of lag suggested by Liu et al. (1998a). A longer period of the aerobic phase provides a higher amount of dilution and decay of the denitrifyi ng enzyme due to the suppression effect of dissolved oxygen on synthesis of de nitrifying enzyme during aeration. Dissolved oxygen concentration in aerobic phase Lisbon et al. (2001) investigated the effect of dissolved oxygen concentration during the aerobic phase on the length of diauxic lag during the following anoxic phase. The authors reported that the average length of diauxic lags in the case of the high dissolved oxygen runs was longer than that in the case of the low dissolved oxygen runs. The average specific growth rates in the anoxic phases following low dissolved oxygen aerobic phases were significantly higher than those in the anoxic phases following high dissolved oxygen aerobic phases. The au thors computed the ratio of biomass concentration at the end of an aerobic phase to the biomass concentration at the beginning of an aerobic phase. Higher valu es of the aerobic biomass ratio indicate higher levels of new biomass formed under aerobic conditions The specific grow th rate during the anoxic phase was inversely correlated with the biomass ratio for the preceding aerobic phase, whereas the diauxic lag of bacteria switching between oxygen and nitrate was directly correlated to the aerobic biomass ratio. This is consistent with a mechanism of

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14 nitrate reductase diluti on by growth under aerobic conditions and indicates that the effect of dissolved oxygen was to influence the rate of aerobic growth and, hence, enzyme dilution. Nitrate exposure history of preceding culture Gouw et al. (2001) examined the effect of nitrate exposure history on the oxygen/nitrate diauxic growth of Pseudomonas denitrificans Their culturing sequence consisted of a pre-culture (bact erial growth in nutrient media that were inoculated from agar plates), an aerobic phase, and an anoxic phase. Three different pre-culture conditions were investigated: (1) anoxic with nitrate presen t, (2) aerobic with nitrate present, and (3) aerobic with ni trate absent. The effect of presence or absence of nitrate during the aerobic phase was also examined. In the case of aerobic pre-cu lture, the diauxic lag was l ong (4.0-9.5 h) if nitrate was absent in pre-culture, whereas the presence of nitrate in pre-culture resulted in shorter lags (1.0-4.5 h). The presence of nitrate in pre-culture partially compensated for absence of nitrate in subsequent long aerobic phases. (The combina tion of aerobic pre-culture and aerobic phase, both without nitrate, gave the longest lags.) In the case of anoxic preculture (with nitrate present), presence of nitrate during the following aerobic phase resulted in relatively short di auxic lags or no lags whereas th ere were always diauxic lags if nitrate was absent during the aerobic pha ses. The authors hypothesized that key denitrification enzymes might be synthesized un der aerobic conditions if nitrate is present. Nitrate concentration in anoxic phase Kodama et al. (1969) investigated the effect of nitrate concentration on the diauxic lag of Pseudomonas stutzeri switching their electron betw een acceptors although they did not focus on diauxic lag switchi ng between oxygen and nitrate. As the bacteria reduced

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15 nitrate, nitrite accumulated unt il all nitrate was removed. The growth was then lagged until the bacteria started reducing nitrite. The authors found that the length of the lag period depended on the initial nitrate con centration (i.e., the higher the nitrate concentration, the longer the lag.) The aut hors gave two possible mechanisms for this effect: (1) repressed development of the ni trite reducing system by nitrate, and (2) competition between nitrate and nitrite for electrons. Modeling of Denitrificatio n in Activated Sludge Several mathematical models have been developed to predict denitrification of activated sludge. Prediction of denitrificat ion of the activated sludge models will be discussed and two new models capable of depi cting diauxic lag of de nitrification will be introduced. Activated Sludge Model No. 1 (ASM1) Activated Sludge Models No. 1, 2, 2d and 3 (Henze et al. 2000) were created by the task group on mathematical modeli ng for design and operation of biological wastewater treatment of the International Water Association. They have become well accepted for modeling of single-sludge biologic al wastewater treatment processes. The four models have similar expressions for growth of heterotrophic biomass on oxygen and nitrate and control the resp ective rates using the same switching functionality. ASM1 will be discussed in the present literature re view because it is the oldest of the four models and thus has the l ongest experience base. The complete matrix representation of ASM1 is given in Table 2-2. In ASM1, the process rate for growth of heterotrop hic biomass on oxygen is expressed by H B O H O O S S S HX S K S S K S, 1 (2-1)

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16 where 1 is the process rate for growth of heterotrophic biomass on oxygen, H is the maximum specific growth rate of heterotrophic biomass, SS is the concentration of readily biodegradable substrate, SO is the dissolved oxygen concentration, KS and KO,H are the half saturation coefficients for a readily degradable substrate and dissolved oxygen, respectively, and XB,H is the concentration of heterotrophic biomass. The process rate for growth of heterotrophic biomass on nitrate (2 ) is expressed by H B g NO NO NO O H O H O S S S HX S K S S K K S K S, , 2 (2-2) where SNO is nitrate plus nitrite nitrogen concentration, KNO is the half saturation coefficient of nitrate nitrogen, and g is the correction factor for anoxic growth of heterotrophic biomass. In equation (2-1), th e effect of oxygen on the rate of growth of heterotrophic biomass on oxygen is portrayed by the following switching function: O OHOS KS (2-3) The term approaches 1.0 when dissolved oxygen concentration is high and approaches zero as the dissolved oxygen concentration a pproaches zero. The effect of dissolved oxygen on the rate of growth of heterotroph ic biomass on nitrate is depicted by the following switching function: , OH OOHK SK (2-4) The term approaches zero when dissolved oxyge n concentration is high and approaches 1.0 as the dissolved oxygen concentration appr oaches zero. Thus, it has the effect of

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17 slowing the growth of hetero trophic biomass on nitrate when dissolved oxygen is present in the medium. Modeling of Denitrification with a Cybern etic Approach for Denitrifying Enzyme Kinetics Liu et al. (1998a) proposed a model of denitrification that relied on a cybernetic approach analogous to that of Kompala et al. (1986) to predict the extent of utilization of two alternative electron acceptors (oxygen, nitrate). The proposed model includes the concentrations of two enzymes, EO and ENO, which stand for concentration of oxygenase and nitrate reductase, respectively. Both the specific levels and activities of these enzymes regulate the growth rate of heterotroph ic biomass. The process rate expressions for growth of heterotrophic biomass on oxygen (1 ) and on nitrate (2 ) from ASM1 were modified and process rate expres sions for enzyme synthesis and decay (9 -12 ) were developed. Since the model was subjec t to be incorporated into ASM1, the order numbers were assigned to the process rates in a manner consistent with that of ASM1. The #1 and #2 were assigned to the process ra te for growth of heterotrophic biomass on oxygen and nitrate, respectively, as in AS M1 and #9 through #12 were assigned to the four additional process rates. The effects of oxygenase level and activity on the process rate for growth of heterotrophic biomass on oxygen (1 ) are expressed in the model of Liu et al. (1998a) by multiplying the ASM1 expression by the term ,max/OOOee as follows: 1, ,max,OOO H BH OOHOeS X eKS (2-5)

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18 where Oe represents the specific level of oxygenase (i.e., ,/OOBHeEX ), O is activity of oxygenase (ranging from 0 to 1), and ,max Oe is the maximum specific level of oxygenase. The effect of nitrate reductase level on the process ra te for growth of heterotrophic biomass on nitrate (2 ) is expressed by 2, ,maxNONONO H gBH NONONOeS X eKS (2-6) where the enzyme variables NOeand NO and parameters ,max Oe are analogous to those in equation (2-6). The process rate for synthesis of oxygenase (9 ) can be expressed by the following: 9, O OOBH OHOS uX KS (2-7) where O represents a synthesis rate coefficient for oxygenase and Ou is cybernetic variable ranging from 0 to 1, which governs the specific oxygenase synthesis rate. The process rate for synthesi s of nitrate reductase (10 ) can be expressed by the following: 10, NO NONOBH NONOS uX KS (2-8) where parameter NO is analogous to that in Equation (2-8) and 1NOOuu. The process rate for decay of oxygenase (11 ) was assumed to be firs t order with respect to oxygenase concentration with the same manner that expresses biomass decay (4 ), as follows: 11 OOE (2-9)

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19 where O is oxygenase the decay coefficient. The process rate for decay of nitrate reductase (12 ) is described as follows: 12 NONOE (2-10) whereNO is the nitrate reducta se decay coefficient. The variables Ou, NOu, O and NO in the above formulati on represent the control actions of the cellular regulatory process of repression-induction and inhibition-action. The cybernetic modeling approach postulates th at the bacteria adjust the values of these variables, as well as the values of ,max Oe and ,max NOe, to maximize their instantaneous growth rate. Kompala et al. (1986) showed the solution of the optimization problem to be 1 12/ //O O ONOu (2-11) 1 1/ max/O O O (2-12) 2 2/ max/NO NO NO (2-13) ,maxO O H Oe (2-14) ,maxNO NO H gNOe (2-15) Liu et al. (1998a) used the above model to successfully simulate the diauxic lags observed in their experiments. In a second paper, Liu et al. (1998b) pointed out the fact that the new model still could not depict longer lags or the effect of length of aerobic phase on the length of

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20 diauxic lag. They modified the process rate expressions for enzyme synthesis and enzyme activity. Although the cybernetic variable u is retained in the new model, the overall skim for regulating denitrification is no longer analogous to that of Kompala et al. (1986), hence the model was referred to as a modified cybernetic approach. The process rate for synthesis of oxygenase (9 ) was modified by adding a second synthesis rate coefficient, ,2 O as follows: 9,1,2, ,max, OO OOBHO OOHOeS X u eKS (2-16) The process rate for synthesis of nitrate reductase (12 ) was modified as follows: 10,1,2, ,max NONO NONOBHNO NONONOeS X u eKS (2-17) The expression for oxygenase activity (O ) was changed to provide a sharper transition from inactive to active enzyme. This was accomp lished utilizing a logistic function of the ratio ,max/OOee as follows: ,max41 1O CO OO e sr ee (2-18) where COr is the value of ,max/OOee at which the oxygenase activity is 0.5 and s is the sharpness parameter, which is the slope of the curve at ,max,/OOCOeer Similarly, the expression of nitrate reductase activity (NO ) was modified as follows: ,max41 1NO CNO NONO e sr ee (2-19) where parameters s and CNOr are analogous to thos e in Equation (2-18).

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21 The maximum specific level of oxygenase (,max Oe) can be calculated using the material balance on OE, when the oxygen is non-limiting and nitrate is absent (1Ou ), as follows: ,1,2 ,maxOO O H OHe b (2-20) Similarly, the maximum specific level of nitrate reductase (,max NOe) can be calculated using the material balance on NOE, when the nitrate is non-limiting and oxygen is absent (1NOu ), as follows: ,1,2 ,maxNONO NO H gNOHe b (2-21) Liu et al. (1998b) reported that the m odified model efficiently de picted the influence of aerobic phase length on growth of Pseudomonas denitrificans and length of diauxic lag. Limitations of the model of Liu et al. (1998b) were its inabilit y to predict the effect of organic substrate limitation on bacterial growth and its inability to simulate growth of denitrifying bacteria when switching from anoxic to aerobic conditions. CasassZambrana (2001) modified the model of Liu et al. (1998b) by changing the growth rate and enzyme synthesis rate expressions, the enzyme activity terms in the growth rate expressions, and the nitrate reduc tase activity expression. Casass -Zambrana (2001) gave the follo wing expression for the process rate for growth of heterotrophic biomass on oxygen (1 ) 1, ,max,max,OOSO H BH OOSSOHOevSS X eKSKS (2-22)

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22 In this expression, a switching function has been added to account for the effect of organic substrate limitation on growth rate. Furthermore, the enzyme activity is expressed relative to its maximum value, ,max O (,max1O ). Analogously, the process rate for growth of heterotrophic biomass on nitrate (2 ) was given as: 2, ,max,maxNONOSNO H gBH NONOSSNONOevSS X eKSKS (2-23) where ,max NO is the maximum nitrate reductase activity (,max1NO ). The process rate for oxygenase synthe sis was modified by adding the switching function of the organic substrate as follows: 9,1,2, ,max, OSO OOBHO OSSOHOeSS X u eKSKS (2-24) Analogously, the process rate for nitrate reduc tase synthesis was m odified as follows: 10,1,2, ,max NOSNO NONOBHNO NOSSNONOeSS X u eKSKS (2-25) A switching term was added to the expre ssion for nitrate reductase activity to depict the inactivation e ffect of dissolved oxygen. O i i e e r s NOS K K eNO NO NO c max ,41 1 (2-26) where iK is the inactivation coefficient. The switching term turns down the enzyme activity in the pr esence of oxygen.

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23 Modeling of Denitrification with Mechanis tic Approach for Denitrifying Enzyme Kinetics Hamilton et al. (2005) introduced a mechanistic biochemical model that depicts denitrifying enzyme dynamics of heterotrophic b acteria. In the model, nitrate enters the cell by active transport mediated by a transpor t protein (Figure 2-1). The model assumes that the genes for the nitrate reductase and the transport protein are part of the same operon, thus the two genes are induced together. In the abse nce of intracellular nitrate, the operator is repressed by th e binding of a protein (repr essor). Nitrate that is transported into the cell can bind with the repr essor protein, causing its release from the operator and allowing transcription of the gene s for the nitrate reductase and the transport protein to take place. The activity of the nitrate transport protei n is inhibited by the presence of dissolved oxygen in the bulk medium. In the following, the process rate expressi ons that appear in the model of Hamilton et al. (2005) are explained. The strategy fo r the numbering of process rates (e.g., 2 for growth of heterotrophic bacter ia on nitrate) follows that of ASM1. Expressions for process rates not included in ASM1 are numbered, beginning with 9. The process rate for growth of heterotr ophic biomass on nitrate is described as follows: 2, ,max,,maxNOi NS H gBH NNOiSSS ES X ESKS (2-27) where,max NE is the maximum level of nitrate reductase concentration and ,,max NOiS is the maximum level of intracellular nitrate concen tration. The process ra te of synthesis of nitrate reductase (9 ) is expressed as follows:

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24 Repressor Nar Repressor NO3 Operator Operator NO3 Transport Protein NO3 NO3 Figure 2-1. Schematics of Mech anistic Denitrification Model ,1, 9, 2,1, BHNOi S N BH BHNOiSSXKS S X KXKSKS (2-28) where N is the specific rate of nitrate reductase synthesis, 1K is the equilibrium constant for the binding of repressor to an inducer molecule (intracellular nitrate), 2K is the equilibrium constant for th e binding of repressor to the op erator. The process rate for uptake of nitrate (10 ) is shown as follows: ,, 10 ,max,NOiOH NNOS S BH NNONOOHOSSK ESS VX EKSKSKS (2-29) where NOiSVis the specific rate of uptake of nitrate, ,maxNE is the maximum level of nitrate reductase and NOK and OHK are half saturation coeffici ents for nitrate and oxygen,

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25 respectively. The process rate for decay of nitrate reductase (11 ) was assumed to be first order with respect to nitrate reductase concentration as follows: 11N E NbE (2-30) where N E b is decay rate of nitrate reductase. The process rate for decay of intracellular nitrate (12 ) is described as follows: 12iNObS (2-31) ,maxNE and ,,maxNOiS can be expected when bacteria are growing exponentially in a batch reactor with non-limiting organic substr ate and nitrate. In that case the time derivatives of those variables are zero, a nd the following maximum expressions of the two components are generated. ,,max,(1) 2.86SNOi H NOiBH HgHV Y SX Y (2-32) ,1,,max ,max, 2,1,,maxNBHNOi N NBH EHgBHNOiXKS EX bbKXKS (2-33)

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26 CHAPTER 3 EXTENSION OF ACTIVATED SLUDGE MODEL NO. 1 TO INCORPORATE DENITRIFYING ENZYME KINETICS In the present study, the denitrification m odeling approaches discussed in Chapter 2 were integrated with ASM1 to generate ex tended versions of ASM1. These are eASM1c, which incorporates a modified cybernetic approach for modeling denitrification, and eASM1m, which incorporates a mechanistic ap proach for modeling denitrification. The extended models are discussed in the following sections. Extension with Cybernetic Approach (eASM1c) The extended model is presented in Table 3-1. The major differences between the extended model and the original version of ASM1 are: Process rate expressions for heterotr ophic growth on oxygen and heterotrophic growth on nitrate were changed. New expressions were added for synthe sis of oxygenase, synthesis of nitrate reductase, decay of oxygenase and decay of nitrate reductase. Auxiliary expressions for enzyme activities, maximum enzyme levels, and cybernetic variables were added. (These do not appear in the model matrix.) Process rates for aerobic growth of hetero trophic biomass [equation (2-22)], anoxic growth of heterotrophic bioma ss [equation (2-23)], synthe sis of oxygenase [equation (224)], and synthesis of nitrate reductase [e quation (2-25)] were taken from Casass Zambrana (2001). Process rates for decay of oxygenase [equation (2-9)] and decay of nitrate reductase [equation (2 -10)] were taken from Liu et al. (1998a). These were integrated with the process rates for decay of heterotrophic biomass, aerobic growth of

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27Table 3-1. Process rates and stoich iometric coefficients of eASM1c. Component i 1 2 3 4 5 6 7 8 9 10 11 12 j Process SI SS XI XS XB,H XB,A XP SO SNO SNH SND XND 1 Aerobic growth of heterotrophs 1 H Y 1 1H HY Y XBi 2 Anoxic growth of heterotrophs 1 H Y 1 1 2.86 H H Y Y XBi 3 Aerobic growth of autotrophs 4.57A AY Y 1AY 1XB A i Y 4 Decay of heterotrophs 1P f -1 P f X BPXBifi 5 Decay of autotrophs 1P f -1 P f X BPXBifi 6 Ammonification of soluble organic nitrogen 1 -1 7 Hydrolysis of entrapped organics 1 -1 8 Hydrolysis of entrapped organic nitrogen 1 -1 9 Synthesis of oxygenase 10 Synthesis of nitrate reductase 11 Decay of oxygenase 12 Decay of ntrate reductase Observed Conversion Rates, ML-3T-1 iijj jr

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28Table 3-1. Process rates and stoichiometr ic coefficients of eASM1c (continued). Component i 13 14 15 j Process SALK EO ENO Process rate, j ML-3T-1 1 Aerobic growth of heterotrophs 14XBi ,max,max,OOSO H BH OOSSOHOeSS X eKSKS 2 Anoxic growth of heterotrophs 1 142.8614 H XB HYi Y ,max,maxNONOSNO H gBH NONOSSNONOeSS X eKSKS 3 Aerobic growth of autotrophs 1 147XB Ai Y ,NHO ABA NHNHOAOSS X KSKS 4 Decay of heterotrophs H BHbX 5 Decay of autotrophs ABAbX 6 Ammonification of soluble organic nitrogen 1 14 aNDBAkSX 7 Hydrolysis of entrapped organics ,, ,,,/ (/)SBHOH ONO hhBH XSBHOHOOHONONOXXK SS kX KXXKSKSKS 8 Hydrolysis of entrapped organic nitrogen 7/NDSXX 9 Synthesis of oxygenase 1 ,1,2, ,max, OSO OOBHO OSSOHOeSS X u eKSKS 10 Synthesis of nitrate reductase 1 ,1,2, ,max1NOSNO NONOBHO NOSSNONOeSS Xu eKSKS 11 Decay of oxygenase -1 OO E 12 Decay of ntrate reductase -1 NONO E Observed Conversion Rates, ML-3T-1 iijj jr

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29 nitrifiers, decay of nitrifiers, hydrolysis, a nd ammonification from ASM1. A fit of the model to experimental data from CasassZambrana (2001) is shown in Figure 3-1. Extension with Mechanistic Approach (eASM1m) The extended model is presented in Table 3-2. The major differences between the extended model and the original version of ASM1 are: The process rate expression for hetero trophic growth on nitrate was changed. New expressions were added for trans port of nitrate across the cell membrane, synthesis of nitrate reductase, and decay of nitrate reductase. Auxiliary expressions for enzyme activities, maximum enzyme levels, and cybernetic variables were added. (These do not appear in the model matrix.) Process rates for anoxic growth of he terotrophic biomass [equation (2-27)], synthesis of nitrate reductase [equation (2-28) ], uptake of nitrate [equation (2-29)], decay of nitrate reductase [equation (2-30)], and decay of intracellular n itrate [equation (2-31)] from Hamilton et al. (2005) were integrated with the process rates for aerobic growth of heterotrophic biomass, nitrif ication, hydrolysis, and ammoni fication from ASM1. A fit of the model to experimental data from Hamilton et al. (2005) is shown in Figure 3-2. Comparison of Extended Versions of ASM1 to the Original Version of ASM1 Modeling performances of ASM1 and ex tended versions of ASM1 (eASM1c, eASM1m) will be evaluated. This will be done by comparing their predictions of denitrification of bacteria switching electron acceptors between oxygen and nitrate in various situations. Since the simulations will be done assuming non-limiting organic substrate and other nutrients (e.g., ammonia nitrogen), the pr ocess rates associated with nitrification, ammonification of organic nitrogen and hydrolys is of particul ate matter will not be taken into account.

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30Table 3-2. Process rates and stoich iometric coefficients of eASM1m. Component i 1 2 3 4 5 6 7 8 9 10 11 12 j Process SI SS XI XS XB,H XB,A XP SO SNO SNH SND XND 1 Aerobic growth of heterotrophs 1 H Y 1 1 H HY Y X Bi 2 Anoxic growth of heterotrophs 1 H Y 1 X Bi 3 Aerobic growth of autotrophs 1 4.57A AY Y 1AY 1XB Ai Y 4 Decay of heterotrophs 1 P f -1 P f X BPXBifi 5 Decay of autotrophs 1 P f -1 P f X BPXBifi 6 Ammonification of soluble organic nitrogen 1 -1 7 Hydrolysis of entrapped organics 1 -1 8 Hydrolysis of entrapped organic nitrogen 1 -1 9 Synthesis of intracellular nitrate -1 10 Synthesis of nitrate reductase 11 Decay of intracellular nitrate 12 Decay of ntrate reductase Observed Conversion Rates, ML-3T-1 iijj jr

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31Table 3-2. Process rates and stoichiometr ic coefficients of eASM1m (continued). Component i 13 14 15 j Process SALK SNOi EN Process rate, j ML-3T-1 1 Aerobic growth of heterotrophs 14XBi ,SO H BH SSOHOSS X KSKS 2 Anoxic growth of heterotrophs 14 86 2 14 1XB H Hi Y Y 1 2.86 H H Y Y , ,max,,maxNOi NS H gBH NNOiSSS ES X ESKS 3 Aerobic growth of autotrophs 1 147XB Ai Y ,NHO ABA NHNHOAOSS X KSKS 4 Decay of heterotrophs H BHbX 5 Decay of autotrophs ABAbX 6 Ammonification of soluble organic nitrogen 1 14 aNDBHkSX 7 Hydrolysis of entrapped organics ,, ,,,/ (/)SBHOH ONO hhBH XSBHOHOOHONONOXXK SS kX KXXKSKSKS 8 Hydrolysis of entrapped organic nitrogen 7/NDSXX 9 Synthesis of intracellular nitrate 1 ,, ,max,NOiOH NNOS S BH NNONOOHOSSK ESS VX EKSKSKS 10 Synthesis of nitrate reductase 1 ,1, 2,1, BHNOi S N BH BHNOiSSXKS S X KXKSKS 11 Decay of intracellular nitrate -1 NOibS 12 Decay of ntrate reductase -1 N E NbE Observed Conversion Rates, ML-3T-1 iijj jr

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32 0 50 100 150 200 250 300 350 5101520 Time(h) Repeat Experimental result Diauxic lag Oxic Anoxic A noxicBiomass (mg cell/L) eASM1c prediction Figure 3-1. Simulation of experi mentally observed diauxic lag of Pseudomonas denitrificans predicted by eASM1c 0 50 100 150 200 250 300 350 05101520 Time (h) simulation experimental data eASM1m prediction Experimental result OxicAnoxic Diauxic lag Biomass (mg cell/L) Figure 3-2. Simulation of experi mentally observed diauxic lag of Pseudomonas denitrificans predicted by eASM1m

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33Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by ASM1 A series of simulations of an intermittently aerated batch reactor was carried out to demonstrate the ASM1 prediction. Table 3-3 shows the characteristics of growth medium used in the simulations, whereas Ta ble 3-4 presents the model parameters of ASM1 used in the simulations. Alternati ng oxic/anoxic conditions were provided to the reactor. At the end of the each oxic period, the condition was changed instantaneously to anoxic. This could be achieved experime ntally by stopping aerati on and then stripping oxygen with nitrogen gas. The anoxic phase wa s held at 1.5 times of the oxic phase. At the end of each anoxic phase the biomass con centration was diluted to 5 mg cell/L. An ultimate state was always reached within 100 cy cles for the given reactor characteristics and model parameters. The results shown always begin with cycle 101. Three different aeration periods, 2 h, 4 h, and 8 h, were tested to evaluate the model prediction of the effect of oxic phase length on the denitrification. Figure 3-3 shows the biomass concentration during cyclic simulati ons As shown in the figures, ASM1 cannot predict diauxic lag under cyclic oxic/anoxic change. We define volumetric denitrification rate (VDR) as the change in nitrate concen tration per unit time and mass specific denitrification rate (MSDR) as the VDR divided by the biomass concentration. Figure 3-4 shows the MSDR and VDR during the cyclic simulation with 4 h of oxic phase. The model could not predict the e ffect of aeration on either MSDR or VDR during the anoxic phases. The MSDR was c onstant throughout the anoxic phases and VDR started to increase i mmediately at the beginni ng of each anoxic phase. To investigate the model prediction of the effect of dissolved oxygen (DO) level on the denitrification of heterotrophic biomass, the simulation of alternating oxic/anoxic

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34 Table 3-3. Characteristics of gr owth medium in batch simulations. Components Initial conditions Units SS 60 mmol glutamic acid/L SNO 4000 mg NO3 --N/L SO 8 (oxic, constant) mg O2/L XB,H 0 (anoxic, constant) mg cell/L Table 3-4. Parameters of AS M1 for the batch simulation. Common Parameters Values Units H 0.6 h-1 g 0.58 bH 0.002 h-1 YH 80 mg dry cells / mmol glutamic acid KS 0.025 mmol glutamic acid / L KO,H 0.065 mg O2 / L KNO 0.77 mg NO3-N / L eASM1c parameters O,1 1.00E-02 h-1 O,2 0.00E-02 h-1 NO,1 1.00E-04 h-1 NO,2 1.00E-04 h-1 O 0.002 h-1 NO 0.0002 h-1 rC,O 0.1 rC,NO 0.7 S 10.9 Ki 0.2 eASM1m parameters VSni 4.66E-02 mg NO3-N /mg dry cells/sec aN 2.45E-08 kat/mg dry cells/sec K1 9.86E+04 (mg NO3-N/L)-1 K2 1.96E+04 (mg NO3-N/L)-1 KNO,i 5.61E-04 mg NO3-N / L KO,i 3.31E-04 mg O2 / L bNO 0.4 h-1

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35 4 h oxic / 6 h anoxic 0 50 100 150 200 250 300 350 400 450 9909951000100510101015 Time (h) Oxic b.OxicOxic AoxicAoxicXB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 500 1000 1500 2000 2500 3000 3500 4000 4500 198019851990199520002005 Time (h) A noxic Oxicc.XB,H (mg cell/L) 2 h oxic / 3 h anoxic 0 10 20 30 40 50 495500505510515520 Time (h) Oxic Anoxi c a.Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c XB,H (mg cell/L) Figure 3-3. Growth of hetero trophic biomass during cyclic simulations with 8 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by ASM1

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36 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 9909951000100510101015 Time (h) 0 2 4 6 8 10 12 14 16 MSDR VDR OxicAnoxic OxicAnoxicOxicMSDR (mg NO 3 -N/mg cell/L) VDR (mg NO 3 -N/mg cell/L) Figure 3-4. Mass specific a nd volumetric denitrification ra te during cyclic simulation, predicted by ASM1 cycling was repeated with va rious levels of DO concentr ation. Three additional DO levels (1, 2 and 4 mg/L) were tested. Figures 3-5, 3-6, and 3-7 show the biomass profile from the repetitive cycling simulations with respect to 4, 2, and 1 mg/L of DO concentration. As shown in the figures, DO concentrations during the oxic phase did not affect either the simulated growth of he terotrophic biomass, the MSDR or the VDR during anoxic phases. Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by eASM1c Growth of heterotrophic biomass switching between oxygen and nitrate as an electron acceptor in a batch reactor at non-limiting substrate concentrations was simulated to illustrate the differences between the predictions of ASM1 and eASM1c and the effects of culture variables on diauxic lag. Characteristics of the growth medium

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37 4 h oxic / 6 h anoxic 0 50 100 150 200 250 300 350 400 450 9909951000100510101015 Time (h) OxicAnoxic Oxic A noxicOxicb.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 500 1000 1500 2000 2500 3000 3500 4000 4500 198019851990199520002005 Time (h) A noxic Oxicc.XB,H (mg cell/L) 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 35 40 45 50 495500505510515520 Time (h) Oxic Anoxi c Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) Figure 3-5. Growth of hetero trophic biomass during cyclic simulations with 4 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by ASM1

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38 4 h oxic / 6 h anoxic 0 50 100 150 200 250 300 350 400 450 9909951000100510101015 Time (h) OxicAnoxic Oxic A noxicOxicb.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 500 1000 1500 2000 2500 3000 3500 4000 4500 198019851990199520002005 Time (h) A noxic Oxicc.XB,H (mg cell/L) 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 35 40 45 50 495500505510515520 Time (h) Oxic Anoxi c Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) Figure 3-6. Growth of hetero trophic biomass during cyclic simulations with 2 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by ASM1

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39 4 h oxic / 6 h anoxic 0 50 100 150 200 250 300 350 400 450 9909951000100510101015 Time (h) OxicAnoxic OxicOxic A noxicb.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 500 1000 1500 2000 2500 3000 3500 4000 4500 198019851990199520002005 Time (h) A noxic Oxicc.XB,H (mg cell/L) 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 35 40 45 50 495500505510515520 Time (h) Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c Anoxi c Oxic Oxic Oxica.XB,H (mg cell/L) Figure 3-7. Growth of hetero trophic biomass during cyclic simulations with 1 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by ASM1

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40 are given in Table 3-3, whereas parameters fo r eASM1c are given in Table 3-4. With the exception of the kinetic and stoichiometric parameters for eASM1c, all simulation conditions were identical to those for the oxic/anoxic cycling simu lations using ASM1 that were discussed in the previous section. Figure 3-8 shows biomass profiles simula ted by ASM1 and eASM1c. These were generated by setting eO/eO,max to 1.0 and eNO/eNO,max to 0 in eASM1c at the beginning of the oxic phase. As apparent from the fi gure, eASM1c can predict diauxic lag of denitrifiers switching between oxygen and nitr ate, whereas ASM1 cannot predict diauxic lag. Figure 3-9 presents nitr ate reductase specific level and activity during the diauxic growth of heterotrophic biomass, as predicted by eASM1c. The specific nitrate reductase level started increasing immediately after the switch. In contrast, the nitrate reductase activity remained low for some time and then began increasing. Noticeable biomass growth resumed shortly after th e increase of enzyme activity. Model runs for a simple batch reactor w ith alternating oxic/anoxic cycling were carried out to demonstrate eASM1c predicti ons of the effect of oxic phase length on dynamics of denitrification in the subseque nt anoxic phases. Three different aerobic phase lengths (2 h, 4 h, and 8 h) with oxic phase DO of 8 mg/L were tested to investigate the effect of aerobic phase length on the dia uxic lag predicted by eA SM1c. Anoxic phase lengths were varied in proporti on to the oxic phase lengths, giving cycle lengths of 5, 10, and 20 hours, respectively. In each case, the simulation was run to an ultimate state. The eASM1c model predicted longer diauxic lags as oxi c phase length increased (Fig. 3-10). This reflects the ability of eASM1c to depict dilution of nitrate re ductase in the biomass during oxic phases. According to the model, the rate of nitrate reducta se synthesis is low

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41 0 100 200 300 400 500 600 700 800 ASM1 Prediction oxicanoxic switch a.XB,H (mg cell/L) 0 100 200 300 400 500 600 700 800 051015 Time (h) switch oxicanoxic diauxic lag eASM1c Predictionb.XB,H (mg cell/L) Figure 3-8. Growth of he terotrophic biomass under oxi c/anoxic switch: a) ASM1 prediction, b) eASM1c prediction relative to the rate of enzyme decay unde r oxic conditions. Figure 3-11 presents the MSDR and VDR during the cyclic simulations The MSDR of biomass at the beginning of the each anoxic phase was zero and then increased rapidly once biomass started growing at the end of the diauxic lag peri od. The VDR at the beginning of the each anoxic phase was also zero and started to increase along with the increase of MSDR. To investigate the model prediction of the effect of DO level on the length of diauxic lag, the simulations of oxic/anoxic cycling with three different cycle lengths

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42 0 0.5 1vNO v NO c. 0 0.5 1 051015 Time (h) eNOvNOeNO,maxvNO,maxd.e NO v NOe NO,ma x v NO,ma x 0 100 200 300 400 500 600 700 800 switch oxicanoxic diauxic lag biomass a.XB,H (mg cell/L) 0 0.0002 0.0004 0.0006eNOeNOb. Figure 3-9. Specific nitrate reductase level and activity of heterotrophic biomass under oxic/anoxic switch: a) Biom ass, b) specific nitrate reductase level, c) nitrate reductase activity, d) ,max,max NONO NONOe e predicted by eASM1c

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43 4 h oxic / 6 h anoxic 0 20 40 60 80 100 120 9909951000100510101015 Time (t) 1.9 h Anoxic Oxic OxicOxic A noxicb.XB,H (mg cell/L) 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 495500505510515520 Time (t) 1 h Oxic Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 500 1000 1500 2000 2500 3000 3500 4000 4500 198019851990199520002005 Time (t) 2.6 h A noxic Oxic c.XB,H (mg cell/L) Figure 3-10. Growth of hetero trophic biomass during cyclic simulations with 8 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by eASM1c

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44 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 9909951000100510101015 Time (h) 0 1 2 3 4 5 MSDR VDR Anoxic AnoxicOxic Oxic Oxic MSDR (mg NO 3 -N/mg cell/L) VDR (mg NO 3 -N/mg cell/L) Figure 3-11. Mass specific and volumetric deni trification rate during cyclic simulation, predicted by eASM1c (5, 10, 20 hours) were repeated with oxic pha se DO concentrations of 4, 2 and 1 mg/L (Figs. 3-12, 3-13, 3-14, respectively). S horter diauxic lags we re predicted as DO concentrations decreased. This happens because eASM1c predicts a lower rate of dilution of ENO due to the lower rate of oxic growth of bacteria as DO is decreased. Two of the terms that affect the process rate for growth of heterotrophic biomass under oxic conditions in eASM1c are the sw itching function for DO and the ratio of specific oxygenase level to the maximum specific oxygenase level ( eO/eO,max). In order to portray the effect of eO/eO,max on oxic phase growth rates, data from the simulations for 1 mg/L and 8 mg/L oxic phase DO and cycle length of 20 hours were re-examined. The ultimate state biomass profiles from these simulations are shown in Figure 315a. The specific growth rates were extracte d from these profiles, as shown in Figure 315b. For a given oxic phase DO, the specific growth rate changes with respect to time

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45 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 495500505510515520 Time (h) 1.0 h Oxic Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) 4 h oxic / 6 h anoxic 0 20 40 60 80 100 120 9909951000100510101015 Time (h) 1.9 h Anoxic Oxic b.Oxic A noxicOxicXB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 1000 2000 3000 4000 5000 6000 198019851990199520002005 Time (h) 2.5 h A noxic Oxic c.XB,H (mg cell/L) Figure 3-12. Growth of hetero trophic biomass during cyclic simulations with 4 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by eASM1c

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46 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 495500505510515520 Time (h) 0.8 h Oxic Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) 4 h oxic / 6 h anoxic 0 20 40 60 80 100 120 9909951000100510101015 Time (h) 1.7 h Anoxic Oxic Oxic A noxic Oxicb.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 1000 2000 3000 4000 5000 6000 198019851990199520002005 Time (h) 2.4 h A noxic Oxic c.XB,H (mg cell/L) Figure 3-13. Growth of hetero trophic biomass during cyclic simulations with 2 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by eASM1c

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47 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 495500505510515520 Time (h) 0.5 h Oxic Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) 4 h oxic / 6 h anoxic 0 20 40 60 80 100 9909951000100510101015 Time (h) 1.4 h Anoxic Oxic OxicOxic A noxicb.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 1000 2000 3000 4000 5000 6000 198019851990199520002005 Time (h) 2.3 h A noxic Oxic c.XB,H (mg cell/L) Figure 3-14. Growth of hetero trophic biomass during cyclic simulations with 1 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by eASM1c

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48 throughout the oxic phase, even though the DO is constant and substrate is non-limiting. This is due to values of eO/eO,max (Fig. 3-15c) that change th roughout the oxic phase as a consequence of oxygenase bi osynthesis. Profiles of eO/eO,max were generally higher at higher oxic phase DO. Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by eASM1m Simulations were carried out to compare pr ediction trends of the mechanistic model (eASM1m) to the cybernetic model (eASM1c) Specifically, the effect of length of aerobic phase in alternating oxic/anoxic grow th and the effect of DO concentration on the predictions of eASM1m were examined by repeating the series of simulations previously performed with eASM1c, as de scribed in the previous section. The experimental conditions (substrate concentrati on, nitrate concentra tion, phase lengths) were same as reported in the previous secti ons. Model parameters used for eASM1m are compared to those used for eASM1c in Table 3-4. It is useful to first examine a single cycle consisting of an oxic phase followed by an anoxic phase in terms of biomass con centration, enzyme concentration, and intracellular nitrate concentra tion (Fig 3-16). The eASM1m predicted a diauxic lag of denitrifiers switching between oxygen and nitrat e. Nitrate reductase and intracellular nitrate concentrations increased immediately from the switch, but the magnitude of the increases were small for several hours. The in crease of nitrate reduc tase and intracellular nitrate concentration then became significan t about four hours after the switch. The biomass started growing as th e product of the nitrate reduc tase level and intracellular nitrate concentration divided by their maximum values started increasing. This term thus regulates the rate of growth of hetero trophic biomass under anoxic condition.

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49 0 100 200 300 400 500 600 20002005201020152020 Time (h) 8 mg/L DO 1 mg/L DO OxicAnoxicaXB,H (mg cell/L) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 20002005201020152020 Time (h) 8 mg/L DO 1 mg/L DO b.e O / e O,max 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 20002005201020152020 Time (h) 8 mg/L DO 1 mg/L DO Model KO,H c.Effective K O,H Figure 3-15. Growth of hetero trophic biomass during cyclic simulations with 8 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by eASM1c

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50 0 30 60SnoiSnoi c. 0 0.5 1 05101520 Time (h) eNOSnoieNO,maxSnoi,maxd.e NO S noie NO,max S noi,max 0 100 200 300 400 500 600 700 800 Switch OxicAnoxic Diauxic lag Biomass a.X B,H (mg cell/L) 0 0.00001eNOeNOb. Figure 3-16. Specific nitrate reductase level and specific intracellular nitrate level of heterotrophic biomass under oxic/anoxic switch: a) Biomass, b) Nitrate reductase level, c) Intracellular nitrate level, d) ,max,maxNONO NONOe e predicted by eASM1c The first comparison made was for the case of long-term cyclic oxic/anoxic operations. In this case, eASM1m predicted longer diauxic lag as the cycle length was increased (data not shown). This is qualit atively similar to the predictions made by

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51 eASM1c. This reflects the ability of eASM1m to depict dilution of nitrate reductase and intracellular nitrate in the biom ass while the rates of synthe sis of nitrate reductase and transfer of nitrate into the cell are low relative to the ra te of decay of enzyme and intracellular nitrate. The mass specific denitrification rate (MSDR) and volumetric denitrification rate (VDR) duri ng the cyclic simulations showed similar trends of those of eASM1c (data not shown). The second comparison made was in terms of DO concentration of the oxic phases. Unlike the prediction of eASM1c, the length of lag was not significan tly influenced by DO concentrations from 1 mg/L to 8 mg/L during the oxic phases (data not shown). Since eASM1m is not capable of predicting the dynamics of oxygenase synthesis, the lower rate of aerobic growth associated with low specific oxygenase level predicted by eASM1c in the previous section could not be the case, which means that DO concentration may affect the specific growth rate only by changing the switching function for DO concentration in the model prediction of eASM1m. This leads to the fact that the 1 mg/L of DO concentration tested in th e simulation was not low enough to result in lower rate of aerobic growth than that of aerobic growth with 8 mg/L. Hence, the simulations were repeated with much lowe r DO concentrations. Figure 3-17, 3-18, and 3-19 show the biomass profile from the altern ating cycling simulation with respect to 1, 0.5 and 0.1 mg/L of DO concentration. Th e eASM1m predicted shorter diauxic lag as DO concentrations during the oxic phase decreases. This is because low DO concentration results in lower rate of aerobi c growth of heterotrophic biomass and lower rate of dilution of nitrate reductase ( ENO).

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52 4 h oxic / 6 h anoxic 0 20 40 60 80 100 120 140 10001005101010151020 Time (h) 0.8 h Anoxic Oxic Oxic A noxicb.XB,H (mg cell/L) 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 500505510515520 Time (h) Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 500 1000 1500 2000 2500 3000 3500 4000 4500 20002005201020152020 Time (h) 3.2 h A noxic Oxic c.XB,H (mg cell/L) Figure 3-17. Growth of hetero trophic biomass during cyclic simulations with 1 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by eASM1m

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53 4 h oxic / 6 h anoxic 0 20 40 60 80 100 120 10001005101010151020 Time (h) 0.7 h Anoxic Oxic Oxic A noxicb.XB,H (mg cell/L) 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 500505510515520 Time (h) Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 500 1000 1500 2000 2500 3000 3500 4000 4500 20002005201020152020 Time (h) 3.0 h A noxic Oxic c.XB,H (mg cell/L) Figure 3-18. Growth of hete rotrophic biomass during cyclic simulations with 0.5 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by eASM1m

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54 4 h oxic / 6 h anoxic 0 10 20 30 40 50 60 70 80 10001005101010151020 Time (h) 0.3 h Anoxic Oxic Oxic A noxicb.XB,H (mg cell/L) 2 h oxic / 3 h anoxic 0 5 10 15 20 25 30 500505510515520 Time (h) Oxic Oxic Oxic Oxic Anoxi c Anoxi c Anoxi c Anoxi c a.XB,H (mg cell/L) 8 h oxic / 12 h anoxic 0 500 1000 1500 2000 20002005201020152020 Time (h) 2.3 h A noxic Oxic c.XB,H (mg cell/L) Figure 3-19. Growth of hete rotrophic biomass during cyclic simulations with 0.1 mg/L of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted by eASM1m.

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55Re-examination of Results from a Previous Study It would be interesting to re visit the studies about the e ffect of change of electron acceptor to the denitrifica tion with a view point of diauxic lag. Baumann et al. (1996) investigated the effect of switching of electron acceptor from oxygen to nitrate in a continuous culture of Paracoccus denitrificans as discussed in the previous chapter. In their study, the biomass concentration slight ly decreased over the 20-hour period as the growth condition was changed fr om aerobic to anaerobic. It was not clear from these data if a growth lag occurred after the sw itch. A simulation was therefore performed using a denitrification model capab le of predicting diauxic lag to investigate if the slight change of biomass concentration was due to diauxic lag. The experimental conditions for the simulati ons are given in Appendix A. The data of Baumann et al. (1996) are shown in Figure 3-20, along with three simulation scenarios. In the first scenario, growth stops immediat ely after the switch fr om aerobic to anaerobic conditions. In this case, the biomass concen tration would then begin to follow the washout curve until growth resumed. In the second scenario, there is no la g, rather, the growth yield decreases because less energy is obtaine d from oxidation of substrate using nitrate instead of oxygen. In the third scenario, th e maximum specific growth rate is decreased, again with no lag. As can be seen in the figure the trend of biomass did not follow washout curve. Therefore, it can be conclude d that the growth lag did not occur in the experiment. Either the second scenario (lower growth yield) or the third scenario (lower maximum specific growth rate) could simulate the trend of biomass in the experimental result well.

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56 0 0.2 0.4 0.6 0.8 1 1.2 200210220230240 Time (h) Experimental data Scenario 1 Scenario 2 Scenario3Biomass (g cell/L) Figure 3-20. Simulation of experiment al results from a previous study.

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57 CHAPTER 4 SIGNIFICANCE OF DENITRIFYING ENZYME DYNAMICS IN BIOLOGICAL NITROGEN REMOVAL PROCESSE S: A SIMULATION STUDY The phenomenon of diauxic lag for bacteria switching between electron donors was discovered at least 60 years ago (M onod, 1942). Subsequently, Kodama et al (1969) observed an analogous lag for bacteria switching between electron acceptors. Experiments with activated sludge and Pseudomonas denitrificans pure culture have established that the diauxic lag of bacteria switching between oxygen and nitrate as electron acceptors can last up to several hour s and depends on the length of aeration period, DO concentration during the aeration period, and the preculture environment that precede anoxic conditions (Liu et al ., 1998a, b; Gouw et al. 2001; Lisbon et al. 2002). Effects of aeration period and DO con centration on diauxic lag of bacteria switching between oxygen and nitrate have been successfully modeled (Liu et al. 1998a, b, Casass-Zambrana, 2001). However, the widely used ASM 1, 2, 2d, and 3 (Henze et al. 2000) are unable to portray th e diauxic lag phenomenon. Th is deficiency could result in sub-optimal operational strategies or de signs and lead to n eedless environmental impact or waste of economic resources. The purpose of the present study was to compare predictions of the widely used ASM1 to a ex tended version of ASM1 that incorporates enzyme kinetic expressions and can portray di auxic lag. Both models were applied to activated sludge process c onfigurations for nitrogen removal involving periodic operation.

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58 Experimental Methods Process configurations and operational condi tions for simulations will be provided. The wastewater composition and model para meters will also be discussed. Process Configurations and Modeling We used two different process configurations to illustrate how predictions of eASM1c differ from those of ASM1: fed-ba tch, and BioDenipho (BDP). The fed-batch reactor had a minimum volume of 4495 m3 at the beginning of a cycle and a maximum volume of up to 6567 m3 at the end of a cycle. Th e BDP process consisted of an anaerobic reactor, two parallel oxidation ditches, a final anoxic reactor, and a final aerated reactor (Fig. 4-1, bottom). R eactor volumes and the corresponding hydraulic residence times are given in Table 4-1. The sequence and lengths of anoxic and oxic phases in the fed-batch proce ss (Fig. 4-1, top) were comp arable to the sequence of unaerated and aerated environments thr ough which mixed liquor passes in the BDP process (Fig. 4-1, bottom). In the fed-batc h process, the reactor was unaerated for the initial 18.0% of the cycle. Similarly, in the BDP process, the mixed liquor initially passed through an unaerated (anaerobic) reactor that comprised 18.0% of the total residence time. Subsequently, aeration in th e fed-batch process was turned on and off to create alternating oxic and anoxic conditions Mixed liquor was exposed to three oxic and three anoxic phases during this time period, which lasted 64.2% of the cycle. In the BDP process, the residence time of the oxidati on ditches was 64.2% of the total. While in the oxidation ditches, the mixed liquor wa s exposed to a sequence of six phases (Fig. 4-2). The last two phases of the fed-batch pr ocess were anoxic (14.3% of cycle) and oxic (3.5% of cycle), respectively. In comparis on, the last two reactor s of the BDP process were anoxic (14.3% of residence time) and oxi c (3.5% of residence time), respectively.

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59 Influent wastewater Mixed fill (anoxic) Influent wastewater Oxic ReactFill Anoxic ReactFill Anoxic React Oxic ReactFraction of cycle or residence time Anaerobic reactor Final anoxic reactor Final oxic reactor Oxic/anoxic Oxidation ditch R-1 Oxic/anoxic Oxidation ditch R-2 Secondary Settling Tank Sludge recycle Waste sludge18.0% 14.3%3.5% 64.2% BDP process Fed batch process Figure 4-1. Process schematics of fed-batc h process (top) and BDP process (bottom) showing the fraction of the cycle length or hydraulic residence time occupied by each phase or part of the processes. R-1 DN R-2 N A B C D EF R-1 DN R-2 N R-1 N R-2 N R-1 N R-2 DN R-1 N R-2 DN R-1 N R-2 N Figure 4-2. Sequence of phase s in the BDP oxidation ditches

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60 Table 4-1. Design of UF BDP wa ter reclamation facility (train 1 of two parallel trains). Hydraulic residence time, h Unit Volume, m3 % of total reactor volume At current ADFa At design ADFb Anaerobic reactor 808 18.0 6.0 3.4 Oxidation ditch 1 1443 32.1 10.7 6.1 Oxidation ditch 2 1443 32.1 10.7 6.1 Final anoxic 643 14.3 4.8 2.7 Final aeration 158 3.5 1.2 0.7 TOTAL 4495 100.0 33.4 18.9 a Current average daily flow is 3230 m3/d to one train b Design ADF is 5700 m3/d to one train Biomass was wasted at the end of each cycl e of the fed-batch process to achieve a solids retention time (SRT) of 25 days. Dela ys for settling of sludge and decanting of supernatant were neglected. Sludge was wasted from the BDP secondary settling tank underflow to achieve an SRT of 25 days. Th e secondary settling tank was modeled as a perfect settler with a sludge recycle ratio of 1.0. Mixing and delays resulting from the secondary settler were neglected. The oxyge n mass transfer coefficient was adjusted under aerated conditions in both processes by a proportional-integral-derivative velocity controller, up to a maximum of 100 d-1. The proportional gain and reset time for the DO controller were tuned using the closed loop method of Ziegler and Nicholes (1942). The oxygen mass transfer coefficient was set to zer o to simulate unaerated conditions. DO in the reactors was determined by mass balance. All reactors were modeled as completely mixed tanks. Effluent concentrations of soluble components were taken as the concentrations in the final React phase in the fed-batch process and as the concentrations in the final oxic reactor in the BDP process. Daily effluent concentrations of the fedbatch reactor and BDP process were reported as effluent, 24-hour flow-weighted average concentrations. Base values in simulations were the following: average daily influent

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61 flow = 5700 m3/d, DO = 1.0 mg/L during oxic phases fed-batch reactor DO set point during final oxic phase = 2.0 mg/L, BDP final oxic reactor DO set point = 2.0 mg/L, temperature = 23C, and pH = 6.8. We define the unaerated volume fraction (UVF) as the ratio of the reaction time under unaerated conditions to th e total reaction time. This parameter gives an indication of the reaction time available for denitrificati on. In the fed-batch process, the relative lengths of the React-Fill sub-phases were ad justed in order to manipulate the UVF, whereas in the BDP process, the relative le ngths of phases in th e oxidation ditch cycle were varied in order to ma nipulate UVF (Table 4-2). Si mulations were run until an ultimate state (i.e., constant component concen trations at any given time of day) was reached. Table 4-2. Sequence of phases in the fed-batch and BDP processes. Fed-batch process BDP process Phases % of Total React-Fill Phase Phases % of Oxidation Ditch Cycle Length Oxic React Fill 1 22.3 13.7 A 13.3 18.8 Anoxic React Fill 1 11.0 19.7 B 20.0 28.2 Oxic React Fill 2 22.3 13.7 C 16.7 3.0 Anoxic React Fill 2 11.0 19.7 D 13.3 18.8 Oxic React Fill 3 22.3 13.7 E 20.0 28.2 Anoxic React Fill 3 11.0 19.7 F 16.7 3.0 Wastewater Composition and Model Parameters Values for the 24-hour flow-weighted aver age wastewater composition (Table 4-3) and kinetic and stoichiometric parameters fo r ASM1 (Table 4-4) were taken from a study of a local nitrogen removal activated sludge plant (Antoniou, 1989; Antoniou et al. 1990; Hamilton et al. 1992). Temperature and pH depende ncies reported by these were used

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62 Table 4-3. 24-hour flow-weighted average wastewater composition. Wastewater Components Average value Units Soluble inert substrate ( SI) Particulate inert substrate ( XI) Readily degradable substrate ( SS) Slowly degradable (par ticulate) substrate ( XS) Soluble inert organic N ( SNI) Particulate inert organic N ( XNI) Nitrate plus nitrite N ( SNO) Ammonia N ( SNH) Soluble degradable organic N ( SND) Particulate degradable organic N ( XND) Active heterotrophic biomass ( XB,H) Active autotrophic biomass ( XB,A) Inert products from decay ( Xp) Alkalinity ( SAlk) DO ( SO) 20.8 102.6 96.7 127.4 1.5 2.7 0.0 22.3 8.0 3.6 0.0 0.0 0.0 6.0 0.0 g COD/m3 g COD/m3 g COD/m3 g COD/m3 g N/m3 g N/m3 g N/m3 g N/m3 g N/m3 g N/m3 g COD/m3 g COD/m3 g COD/m3 mol HCO3 -/m3 g COD/m3 for the following model parameters: hetero trophic and autotrophic maximum specific growth rates (H A ), heterotrophic decay rate ( bH), hydrolysis rate ( kh), ammonification rate ( ks), and hydrolysis halfsaturation coefficient ( KX). All other parameters were assumed to not change with temperature or pH. The value used for the ammonia half-saturation coefficient for autotrophs ( KNH) was that found by Antoniou (1989) in experiments using the same wastewat er that the influent composition was based on. The remaining parameter values for ASM1 were taken from Henze et al. (1986). New parameters for the eASM1c model, as we ll as the maximum speci fic growth rate of heterotrophs used in eASM1c, were take n from Casass-Zambrana (2001). Diurnally Varying Flow and Component Co ncentrations in Influent Wastewater Typical diurnal flow and concentrati on patterns from Metcalf and Eddy (1991) were normalized. Influent flow was varied according to the normalized flow variation curve, soluble components of the influent wastewater were varied according to the normalized biochemical oxygen demand variat ion curve, and particulate components

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63 Table 4-4. Stoichiometric and kinetic parameters in the ASM1 and eASM1 models. Parameter Symbol Valuea Typical Units Yields Heterotrophic Autotrophic Biomass fraction yielding particulates YH YA p 0.67 0.24 0.08 g cell COD/g COD utilized g cell COD/g N utilized dimensionless Composition Mass N/mass COD in biomass Mass N/mass COD in biomass products iXB iXP 0.086 0.06 g N/g COD g N/g COD Maximum Specific Rates Heterotrophic growth for ASM1 Heterotrophic growth for eASM1 Autotrophic growth Hydrolysis Rate Coefficients Ammonification Heterotrophic decay Autotrophic decay H H A kh ka bH bA 7.4b 14.4c 0.644d 4.171b 0.098b 0.871b 0.1 day-1 day-1 day-1 g particulate COD /(g cell COD d) m3/(g cell COD day) day-1 day-1 Half Saturation Coefficients Substrate coeff. for heterotrophs Nitrate coeff. for heterotrophs Ammonia coeff. for autotrophs Oxygen coeff. for heterotrophs Oxygen coeff. for autotrophs Hydrolysis of particulate organics KS KNO KNH KO,H KO,A KX 20 0.5 0.2e 0.2 0.4 0.042b g COD/m3 g NO3-N/m3 g NH4-N/m3 g COD/m3 g COD/m3 g particulate COD /(g cell COD) Anoxic Correction Factors Growth Hydrolysis G H 0.7f 0.4 dimensionless dimensionless Enzyme rate coefficients eASM1cc Oxygenase synthesis Nitrate reductase synthesis Nitrate reductase synthesis Oxygenase decay Nitrate reductase decay Enzyme activity coefficients eO/eO ,max at which the activity is 50% eNO/eNO ,max at which the activity is 50% Sharpness parameter Oxygen inactivation coefficient O,1 O,2 NO,1 NO,2 O NO rC,O rC,NO s Ki 0.24 0 0.0024 0.0024 0.0871 0.0871 0.1 0.7 10.9 0.2 day-1 day-1 day-1 day-1 day-1 day-1 dimensionless dimensionless dimensionless g COD/m3 aAt 20 C and neutral pH unless otherwise specified; a ssumed not to change with temperature or pH bAt T = 23 C (Hamilton et al ., 1992) cCasass-Zambrana (2001) dAntoniou et al (1990); T = 23 C and pH = 6.8 eAntoniou (1989) fFor wastewaters from anaerobic sewers. According to Henze et al (1986), G falls in the range 0.6-1.0, the lower value being for waters from anaerobic sewers.

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64 were varied according to the normalized suspen ded solids variation curve. The resulting patterns were shown in Potter et al. (1996). Results and Discussion Results from the simulations will be show n and discussed. The results from the fed-batch process will be discussed first followed by those from the BDP process. Simulations of Fed-Batch Process Simulations of the fed-batch process with ASM1 indicated that the optimum UVF was in the range of 63-66% (Fig. 4-3a). Exceeding this optimum lead to decreased performance because of insufficient nitrifi cation, whereas operati on below the optimum led to insufficient denitrification. Figure 4-3 also shows that the cycle length impacts the performance of the fed-batch process. At UVF below the optimum, ASM1 predicts that shorter cycle lengths cause performance to deteri orate. This is due to an increase of the number of switches between oxic and anoxic phases per day as the cycle length is decreased. At each switch, residual DO must be consumed before denitrification can proceed, thus limiting the time available for den itrification. Furthermore, some organic matter is consumed to reduce the oxygen, decreasing the quantity available for reducing nitrate. Excessively long cycles at UVF below its optimum also lead to poor performance. This is because nitrate can be exhausted before the end of an anoxic phase, so that the remainder of the phase is not effectively utiliz ed. At UVF above the optimum, performance deteriorates as cycle length is in creased (Fig. Fig. 4-3a), due to failure of nitrification. The eASM1c model (Fig. 4-3b) predicts an optimum for UVF at a somewhat higher range (65-68%) than found with ASM1. At UVF below the optimum, performance improves as the cycle length is in creased. This is because bacteria have

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65 2 4 6 8 10 12a.2 h 3 h 8 h 1 hSbN (mg-N/L) 0 5 10 15 20 25 30 35 40 5055606570 UVF (%) b. 2 h 3 h 8 h 1 hSbN (mg-N/L) Figure 4-3. Effects of an oxic volume fraction and cycle length on performance of fedbatch process predicted by ASM1 and eASM1c: a). as predicted by ASM1; b). as predicted by eASM1c. (SbN is the sum of ammonia, nitrate, and soluble organic nitrogen.) more time after the end of the lag period to car ry out denitrification. At UVF above the optimum, performance declines due to failure of nitrification. Simulations of BDP Process Our simulation of the BDP process with AS M1 (Fig. 4-4a) gave results that were comparable to those of Potter et al. (1996). An optimum UVF in the range of 55-57% was predicted. In comparison, the UVF used for the University of Florida BDP process is 61%. Cycle length had only moderate impact on performance at UVF below the

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66 4 5 6 7 8SbN (mg-N/L)a.0.5 h 1 h 2 h 3 h 6 8 10 12 14 16 52545658606264 UVF (%)SbN (mg-N/L)b.1 h 2 h 3 h 0.5 h Figure 4-4. Effect of unaer ated volume fraction (UVF) a nd cycle length on performance of BDP process: a). Predicted by ASM1; b). Predicted by eASM1c. (SbN is the sum of ammonia, nitrate, and soluble organic nitrogen.) optimum. At UVF above the optimum, perfor mance deteriorated w ith increasing cycle length, due to impairment of nitrification. An optimum UVF in the range of 57-61% was predicted by the eASM1c model (Fig. 4-4b) Cycle length had significant impact on performance when UVF was below the optimum. In this operational regime, increasing cycle length improved performance. This wa s consistent with the prediction of the eASM1c applied to the fed-batch process. Above the optimum UV F, cycle length had only moderate impact on performance.

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67 Optimum Cycle Length as a Function of UVF Both the ASM1 and eASM1c models pred ict that the optimal cycle length is a function of unaerated volume fraction (Fig. 45). However, optimal cycle lengths based on eASM1c are considerably longer than op timal cycle lengths based on ASM1. For example, at an UVF of 65% in the fed-batc h process, the best cy cle length according to eASM1c was about 7 hours, whereas the opt imal cycle length according to ASM1 was 0 1 2 3 52545658606264 UVF (%) ASM1 eASM1cb.Optymum cycle length 0 2 4 6 8 ASM1 eASM1ca.Optymum cycle length Figure 4-5. Optimum cycle lengths of fedbatch and BDP processes as a function of unaerated volume fraction (UVF): a). Fe d-batch process; b). BDP process. (Dashed lines indicate range of UVF and cycle length tested.)

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68 less than 2 hours (Fig. 4-5a). A similar di chotomy was noted for th e BDP process (Fig 45b). For example, at a UVF of 57%, an optim um cycle length of 0.6 h was predicted by ASM1, whereas an optimum of 2.6 h was pr edicted by eASM1c. In comparison, the cycle length employed at the University of Florida BDP process is 1.8 h. Conclusions A biochemical model (eASM1c) capable of simulating the diauxic lag of denitrifying bacteria switchi ng between oxygen and nitrate as electron acceptors was applied to two different process configura tions that are commonly used for nitrogen removal from wastewater and involve peri odic operation: fed-ba tch and BDP. Its predictions were compared to those of the industry-standard ASM1, which cannot portray diauxic lags. In simulations of the fedbatch process, the eASM1c model predicted slightly higher optimal values for the unaer ated volume fraction (UVF) and substantially higher optimal cycle lengths. Similar results were obtained in simulation of the BDP process. The eASM1c model predictions of optimal BDP UVF and cycle length are more consistent with operation of the University of Florida BDP process than are the ASM1 predictions.

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69 CHAPTER 5 OBJECTIVES A number of researchers (Liu et al. 1998a, b; Gouw et al. 2001; CasassZambrana, 2002; Lisbon et al. 2002) have investigated the phenomenon of diauxic lag of bacteria switching between oxygen and nitr ate. The observed dynamics could be successfully modeled by the proposed extended version of ASM1 (eASM1c, eASM1m). However, these studies were performed afte r only one or two switches between oxic and anoxic conditions under non-limiting carbon substrate conditions. Observation of growth and denitrificati on dynamics of cultures that have reached an ultimate state after rep eated oxic/anoxic cycling is n eeded to obtain representative experimental data and verify model pred iction. The objective of present study is, therefore, to investigate grow th dynamic of model bacterium, Pseudomonas denitrificans in oxic/anoxic cycling conditions under continuous flow reactor. Major objectives of the present study are to 1. Develop an experimental system capab le of achieving bacterial pure culture under continuous flow conditions. 2. Verify performance of the experimental system in terms of reproducibility of experimental data afte r steady state growth. 3. Study diauxic lag of Pseudomonas denitrificans under continuous flow conditions and verify eASM1m pred ictions on experimental results. 4. Study diauxic lag of Pseudomonas. denitrificans in oxic/anoxic cycling conditions and verify eASM1m prediction on experimental results. More specific objectives of each topic will be introduced in each corresponding chapter.

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70 CHAPTER 6 GENERAL MATERIALS AND METHODS An experimental system was developed to achieve pure cult ure in a continuous flow system. The system was used for al l continuous experiments in the following chapters. In this chapter, the continuous fl ow reactor system will be discussed along with general experimental protocols such as preparation of bacter ia and methods for analytical measurements. Specific experi mental protocols and condition s pertinent to each type of experiment will be given in each corresponding chapter. Bacterial Cultivation Experimental protocols for preservation and culturing of bacteria will be discussed. Reviving of freeze-dried bacteria from ATCC, preservation of bacteria by deep-freezing, reviving of bacteria from preserved stock and preparation of preculture will be discussed in detail. Reviving Freeze-Dried Bacteria and D eep-Freezing of Bacterial Cultures Freeze-dried Pseudomonas denitrificans ( P. denitrificans ATCC 13867) or Paracoccus pantotrophus ( P. pantotrophus ATCC 35512) were revived in 250 mL Erlenmeyer flasks containing 125 mL of 8 g/ L nutrient broth contai ning beef extract and peptone (Sigma N7519) and agitated at 250 rev/min in an incubator-shaker (New Brunswick Scientific, Model C24) at 35 C for two days. The revived bacteria were cultivated on Tryptic soy agar in Petri dishes at 35 C for three days. Bacteria from the agar plates were used to i noculate 250 mL Erlenmeyer flas ks containing 125 mL of 8 g/L nutrient broth and grown in the incu bator-shaker at 250 rev/min and 35 C for a day. A

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71 volume of 0.5 mL of bacter ial suspension was combined with 0.5 mL of solution consisting of 60% nutrient solution and 40% gl ycerol (v/v) in 1.5 mL microcentrifuge tubes. The microcentrifuge tubes were flash-fr ozen in liquid nitrogen and then stored at 85 C. Solutions were prepared using deionized (D.I.) water and were autoclaved at 130 C and 1.5 kg/cm2 for 20 minutes before use. Reviving of Frozen Bacteria A portion of frozen material from a micr ocentrifuge tube was scratched off and allowed to fall in a 250 mL Erlenmeyer flask containing 125 mL of 8 g/L nutrient broth. The flask was placed in the incubator-s haker and agitated at 250 rev/min and 35 C for one to two days. The revived bacteria were cultivated on Tryptic soy agar plates at 35 C for one to three days. The agar plates were preserved in a refrigerator at 4 C for up to two weeks. Preculture Procedure Bacteria from the refrigerated agar plates were grown in preculture before they were used in subsequent experiments. Th e bacteria were inoculated into 250 mL Erlenmeyer flasks containing 125 mL of grow th medium containing proper carbon source and ammonia as the nitrogen source. Aer obic preculture was achie ved by agitating the bacterial suspensions at 250 rev/min and 35 C in the incubator-shaker. Preculture periods were 12 hours to 24 hours. Reactors The reactor system for continuous flow expe riments will be disc ussed. The main fermentor and its ancillary components, feed reservoir and its ancillary components,

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72 autoclaving procedure and in stallation of experimental system, and initiation of continuous reactor runs will be discussed in detail. Overall Layout Experiments were performed using a 2 L glass fermentor modified for continuous feeding and withdrawal, electr on acceptor switching and samp ling. Figure 3-1 presents the overall experimental confi guration, including the fermen tor, feeding and effluent system, gas supply system, sampling system, and computerized operational system for liquid and gas flow control. Feed solution was stored in an aseptic feed reservoir and was fed to the fermentor by a peristaltic pump. The medium was pumped from the fermentor through an effluent line. Fermentor Assembly A New Brunswick Scientific Bioflo 2000 fermentor or Bioflo 110 fermentor was used for all experiments. Figure 3-2 shows a side view of Bioflo 2000 fermentor. The fermentor consists of a 2 L glass jar, a h eadplate assembly and a baffle system. The headplate assembly includes vessel mounting ar ms to be mounted to the main frame and impeller assembly to be connected to the main controller. Gas was input to the fermentor liquid through a gas sparger. The main contro ller installed in the main frame controlled temperature and agitation of the fermentor. Temperature of the fermentor was maintained at 35 C and bacterial suspension in the ferm entor was agitated at 200 rev/min. A simplified drawing of the fermentor and ancillary equipment (fermentor assembly) is shown in Figure 3-3. The asse mbly includes the fermentor, gas supply, gas outflow, feed, and effluent. The fermentor was fed by a peristaltic pump (MasterFlex, Cole-Parmer) using Tygon tubing (6409-13). The solution from the feed reservoir was filtered by two 0.2 m Domnick Hunter Propor PES Capsule filters (model No. FEMSE-

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73 Feed reservoir assembly Gas cylinders Fermentor assembly Effluent pump 30C 0C 4.0 4.2 Gas humidifying flask Feed pump Feed reservoir filling pump Figure 6-1. Overall layout of experimental configuration. HeadPlate assembly V essel mounting arms Gas sparger Impeller assembly Baffle system Figure 6-2. Side view of New Brunswick Bioflo 2000 Fermentor.

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74 Peristaltic pump Feed reservoir assembly Peristaltic pump Gas humidifying flask Gas outlet flask Fermenter From gas tanks Gas in Gas out Feed Effluent Figure 6-3. Fermentor assembly. 020GG-PSX, filter material: polyethersulphone) in series to protect both the fermentor and the feed reservoir from contamination. Liquid level in the fermentor was maintained by pumping out of a length of tubing that ex tended from the top of the fermentor to the desired liquid level. The working volume of th e fermentor was set at 1 L. The potential pumping rate of the effluent line was set to exceed the influent pumping rate. Input gas was humidified by being passed through an Er lenmeyer flask contai ning D.I. water and supplied to the fermentor through three 0.3 m Whatman Hepa-Vent Glass Microfiber filters in series. The vent gas from the fermentor was passed through a gas outlet flask (Erlenmeyer flask containing 6,000 mg/L sodi um hypochlorite solution) to prevent back contamination. The sodium hypochlorite so lution was prepared by adding 30 mL of commercial bleach solution containing 6% of sodium hypochlorite to 270 mL of D.I. water. 0.3 m HepaVent filter 0.2 m Capsule filter 0.2 m Capsule filter

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75 Feed Reservoir Assembly A 12 L glass jar was used as a feed reservoir (Fig. 3-4). Feed solution was pumped into the reservoir through silicone tu bing using a peristaltic pump (MasterFlex, Cole-Parmer). Two 0.2 m Domnick Hunter Propor Capsul e filters in series were present in the filling line to remove contamin ation. Air was forced out of the feed reservoir during the filling opera tion and sucked into the feed reservoir while its contents were pumped to the fermentor. This air was passed through three 0.3 m Whatman Hepa-Vent Glass Microfiber filters in series When nitrate was present in the feed, nutrients in the feed solution were divided in to two groups and prepared in two separate feed solutions in two feed reservoir assemb lies. This was because significant biomass accumulation was observed at the space betw een two capsule filters connected to fermentor and the feed reservoir, resulting in significant pH increase due to high level of denitrification at the biomass accumulation. Another capsule filter and necessary tubing were installed to the fermentor and two pu mp heads were instal led to the fermentor feeding pump for two feed solutions. Autoclaving Procedure and Aseptic Connect ion of Feed Reservoir Assembly to Fermentor Assembly All components of the feed reservoir assembly were autoclaved together. All components of the fermentor assembly (one capsu le filter on the feed line, the gas filters, and the gas outlet flask) were also autoclaved together. A volume of 2 L of D.I. water was added to the feed reservoir before autocl aving to ensure that proper temperature and pressure were reached inside of the reservoi r. (This was not necessary for the fermentor because of its small size.) Autoclaving was performed at 130 C and 1.5 kg/cm2 for 20 minutes. After being autoclaved and allowed to be cool down to room temperature, D.I.

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76 to fermentor Peristaltic pump Three hole stopper Feed solution Sterilized Feed solution Figure 6-4. Feed reservoir assembly. water was removed from the reservoir with a peristaltic pump (MasterFlex, Cole-Parmer) using the outlet line. Feed solution was prepared with D.I. wate r in a 20 L plastic c ontainer. After the feed reservoir was filled with feed solution, the outlet line to the fermentor was connected to the filter on the fermentor inlet. The outlet line of the gas humidifying flask was connected to the outermost gas filter on the gas inlet line of the fermentor assembly. Finally, an effluent pump was attached to the effluent line of the fermentor. Inoculation of Fermentor and Initiation of Startup Phase The fermentor was inoculated with precu ltured bacteria in a Labconco Purifier Class 2 Safety Cabinet. A funnel was insert ed into an opening on the headplate of the fermentor before the fermentor assembly was autoclaved. A volume of 2 L of the appropriate autoclaved nutrient solution was first added thro ugh the funnel; then 0.2 m Capsule filter 0.3 m HepaVent filter 0.2 m Capsule filter

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77 precultured bacteria were poured into the fermentor through the funnel. Since direct sampling from the reactor medium could resu lt in contamination, the initial biomass absorbance was not measured. The amount of preculture added was sufficient to impart a slight cloudiness (0.05 to 0.1 absorbance at 550 nm). The funnel was then removed from the fermentor and the opening was closed. Be fore turning on the feed, cells were grown under startup phase to approximately 0.4-0.6, depending on the experiment. Air flow rate was set to the maximum value allowed to the fermentor to prevent oxygen limitation because of level of carbon substrate during startup phase. Initiation of Continuous Flow Phase Continuous mode was initiated by starting feed and effluent flows. Feed and effluent lines and corresponding inlet and out let were thoroughly ri nsed with ethanol before they were connected, to avoid c ontamination. Connections were made immediately after rinsing. Steady state expe riments were run for at least six hydraulic residence times or until biomass absorbance becomes stable. Air flow rates in all oxic continuous flow phases were set to 3.0 L/min, which resulted in 70% of the saturation DO concentration at the given temperature, in a typical oxic con tinuous flow phase. DO concentrations during all other oxic continuous flow phases were assumed to be 70% of the saturation concentration because oxygen requ irement from substrate consumption rate was quite consistent for all experiments. Nitrogen flow rate was set to the same level as that of air during all anoxic continuo us flow phase which resulted in zero DO concentration. Sampling from Fermentor Samples for COD, NO2 --N, NO3 --N, and biomass were manually taken from the effluent line of the fermentor. A biomass sample for contamination monitoring was taken

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78 at the end of each experiment. This sample was obtained using a syringe connected to a sampling port on the headplate of the ferm entor. Samples for biomass absorbance measurement were taken either from the effl uent line the absorbance sampling line. Monitoring of Contamination of Pure Culture During the continuous experiments, bacter ial suspensions from reactor effluent were observed under microscope. The bacter ial suspensions were Gram-stained and observed under microscope. Morphologies of cells such as shape and size and motility of cells were observed. The bacterial suspen sions were inoculated on Tryptic soy agar plates and incubated at 35 C for three days, and then the bacterial colonies were compared to the known colonies with respect to their shape, color and merging patterns. Reactor suspensions were considered as being contaminated if an indication of contamination was detected with respect to microscopy, Gram-stain or colony observation. Analytical Measurements Protocols for analytical measurement will be discussed. Biomass absorbance, COD, nitrate, nitrite, and nitrat e reductase enzyme activity were measured as needed. Biomass Absorbance Cell concentration of a bacterial suspension was estimated by measuring absorbance of the bacterial suspension at 550 nm using Spectronic Unicam Genesys 10 series spectrophotometers. A plastic cuvette or a quartz cuvette a w ith path length of 1 cm was used for manual measurements and a quartz flow cell (Spectronic Genesys 10 series spectrophotometers flowcell) with a pa th length of 1 cm was used in case of automatic measurement.

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79 Chemical Oxygen Demand Chemical oxygen demand (COD) of feed was measured using HACH low-range COD tubes and COD of reactor effluent was measured using HACG ultra-low COD tubes. Effluent was filtered through a 0.2 m cellulose acetate membrane before analysis. Nitrate and Nitrite Nitrate was measured using the HACH chromotropic acid method (NitraVer TestN Tube 26053-45). Nitrite was meas ured using the HACH diazotization method (NitriVer TestN Tube 26083-45). Effluent was filtered through a 0.2 m cellulose acetate membrane before analysis. Nitrate Reductase Activity Nitrate reductase level was measured by a nitrate reductase enzyme assay using benzyl viologen (Jones et al. 1976). Cells in the fermento r medium were harvested by centrifugation (10,000 xg for 10 minutes at 4 C) and washed with 20 mM Tris buffer solution (pH 7). The harvesting was repeated once and cells were resuspended in the buffer solution. These operations were carried out with in 20 minutes. The assay method was modified from the ni trate reductase enzyme assay presented by Jones and Garland (1976). The reaction was performed in a 1 cm optical path borosilicate cuvette. Solution containing 0. 3 mM benzyl viologen and 20 mM Tris buffer was added to fill the cuvette approximately to one half of the volume; then 200 L of the resuspended biomass was added, followed by 20 L of 20 mM dithionite. A few 3 mm glass beads were added to enhance mixing and the cuvette was filled with solution containing 0.3 mM benzyle vi ologen and 20 mM Tris buffer to the top. Finally the cuvette was sealed with a Wheaton seal, leaving no headspace.

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80 The final concentration of benzyl viol ogen was 0.3 mM and the absorbance was approximately 1.8 at 550 nm. The final bi omass concentration was approximately 55 mg dry cell/L. Washing, resuspension, and prepar ation of the cuvette were performed in an anaerobic chamber (Coy Labor atory Type `A'). After 3 minutes of absorbance monitoring using a Spectronic Unicam Gene sys 10 series spectrophotometer at 550 nm, the enzyme assay reaction wa s initiated by injecting 35 L of 20 mM nitrate solution into the cuvette to give a final con centration of 6 mM nitrate. The cuvette was then inverted twice and the initial rate of decolorization was measured. To measure only periplasmic nitrate re ductase, sodium azide, which inhibits membrane-bound nitrate reductase (C raske and Ferguson, 1986; Sears et al. 1993), was added to the assay to give 100 mM NaN3 in the final assay solution. The unit of nitrate reductase enzyme activity is mol benzyl viol ogen/mg biomass/L/sec. In later chapters, this unit will be specified as units. Membrane-bound nitrate reductase activ ity was calculated by subtracting periplasmic nitrate reductase activity from total nitrate reductase activity. If an enzyme activity value was less than 10-13 units, below which has been known as a base line activity from previous studies (d ata not published), the value was c onsidered to be zero.

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81 CHAPTER 7 METHOD FOR ACHIEVING REPRODUCIBLE INITIAL CULTURE STATES IN STUDY OF BACTERIAL DENI TRIFICATION KINETICS Introduction Diauxic lag of bacteria sw itching between oxygen and nitrate has been reported in a number of studies (Liu et al. 1998a, b; Gouw et al. 2001; Casass-Zambrana, 2002; Lisbon et al. 2002). In those studies, which consisted of a short-term (less than 0.5 days) batch aerobic phase followed by a batch anoxic phase, length of diauxic lag and specific growth rates of pure cultures of bacteria varied significantly, even with identical experimental procedures and previous culture histories. For example, lengths of diauxic lag following an anoxic preculture and aerobic batch phase varied from as short as 2 hours to as long as 10 hours (Gouw et al. 2001). Since those expe riments were carried out with direct inoculation fr om bacteria preserved on agar plates (Casass-Zambrana, 2002) or after batch preculture phase (Gouw et al. 2001; Casass-Zambrana 2002; Lisbon et al. 2002) and the aerobic batch phases were re latively short (0.5 days or less), there might be no opportunity for the bacter ial populations to reach a consistent physiological state before the anoxic batch pha ses. This inconsistency complicated the study of diauxic lag because significant vari ation in experimental results led to a significant amount of trials and efforts for a si ngle set of experiments. For example, as many as 11 trials were necessary to identify an experimental trend in the study of nitrate exposure history.

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82 Theoretically, growing bacteria in a chem ostat until a steady state is reached would provide reproducible initial condit ions in terms of physiological state of bacter ia. In such steady state growth conditions, physiological conditions of ba cteria are expected to be consistent regardless of their previous culture history. Th e objective of the present study was, therefore, to determin e whether achieving a steady stat e during the aerobic growth phase would lead to more reproducible result s during the subsequent anoxic batch phase, including length of diauxi c lag and specific growth rate of bacteria. Materials and Methods Experimental procedures specific to this se t of research will be discussed. Other general procedures related to continuous growth of bacteria were consistent with the procedures described in Chapter 6. Four experiments were carried out, with tw o duplicate trials per experiment. Each trial consisted of an oxic preculture phase a nd oxic continuous flow phase, starting with a startup phase, that enabled the bacteria to reach constant growth conditions, and an anoxic batch phase that allowed m easurement of diauxic lag. The P. denitrificans were grown with malate as a carbon substrate, whereas P. pantotrophus were grown with two alternative carbon substrates: malate or ace tate. These substrates were chosen to represent differing redox states, with mala te being highest and acetate lowest. The nutrient media used with P. denitrificans were made up as r ecommended by Kornaros et al. (1996) and the media used for P. pantotrophus were made up as recommended by ATCC. The compositions of nutrient media for P. pantotrophus and P. denitrificans are shown in Tables 7-1 and 7-2, respectively. The carbon substrates in feed solutions were varied depending on the experiments as shown in Table 7-3. With exception of Trial

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83 Table 7-1. Composition of nutrient solution for P. pantotrophus Chemicals g/L in D.I. water Inorganic salts NH4Cl 0.3 MgSO4 7H2O 0.1 Vishniac and Santer Trace Element Solution 2 mL Phosphates Na2HPO4 4.2 KH2PO4 1.5 Carbon source -D,L-malic acid 1.297a -Sodium acetate 1.36a Nitrateb KNO3 2.88b (400c) aVaried in feed solutions bOnly in anoxic batch phase. cmg NO3 -N/L Table 7-2. Composition of nutrient solution for P. denitrificans. Chemicals g/L in D.I. water Inorganic salts NaCl 1 NH4Cl 1 MgSO4 7H2O 0.2 CaCl2 2H2O 0.0264 Trace metals 1 dropa Phosphates K2HPO4 5 KH2PO4 1.5 Carbon source -D,L-malic acid 6.45b Nitratec KNO3 2.88 (400d) aTrace metal solutions containing 0.5%(w/v) each of CuSO4, FeCl3, MnCl2, and Na2MoO4 2H2O. bVaried in feed solutions cOnly in anoxic batch phases and feed solution in Trial 2, Experiment 4. dmg NO3 -N/L Table 7-3. Amount of carbon substrate in feed solutions. Carbon substrate g/L in D.I. water Experiment 1 Malic Acid 0.42 (300)a Experiment 2 Sodium Acetate 0.38 (300)a Experiment 3 Sodium Acetate 1.28 (1000)a Experiment 4 Malic Acid 1.39 (1000)a amg COD/L 2 of Experiment 4, nitrate was present in the feed solution and nutrients in the feed solution were divided into two groups and prep ared in two separate feed solutions as discussed in Chapter 6 (Table 7-4). The dilution rate was set at either 0.1 h-1 or 0.03 h-1.

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84 Table 7-4. Nutrients in two feed solutions. Feed 1 Feed 2 Carbon source -D,L-malic acid Phosphate buffers K2HPO4 Inorganic salts NaCl KH2PO4 MgSO4 7H2O Nitrate KNO3 CaCl2 2H2O Inorganic salts NH4Cl Trace metals aThe amount of all ingredients were doubled so that final concentrations are achieved after the two feed solutions were mixed in the reactor. The oxic continuous flow phase was carried out as described in Chapter 6. The anoxic batch phase was carried out as follows. Feeding and withdrawal of effluent were stopped and gas supply was switc hed from oxygen to nitrogen. An aliquot of reactor medium was removed through a sampling outlet. Nutrient solution was added to dilute the biomass concentration in the reactor and increase carbon substrate and nitrate concentrations to target levels. This was accomplished either by pouring nutrient solution through a funnel or by rapidly pumpi ng the solution into the reactor through a capsule filter. In the former case, the por t used for insertion of biomass and nutrient solution at the beginning of the startup phase was reopened and an autoclaved funnel was installed. In the latter case, the nutrient solution was rapidly pumped into the fermentor through a 0.2 m capsule filter (Domnick Hunter Propor PES) that had been previously connected to and autoclaved with the fermen tor at the beginning of the experiments. A peristaltic pump (MasterFlex, Cole-Parmer) a nd either silicon or Norprene tubing were used to convey the nutrient solution. Biomass concentration (absorbance) wa s monitored during the oxic continuous phase at least twice daily. The COD concen tration was monitored intermittently during the continuous phase. A final set of biom ass and COD measurements was made after

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85 steady state was achieved, within two hours to switch to anoxic conditions. Nitrate and nitrite were measured during the continuous phase when nitr ate was present in the feed solution. Activities of membrane-bound and pe riplasmic nitrate reductase were measured during the steady states. Afte r the switch to the anoxic conditions, biomass absorbance was measured at a frequency appropriate to the stage of growth. Results The results with P. pantotrophus will be discussed first, followed by discussion about results with P. denitrificans Paracoccus pantotrophus Results of a typical trial, beginning with the oxic continuous flow phase, are shown in Figure 7-1a. During the first few hours of dilution, biomass absorbance continued the increasing trend that began during the preced ing oxic startup phase. Eventually, as substrate was depleted, biomass absorbance st arted decreasing and gradually approached an approximately constant leve l in the range of Hours 55 to 70. Finally, at Hour 70 (7 hydraulic residence times (HRTs )), the culture was switched to the anoxic batch phase. This phase was continued until the maximu m specific growth rate was observed. A detailed view of the anoxic batch phase for th e present trial is given in Figure 7-2a and discussed later in this section. Two trials with dilution rate of 0.1 h-1 during the oxic continuous flow phases were carried out with 300 mg/L malate as COD as carbon substrate. Biomass concentration profiles (Figs. 7-1a, 7-1b) show that steady state growth was achieved within 5.5 HRTs of the beginning of the oxic continuous flow phase in both cases. Mean biomass absorbance during the last 1.5 hydraulic resi dence times was 0.165 and 0.167 for the two

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86 0 0.1 0.2 0.3 0.4 0.5 01020304050607080 Time (h) b.Biomass absorbance 0 0.1 0.2 0.3 0.4 0.5a.Biomass absorbance Figure 7-1. Biomass absorbance profile of E xperimental 1: a) Trial 1; b) Trial 2.

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87 trials, with coefficients of variation of 0.4% and 1.6%, respectively. Geometric averages of total nitrate reductase activities during th e steady state portion of the trials were 10-11.8 units in Trial 1 and 10-10.8 units in Trial 2. For conve nience in Table 6-4, these are expressed as the negative base 10 logarithms of the respective activities, which we refer to as pA (total or periplasmic). The total pAs were 11.8 and 10.8 and their coefficient of variation was 5.1 and 0.8%. The periplasmic pAs were 11.9 in Trial 1 and 10.8 in Trial 2 and the corresponding coefficients of va riation were 0.7% for both trials. Figures 7-2a and 7-3a presen t arithmetic plots of bioma ss concentration versus time for the anoxic batch phase of the tw o respective trials. In both cases, P. pantotrophus started growing immediately after the switch from oxic continuous flow phase to anoxic batch phase. Figures 7-2b and 7-3b show semi -log plots of biomass concentration versus time for the same two trials. Observed sp ecific growth rates dur ing the anoxic batch phase are found from the slopes of the linear best fit lines to the semi-log data. The highest observed specific growth rates duri ng the exponential growth period were 0.45 h-1 and 0.46 h-1, respectively. Two trials were carried out with acetate as carbon substrate at the same dilution rate of 0.1 h-1 and the same feed COD concentration of 300 mg/L. In these cases, 5.5 HRTs were not quite sufficient to reach a steady st ate during the oxic continuous flow phases. This was evidenced by slight decreases of bi omass absorbance over the next 1.5 HRTs, as can be seen in Figures 7-4a and 7-4b. Av erage biomass absorbance during the last 1.5 HRTs was higher than the results with malate with values of 0.216 and 0.196, with the respective coefficients of variation of 4.9 and 2.2%. The total pA were 11.3 (Trial 1) and 10.9 (Trial 2) with the co rresponding coefficients of variation of 1.5 and 1.0%,

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88 0 0.1 0.2 0.3 0.4 0.5 a.Biomass absorbance y = 0.46x 34.90 R2 = 1.00 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 66.068.070.072.074.0 time (h) b.ln(Biomass absorbance) Figure 7-2. Biomass absorbance during anoxic ba tch phase (Trial 1, Experimental 1): a) Arithmetic plot; b) Semi-log plot.

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89 y = 0.45x 34.64 R2 = 1.00 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 66.068.070.072.074.0 time (h) b.ln(Biomass absorbance) 0 0.1 0.2 0.3 0.4 0.5 a.Biomass absorbance Figure 7-3. Biomass absorbance during anoxic ba tch phase (Trial 2, Experimental 1): a) Arithmetic plot; b) Semi-log plot.

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90 0 0.1 0.2 0.3 0.4 0.5 01020304050607080 Time (h) b.Biomass absorbance 0 0.1 0.2 0.3 0.4 0.5a.Biomass absorbance Figure 7-4. Biomass absorbance profile of E xperimental 2: a) Trial 1; b) Trial 2.

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91 respectively. The periplasmic pA were 11.2 (T rial 1) and 10.9 (Trial 2) with respective coefficients of variation of 0.1 and 0.7%. Growth of P. pantotrophus during the anoxic batch phase could be characterized by short periods of slow (but not zero) growth with lower specific growth rate, followed by periods of exponential growth with maximum sp ecific growth rate (Figs 7-5a, 7-5b, 7-6a, 7-6b). We define lag as a period during whic h bacteria grow more slowly than their maximum specific growth rate, which we cons ider to be the highe st observed specific growth rate found in any interval during the exponential growth period. The length of diauxic lag, therefore, coul d be obtained by calculating time delay resulting from zero or slow growth periods. As shown in Figures 7-5b and 7-6b, in a semi-log plot of the growth data, a trend line can be drawn through the data that give the highest specific growth rate. A virtual line re presenting the trend of biomass absorbance, as it would be if the bacteria grew at their maximum rate, can be drawn beginning at time zero. The horizontal distance between the virtual trend and the observed trend thus gives the lag length. Using this procedure, the lengths diau xic lag of the two trials were found to be 0.68 hours and 0.74 hours, respectively. Th e highest observed anoxic specific growth rates during exponential growth periods of the two trials were 0.229 h-1 and 0.243 h-1, respectively. Experiments with acetate were repeated at a lower dilution rate (0.03 h-1) and higher feed COD concentration (1000 mg/L COD). Steady states were reached after three HRTs in both trials (Fig 7-7a, 7-7b) Average steady state biomass absorbance during the last 1.5 hydraulic residence times was 0.483 in Trial 1 and 0.557 in Trial 2, with the corresponding coefficients of variati on of 1.5 and 10.1%, respectively. The total

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92 0 0.1 0.2 0.3 0.4 a.Biomass absorbance y = 0.06x 7.17 R2 = 0.89 y = 0.23x 19.05 R2 = 1.00 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 65.070.075.080.0 Time (h) b.ln(Biomass absorbance)0.68 hours Figure 7-5. Biomass absorbance during anoxic ba tch phase (Trial 1, Experimental 2): a) Arithmetic plot; b) Semi-log plot.

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93 0 0.1 0.2 0.3 0.4 a.Biomass absorbance y = 0.064x 7.295 R2 = 0.921 y = 0.24x 19.47 R2 = 1.00 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 65.070.075.080.0 Time (h) b.ln(Biomass absorbance)0.74 hours Figure 7-6. Biomass absorbance during anoxic ba tch phase (Trial 2, Experimental 2): a) Arithmetic plot; b) Semi-log plot.

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94 0 0.2 0.4 0.6 0.8 1 1.2 020406080100120140160 Time (h) Biomass absorbanceb. 0 0.2 0.4 0.6 0.8 1 1.2a.Biomass absorbance Figure 7-7. Biomass absorbance profile of E xperimental 3: a) Trial 1; b) Trial 2.

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95 pA and periplasmic pA were during the steady state portion of Trial 2 were 11.1 (Trial 1) and 11.1 (Trial 2) with respective coeffici ents of variation of 0.0 and 0.2%. The anoxic batch growth curves obtained following oxic continuous flow phases at D=0.03 h-1 were similar to those obtained following oxic continuous flow phases at D=0.1 h-1 (Fig 7-8a, 7-8b, 7-9a, 7-9b). However, the lengths of the initial slow growth periods were 0.45 and 0.56 hours, respectively, which were shorter th an those obtained at the higher dilution rate. The highest obser ved anoxic specific growth rates during exponential growth periods were 0.229 h-1 and 0.210 h-1, respectively. These were comparable to those obtained at the higher dilution rate. Pseudomonas denitrificans Growth of P. denitrificans did not reach a steady stat e after 5 HRTs of oxic continuous flow phase (Fig. 7-10a, 7-10b). Instea d, they exhibited oscillatory behaviors. Therefore, we can characterize the oxic contin uous flow phase in terms of an ultimate state, rather than stea dy state. We considered the ulti mate state to begin after 3 HRTs (100 hours), considering the repeating patterns within each biomass pr ofile. The mean biomass absorbance between Hour 100 and th e switch point (Hour 173) of Trial 1 was 0.48 and the coefficient of va riation was 8.9%. The corresp onding values in Trial 2 were 0.56 and 11.6%, respectively. The total pA in Trial 2 was 13.8 with coefficient of variation of 2.5%. After switching to batch anoxic mode, growth of P. denitrificans in both trials lagged for several hours (Figs. 7-11 and 7-12). In Trial 1, the lag lasted for 4.6 hours, whereas in Trial 2, the lag lasted 5.0 hours. The specific growth rates, were low (0.055 and 0.046 h-1, respectively) during the lag phase s and much higher (0.291 and 0.343 h-1, respectively) after the lags e nded (Figs. 7-11b and 7-12b).

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96 0 0.1 0.2 0.3 0.4 0.5 0.6 a.Biomass absorbance y = 0.11x 18.22 R2 = 1.00 y = 0.23x 33.62 R2 = 1.00 -2.5 -2 -1.5 -1 -0.5 0 137138139140141142143 Time (h) ln(Biomass absorbance)b.0.45 hours Figure 7-8. Biomass absorbance during anoxic ba tch phase (Trial 1, Experimental 3): a) Arithmetic plot; b) Semi-log plot.

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97 0 0.1 0.2 0.3 0.4 0.5 0.6 a.Biomass absorbance y = 0.13x 20.09 R2 = 1.00 y = 0.21x 32.23 R2 = 1.00 -2.5 -2 -1.5 -1 -0.5 0 140141142143144145146147 Time (h) b.ln(Biomass absorbance)0.56 hours Figure 7-9. Biomass absorbance during anoxic ba tch phase (Trial 2, Experimental 3): a) Arithmetic plot; b) Semi-log plot.

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98 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 050100150200 Time (h) b.Biomass absorbance 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1a.Biomass absorbance Figure 7-10. Biomass absorbance profile of E xperimental 4: a) Trial 1; b) Trial 2.

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99 y = 0.05x 11.68 R2 = 0.96 y = 0.31x 57.88 R2 = 1.00 -2.5 -2 -1.5 -1 -0.5 0 0.5 170172174176178180182 Time (h) b.ln(Biomass absorbance)4.6 hours 0 0.2 0.4 0.6 0.8 1 a.Biomass absorbance Figure 7-11. Biomass absorbance during anoxic batch phase (Trial 1, Experimental 4): a) Arithmetic plot; b) Semi-log plot.

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100 0 0.2 0.4 0.6 0.8 1 a.Biomass absorbance y = 0.03x 7.29 R2 = 0.42 y = 0.36x 73.69 R2 = 1.00 -3 -2.5 -2 -1.5 -1 -0.5 0 189191193195197199201 Time (h) b.ln(Biomass absorbance)4.6 hours Figure 7-12. Biomass absorbance during anoxic batch phase (Trial 2, Experimental 4); a) Arithmetic plot b) Semi-log plot.

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101 Discussion Continuous flow pure cultures of bacteria were successfully achieved with the experimental configuration developed in the present study. No indication of contamination was detected in the experiment s shown in this study. Reactor suspension could be maintained without contamin ation for at least 180 hours at 0.03 h-1 of dilution rate. Population of a bacterial cu lture in a CSTR can be sh ifted, if the reactor is contaminated by bacteria having higher growth rate in given condi tions. Contamination with slower growing bacteria also can affect population dyna mics of a continuous culture if contaminants are capable of attaching to any reactor surface. In that case, contaminants may grow on surfaces such as the reactor wa ll and be released from the surfaces. The achievement of proper experimental configurat ion is very important to this study because observed growth response of culture under give n conditions may drastically change if the culture is contaminated with bacteria ha ving different growth characteristics. Serious population shift or partial population change of cultures had indeed occurred because of contamination, in experi ments carried out during the earlier stages of development of the present experimental conf iguration (data not show n). In those cases, the reactor system was not completely developed regardi ng protection from contamination. Even after the current ve rsion of experimental configuration was established, contamination in reactor susp ensions was found occasio nally after two to three HRTs from the inoculation, indicating that contaminants might be brought in during the inoculation. Once experiments were suc cessfully carried out w ithout contamination, pure cultures could be maintained up to given lengths of operational periods.

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102 Lengths of diauxic lag of P. pantotrophus were zero or less than 1 hour, whereas those of P. denitrificans were significantly longer, on the order of several hours (Table 75). This difference between the two species wa s compatible to their difference in nitrate reductase enzyme activities during oxic continuous flow phase. Growths of P. pantotrophus always showed non-zero total nitrate reductase activities, most of which was periplasmic activity, whereas tota l nitrate reductase activities of P. denitrificans were nearly zero (Table 7-6). The results of enzyme activity are consistent with the fact that P. pantotrophus have both membrane-bound nitrate reductase an d periplasmic nitrate reductase, whereas P. denitrificans have only membrane-bound nitrate re ductase (Casasus-Zambrana, 2005). Membrane-bound nitrate reductase is inactivat ed and its synthesis is repressed under aerobic conditions, whereas periplasmic nitrate reductase is active and synthesized under aerobic conditions (Richardson and Ferguson, 1992; Warnecke-Eberz and Friedrich, 1993; Moreno-Vivian et al. 1999). The role of periplasmic nitr ate reductase is still under debate. Some researchers believe that it removes excess reducing equivalents inside the cell under aerobic conditions (Richardson and Ferguson, 1992). Whether the enzyme is responsible for anoxic growth of bacteria is not clear, however Moreno-Vivin et al. (1999) has noted that the periplasmic reductase facilitates metabolic transition from aerobic to anoxic conditions. The results of the present study suggest that peri plasmic nitrate reductase is able to support growth of bacteria under anoxic conditions, at least during the initial anoxic growth of bacteria following an exte nded oxic continuous growth phase. As a result, the diauxic la g is shortened.

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103Table 7-5. Summary of experimental results of anoxic batch phases. Length of lag (h) Bacterial species Subs trate Dilution rate (h-1) Trial-1 Trial-2 Average Cv (%) Malate 0.1000.0000.00 0.10.680.740.7115.24 P. pantotrophus Acetate 0.030.450.560.50414.44 P. denitrificans Malate 0.034.584.61 4.5940.40 Anoxic maximum specific growth rate (h-1) Bacterial species Subs trate Dilution rate (h-1) Trial-1 Trial-2 Average Cv (%) Malate 0.10.46470.45480.4601.52 0.10.2290.2430.2364.26 P. pantotrophus Acetate 0.030.220.210.2153.29 P. denitrificans Malate 0.030.310.36 0.33810.34

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104Table 7-6. Summary of e xperimental results of oxic continuous flow phases. Biomass absorbance over last 1.5 HRT Trial-1 Trial-2 Averages of Both Trials Bacterial species Subs trate Dilution rate (h-1) Average Cv (%) AverageCv (%) Average Cv (%) Malate 0.10.160.40.171.60.171.0 0.10.224.90.202.20.216.9 P. pantotrophus Acetate 0.030.481.50.561.50.5210.1P. denitrificans Malate 0.030.530.30.620.5 0.5811.4 -log10(Total nitrate reductase level) Trial 1 Trial 2 Averages of Both Trials Bacterial species Subs trate Dilution rate (h-1) Average Cv (%) AverageCv (%) AverageCv (%) Malate 0.111.85.110.80.811.36.1 0.111.31.510.91.011.12.7 P. pantotrophus Acetate 0.0311.10.0P. denitrificans Malate 0.0313.82.5 -log10(Periplasmic nitrate reductase level) Trial 1 Trial 2 Averages of Both Trials Bacterial species Subs trate Dilution rate (h-1) Average Cv (%) Average Cv (%) Average Cv (%) Malate 0.111.90.710.8 0.711.36.5 0.111.20.110.9 0.711.12.4 P. pantotrophus Acetate 0.0311.1 0.2P. denitrificans Malate 0.03

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105 The average length of diauxic lag of P. pantotrophus grown on malate was significantly less (p < 0.05) than that of thes e bacteria grown on acetate (Table 7-5). The average maximum specific growth rate of the bacteria grown on malate was significantly higher (p < 0.01) than that of the bacteria grown on acetat e. The membrane-bound nitrate reductase levels were near zero as expected whereas periplasmic nitrate reductase levels were substantially higher. There was no si gnificant difference (p < 0.05) between the average periplasmic pA of bacteria grown on malate and the average periplasmic pA of bacteria grown on acetate. These results are different from the fi nding of Richardson and Ferguson (1992) because they found that the level of Nap activity during aerobic growth increased with the higher redox potential of the carbon substrate in co ntinuous cu ltures of Thiosphaera pantotropha (This organism was later reclassified as P. pantotrophus. ) This discrepancy can be e xplained by magnitude of re dox potential of the two carbon substrates and accuracy of enzyme meas urement in the present study. The redox potential of malate and acetate in terms of oxygen requirement per unit mass is 0.72 g COD/g malate and 1.067 g COD/ g acetate, respectively. Since the difference in the redox potential between the two substrates is not substantial, corres ponding differences of Nap activities might be detected if the enzy me analysis is not sensitive or accurate enough. Therefore, further study to explain variation of length of lag and maximum specific growth rate depending on carbon substr ate is necessary. Length of diauxic lag and maximum specific growth rate were not significantly different depending on dilution rate. It was found that there were no significant differences in the length of diauxic lag during anoxic batch growth, regardless of pres ence or absence of nitrate in the preceding

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106 oxic continuous flow phase, from the results with P. denitrificans (Table 7-5). This is quite opposite to the results from the study of effect of nitrate e xposure history, which showed that presence of nitrate during preceding aerobic growth shortened the diauxic lag during anoxic growth (Gouw et al. 2001). For example, P. denitrificans had very short or no lags during anoxic growth fo llowing aerobic growth when nitrate was presence, whereas the lengths of lag were re latively longer when nitrate was absence in the media during the preceding ae robic growth. The difference in the length of lag in the previous study, therefore, might be resu lted from inconsistent initial physiological condition of cultures, not nece ssarily from presence of nitr ate in the preceding aerobic cultures. Theoretically, it is reasonable to assume that synthesis of denitrification machinery cannot be expected if DO con centration is high enough, for example, significantly higher than the inactivation coe fficient. On the other hand, it could be possible that oxygen limitations occurred dur ing the preceding aerobic growth followed by anoxic growth, leading to so me denitrification during the aerobic phase when nitrate was present and, eventually, shorter dia uxic lag during the following anoxic phases. Coefficients of variation of steady st ate biomass absorbance during the oxic continuous flow phase of six of the eight tria ls were below 2.0 % (Table 7-6). Somewhat higher coefficients of variation were observed (4.9%, 3.8%) for the experiments with P. pantotrophus at 0.1 h-1 of dilution rate because those cu ltures did not reach at complete steady state. Coefficients of variation of steady state biomass absorbance between paired experiments ranged variously. For example, th e coefficient for variation in the case of P. pantotrophus at dilution rate of 0.1 h-1 was 0.99% whereas those of the case with P.

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107 pantotrophus at dilution rate of 0.03 h-1 and P. denitrificans with dilution rate of 0.03 h-1 were 10.1 and 11.36, respectively. Theses variations could be resulted fr om a number of factors. Feed COD concentration could vary in spite of exactly identical am ount of carbon source used for each pair of duplicated experiment and zero co ntamination in the feed reservoir for all experiments because there were some bi omass accumulation at the space between the fermentor side and the feed reservoir side ca psule filters in some cases. Since the space could not be sterilized co mpletely, contaminants coul d grow and reduce feed COD concentrations, leading to variation of biomass absorbance at steady states. Magnitude of biomass accumulation on the inside wall and surface of other structures could also change, leading to COD change in biomass suspension and, consequently, various level of biomass absorbance. Substantial amount of biomass accumulation observed at the effluent line also could biomass absorbance by either biomass accumulation on the inner surface or release of accumulated biomass from the surface. Five out of the seven measurements of membrane-bound pA were effectively zero (Table 7-6). This is consistent with the expectation that membrane-bound nitrate reductase is not synthesized under aerobic conditions and ther efore would be lost due to dilution and decay during the long aerobic phase. Membrane-bound pA values were higher than zero in two cases, which was also observed in previous studies (data not reported). This might be because of error in enzyme assay procedure. Since benzyl viologen can be easily oxidized by trace am ount of DO in assay, any oxygen intrusion during the procedure can be a source of significant error.

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108 Experimental results from the anoxic batch phases of the experiments are summarized in Table 7-5. The highest anox ic specific growth rates during the anoxic batch phase of each pair of duplicated experime nts had coefficients of variation of less than 12%. Coefficients of variation for length of diauxic lag (paired) ranged from 5.44 to 12.7%. In the previous expe rimental works from Gouw et al. (2001) and Lisbon et al. (2002), there were no continuous phases pre ceding the anoxic batch phases. Maximum specific growth rates obtained under the same experimental conditions were characterized by coefficients of variation ranging from 0% to 33%, whereas lengths of diauxic lag obtained under identical experi mental conditions were charac terized by coefficients of variation ranging from 0% to 140%. The variability of normalized data from th e present study was compared to that of normalized data from the previous studies in Table 7-7. The variance of lag lengths found in the present study was significantly lower (p<0.01) than the variances of lag lengths from the stud ies of either Gouw et al. (2001) or Lisbon et al. (2002). Furthermore, the variance of highest observed specific growth rate data collected in the present study was significantly lower (p<0.01) than that of the data collected by Lisbon et al. (2002). Therefore, it has been found that achieving steady state growth condition as initial stage for study of dia uxic lag of denitrifiers clea rly improved reproducibility of experimental results.

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109 Table 7-7. Comparison of experimental data. Study Length of lag Anoxic Variable Present Study Gouw et al. (2001) Lisbon et al. (2002) Present Study Lisbon et al. (2002) Number of observations 8 39 32 8 29 Coefficient of variation 5.81 116.56 52.74 4.44 20.63 Variance of normalized data 0.00341.35860.27810.00200.0426 p-values from F-test 4.8E-071.7E-06 1.1E-03

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110 CHAPTER 8 DIAUXIC LAG OF DENITRIFYING BAC TERIA IN A CONTINUOUS FLOW REACTORI. SINGLE SWITCH FROM OXIC TO ANOXIC CONDITIONS Introduction Several researchers have studied diauxic la g of denitrifiers us ing activated sludge or pure cultures of deni trifying bacteria (Liu et al. 1998a, b; Gouw et al. 2001; Casass Zambrana, 2002; Lisbon et al. 2002). Growth dynamics of de nitrifiers were observed in only a few series of batch growth cond itions with non-limiting carbon substrate and nitrate in those studies. Such batch experime ntal conditions might not be appropriate for the study of diauxic lag of de nitrifiers in several ways First, non-limiting nutrient concentrations including carbon substrate and nitrate nitrogen are not pertinent to realworld situations. In biological wastewater treatment processes in continuous flow configurations, substrate concentrations are comparable to their half saturation coefficients, which can be described as substrate-limiting conditions. In such conditions, the growth rates of denitrifiers ar e much lower than those in batch conditions with non-limiting substrates, hence, the growth responses of bacteria may be different. Second, the previous studies were performe d in only a few series of batch growth conditions switching between oxic and anoxic conditions. Such conditions also might not be suitable to portray the real-world situ ation because denitrifiers in real wastewater treatment processes undergo alte rnating oxic/anoxic switching a long period of time. The effect of alternating cycling situation on diauxic lag, indeed has never been observed so far.

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111 It is necessary, therefore, to investigat e growth dynamics of denitrifiers in continuous growth conditions switching between oxic and anoxic conditions in order to better understand their growth responses to change of electron acceptors from oxygen to nitrate. Such conditions can be achieved by running a continuously fed CSTR and switching gas supplies between oxygen and nitr ogen. A series of experiments were performed using a continuous flow reactor and growth dynamics of P. denitrificans were studied. First, bacteria grown under oxic cont inuous flow phase were exposed to anoxic continuous flow phase to observe diauxic la g under single switch conditions. As a second stage of experiment, bacteria were th en exposed to alternat ing oxic/anoxic cycling conditions at the end of select ed experiments. Predictions of eASM1m were evaluated regarding capabilities of depicting growth dynami cs under those conditions. The study on diauxic lag under single sw itch between oxic to anoxic continuous flow conditions will be discussed here. Eff ects of lengths of the oxic continuous flow phase and types of feed inlet configurations were evaluated as experimental conditions. The second part of the study will be discu ssed in the following chapter. Materials and Methods Experimental and modeling procedures specific to this chapter will be discussed. General procedures to grow bacteria in a cont inuous flow reactor were consistent with the procedures described in Chapter 6. Experimental Procedures A set of experiment consisted of a precult ure phase, an oxic continuous flow phase, starting with a startup phase, and an anoxic continuous flow phase. The P. denitrificans were first grown in oxic preculture for 12 hour s and inoculated to the fermentor. The oxic continuous flow phase was initiated and continued for 40 to 120 hours (4 to 12

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112 HRTs). Bacteria were then exposed to the anoxic continuous flow phase for several HRTs until diauxic lag was finished and growth of bacteria was resumed. The dilution rate was set at 0.1 h-1. The nutrient media for P. denitrificans were prepared as recommended by Kornaros et al. (1996) with malate as carbon source and a mmonium nitrogen as nitrogen source. General compositions of nutrien t solution are shown in Table 8-1. Concentrations of malate, ammonia in the nutrient media were tailored to each stage of an experiment (Table 8-2). For continuous flow phases, nutrients were divide d into two separate solutions as discussed in Chapter 6 (Table 8-3). A total of 6 experiments were carried out as summarized in Table 8-4. We call a set of experiment a trial. Lengths of oxic c ontinuous flow phases were varied in trials. Two different length, one equal to 4 HRTs and the other longer than 9 HRTs, were tested. Three different feed inlet configurations were evaluated as a part of the experimental work (Fig. 8-1). As seen in the figure, two feed solutions were mixe d together at the end of two inlets in Type 1 feed inlet configuration which resu lted in substantial amount of biomass accumulation. In Type 2 configurati on, one feed inlet was submerged into the medium and substantial amount of biomass accumulation was also observed at the tubing connecting the one end of feed inlet and reacto r suspension. In Type 3 configuration, two feed inlet was completely separated from each other and no biomass accumulation was observed at the feed inlet. Changes of electron acceptors betwee n oxygen and nitrate were achieved by switching between gas supplies. In oxic c ontinuous flow phases, oxygen was provided as an electron acceptor by sparging compressed ai r through the bacterial suspension in the

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113 Table 8-1. Experimental conditions. Trial Length of oxic phase (HRT ) Feed inlet configuration 1 12 Type 1 2 4 Type 2 3 4 Type 2 4 10 Type 2 5 12 Type 3 6 9 Type 3 Table 8-2. Composition of nutrient solution for P. denitrificans. Chemicals g/L in D.I. water Carbon source -D,L-malic acid Varied depending on phases Nitrate KNO3 2.88 (400a) Phosphate buffers K2HPO4 5 (10b) KH2PO4 1.5 (3b) Inorganic salts NaCl 1 NH4Cl Varied depending on phases MgSO4 7H2O 0.2 CaCl2 2H2O 0.0264 Trace metals 1 dropc amg NO3 -N/L bin nutrient solutions for precu lture and startup phase. cTrace metal solutions containing 0.5%(w/v) each of CuSO4, FeCl3, MnCl2, and Na2MoO4 2H2O. Table 8-3. Amount of carbon substrate, am monia in nutrient solu tion of each stage. NH4Cl (g/L in D.I. water) -D,L-malic acid (g/L in D.I. water) Preculture 0.5 2.78 (2000a) Startup 0.25 1.11 (800 a) Continuous flow (feed) 0.25 0.56 (400 a) amg COD/L Table 8-4. Nutrients in two feed solutions. Feed 1a Feed 2a Carbon source -D,L-malic acid Phosphate buffers K2HPO4 Inorganic salts NaCl KH2PO4 MgSO4 7H2O Nitrate KNO3 CaCl2 2H2O Inorganic salts NH4Cl Trace metals aThe amount of all ingredients were doubled so that final concentrations are achieved after the two feed solutions were mixed in the reactor.

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114 Feed 1Feed 2Type 1 Feed 1Feed 2Type 2 Feed 1 Feed 2Type 3Biomass accumulation Figure 8-1. Feed inlet configurations. fermentor. In anoxic continuous flow phases, nitrate in the feed solution served as the electron acceptor and comp ressed nitrogen gas was sparged through the bacterial suspension to strip out residual DO and to pr event any oxygen penetra tion from the headspace. Biomass absorbance was collected as desc ribed in Chapter 6 and monitored during the oxic continuous flow phase s at least twice daily. After the switch to anoxic continuous flow phases, biomass absorbance wa s measured at a fre quency appropriate to the stage of growth. Modeling Experimental results from selected trials were simulated using eASM1m. The process rates and stoichiometric coefficients of the eASM1m are s hown in Table 3-2. Since there were no nitrifiers in the syst em and ammonia nitrogen was always provided in non-limiting amount, the process rates and components related to nitrification and ammonification of organic ni trogen were not taken into account. Hydrolysis of

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115 particulate COD was assumed to be negligence because the dilution rate was significantly greater than decay rates. Bacterial growth in the react or was simulated with assumption of completely stirred tank reactor (CSTR) without bi omass separation, which is co nsistent with the present reactor configuration. Figure 8-2 shows the reactor, liquid flow and components, where Q is feed flow rate and V is liquid volume of the reactor. Other notations are consistent with those described in Chapter 3. Time de rivative of each component is calculated from a mass balance on a component is calculated as follows: 0 CdCQQ CCr dtVV (8-1) where C is concentration of a component, C0 is the feed concentr ation of the component and rC is sum of reaction rates relate to the component. DO concentration was assumed to be equal to the saturati on concentration during oxic cont inuous flow phases and zero during anoxic continuous flow phases. Results Results from experiments and model simulations will be discussed. Growth dynamics of P. denitrificans after a single switch from oxic to anoxic conditions were studied and results from selected tr ials were simulated using eASM1m. Determination of Diauxic Lag und er Continuous Flow Conditions A procedure was developed to quantify di auxic lag after a switch from oxic to anoxic conditions while the reactor was opera ted in a continuous fl ow mode (virtual batch curve method). A goal of this approach was to characterize diauxic lag in a manner which is consistent with the defi nition of diauxic lag, as discussed in Chapter 7. In this approach, the experimental biomass absorbance values are adjusted to give a virtual batch

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116 XB,H,0SS,0SNO,0SNOi,0SEN,0Q XB,HSSSNOSNOiSEN Q V CSTR Figure 8-2. Flow and compone nts around CSTR in simulation. growth curve. This is done by integrating specific growth rates estimated from the biomass absorbance data. In this section, the procedure is demonstrated using biomass absorbance values generated from the eASM1m model. The relationship between the specific rate of change of biomass concentration and the net specific growth rate of bacteria unde r a CSTR can be found from the mass balance on biomass around the CSTR as follows: ,,, 0()BH BHBHHHBHdX QQ X XbX dtVV (8-2) Since Q/V is equal to the dilution rate (D) and biomass concentration in the feed is zero, the equation can be simplified as follows: ,1BH Net BHdX D dtX (8-3)

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117 where Net is equal to H Hb and ,1BH BHdX dtX is the specific rate of change of biomass concentration. The biomass profile from eASM1m was pl otted every 0.5 hours (Fig 8-3a), which was a typical time interval of biomass abso rbance measurements in the experimental trials. Note that biomass absorbance is proportional to biomass concentration. We assume that the time interval is small so that the specific rate of change of biomass absorbance is constant during the time interval. Then, Net can be calculated from the specific rate of change of biomass absorbance as follows: 1, 11nnNet nn A D tA (8-4) where 1 nnNet is the net specific growth rate between the nth and n+1th measurements and 11nn A tA is specific rate of change of biomass absorbance between nth and n+1th measurements, which is calculated as follows: n+1n 1 11ln()ln()nn nn AAA tAtt (8-5) where tn and tn+1 are the times at the nth and n+1th measurements, respectively. Table 8-5 shows the original biomass absorbance data in this example, the specific rates of change of biomass absorbance, and th e net specific growth rate during each time interval. Using the net specific growth rates, a vi rtual batch growth curve is generated as follows. Starting from the initial biomass absorbance (A1) at time zero after the switch (t1), the next virtual biomass absorbance at t2 (2VirtualA) is calculated by

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118 0 0.2 0.4 0.6 0.8 1Biomass absorbance b. 0 0.05 0.1 0.15 0.2 0.25Biomass absorbancea. y = 0.273x 29.689 R2 = 1.000 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 949698100102104106108 Time (h) ln(Biomass absorbance)c. Figure 8-3. Determination of diauxic lag under continuous flow c ondition using virtual batch curve method.

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119 Table 8-5. Calculation procedures of virtual batch curve method. t n (nA) ln()nA 11nn A tA 1 nnNet nVirtualA ln()nVirtualA 94 1 0.205 -1.59----0.205a -1.59 94.5 2 0.194 -1.64-0.102-0.0020.204 -1.59 95 3 0.185 -1.69-0.102-0.0020.204 -1.59 95.5 4 0.176 -1.74-0.102-0.0020.204 -1.59 96 5 0.167 -1.79-0.102-0.0020.204 -1.59 96.5 6 0.159 -1.84-0.102-0.0020.204 -1.59 97 7 0.151 -1.89-0.102-0.0020.203 -1.59 97.5 8 0.143 -1.94-0.102-0.0020.203 -1.59 98 9 0.136 -1.99-0.102-0.0020.203 -1.59 98.5 10 0.129 -2.05-0.102-0.0020.203 -1.60 99 11 0.123 -2.10-0.102-0.0020.203 -1.60 99.5 12 0.117 -2.15-0.102-0.0020.202 -1.60 100 13 0.111 -2.20-0.102-0.0020.202 -1.60 100.5 14 0.105 -2.25-0.102-0.0020.202 -1.60 101 15 0.100 -2.30-0.102-0.0020.202 -1.60 101.5 16 0.095 -2.35-0.102-0.0020.202 -1.60 102 17 0.091 -2.40-0.1000.0000.202 -1.60 102.5 18 0.086 -2.45-0.0930.0070.202 -1.60 103 19 0.084 -2.48-0.0690.0310.205 -1.58 103.5 20 0.083 -2.49-0.0150.0850.214 -1.54 aEqual to initial biomass absorbance in original data set (third column). 211 1,2 2()ln() VirtualNetttAAe (8-6) assuming that biomass absorbance increases with the adjusted specific growth rate during the period between t1 and t2 as follows: 2 1, 21 21ln()ln()Virtual NetAA tt (8-7) The consecutive virtual biomass absorbance values are calculated with the same procedure as follows: 1 1 1()ln()NetnnVirtual nnn nttA VirtualAe (8-8) The biomass absorbance values in virtual ba tch curve are shown in Table 8-5 and Figure 8-3b.

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120 We defined lag, in Chapter 7, as a peri od during which bacteria grow more slowly than their maximum specific growth rate, whic h we consider to be the highest specific growth rate found in any interv al during the exponential growth period. In a continuous flow condition, however, bacteria may not exhibit exponential growth during recovery period because of carbon substrat e limitation. Therefore, we define lag under continuous flow conditions as a period during which bacter ia grow more slowly than their highest specific growth rate found in any interval during the recovery growth period following the initial lag period. The highest observed anoxic specific growth rate and length of diauxic lag are quantified using the same pro cedure described in Chapter 6 (Fig. 8-3c). At least three data points, which result in the highest R2 value in linear regression, are used to calculate highest observed an oxic specific growth rate. The validity of the proce dure was confirmed by comparing the highest anoxic specific growth rate calculated by the procedur e to that calculated from a simulation. Specific growth rate of bacteria were calculate d in the simulation as follows. The rate of change of biomass concentration is time de rivative of biomass absorbance concentration from mass balance of biomass concentra tion around the reactor as follows: 0,, ,, B HBH BHBHXdX QQ X Xr dtVV (8-9) The specific rate of change of bioma ss concentration was then calculated by dividing the time derivative of biomass concentration at each time step in the integration by the corresponding biomass concentration duri ng the simulation. Then, the specific rates were adjusted for continuous flow by adding the dilution rate resulting in the observed specific growth rate as follows:

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121 ,1BH Observed BHdX D dtX (8-10) During the simulation, the observed anoxic sp ecific growth rates were recorded for each time step. The highest observed specific growth rate calculated from the simulation during the recovery period after the lag was 0.274 h-1, which is almost identical to the net specific growth rate calculated from the virtual batch curve method (0.273 h-1). Experimental Results The variation of biomass ab sorbance in Trial 1 is shown in Figure 8-4. The continuous flow with oxygen as an electron acceptor was maintained for 12 HRTs (120 hours). After an initial ra pid increase and decrease in biomass absorbance, the absorbance continued to decline slowly up to the 120 hour point. The electron acceptor was then switched to nitrate. Biomass abso rbance began a rapid decrease at this point, reached a minimum, increased, and then even tually attained a steady level which was somewhat lower than the biomass absorbance immediately before the switch. A close-up view of the time following the sw itch is given in Figure 8-5. A curve that would represent the d ilution of non-growing biomass is superimposed on the experimental data. By comparing the biomass trend to this line, it is apparent that the growth stopped immediately af ter the switch and did not resu me for several hours. The length of diauxic lag, as determined by the virtual batch curve me thod, was 4.8 hours. The highest observed anoxic spec ific growth rate was 0.25 h-1 (Fig. 8-6). Substantial biomass accumulation was obs erved at a reactor component and adjacent reactor wall, where feed solution from the top plate drops as shown in Figure 8-1. When the top plate was dissembled from the r eactor at the end of the trial, significant amount of biomass accumulation was also obs erved under the top plate where the two

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122 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 020406080100120140160 Time (h) Oxic Anoxic Switch HRT = 10 hoursBiomass absorbance Figure 8-4. Biomass abso rbance profile (Trial 1). 0 0.05 0.1 0.15 0.2 0.25 0102030 Time (h)Biomass absorbanceWashout curve Figure 8-5. Biomass absorbance profile during anoxic continuous flow phase (Trial 1).

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123 y = 0.25x 2.89 R2 = 1.00 -2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 051015 Time (h) ln(biomass absorbance)y = 0.25x 1.7 4.5 hours Figure 8-6. Determination of diauxic lag (Trial 1). feed solutions (one contai ning nitrate, ammonia and phos phate buffer and the other containing carbon substrate and ot her minerals) met (Fig 8-1). Trial 2 was performed with a new feed inle t configuration (Type 2) because of the significant biomass accumulation observed in Tr ial 1 with the Type 1 configuration (Fig 8-1). The switch from oxic to anoxic c onditions was made after 4 HRTs. Biomass absorbance started decreasing immediately after the switch (Fig. 8-7). The rate of decrease of biomass gradually became sma ller but never reached zero for 15 hours after the switch (Fig. 8-8). Since growth was not resumed after the surprisingly long pe riod, the trial was seceded and bacteria were grown under oxic c ontinuous flow phase again (Trial 3). The anoxic continuous flow phase was initiated 4 HRTs after the oxic c ontinuous flow phase began (Fig 8-9). Trial 3 showed a similar bi omass absorbance patter n to that of Trial 2 up to 15 hours after the switch (Fig 8-10). Bi omass absorbance was constant between

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124 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0204060 Time (h) Oxic Anoxic Switch HRT = 10 hoursBiomass absorbance Figure 8-7. Biomass abso rbance profile (Trial 2). 0 0.05 0.1 0.15 0.2 0.25 0102030 Time (h)Biomass absorbanWashout curve Figure 8-8. Biomass absorbance profile duri ng anoxic continuous flow phase (Trial 2).

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125 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 020406080100120140 Time (h) OxicAnoxic Switch HRT = 10 hoursBiomass absorbance Figure 8-9. Biomass abso rbance profile (Trial 3). 0 0.05 0.1 0.15 0.2 0.25 0102030405060 Time (h)Biomass absorbanceWashout curve Figure 8-10. Biomass absorbance profile durin g anoxic continuous flow phase (Trial 3).

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126 Hours 30 and 40 and subsequently began an in creasing trend that continued for next 40 hours. The length of diauxic lag and the hi ghest observed anoxic sp ecific growth rate from the virtual batch curve method were 26 hours and 0.18 h-1, respectively (Fig. 8-11). The feed inlet was not inspected for biomass accumulation at the end of either Trial 2 or 3. The diauxic lag measured in Trial 3 was mu ch longer than that in Trial 1. There were two differences between Trial 1 and Tria l 3: length of oxic phase and feed inlet configuration. Note that the length of oxic phase was not expected to play a role because of oxic history of culture. Therefore, Trial 4 was performed with the Type 2 feed inlet configuration, with an oxic pha se length of the same order (10 HRTs) as that in Trial 1 to determine whether or not the difference in la g lengths between Trials 1 and 3 was due to the change in feed inlet configurations. The biomass absorbance gradually decreas ed throughout the oxic phase in Trial 4 (Fig. 8-12), with a pattern similar to that seen in Trial 1 (Fig. 8-4). Biomass absorbance began decreasing immediately af ter the switch from oxic to anoxic conditions (Fig. 8-13). From the virtual batch curve method, the le ngth of diauxic lag was 7.8 hours and the highest observed anoxic specifi c growth rate was 0.3 h-1 (Fig. 8-14). At the end of a consecutive experiment after Trial 4, however, significant amount of biomass accumulation was also observed inside of the feed line which was submerged to the reactor suspension (Fig. 8-1). Trial 5 was performed with a further modi fication of feed inlet configuration in order to prevent biomass accumulation at the feed inlet (Type 3). Biomass absorbance reached a steady state value 8 HRTs after the startup rather than decreased gradually

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127 y = 0.16x 5.97 R2 = 1.00 -3 -2 -1 0 1 2 3 4 5 6 0102030405060 Time (h) y = 0.16x 1.6ln(biomass absorbance) 30 hours Figure 8-11. Determination of diauxic lag (Trial 3). 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 020406080100120140 Time (h) OxicAnoxic Switch HRT = 10 hoursBiomass absorbance Figure 8-12. Biomass absorbance profile (Trial 4).

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128 0 0.05 0.1 0.15 0.2 0.25 0102030 Time (h)Biomass absorbanceW as h out cu rv e Figure 8-13. Biomass absorbance profile durin g anoxic continuous flow phase (Trial 4). y = 0.30x 4.12 R2 = 1.00 -2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 051015 Time (h)ln(biomass absorbance) y = 0.30x 1.8 7.8 hours Figure 8-14. Determination of diauxic lag (Trial 4).

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129 throughout the oxic continuous flow phase (F ig. 8-15). Biomass absorbance which was somewhat higher than the steady state value was observed from 7 to 9 HRTs when the temperature control malfunctioned resulting in sl ightly higher temperature in the reactor medium. The steady state biomass absorbance during the last 2 HRTs was 0.224. After the anoxic continuous flow phase was initia ted at 12 HRT, biomass profile showed similar pattern seen in Trial 1 (Fig. 8-16). However, the lengths of diauxic lag from the virtual batch curve method was 9.7 hours, whic h was longer than that of Trials 1 and 4 (Fig. 8-17). Biomass absorbance then reached a stabilized va lue within 24 hours after the switch. Average biomass absorbance during the last 1 HRT was 0.163, which was lower than that in the oxic continuous flow phase. Trial 6 was performed with the identical experimental conditions to those of Trial 5, except the length of oxic continuous flow phase (10 HRTs). Biomass absorbance 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 020406080100120140160 Time (h) OxicAnoxic Switch HRT = 10 hoursBiomass absorbance Figure 8-15. Biomass absorbance profile (Trial 5).

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130 0 0.05 0.1 0.15 0.2 0.25 0102030 Time (h)Biomass absorba n Washout curve Figure 8-16. Biomass absorbance profile durin g anoxic continuous flow phase (Trial 5). y = 0.23x 3.73 R2 = 1.00 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 051015 Time (h) y = 0.23x 1.7ln(biomass absorbance) 9.5 hours Figure 8-17. Determination of diauxic lag (Trial 5).

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131 reached a steady state value from 6 HRT (Fig. 8-18). The steady state biomass absorbance during the last 2 HRTs was 0.209. Biomass profile during the anoxic continuous flow phase was similar to that of Trial 5 (Fig. 8-19). The observed length of diauxic lag and the highest obs erved anoxic specific growth rate from the virtual batch curve method were 9.5 hours and 0.234 h-1, respectively (Fig. 8-20). Modeling Results A series of simulations were performe d using eASM1m to compare the model predictions with the experimental results. Th e parameter values were calibrated to fit a biomass profile from a selected trial. Then, the capability of model to predict the effect of different length of oxic continuou s flow phase was evaluated. The eASM1m parameter values were modified from Chapter 3. The half saturation coefficient for carbon substrate (SK) and the yield coefficient ( H Y) from Hamilton 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 020406080100120140 Time (h) OxicAnoxic Switch HRT = 10 hoursBiomass absorbance Figure 8-18. Biomass absorbance profile (Trial 6).

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132 0 0.05 0.1 0.15 0.2 0.25 0102030 Time (h)Biomass absorbanceWashout curve Figure 8-19. Biomass absorbance profile durin g anoxic continuous flow phase (Trial 6). y = 0.23x 3.82 R2 = 1.00 -2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 051015 Time (h)ln(biomass absorbance) y = 0.23x 1.6 9.7 hours Figure 8-20. Determination of diauxic lag (Trial 6).

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133 et al. (2005) were replaced with the ASM1 defau lt values because prop er values of those were not available for P. denitrificans with malate as carbon substrate. The specific rate of uptake of nitrate (, NOiSV) was corrected because the ma ximum level of intracellular nitrate concentration (,,max NOiS) becomes negative with the change of the yield coefficient ( H Y). As shown in Chapter 2, the maximum level of intracellular nitrate concentration (,,maxNOiS) is a function of the specific rate of uptake of nitrate (, NOiSV) and the yield coefficient ( H Y) as follows: ,,,max,(1) 2.86NOiS H NOiBH HgHV Y SX Y (8-11) The specific rate of uptake of nitrate (, NOiSV) was changed to result in the maximum level of intracellular nitrate concentration (,,maxNOiS) equal to the previous value. The remaining parameter values were identical to those in eASM1m described in Chapter 3. The parameter values are summarized in Table 8-6. A series of simulation were performed to f it the biomass profile from Trial 6. The initial conditions for the simulation were consis tent with those for the experimental trial (Table 8-7). Biomass concentrations in mg COD/L unit from simulation were converted to biomass absorbance as follows: COD VSS1 m Dry cellBiomass absorbance Biomass absorbance 690 m Dry cell L1.42 m COD0.85 m VSS L mgmgg g gg (8-12) The conversion between g dry cell/absorbance was changed until the least sum of square errors was obtained between the biomass absorbance values from the experiment and those from the simulation during the last three HRTs of the oxic continuous flow phase.

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134 Table 8-6. Parameters of eASM1m for simulation. Common Parameters Values Units H 0.6 h-1 g 0.58 bH 0.002 h-1 YH 0.7 g biomass COD/ g substrate COD KS 20 mg COD/L KO,H 0.065 mg O2 / L KNO 0.77 mg NO3-N / L eASM1m parameters VSni 6.28E-02 mg NO3-N /mg biomass COD/sec aN 2.45E-08 kat/mg biomass COD/sec K1 9.86E+04 (mg NO3-N/L)-1 K2 1.96E+04 (mg NO3-N/L)-1 KNO,I 5.61E-04 mg NO3-N / L KO,i 3.31E-04 mg O2 / L bEN 0.4 h-1 Table 8-7. Initial conditions for eASM1m simulations. Component Concentrations XB,H 200 mg biomass COD/L SS 600 mg COD/L Nitrate 400 mg NO3 -N/L The eASM1m was able to predict grow th dynamics of bacteria switching the electron acceptors from oxygen to nitrate under a continuous flow growth condition with a similar pattern to that from experiments (Fig. 8-21). As the electron acceptor changed from oxygen to nitrate at Time 94 hour, bi omass absorbance started decreasing as expected by washout due to dilution. From 8 hours after the switch, the rate of decrease of biomass absorbance became smaller and biomass absorbance started increasing from 10 hours after the switch. The length of dia uxic lag and highest anoxic specific growth rate calculated using the virtual batch curve method were 9.4 hours and 0.28 h-1, respectively, which were comparable to t hose from the experiment (9.5 hours and 023 h-1, respectively).

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135 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 020406080100120140 Time (h) Experiment Simulation Washout curve OxicBiomass absorbanceAnoxic Switch Figure 8-21. Simulation of expe rimental result (Trial 6). Calibrations of model parameters were performed for better prediction of experimental results. Steady state biomass ab sorbance is a function of the half saturation coefficient for carbon substrate (SK) and yield coefficient ( H Y) as follows: 0,() ()HSS BH H gSSYKS Q X VSS (8-13) The equation was induced from a mass balan ce on the carbon substrate. Therefore, steady state biomass absorbance can be increased by increasing either the half saturation coefficient for carbon substrate (SK) or the yield coefficient ( H Y) or both. Since substrate COD data were not available for the trial, we assume that the yield coefficient under anoxic conditions is lower than that under oxic conditions. The specific rate of uptake of nitrate (, NOiSV) was adjusted to compensate the change of the maximum level of intracellular nitrat e concentration (,,maxNOiS) and length of the diauxic lag. This was done

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136 until the least sum of square errors was obt ained between the biomass absorbance values from the experiment and those from the si mulation during the first three HRTs of the anoxic continuous flow phase. The eASM1m predicted a biomass absorbance profile much closer to that from the experiment with the modified parameter va lues shown in Table 8-8 (Fig. 8-22). The length of diauxic lag and the highest observed anoxic specif ic growth rate during the recovery period are 9.8 hours and 0.27 h-1 from the virtual batch curve method. The eASM1m also predicted a biomass profile sim ilar to that from Trial 5 with the same parameter values (Fig. 8-23). Additional simulation was performed to i nvestigate the effect of length of oxic continuous flow phase. The oxic continuous fl ow phase was lasted for 4 HRTs, same as those of Trials 2 and 3 (Fig. 8-24). Biomass absorbance profile from the simulation showed almost exactly same trend seen in th e previous simulations. Length of diauxic lag and the highest observed a noxic specific growth rate fr om the virtual batch curve method were 9.9 hours and 0.28 h-1, respectively, indicating th at eASM1m model is not capable of predicting the exte nsively long diauxic lag found in Trial 2 and 3. with the given parameters. Discussion The results of the present study were th e first observation of diauxic lag of P. denitrificans under continuous flow conditions. Lengths of diauxic lags under continuous flow conditions could be characterized by th e virtual batch curve method developed in the present study. The general char acteristics of growth dynamics of P. denitrificans were consistent with predictions based on the previous knowledge as follows. The eASM1m generated biomass and substrate prof ile during a diauxie in a continuous flow

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137 Table 8-8. Parameters of eASM1m after calibration. Common Parameters Values Units H 0.6 h-1 g 0.58 bH 0.002 h-1 YH 0.7 g biomass COD/ g substrate COD YH, Anoxic 0.47 g biomass COD/ g substrate COD KS 20 mg COD/L KO,H 0.065 mg O2 / L KNO 0.77 mg NO3-N / L eASM1m parameters VSni 1.74E-01 mg NO3-N /mg biomass COD/sec aN 2.45E-08 kat/mg biomass COD/sec K1 9.86E+04 (mg NO3-N/L)-1 K2 1.96E+04 (mg NO3-N/L)-1 KNO,I 5.61E-04 mg NO3-N / L KO,i 3.31E-04 mg O2 / L bEN 0.4 h-1 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 020406080100120140 Time (h) Experiment SimulationBiomass absorbanceOxicAnoxic Switch Figure 8-22. Simulation of e xperimental result with calibrated parameters (Trial 6).

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138 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 020406080100120140 Time (h) Experiment SimulationBiomass absorbanceOxicAnoxic Switch Figure 8-23. Simulation of expe rimental result with calibrated parameters (Trial 5). 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 020406080100120140 Time (h) Experiment SimulationBiomass absorbance OxicAnoxic Switch Figure 8-24. Simulation of experimental result (short oxic continuous flow phase).

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139 reactor are shown in Figure 8-25. Bacteria stop growing immediat ely after the switch from oxic to anoxic conditions because of diauxi c lag and, therefore, they are washed out from the reactor by dilution of feed solution. This is reflected by a decrease of biomass absorbance. The rate of decr ease is equal to the sum of the dilution and specific decay rate. Meanwhile, the concen tration of carbon substrate increases as the reactor suspension is diluted with feed solution. The carbon substrate concentration approaches the feed concentration. As bacteria start growing, the rate of change of biomass absorbance decreases and becomes lower than dilution rate. When growth rate becomes equal to dilution rate, the biomass absorbance stays constant. The carbon substrate concentrati on is highest near this period. The concentration depends on le ngth of diauxic lag and magnitude of change of growth rate. As the growth rate further increases, observed growth rate also increases, which results in gross increase of biomass ab sorbance. Exponential growth of bacteria can be possible if concentration of carbon s ubstrate accumulated dur ing lag period is high enough, of course depending on carbon subs trate half saturation coefficient. Concentration of carbon substrate rapidly decr eases as bacteria gr ow exponentially and finally exponential phase is terminated as bacteria exhaust carbon source. Biomass absorbance, hence, reaches at a maximum a nd gradually reaches at steady state anoxic value. Although general profiles of biomass absorbance from the experimental results followed the above prediction, there were so me unexpected phenomena also. First, biomass absorbance under oxic continuous flow phases with Type 1 and Type 2 (Trail 1 and Trial 4, respectively) continuously decreased throughout the periods whereas

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140 0 0.05 0.1 0.15 0.2 0.25 Biomass absorbance Washout curveBiomass absorbanceOxicAnoxic Switch a. 0 50 100 150 200 250 300 350 400 9095100105110115120 Time (h)S S (mg COD/L)b. Figure 8-25. Change of biomass absorban ce and carbon substrate concentration during diauxic lag and recovery of growth, predicted by eASM1m: a) Biomass absorbance; b) Carbon substrate concentration. biomass absorbance reached at steady state dur ing oxic continuous flow phase with Type 3 (Trials 5 and 6) (Figs. 8-4, 8-12, 8-15, 818). Biomass absorbance level at the end of oxic continuous flow phase with Type 1 and Type 2 was lower than that with Type 3

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141 (Trial 5, Trial 6). Furthermore, lengths of diauxic lag during anoxic continuous flow phase were significantly shorter with Type 1 than with Type 3 (p < 0.05) (Table 8-9). Length of diauxic lag with Type 2 (Trial 4) was lower than average length of diauxic lag from Type 5, although p value was found to be greater than 0.05 as shown in Table 8-9, because of one sample. These results were effectively correlated with the extent of biomass accumulation at the feed inlet structures. Type 1 and T ype 2 configuration resulted in substantial amount of biomass accumulation whereas there was no accumulation with Type 3 configuration. The difference in biomass absorbance level during oxic continuous flow phases according to different type of feed inlet configuration can be explained by the different magnitudes of biomass accumulation at the feed inlet. As bacteria are accumulated at the feed inlet and consume C OD from the feed solution, the actual feed COD concentration at the reac tor suspension becomes lower, leading to lower biomass concentration than that in zero biomass accumulation situations. As magnitude of biomass accumulation became more as time goes by, biomass concentration could decrease according to increasi ng amount of the accumulation. The differences in length of diauxic lag might be because of differe nce in magnitude of biomass accumulation at the feed line if the accumulation is a good source of fresh bacteria which already develop denitrification capabilities. This could be possible because oxygen limitation is expected inside of accumulated biomass. Lengths of diauxic lags were substan tially affected by length of preceding oxic continuous flow phases. Length of diauxic lag after 4 HRTs of oxic continuous flow phase (Trial 3) was significantly longer (p < 0.01) than those af ter at least 9 HRTs of oxic

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142Table 8-9. Summary of experimental results. Trial Feed inlet configuration (Type) Length of oxic continuous flow phase (H RTs) Length of lag (h) 1 1 12 (Long) 4.5 (0.02a) 3 2 4 (Short) 26 (0.007a) 4 2 10 (Long) 7.8 (0.06a) 5 3 12 (Long) 9.5 6 3 9 (Long) 9.7 Average (Trials 5, 6) 9.6 CV (%) 1.5 ap-value from t-test with the result from T ype 3 (two-sample assuming equal variances)

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143 continuous flow phases (Trial 5, Trial 6) (T able 8-9). Biomass ab sorbance profile during anoxic continuous flow phase after 4 HRT of oxic continuous flow phase of another trial (Trial 2) was similar to that of Trial 3 up to 15 hours after the switch, indicating that the length of diauxic lag could have been similar to that of Trial 3, if the trial had extended further. This result cannot be explained by our previous knowledge, regarding modeling concepts. Since bacteria were never gr own under anoxic condition before they were exposed to the anoxic continuous flow phase, th e relative levels of nitrate reductase and intracellular nitrate woul d be expected to be at baseline level at the outset of the oxic phase. Thus, regardless of th e length of the oxic continuous flow phase, the length of the diauxic lag should be consistent. Although the feed inlet configurations were different for those two cases (Type 2 was used for Trials 2 and 3, and Type 3 was us ed for Trials 5 and 6 (Table 8-1)), it still cannot explain this phenomenon. Type 2 feed inlet resulted in substantial amount of biomass accumulation at the inlet and relativ ely shorter diauxic lag when length oxic continuous flow phase was long (Trial 4), as discussed above. This new finding leads to the fact that diauxic lag of bacter ia switching between oxygen and nitrate may be regulated by certa in growth mechanisms different from growth dynamics associated with synthesis of nitrate reductase and transport of nitrate across cell membrane. It ha s to be noted that there was a difference in growth characteristics at the point wh en the anoxic continuous flow phases began. In the case of trials with long oxic continuous flow phase, steady state growths of bacteria were achieved before the switch. On the ot her hand, switch between oxic and anoxic

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144 conditions was made only a few HR Ts after bacteria finished th eir fast growth in startup phase. Whether or not the difference in gr owth stage during oxic continuous flow phase resulted in the difference in e xperimental results is not clear. Further research is needed to investigate these various growth responses. It has to be noted that the finding from the present study about longer oxic continuous flow phase resulting in shorter diau xic lag is not necessarily controversial to the previous finding about the e ffect of length of oxic phase on length of diauxic lag of denitrifiers (Bonin et al., 1989; Liu et al., 1998b). This is because there is one important difference in experimental condition as follows In the previous research, there were preceding anoxic phases before bacteria were exposed to oxic phases so bacteria. Lengths of diauxic lag in following anoxic phase s were considered to be controlled by the amount of denitrification enzyme in the ce ll according to the different magnitude of dilution and decay of denitrification enzyme during different length of oxic phases. Therefore, longer diauxic lag is expected as length of oxic phase becomes longer. However, since there was no preceding anoxic phase in present study, there should be no difference in magnitude of den itrification enzyme regardless of length of oxic continuous flow phases. Difficulties were found regarding calibrations of model parameters for eASM1m. Conceptually length of diauxic lag of bacteria is expected to be increased by decreasing the specific rate of nitrate reductase synthesis (N ) and/or the specific rate of uptake of nitrate (, NOiSV), or decreasing the decay rate of biomass (b) and/or decay rate of nitrate reductase (N E b). However, modeling results with manipulations of those parameters

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145 were not consistent with our prediction. This is because of the nature of the mechanistic biochemical denitrification model from Hamilton et al. (2005). Decreasing the specific rate of nitrate reductase synthesis (N ) results in not only the decrease of the rate of synthesis of nitrate reductase, but also the decrease of the maximum level of nitrate reductase (,maxNE) because the level is proportional to the specific rate of nitrat e reductase synthesis (N ) as follows: ,1,,max ,max, 2,1,,maxNBHNOi N NBH EHgBHNOiXKS EX bbKXKS (8-14) Note that the specific level of nitrate reductase (EN/EN,max) controls the anoxic growth rate, synthesis of nitrate reductase and transport of nitrate ac ross the cell membrane, rather than the absolute amount of nitrate reductase Therefore, changing the specific rate of nitrate reductase synthesis (N ) does not have noticeable effect on length of diauxic lag. Decreasing the specific rate of uptake of nitrate (, NOiSV) also decrease the maximum level of intracellular nitrate concentration (,,max NOiS) but the magnitude of decrease of the maximum level could be much higher than that of the specific rate of uptake of nitrate as follows: ,,max,(1) 2.86SNOi H NOiBH HgHV Y SX Y (8-15) Again, the specific level of intracellular nitrate (SNO,i/ SNO,i,max) controls the anoxic growth rate rather than the absolu te amount of nitrate reductase. Therefore, decreasing the specific rate of upt ake of nitrate (, NOiSV) results in the increase of length of diauxic lag.

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146 Changes of the decay coefficient of intrac ellular nitrate reductase and the biomass decay coefficient also result in patterns which are not consistent with the modeling concept. Proper modifications have to be made to the structure of the mechanistic biochemical denitrification model to overcom e the conceptual discrepancies found in the present study.

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147 CHAPTER 9 DIAUXIC LAG OF DENITRIFYING BAC TERIA IN A CONTINUOUS FLOW REACTORII. ALTERNATING OX IC/ANOXIC CONDITIONS Introduction It has been found that cyc ling between oxic/anoxic condi tions may have substantial effects on denitrification in biological nitrogen removal processes (Kos et al., 1992; Lee et al., 2004). Recently, Lee et al. (2004) studied the effect of alternating oxic/anoxic cycling on diauxic lag of denitr ifiers with an extended (eASM1 c) and original version of ASM1. The eASM1c predicted substantially longer optimal cycle lengths for periodical nitrogen removal processes than ASM1. Such long cycle lengths were consistent with the operational strategy of the BioDenipho pr ocess at the University of Florida. Furthermore, there was a critical cycle length, below which denitrification would not take place, according to eASM1c predictions. This was consistent with the results of Kos et al. (1992), which indicated the existence of a minimum hydraulic residence time of the anoxic reactor, below which no denitrification took place. The earlier studies on diauxi c lag of denitrifying bacter ia were performed within only a few switches between oxic and anoxic conditions under batch reactors (Liu et al., 1998a, b; Gouw et al., 2001; Casasus-Zambrana, 2002; Lisbon et al., 2002). Diauxic lag of denitrifiers in alternating oxic/anoxic cycling under continuous flow conditions has never been studied. In the present study, diauxic lag of P. denitrificans under such conditions was studied. Simulations were first performed to verify effect of cycl e lengths on diauxic lag

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148 of bacteria under such conditions. Altern ating oxic/anoxic cycli ng experiments were performed using a continuous flow reactor and effect of cycle length was evaluated. Materials and Methods Information regarding the modeling and th e experiments in this chapter will be discussed. General procedures for experi ments with a continuous flow reactor are described in Chapter 6. The extended AS M1 (eASM1m) for simulation of growth dynamics of bacteria is described in Chapter 3. Alternating Oxic/Anoxic Cycling under Continuous Flow Conditions The growth of P. denitrificans in alternating oxic/anoxi c cycling under continuous flow conditions was achieved as a consecutive experimental stage following the selected experimental trials in Chapter 8. This was to verify that bacteria could grow under both oxic and anoxic continuous flow phase before they were exposed to alternating cycling. Therefore, a typical set of experiment consis ted of a preliminary growth period (an oxic preculture, an oxic continuous flow phase, and an anoxic continuous flow phase) and a cycling continuous flow period consisting of alternating oxic/anoxic continuous flow phases. The experimental conditions including the compositions of the feed solutions, temperature of the reactor suspension, and the dilution rate were identical to those of the preliminary periods as described in Chapter 8. Lengths of oxic continuous flow phases were set at twice of those of anoxic continuous flow phases in cycling continuous periods. This resulted in 33.3% of unaerated volume fraction (UVF), which was reported as an optimum ratio predicted by eASM1c in a process optimization (Lee et al., 2004). Cycle lengths of cycling continuous periods were determined from an initial set of simulati ons using eASM1m.

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149 Changes of electron acceptors betwee n oxygen and nitrate were achieved by switching between gas supplies as described in Chapter 8. Changes of gas supplies were made either manually or automatically by a solenoid valve that was interfaced to a computer (Fig. 9-1). Biomass absorbance was measured as desc ribed in Chapter 6 and monitored during selected anoxic continuous flow phases in cy cling continuous periods at least once hourly. Biomass absorbance was monitored during sele cted oxic continuous flow phases with a frequency appropriate to the change of biomass absorbance COD, nitrate and nitrite concentration and nitrate reductase activity we re measured at the beginning and the end of selected anoxic phases. At the end of the cycling continuo us flow period of a selected experiment (24 hour cycle length), samples we re also collected from the sampling port installed on the top plate, to evaluate the effect of biomass accumulation on the component concentrations in the effluent. Modeling A series of simulations using eASM1m were carried out to inves tigate the effect of alternating oxic/anoxic cycling on diauxic lag of P. denitrificans. Various cycle lengths were tested to verify the effect of cycle lengths on diauxic lag of P. denitrificans and to determine experimental cycle lengths for the cycling experiments. Parameter values for the simulations are shown in Chapter 8 (Table 8-8). Each set of simulation was performed w ith the same operational strategy of a reactor experiment, consisting of a prelimin ary growth period and a cycling continuous flow period. Experimental conditions for simulations such as model assumptions and reactor conditions were consistent to those in Chapter 8, except the strategies for change of electron acceptors.

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150 Solenoid valve or manual switch NITROGEN AIRTo fermentor Gas humidifying flask Automatic operation program Valve controller Figure 9-1. Schematic vi ew of gas supply system. The test cycle lengths for the simulations were chosen based on the length of diauxic lag in Trial 6 in Chapter 8 (9.5 hours) or the length of oxi c continuous flow phase in Trial 6 in Chapter 8. Hereafter, we call the diauxic lag duri ng the anoxic continuous flow phase in Trial 6 of Chapter 8 initial lag. With cycle lengths of 6 and 12 hours, length of an anoxic phase under cycling are 2 and 4 hours, respectiv ely, which are less than half of the length of th e initial lag. In case of 24hour cycle length, length of an anoxic phase is 8 hours, which is comparable to that of the initial lag. Length of an anoxic phase under cycling is greater than that of the initial lag with cycle lengths longer than 24 hours. With 180-hour cycle length, le ngth of an oxic phase under cycling is 12 HRTs, which is comparable to the length of the oxic continuous flow phase in the preliminary growth period (9.4 HRTs). Afte r the oxic/anoxic cycling experiments were performed, additional simulations were performed to fit the biomass absorbance profiles.

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151 Results Results from the present study will be disc ussed. Simulations were performed to predict effect of alternating oxic/an oxic cycling on growth dynamics of P. denitrificans and test cycle lengths for experiments were de termined. Growth of bacteria then studied in oxic/anoxic cycling under continuous flow reactor experiments. Additional simulations were performed to predict the experimental results using eASM1m. Preliminary Simulations Biomass absorbance profile of a typical set of simulation is shown in Figure 9-2 (6hour cycle length). After the preliminary gr owth period was carried out, bacteria were exposed to the alternating oxic/anoxic cycli ng at 14.5 HRT. An effective ultimate state was achieved 3 HRTs after the cycling was init iated, as indicated by a repeating pattern of biomass absorbance change in each cycl e. A closer view of biomass absorbance change during the ultimate stat e is shown in Figure 9-2. Biomass absorbance continued to decrease during each anoxic phase. The ra te of decrease of biomass absorbance was comparable to that of the washout curve of a non-growing culture (Fig. 9-3). Biomass absorbance increased and gradually reached a steady level in each oxic phase. Biomass absorbance change during an ultim ate state of the alternating oxic/anoxic cycling with 12-hour cycle leng th is shown in Figure 9-4. Biomass started decreasing immediately after each anoxic phase initiated. The rate of change of biomass absorbance decreased gradually over few hours and reached zero as indicated by the flat shape of the curve. However, biomass absorbance did not significantly increase until the end of each anoxic phase. With a longer cycle length (24 hours), bi omass absorbance star ted increasing after the initial decrease of biomass (Fig. 9-5). The length of diauxic lag found by the virtual

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152 0 0.1 0.2 0.3 0.4 0.5 0.6 0255075100125150175200 Time (h)Biomass absorbance Preliminary growth periodCycling continuous flow periodOxic continuous flow phase Anoxic continuous flow phase Figure 9-2. Biomass absorbance prof ile from a typical simulation.

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153 0 0.05 0.1 0.15 0.2 0.25 0.3 172184196208220 Time (h) Biomass Washout curve Oxic Anoxic Biomass absorbance Figure 9-3. Biomass absorbance profile duri ng ultimate state in a lternating oxic/anoxic cycling (6-hour cycle length). 0 0.05 0.1 0.15 0.2 0.25 0.3 172184196208220 Time (h) Biomass Washout curve Oxic Anoxic Biomass absorbance Figure 9-4. Biomass absorbance profile duri ng ultimate state in a lternating oxic/anoxic cycling (12-hour cycle length).

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154 batch curve method developed in Chapter 8 wa s 3.4 hours, which was much shorter than that of the initial lag. Th e highest observed anoxic specific growth rate was 0.17 h-1, which was lower than that during the initia l lag. When much longer cycle length (48 hours) was tested, biomass absorbance cha nged with a similar pattern (Fig. 9-6). Meanwhile, the relative length of peri od during which biomass absorbance was maintained to a level similar to a steady stat e level became longer than that in the 24-hour cycling. The length of diauxic lag was 5.5 hours, which was longer than the one with 24hour cycle length, but still shorter th an that of the initial lag. When an extensively long cycle length (180 HRTs) was applied to alternating oxic/anoxic cycling, biomass absorbance duri ng each cycle changed w ith a pattern profile, which was almost exactly identical to that duri ng the first switch (Fig. 9-7). The length of diauxic lag and the highest observed a noxic specific growth ra te calculated by the virtual batch curve method were equal to those of the initial switch. From these results, two desirable cycle lengt hs for the experiments were found. No substantial recovery of growth of bacteria would be expected with short cycle lengths (12 hours or shorter). Substantial recovery of gr owth of bacteria would be expected with long cycle lengths (24 hours or longer). Experiments Results from experiments with continuous flow reactor will be di scussed. Two test cycle lengths, 12 hours of short cycle lengt h and 24 hours of long cycle length were applied to alternating oxic/a noxic cycling experiments. Test cycle lengths for the experiments were chosen based on the desira ble cycle lengths determined in the above simulations. Although short cycle experi ment was conducted before the above simulation were performed, similar desirable cycle lengths were pr eviously determined

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155 0 0.05 0.1 0.15 0.2 0.25 0.3 172184196208220 Time (h) Biomass Washout curveBiomass absorbance OxicAnoxicOxic Anoxic Figure 9-5. Biomass absorbance profile duri ng ultimate state in a lternating oxic/anoxic cycling (24-hour cycle length). 0 0.05 0.1 0.15 0.2 0.25 0.3 172184196208220 Time (h) Biomass Washout curve OxicAnoxicBiomass absorbance Figure 9-6. Biomass absorbance profile duri ng ultimate state in a lternating oxic/anoxic cycling (48-hour cycle length).

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156 0 0.1 0.2 0.3 0.4 0.5 0.6 0100200300400500 Time (h) OxicAnoxicOxicAnoxicBiomass absorbance Figure 9-7. Biomass absorbance profile in altern ating oxic/anoxic cycling ( 180-hour cycle length).

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157 from simulations using parameter values base d on results from Trial 5 in Chapter 8, biomass absorbance profile was very similar to that of the trial which the current simulations were based on (Trial 6). For short cycle length, 12 hours were chosen instead of 6 hours because of the operational limitations of the experimental system. For long cycle length, 24 hours were c hosen for similar reasons. Short cycle length (12 hours) The alternating cycling of 12 hour-cycle length experiment was conducted followed by Trial 5 of Chapter 8 (Fig. 9-8). The cy cling continuous period was initiated after biomass absorbance was stabilized after the initial lag unde r the anoxic continuous flow phase. A detailed view of biomass absorban ce profile during the cycling continuous flow period is shown in Figure 9-9. From the th ird cycle, biomass absorbance continued to decrease throughout each anoxic phase. There was no indication of change of rate of decrease of biomass absorbance below the dilution rate during each anoxic phase. Biomass absorbance increased during each oxic phase with a consistent pattern. An effective ultimate state can be found during the last three cycles. The reactor suspension was significantly contaminated with bact eria having a different cell morphology and colony shape from the 8th cycle. Even after the contamin ation found, biomass absorbance during each anoxic phase conti nuously decreased and there was no significant increase of biomass absorbance. The overall biomass absorbance level was increased after the suspension was contaminated. Duri ng the anoxic phase of 11th cycle, there was neither significant decrease of nitrate, increas e of nitrite nor increase of nitrate reductase activity. The rate of change of biomass absorbance during the anoxic phases was significantly higher than that of a washout curve as indicated in the figure. The average

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158 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 050100150200250 Time (h) Biomass absorbancePreliminary growth periodOxic continuous flow phase Anoxic continuous flow phaseCycling continuous flow period Figure 9-8. Overall biomass absorb ance profile of short cycle length experiment (12 hour-cycle length).

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159 0.05 0.10 0.15 0.20 0.25 0.30 01224364860728496108120132144 Time (h) Washout Initiation of cyclingBiomass absorbanc e Anoxic Oxic Oxic Oxic Oxic Oxic Oxic Oxic Oxic Oxic Oxic Oxic Oxic Anoxic Anoxic Anoxic Anoxic Anoxic Anoxic Anoxic Anoxic Anoxic Anoxic Figure 9-9. Biomass absorbance profile in al ternating cycling (12 hou r-cycle length).

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160 rate of the decrease of bi omass absorbance during the firs t two hours of the last three anoxic phases was 0.21 h-1 with coefficient of variation of 1.3 %, which was more than twice of the dilution rate (0.1 h-1). The average rate during the second half of each anoxic phase was 0.15 h-1 with coefficient of variation of 0.7 %, which was still significantly higher than the dilution rate. Long cycle length (24 hours) The cycling continuous flow period of th e experiment with 24-hour cycle length was initiated at the end of the anoxic continuous flow phase of Trial 6 in Chapter 8 (Fig. 9-10). A closer view of biomass absorban ce profile during the cy cling continuous flow period is shown in Figure 9-11. Bioma ss absorbance started decreasing rapidly, immediately after the first anoxic phase was init iated. The rate of decrease of biomass absorbance became slightly lower, below the dilution rate as compared with a washout curve of non-growing bacteria (Fig. 9-11). During the following anoxic phases, biomass absorbance continuously decreased and there was no indication of significant increase of biomass absorbance utile the end of the cycling. An effectiv e ultimate state can be found during the last two cycles, indi cated by a repeating pattern of biomass absorbance change. Biomass absorbance continuously increased dur ing the first two hours of each oxic phase with a pattern similar to an e xponential growth, and gradually r eached a stabilized value. A detailed view of biomass absorbance prof ile during the last tw o cycles are shown in Figure 9-12a. The decrease of biomass absorbance during the initial stage of each anoxic phase was substantially faster than th at due to the washout. Average rate of change of biomass absorbance during the firs t two hours of each anoxic phase was 0.18 h1, with coefficient of variation of 6.4%, wh ich was almost twice of the dilution rate. Decrease of biomass absorbance become slower during the rest of each anoxic phase, as

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161 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 050100150200250300 Time (h) Oxic continuous flow phase Anoxic continuous flow phaseCycling continuous flow period Final anoxic continuous flow phase Preliminary growth periodBiomass absorbance Figure 9-10. Overall biomass absorbance profile of long cycle length experiment (24-hour cycle length).

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162 0.05 0.10 0.15 0.20 0.25 0.30 024487296120144 Time (h) Washout curveOxic Oxic Oxic Oxic Oxic Oxic Anoxic Anoxic Anoxic Anoxic Anoxic Anoxic Initiation of cyclingBiomass absorbance Figure 9-11. Biomass absorbance profile in alternating cycling ( 24-hour cycle length).

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163 0 50 100 150 200 250 300 96108120132144 Time (h) Effluent Reactor No growth (simulated)COD (mg COD/L) c. 0 100 200 300 400 500 600 0 2 4 6 8 10 12 Nitrate (Effluent) Nitrate (Reactor) Nitrite (Effluent) Nitrite (Reactor)Nitrate (mg NO 3 -N/L) Nitrite (mg NO 2 -N/L) b. 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Biomass Washout curve OxicBiomass absorbanceAnoxicOxicAnxoica. Figure 9-12. Component concentrations duri ng ultimate state (24-hour cycle length): a) Biomass absorbance; b) Nitrate and nitrite; c) COD.

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164 indicated by a less stiff biomass absorbance curve during the second half of each anoxic phase. The average rate of decrease of biom ass absorbance during the last three hours of each anoxic phase was 0.08 h-1, with coefficient of variation of 6%. Concentration of nitrate, n itrite, and COD, was monitored from the effluent at the beginning and end of each phase during the last two cycles. At the end of the anoxic phase of the last cycle, the sampling port inst alled to the top plate of the fermentor was opened and samples for measurements were co llected directly from the fermentor. Measurements from the direct sampling were compared to those from the effluent. The effluent nitrate concentration decreased by approximately 50 mg NO3 --N/L during the two anoxic phase whereas nitrate concentration from the direct sampling was only slightly lower than that from the efflue nt at the beginning of the anoxic phase (Fig. 9-12b). The effluent nitrite concentrati on increased from zero to about 7 mg NO2 --N /L during the two anoxic phases whereas nitrite wa s not detected from the direct sampling from reactor suspension at the end of the last anoxic phase. The effluent COD increased during the two anoxic phase but the magnitude of increase was smaller than that expected by increase due to zero COD consumption (F ig. 9-12c). However, COD concentration from the direct sampling at the end of the last anoxic phase was comparable to that expected by zero COD consumption. Both tota l and periplasmic nitrate reductase activity were found to be zero at the e nd of the last anoxic phase. The last anoxic phase was extended for a nother 4 HRTs to observe entire diauxic lag and recovery of growth (Fig. 9-13a). Th e rate of decrease of biomass absorbance gradually became lower until Hour 150 and biomass absorbance reached a steady value and did not change for 4 hours. Biomass absorbance increased from Hour 154 to 161

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165 0.E+00 1.E-12 2.E-12 3.E-12 4.E-12 5.E-12 6.E-12 7.E-12 8.E-12 9.E-12 1.E-11 136146156166176186196 Time (h) Total PeriplasmicNitrate reductase activity c. 0 100 200 300 400 500 600 0 5 10 15 20 25 30 Nitrate (mg NO3-N/L) Nitrite (mg NO2-N/L)Nitrite (mg NO 2 --N/L) Nitrate (mg NO 3 --N/L) b. 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Biomass Washout curveBiomass absorbancea. Figure 9-13. Component concentrations dur ing the final anoxic phase (24-hour cycle length): a); Biomass absorbance; b): Nitr ate and nitrite; c): Nitrate reductase activities.

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166 (data not available) and then gradually appr oached to a steady state level over the next two HRTs. The length of diauxic lag and the highest obs erved anoxic specific growth rate were calculated using the virtual batch curve met hod. Since detailed biomass absorbance profile was not available duri ng the recovery period, the hi ghest observed anoxic specific growth rate was calculated from linear regres sion of the two data point at Hour 154 and Hour 161 in the virtual batch cu rve. The length of diauxic lag calculated by the virtual batch curve method was 15 hours or longer because of the approximation. The highest observed anoxic specific growth rate was 0.19 h-1 or higher. Concentration of nitrate and nitrite and nitrate reductase enzyme activity were monitored from sample directly collected from the reactor suspension. The decrease of nitrate and the accumulation of nitrate in the medium were marginal, when biomass absorbance was still decreasing (Fig. 9-13b) After biomass abso rbance increased, the decrease of nitrate concentration from the previous measurement was 55 mg NO3 --N and nitrite started being detected As biomass absorbance gradually reached a steady value, the decrease of nitrate and increase of nitrite in the suspension became greater. The total and periplasmic nitrate reductase activities were zero when biomass absorbance was still decreasing. After biomass absorbance increas ed, the total nitrate reductase activity was 10-11.1 units and periplasmic activity was still zero (Fig. 9-13c). Simulations to Predict Experi mental Results using eASM1m Efforts were made to predict the biomass absorbance profiles found in the experiment using eASM1m. A new release coefficient for the intracellular nitrate (iNOS) was introduced in order to better predict th e zero anoxic growth of bacteria during the

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167 oxic/anoxic cycling operations. The process rate for release of th e intracellular nitrate was modified by replacing the existing re lease coefficient with the new release coefficient as follows: 12NOi iSNObS (9-1) where NO iSbis the new release coefficient fo r the intracellular nitrate. The diauxic lag of bacteria during the cyc ling operations in the simulations could be increased by increasing the release coeffi cient for the intracellular nitrate. The biomass profiles from the simulations with 12 hour and 24 hour cycle length are shown in Figures 9-14 and 9-15, respectively. The zero anoxic growth found in the oxic/anoxic cycling experiments with both cycle lengths could be predicted by eASM1m with slight modifications of the release coeffi cient for intracellular nitrate (NO iSb) and the specific rate of uptake of nitrate (, NOiSV) (Table 9-1). However, the model could not predict the fast decrease of biomass absorbance obser ved in the experiment s without significantly increasing the biomass decay coefficient. At the end of the cycling simulation with 24 hour cycle length, the eASM1m also could not predict the longer di auxic lag found at the end of the cycling experiments w ith 24 hour cycle length (Fig 9-16). The process rate of synthesis of nitrate re ductase was then modified to give much more sigmoidal change of nitrate reductase as follows: 2 ,1, 9, 2 2,1, BHNOi S NBH SS BHNOiXKS S X KS KXKS (9-2) However, the extended length of lag at the end of the cycling still could not be predicted by eASM1m with various changes of the equilibrium constants (K1 and K2).

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168 0 0.05 0.1 0.15 0.2 0.25 0.3 172184196208220 Time (h) Biomass (Simulation) Biomass (Experiment)Oxic Anoxic Biomass absorbance Figure 9-14. Simulation of experime ntal results (12 hour cycle length) 0 0.05 0.1 0.15 0.2 0.25 0.3 172184196208220 Time (h) Biomass (Simulation) Washout curve Biomass (Experiment)Biomass absorbanceOxicAnoxicOxicAnoxic Figure 9-15. Simulation of experime ntal results (24 hour cycle length)

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169 Table 9-1. Parameters of eASM1m for simulations. Common Parameters Values Units H 0.6 h-1 g 0.58 bH 0.002 h-1 YH 0.7 g biomass COD/ g substrate COD YH, Anoxic 0.47 g biomass COD/ g substrate COD KS 20 mg COD/L KO,H 0.065 mg O2 / L KNO 0.77 mg NO3-N / L eASM1m parameters VSni 1.6E-01 mg NO3-N /mg biomass COD/sec aN 2.45E-08 kat/mg biomass COD/sec K1 9.86E+04 (mg NO3-N/L)-1 K2 1.96E+04 (mg NO3-N/L)-1 KNO,I 5.61E-04 mg NO3-N / L KO,i 3.31E-04 mg O2 / L bEN 0.4 h-1 bSNOi 0.35 h-1 0 0.05 0.1 0.15 0.2 0.25 0.3 340352364376388 Time (h) Biomass (Simulation) Biomass (Experiment) OxicAnoxicBiomass absorbance Figure 9-16. Simulation of experimental results (final anoxic phase, 24 hour cycle length)

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170Discussion Results from the simulations and the experiments will be compared and analyzed. New findings will be discussed and compar ed to the literatures, and corresponding hypotheses will be introduced. Effect of Alternating Cycling on Growth of P. denitrificans Predicted by eASM1m The results from the simulations can be explained with modeling concepts of eASM1m. With cycle lengths shorter than a certain minimum value (6 h and 12 h), no significant anoxic growth of bacteria is expe cted because bacteria lose their nitrate reductase and intracellular nitrate during each oxic phase, and do not have enough time to re-synthesis those machiner ies (Figs. 9-3, 9-4). As longer cycle lengths (24 h and 48 h) ar e given, length of an anoxic phase is comparable to or longer than the length of the initial lag. Bacteria are able to grow after the diauxic lag during each anoxic phase during an ultimate state (Figs. 9-5, 9-6). The length of lag in each anoxic phase is shorter th an the initial lag becau se bacteria are able to retain carryover nitrate reductase and in tracellular nitrate at the beginning of each anoxic phase. This is because the length of oxic phase is not long enough for enzyme and intracellular nitrate to be tota lly washed out or decay. If the cycle length is extensively long, so that the length of an oxic phase is comparable to the length of initial oxic continuous flow phase, bacteria lose all denitrification machinery duri ng each oxic phase (Fig. 9-7). The physiological state of bacteria at the beginning of each anoxic phase is identical to that at the beginning of the initial anoxic continuous flow phase. Theref ore, the length of di auxic lag during each anoxic phase is same as that of the initial lag.

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171 Likewise, the effect of alternating oxic/anoxic cycling on diauxic lag under continuous flow condition predicted by eASM1m model was consistent with the finding in Chapter 4 (Lee et al., 2004). Cycle lengths showed significant effects on growth dynamics of bacteria and there was a cri tical cycle length below which no significant anoxic growth takes place. Growth Dynamics of P. denitrificans in Alternating Oxic/Anoxic Cycling under Continuous Flow Co ndition-Experiment The experimental results observed in the present study were the first observation of diauxic lag of P. denitrificans in alternating cycling under continuous flow conditions. There was no indication of significant anoxic growth of bacteria in each anoxic phase during alternating oxic/anoxic cycl ing, regardless of cycle length. This is quite surprising because eASM1m predicted substantial anoxic growth of bacteria with 24 hour-cycle length. Even with 12 hour-cycle length, there was some indication of recovery of growth at the end of each anoxic phase although no significant biomass absorbance increase was found, as predicted by eASM1m. These results imply that the magn itude of decrease of nitrate reductase and intracellu lar nitrate during oxic phases in cycling could be more than the model prediction, which leads to needs for further calibration of the eASM1m parameters. Changes of nitrate, nitrite and COD concen tration in the effluent at the beginning and the end of the two anoxic phases in the ultimate state of 24-hour cycle length might indicate that there were some denitrification activities. However, nitrate and nitrite concentration in the reactor su spension at the end of the last anoxic phase were almost closed to those at the beginni ng. This indicates that the change of nitrate and nitrite concentrations in the effluent might be resu lted from anoxic growth of bacteria at the

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172 effluent tubing. After several HRTs from the beginning of continuous flow experiments, substantial amount of biomass accumulation wa s observed at the effluent tubing. More surprising finding was the results fr om the last anoxic phase, which was extended for a few more HRTs under the sa me experimental conditions. The anoxic growth of bacteria was not reinitiated for 18 hours after the switch from oxic to anoxic conditions, leading to an exte nsively long diauxic lag, wh ich was significantly longer than the initial lag (p < 0.05, t-test for two samples assuming equal variances), which we previously considered to be the maximum. No significant nitrate removal, increase of nitrite, or increase of enzyme activity was found until bacteria st arted their growth. Although the phenomenon was observed only once at the end of the last anoxic phase, it is quite obvious that the length of diauxic la g became longer after bacteria were exposed to alternating oxic/anoxic cycl ing, judging from the reproduci bility of the experimental results from the current experimental system. This cannot be explained by our modeling concept; theore tically, the initial lag would be the longest lag whatsoever because the relative levels of nitrate reductase and intracellular nitrate at the beginning of the initial lag are techni cally minima after extensively long oxic continuous flow phase. Th is result is somewhat consistent with the new finding from Chapter 8; the growth patte rns of bacteria duri ng oxic phases (fast and exponential like growth versus sl ow or steady state growth) s eemed to have an effect on diauxic lag of bacteria duri ng the subsequent anoxic phases. When an extensively long diauxic lag was observed (Trial s 2 and 3, Chapter 8), it was not long ago after bacteria grew very fast during the st artup phase and the first fe w hours of the oxic continuous flow phase. The net specific growth rate dur ing the first few hours of the oxic continuous

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173 flow phase was approximately 1.0 h-1, which is technically close to their maximum rate and almost 10 times of the dilution rate (0.1 h-1). In the present results, bacteria started losing their denitrification capabilities from the first oxic phase in the cycling continuous flow period, indicat ed by the immediate washout of biomass during the subsequent anox ic phase. In the subsequent oxic phases, bacteria grew fast during the first few hours wi th a similar growth patterns to a typical exponential growth. The net specific growth rates during those fast growth periods were approximately 0.4 h-1, which was less than the maximum rate but still significantly higher than the dilution rate. The washout during a noxic phase and the fast growth during oxic phase were continuously repeated through out the cycling continuous flow phase. These fast oxic growths of bacteria are quite diffe rent from the steady state growth before the initial lag. Therefore, on e possible reason for these cont roversial phenomena would be the difference in growth patterns dur ing the preceding oxic phases. The difference in growth pattern could result in various levels of physiological state of bacterial cells. The physio logical adaptation of bacterial cell is well summarized in a comprehensive conceptual model based on a number of previous literatures focused on growth dynamics of bacteria regarding physiological adap tation of intracellular components (Daigger and Grady, 1982). In the m odel, relative level of each intracellular components including RNA, proteins, and othe r components, which c ontribute directly or indirectly to change of the specific growth rate of bact eria, are regulated by proper processes and flow of inform ation, resulting in various le vels of components depending on growth conditions represente d by specific growth rates. According to the model, relative levels of re presentative intrace llular components are ma intained high when

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174 bacteria grow with high specific growth rates. On the other hand, the relative levels of those components are low in low specific growth rate conditions, because of less need for amount of machineries. The various levels of those components and control machineries may result in dynamic response of bacteria in transient c onditions (e.g., change s of specific growth rates). For example, in case of transition from low to high diluti on rate in continuous flow condition, bacteria cannot increase their specific growth rate instantly, because the relative levels of gr owth-relating intracellular compone nts including RNA and proteins are low. Bacteria cannot increase the leve ls of the necessary components instantly because of the regulation and the control of each process related to the components. Whereas, in the case of transition from hi gh to low specific growth rate conditions, bacteria sustain their high growth rate fo r a period of time, although not very long, because of available intracellular substrate and energy. As time goes by, bacteria adjust their specific growth rate to the diluti on rate but they sustain their intracellular components such as macromolecules including RNA and proteins. This is because the relative levels of those co mponents will decrease by decay, not by the decrease of specific growth rate. High levels of macromolecules includi ng RNA and proteins may lead to high energy requirements for maintenance includi ng turnover and repair or re-synthesis of those macromolecules. Morita (1997) reported that maintenance energy for macromolecules in cells is a major energy re quirement for starving cells along with other maintenance mechanisms including the contro l of chemical balances and motility, based

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175 on his summary on earlier literatures. He quoted that maintenance energy requirement for growing cells is likely to be higher than that of starvi ng cells (Pirt, 1966). The difference in length of diauxic lag in the present study can be explained by the difference in energy requirements for maintenance of macromolecules in the preceding oxic phase. We shall call this accelerated maintenance energy theory. As air supply is stopped and anoxic condition is initiated, di auxic lag begins because of short of denitrification machineries. Bacteria gene rate limited amount of energy using nitrate respiration with limited or minimum amount of denitrification machiner ies. If bacteria undergo fast growth period in the pre ceding oxic phase, further synthesis of denitrification machinery could be limited b ecause of high energy requirements for the maintenance of the remaining macromolecules carried over from the preceding oxic phase. This may lead to extensively long dia uxic lags, as seen in the present study. After long oxic continuous flow phase (i.e., steady state condition has been achieved), relative levels of macromolecules would be much lower than those after fast growth situations, leading to less en ergy requirements for maintenance of macromolecules. As an anoxic condition begins bacteria still expe rience diauxic lag as well because of short of proper growth mach ineries. However, growth recovery of bacteria from diauxic lag would be much fa ster than the previous case because of the lower energy requirement due to lower relative le vel of macromolecules in the cell. Suggestion of a Preliminary Modeling Co ncept to Show the Effect of Growth Patterns during Oxic Phase on Diauxic Lag of P. denitrificans The effect of growth stage of bacteria during oxic phases on di auxic lag could be predicted by modeling if amount of macromolecules and energy availability in the cells are taken into account. Proper components such as representative macromolecules (e.g.,

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176 RNA or enzymes associated with aerobic resp iration) and energy ca rriers (e.g., ATP), and corresponding process rate for synthesis and decay of those components can be developed. The decay of macromolecules co uld be associated with consumption of energy carrier so that the model can predic t change of energy requirement depending turnover of macromolecules. Then, process rate for synthesis of nitrat e reductase and transport of nitrate across the cell membrane could be modified to be controlled by relative amount of available energy carrier in the cell. Th e process rates related to synt hesis of nitrate reductase and intracellular nitrate can also be modified to be controlled by relative amount of macromolecules. Another way to regulat ing denitrification mechanism would be controlling decays of nitrate reductase and in tracellular nitrate with relative amount of macromolecules. Fast Decrease of Biomass Absorbance during Lag Period Fast decrease of biomass concentration, ra te of which was significantly higher than that of dilution rate, is an other surprising finding. This was consistently observed throughout the cycling experiments. Since biomass absorbance will be changed only by dilution and decay in ideal CSTR where bacter ia do not grow because of diauxic lag, the rate of change of biomass absorbance must be exactly equal to the summation of dilution and decay rate. One possible reason could be fast de cay of biomass because of endogenous respiration due to high energy requirements for maintenance of macromolecules. However, significant amount of destructions of cell structures (e .g., cannibalization of RNA and proteins) has to take place to resu lt in such high washout rates found in the experiments. Such destructions of cell st ructures are also energy dependent mechanism

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177 but we expect that the energy availabilities in ce lls are extremely limited because oxygen was not present in the medium and only limite d amount of nitrate would be utilized in terms of energy production, because of limite d level of denitrification capability. Therefore, it is not clear whether or not th e high washout rates were resulted from high decay rates. Another reason could be the change of su rface properties of bacteria, which could affect their ability to atta ch to the reactor surfaces, both below and above the liquid surface or to coagulate together. Throughout the experiment, substantial amount of biomass accumulation was observed on reactor wa ll, feed tubing and effluent tubing, or any other surface facing to bacterial suspension. It has been well known that bacteria increase thei r attachment property by producing adhesive materials, for example, glycocalyx, when growth condition becomes less favorable in environment as summar ized by Morita (1997). Conceptually, attachment could be an effective surviving st rategy for bacteria because it could be an efficient way to avoid being removed from thei r environment. They can attach to surface, or coagulate together to promote their se dimentation, both of which have been well utilized in wastewater treatment processe s (biofilm, activated sludge floc, etc). According to the summary of Morita ( 1997), increase of attachment property was observed when bacteria experience limited carbon substrate conditi ons or starvation conditions. Furthermore, it has been found th at the attachment of bacteria to surface could increase metabolic activity when the nutrient level is under threshold level. The change of adhesive property of bacteria coul d affect observed biomass absorbance in two ways. First, it could f acilitate accumulation of biomass on surfaces

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178 exposed to biomass suspension, such as fe rmentor wall, impeller, or other fermentor structures. If the rate of transport of bioma ss from suspension to those surfaces is high, the observed rate of change of biomass abso rbance could be higher than dilution rate. Because of the small volume of the reactor (1 L), the specific area of reactor wall and reactor structures facing bacterial suspension ar e expected to be very high, which is an effective condition for bacteria in the suspension to enc ounter to the surfaces. Second, the change of surface property of cells can facilitate the coagulation of bacteria in suspension, leading to changes of the correlation between optical density and actual biomass concentration. Indeed, the coagulated bacterial cells were frequently found throughout the experiments under micros copic observation, although it was not clear whether the magnitude of coagulation was higher during the di auxic lag period than oxic periods. Whether or not bacteria change their su rface property during diauxic lag period is not known. Since biomass accumulation on reactor surface was not quantified in the present study, further study is necessary to prove the hypothesis.

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179 CHAPTER 10 SUMMARY AND CONCLUSIONS Activated sludge model No. 1 (ASM1) was extended by incorporating two denitrification models, one w ith cybernetic approach and the other with mechanistic approach, for better prediction of growth dynami cs of denitrifiers in wastewater treatment processes. The extended versions of AS M1 (eASM1c and eASM1m) were capable of depicting diauxic lag of denitrifiers under oxic/anoxic cyc ling conditions. A simulation study was performed to eval uate ASM1 and eASM1c predictions on the effect of alternating oxic/anoxic cy cling on nitrogen removing performance of BioDenipho and fed-batch processes (Lee et al., 2004). The eASM1c predicted substantially longer opti mal cycle lengths for both processe s, which are more consistent with the operational strategy of BioDenipho pr ocess at the University of Florida. Furthermore, eASM1c predicted a critical cycle length, below wh ich denitrification would stop. Because of limitations of this study, however, needs for further study were recognized to investigate growth dynamics of denitrifying bacter ia under alternating oxic/anoxic cycling under c ontinuous flow conditions. A continuous flow bacteria l pure culture system was developed to investigate diauxic lag of denitrifying bacteria under continuous flow conditions. Contamination free culture conditions were maintained with th e system up to 30 HRTs at dilution rate of 0.1 h-1 (300 hours) and 6 HRTs at dilution rate of 0.03 h-1 of dilution (200 hours). Reproducibility of experimental results could be nicely improved by achieving steady state continuous cultures, as initial stages for study of growth dynamics of

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180 denitrifiers. It was found that presence of pe riplasmic nitrate reducta se in bacterial cells significantly affected growth dyna mics, resulting in zero or sh ort diauxic lag. Presence of nitrate in the media during the oxic phase was found to have no effect on the length of diauxic lag of P. denitrificans, which was contradictory to the previous finding (Gouw et al., 2001). Growth dynamics of P. denitrificans in single switch from oxic to anoxic conditions was examined using the continuous flow experime ntal system. Diauxic lag under continuous flow conditions could be characterized by washout of biomass by dilution during lag period followed by gr adual resumption of growth, which was consistent with our predictions, based on the modeling concepts. The virtual batch curve method was developed to quantify diauxic la g under continuous flow conditions. The eASM1m could predict diauxic lag under continuous flow condition with slight modification of parameters. Biomass accumulation at the feed inlet strongly affected experimental results, resulting in shorter diauxic lag and decrease of steady st ate biomass absorbance level under oxic continuous flow conditions. Faster growth of bacteria during preceding oxic phase seemed to increase length of diauxic lag during consecutive anoxic phase, which could not be predicted by eASM1m. Simulations were performed to investigat e the effect of al ternating oxic/anoxic cycling on growth dynamics of P. denitrificans under continuous flow conditions and determine the test cycle lengths for the e xperiments. The predictions of eASM1m on growth dynamics of P. denitrificans in alternating oxic/anoxic cycling under continuous flow conditions were consistent with the results of Lee et al. (2004), in that cycle length

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181 plays important role on growth dynamics of bacteria and there was a minimum cycle length below which denitrification stops. It has been found that P. denitrificans could not establish si gnificant anoxic growth during alternating oxic/anoxic cycling under continuous flow conditions, for 10 HRTs with either 12 hour cycle length (4 hour a noxic phase) or 24 hour cycle length (8 hour anoxic phase). Model parameters from single switch experiment were not adequate to predict culture behavior duri ng extended cycling. Introduc tion of a new rate parameter for release of intracellular nitrate gave improved fits. Significantly fast decrease of bacterial concentration was observed during each anoxic pha se of alternating cycling periods. This may be explained by ch ange of surface property of bacteria. Length of diauxic lag was significantly increased after bacteria experienced alternating oxic/anoxic cycling. During each oxic phase, fast growth of bacteria was observed, which was similar to that of oxi c continuous flow phase which resulted in extensively long diauxic lag. This indicates that physiolo gical conditions of bacteria during the preceding oxic conditions, other th an dinitrification dynamics, might have significant effects on diauxic la g of bacteria during the following anoxic phases. This may be explained by the accelerated maintenan ce theory. The eASM1m could not depict this effect.

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182 CHAPTER 11 FUTURE WORK The fact that diauxic lag of P. denitrificans became longer after bacteria undergo alternating oxic/anoxic cycling could be important to oxic/anoxic cycling situations taking place in real wastewater treatment processes utilizing oxic/anoxic cycling operational strategy. Accordi ng to the accelerated maintenan ce theory developed in the present study, if fast aerobic growth takes place during each oxic c ondition in alternating cycling, level of macromolecules and co rresponding maintenance energy requirement increase, and diauxic lag during anoxic conditio ns could be extensively long, leading to poor denitrification performance. The accelerated maintenance theory has to be proved with quantified levels of aerobic growth, for example, specific growth ra tes. This can be implemented by growing bacteria under a batch cond ition with excess carbon s ource and under oxic continuous flow conditions with several different dilu tion rates and then exposing bacteria under anoxic continuous flow conditions to monitor diauxic lag. Higher level of maintenance energy resulted from fast aerobic growth may or may not lead to higher decay rate due to turnover of macromolecules such as RNA and pr oteins as discussed in Chapter 9. This would be tested by measuring decay rate after growing bact eria under various levels of dilution rate. Change of surface property of P. denitrificans during diauxic lag in oxic/anoxic switch also has to be studied by correct quantifica tion of attached biomass to the reactor surfaces.

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183 Feeding pattern for such alternating oxic /anoxic cycling processes would be very important if the present theory is true. This is because such high level of aerobic growth of denitrifiers could be avoided by lim iting the amount of carbon source introduced during oxic phase in a periodical process or into oxic zone in a continuous process. It has to be noted that e xperimental conditions in th e present study would not be pertinent to real world situations in seve ral ways, regarding the hypothesis. The mean cell residence time in the present study wa s 10 hours because of several operational limitations, which was significantly shorter th an the solid retention time in a typical biological nitrogen removal proce ss, which is on the order of se veral days. In such short retention time environments, observed speci fic growth rate of bacteria would be substantially higher than those of real world situations, resu lting in much higher level of aerobic growth of denitrifiers. Also, the concentration of carbon substrate in feed solution and DO concentration in the media we re higher than those in real processes, which may also lead to higher level of aerobi c growth during oxic conditions. Therefore, further research has to be done with proper experimental conditions. It would be interesting to study growth dynamics of bacteria having periplasmic nitrate reductase (e.g., P. pantotrophus). The length of diauxic lag of P. pantotrophus was relatively short, on the order of zero to one hour, with similar conditions with which P. denitrificans experienced several hour s of lag as discussed in Chapter 7. No significant diauxic lag was observed duri ng a single switch between oxic/anoxic conditions under a continuous flow conditi on according to the results of Baumann et al. (1996). Assuming that similar physiologica l adaptations may take place during oxic conditions, whether or not th e length of diauxic lag of P. pantotrophus becomes longer

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184 after bacteria experience a lternating oxic/anoxic cycling conditions would be a question of interest. Besides the above discussi on about level of aerobic growth, several other limitations of the present study also have to be taken into account. Nitrate concentration was non-limiting (400 mg NO3 --N/L), which was significantly higher than that in typical anoxic zone in real wastewat er treatment plants. Single population denitrifying culture may not necessarily represent growth dynamics of complex ecosystem in activated sludge. Effect of species and growth ch aracteristics of other kinds of bacteria in activated sludge flocs could be significant. Also, competition between facultative denitrifiers and obligate aerobic bacteria on carbon substrate has to be considered. Temperature may have significant effect on growth dynamics of den itrifiers. Proper research has to be conducted to overcome the current limitati ons and investigate unknown mysteries.

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185 APPENDIX A SIMULATIONS OF RESULTS FROM A PREVIOUS STUDY The simulation was performed using eASM 1m. Since eASM1m parameters for Paracoccus denitrificans did not exist at that time, they were assumed to be same as those from the present study (Table 3-4). Th e simulations were performed in the same manner as that of the experiment of Baumann et al. (1996). Dilution rate was set to the value in the experiment (0.03 h-1). The simulations were started with the aerobic condition for 333 hours, which is 10 times of the mean cell residence time, and switched to the anoxic condition and continued for another 10 mean cell resi dence time. Since Baumann et al. (1996) did not specify their substrate concentration of the feed solution, it was set to 2550 mg COD/L, which generates similar steady state biomass con centration in the aerobic condition in the simulation to that of Bauman et al. (1996). Nitrate concentrati on of the feed solution was set to 2550 mg NO3 --N/L to ensure non-limiting nitrate conc entration. Initial intracellular nitrate concentration and nitr ate reductase level were set to their maximum values. Biomass, substrate, intracellular nitrate, and nitrate reductase level were monitored.

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186 APPENDIX B MEASUREMENTS USING HACH TEST TUBES Procedures for measurements using HACH test tubes will be discussed. First general procedures for the preparation of samples will be presented and the specific procedures for HACH test tubes used in this study will be mentioned. All samples for HACH test tube measurements were first filtered using 0.2 um acetate cellulose filter. The filtered samp les were diluted depending on the expected concentration and the detection limit of the test tube. At least 5 mL of the filtered sample was used for dilution. The sample was take n using a glass pipette and inserted into a mass cylinder with proper volume for the dilution The pipette was rinsed with D.I. water and the rinsed D.I. water was added to the mass cylinder three times. The mass cylinder was filled with D.I. water up to a level that gives a proper dilution. The diluted sample was moved to an Erlenmeyer flask and mixed with a magnetic stirrer for 10 seconds. The maximum magnitude of a dilution was 10 fold at a time. Measurement samples were taken by a pipett er with 1 mL disposable plastic pipette tips and dispensed to test tubes. The rest of assay procedures were performed as indicated by HACH.

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187 LIST OF REFERENCES Antoniou, P. (1989) Determination of biokinetic coeffi cients for nitrification in the activated sludge process. M.E. Thesis, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida. Antoniou, P., Hamilton, J., Koopman, B., Ja in, R., Holloway, B., Lyberatos, G. and Svoronos, S.A. (1990) Effect of temper ature and pH on the maximum effective specific growth of nitrifying bacteria. Water Res., 24, 97-101. Baumann, B., Snozzi, M., Zehnder, J.B. a nd Van der Meer, J.R. (1996) Dynamics of denitrification activity of Paracoccus denitrificans in continuous culture during aerobic-anaerobic changes. J. Bacteriol., 178, 4367-4374. Baumann, B., Snozzi, M., Van der Meer, J.R. and Zehnder, J.B. (1997a) Development of stable denitrifying cultures during repeated aerobic-anaerobic transient periods. Water Res., 31, 1947-1954. Baumann, B., Van der Meer, J.R., Snozzi, M. and Zehnder, J.B. (1997b) Inhibition of denitrification activity but not of mRNA induction in Paracoccus denitrificans by nitrite at a suboptimal pH. Antonie van Leeuwenhoek, 72, 183-189. Bonin, P., Gilewicz, M. and Bertrand, J. C. (1989) Effects of oxygen on each step of denitrification on Pseudomonas nautica. Canadian J. Microbiol. 35, 1061-1064. Casass-Zambrana, A.I. (2001) Effect of exposure to o xygen on the diauxic lag. M.S. Thesis, Department of Chemical Engineeri ng, University of Florida, Gainesville, Florida. Casass-Zambrana, A.I. (2005) Effect of enzymes on the diauxic lag of denitrifying bacteria switching between electron acceptors. Ph.D. Dissertation, Department of Chemical Engineering, University of Florida, Gainesville, Florida. Craske, A. and Ferguson S. (1986) Th e respiratory nitrate reductase from Paracoccus denitrificans. Molecular characteristics and kinetic properties. Europian J. Biochem., 158, 429-436. Daigger, G.T and Grady, C.P. (1982) The dynamics of microbial growth on soluble substrates: a unifying theory. Water. Res., 16, 365-382.

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188 Gouw, M., Bozic, R., Koopman, B. and Svoronos S.A. (2001) Effect of nitrate exposure history on the oxygen/nitrat e diauxic growth of Pseudomonas denitrificans. Water Res., 35, 2794-2798. Hamilton, J., Jain, R., Antoniou, P., Svoronos S.A., Koopman, B. and Lyberatos, G. (1992) Modeling and pilot-scale experiment al verification for predenitrification process. J. Environ. Eng., ASCE, 118, 38-55. Hamilton, R., Casass, A., Rasche, M., Na rang, A., Svoronos, S.A. and Koopman, B. (2005) A structured model for denitrifier diauxic growth. Biotech. Bioeng., 90, 501. Henze, M., Grady, C.P., Gujer, W., Marais, G.R. and Matsuo, T. (1986) Activated sludge model no. 1. IAWPRC Scientific and Techni cal Reports 1, IAWPRC, London. Henze, M., Gujer, W., Mino, T. and Loosdrecht, M. (2000) Activated sludge models ASM1, ASM2, ASM2d and ASM3. IWA Publishing, London. Jones, R.W., Gray, T.A. and Garland, P.B. (1976) A study of the permeability of the cytoplasmic membrane of Escherichia coli to reduced and oxidized benzyl viologen and methyl viologen cations: complications in the use of viologens as redox mediators for membrane-bound enzymes. Biochem. Society Trans., 4, 671-673. Kodama, T., Shimada, K. and Mori, T. (1969) Studies on anaerobic biphasic growth of a denitrifying bacterium, Pseudomonas stutzeri. Plant and Cell Physiol., 10, 855865. Kompala, D.S., Ramkrishna D., Jansen, N.B. and Tsao, G.T. (1986) Investigation of bacterial growth on mixed substrates: e xperimental evaluation of cybernetic models. Biotechnol. Bioeng., 28, 1044-1055. Kornaros, M., Zafiri, C. and Lyberatos, G. (1996) Kinetics of denitrification by Pseudomonas denitrificans under growth conditions limited by carbon and/or nitrate or nitrite. Water Environ. Res., 68, 934-945. Kos, M., Wanner, J., orm, I. and Grau, P. (1992) R-D-N activated sludge process. Water Sci. Tech. 25, 151-160. Lee, D.U., Casass-Zambrana, A.I., Hamilton, R., Svoronos, S.A., Lee, S.I. and Koopman, B. (2004) Significance of deni trifying enzyme dynamics in biological nitrogen removal proce sses: a simulation study. Water Sci. Tech., 49, 265-274. Lisbon, K., McKean, M., Shekar, S., Svoronos S.A. and Koopman, B. (2002) Effect of DO on oxic/anoxic diauxic lag of Pseudomonas denitrificans. J. Environ. Eng., ASCE, 128, 391-394. Liu, P.H., Zhan, G., Svoronos, S.A. and Koop man, B. (1998a) Diauxic lag from changing electron acceptors in acti vated sludge treatment. Water Res., 32, 3452-3460.

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189 Liu, P.H., Svoronos, S.A. and Koopman, B. (1998b) Experimental and modeling study of diauxic lag of Pseudomonas denitrificans switching from oxic to anoxic conditions. Biotech. Bioeng., 60, 649-655. Metcalf and Eddy, Inc. (1991) Wastewater engineering: tr eatment, disposal and reuse. McGraw-Hill, New York, NY. Monod J. (1942) Recherches sur la cr oissance des culture s bacteriennes. Actual Sci. Ind., 911, 1-215. Monod, J. (1949) The growth of bacterial cultures. Annual Rev. Microbiol., 3, 371-394. Moreno-Vivin, C. Cabello, P., Martnez-Luque M., Blasco, R. and Castillo, F. (1999) Minireview-prokaryotic nitrate reducti on: molecular proper ties and functional distinction among bacteria l nitrate reductases. J. Bacteriol., 181, 6573-6584. Morita, R.Y. (1997) Bacteria in oligotrophic environments. Chapman & Hall, New York, NY. Oh, J. and Silverstein, J. (1999) Effect of air on-off cycles on activated-sludge denitrification. Water Environ. Res., 71, 1276-1282. ONeil, M. and Horan, N.J. (1995) Ac hieving simultaneous nitrification and denitrification of wastewaters at reduced cost. Water Sci. Tech. 32, 303-312. Pirt, S.J. (1966) The maintenance en ergy of bacteria on growing culture. Proc. Roy. Soc. 163, 224-231. Potter, P.G., Koopman, B. and Svoronos, S.A. (1996) Optimization of a periodic biological process for nitroge n removal from wastewater. Water Res. 30, 142-152. Richardson, D.J. and Ferguson, S.J. (1992) The influence of carbon substrate on the activity of the periplasmic nitrat e reductase in aerobically grown Thiosphaera pantotropha. Arch. Microbiol., 157, 535-537. Robertson, L.A. and Kuenen, J.G. (1984) Aer obic denitrification: a controversy revived. Arch. Microbiol., 139, 351-354. Sears H.J, Ferguson S.J, Richardson D.J a nd Spiro S (1993) The identification of a periplasmic nitrate reductase in Paracoccus denitrificans. FEMS Microbiol. Lett., 113, 107-112. Simpkin, T.J. and Boyle, W. C. (1985) The regulation of the synthesis of denitrifying enzymes in activated sludge. In Proceedings of the 40th Industrial Waste Conference, Purdue University, West Lafayette, IN

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190 Waki, T., Murayama, K., Kawato, Y. and Ichika wa, K. (1980) Transient characteristics of Paracoccus denitrificans with changes between aerobic and anaerobic conditions. J. Ferment. Tech., 58, 243-249. Warnecke-Eberz, U. and Friedrich, B. ( 1993) Three nitrate reductase activities in Alcaligenes eutrophus. Arch. Microbiol., 159, 405-409. Ziegler, J.G. and Nicholes, N.B. (1942) Op timum settings for au tomatic controllers. Transactions ASME, 64, 759-768.

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191 BIOGRAPHICAL SKETCH Dong-Uk (Don) Lee was born in Seoul, South Korea, on December 8th, 1972. He received a Bachelor of Science in environm ental engineering from Inha University in Inchon, South Korea, in 1997. He continue d his study in the Graduate School of Inha University. Until he received a Master of Science in 1999, he worked on a number of research projects related to biological treatment of domestic waste and landfill leachate, and evaluation of pollution loading from non-point sources. He joined Geoen Tech., Ltd. in South Korea as a researcher and engineer in 1999, and particip ated in researches related to biological treatment of food waste and wastewater, and rela ted process design. In 2001, he began his doctor al study in environmental e ngineering sciences at the University of Florida in Gainesville as an Alumni Fellow and continued his study on biological wastewater treatment. He partic ipated in a number of research projects including modification of ASM1 by incorpor ating denitrification models, wastewater treatment process optimization, development of continuous flow pure culture system, and diauxic lag of denitrifies in oxic/anoxic cycling under continuous flow conditions. He was qualified as a doctoral candidate in 2003 and received a Doctor of Philosophy in environmental engineering scienc es from the University of Florida in August 2005. He then started his professional carrier in an environmental consulting firm, Jones Edmunds and Associates, at Gainesville in August 2005.


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DIAUXIC LAG OF DENITRIFYING BACTERIA IN OXIC/ANOXIC CYCLING
UNDER CONTINUOUS FLOW CONDITIONS
















By

DONG-UK LEE


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2005

































Copyright 2005

by

Dong-Uk Lee

































To God, MJ and my family.















ACKNOWLEDGMENTS

I would like to truly thank my co-advisors, Dr. Ben Koopman and Dr. Spyros

Svoronos, for their guidance and advice throughout my graduate study. Their passion

and sincerity toward academic research and teaching will be strong guiding lights for the

rest of my life. I also have to acknowledge the other members of my committee, Dr.

Angela Lindner, Dr. Atul Narang, and Dr. Samuel Farrah for, their advice and help on my

research since I asked them to be on my committee.

I would like to appreciate the Alumni Fellowship from the University of Florida for

my entire doctoral study. I thank Mr. Chuck Fender and the fellows in the Physical Plant

Division of University of Florida at the Water Reclamation Facility for their help and

friendliness.

I have to thank the fellows in our research group, Anna I. Casasus-Zambrana, Ryan

K. Hamilton, Dr. Sung-Hoon Woo, Kiran Durvasula, and Adrian Vega, for their help,

support and friendship. Also, I thank Jao Jue, Gautam Kini, Vijay Krishna and other

fellows in the Academic Interface Lab for being good friends of mine.

Finally, I would like to thank my family and MJ for their endless love, prayer, and,

most of all, for being my family. I truly thank my God for preparing everything and

leading me here.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES ..................................................................... ........... .. ix

LIST OF FIGURES ......... ............................... ........ ............ xi

ABSTRACT ........ ........................... ...... ...................... xvi

CHAPTER

1 INTRODUCTION ............... ................. ........... ................. ... ..... 1

2 REVIEW OF LITERATURE ......................................................... .............. 4

Dynamics of Heterotrophic, Denitrifying Bacteria Switching between Electron
A c c ep to rs .............................................................. ................ .. 4
Effects of Alternating Electron Acceptors ............... ........ ... .... ........... 5
Diauxic Lag of Bacteria Switching between Electron Acceptors .....................8
Factors Affecting the Diauxic Lag of Heterotrophic, Denitrifying Bacteria.......12
B bacterial sp ecies .............................................. .. ........ ............... 12
Length of aerobic phase ........................ ............. ............... 12
Dissolved oxygen concentration in aerobic phase ......................................13
Nitrate exposure history of preceding culture................ ............... 14
Nitrate concentration in anoxic phase ............................................... 14
M odeling of Denitrification in Activated Sludge................................................. 15
Activated Sludge M odel No. 1 (ASM 1)........................................................... 15
Modeling of Denitrification with a Cybernetic Approach for Denitrifying
E n zy m e K in etics ..... .... ........ ........ .. ................ ... .... .... .......................... 17
Modeling of Denitrification with Mechanistic Approach for Denitrifying
Enzym e K inetics ........... .. .. .................. ............................ 23

3 EXTENSION OF ACTIVATED SLUDGE MODEL NO. 1 TO INCORPORATE
DENITRIFYING ENZYME KINETICS ............................ ..................... 26

Extension with Cybernetic Approach (eASMlc)...................................................26
Extension with Mechanistic Approach (eASMlm).................. ....................29
Comparison of Extended Versions of ASM1 to the Original Version of ASM1 .......29









Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate
by A SM 1 ............. ......................... ... ............ ...............33
Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate
by eA SM lc .............. ... ..................... ............ .......... ...............36
Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate
b y eA SM lm .................... ............................ ......... ............... 4 8
Re-examination of Results from a Previous Study.............................................55

4 SIGNIFICANCE OF DENITRIFYING ENZYME DYNAMICS IN
BIOLOGICAL NITROGEN REMOVAL PROCESSES: A SIMULATION
S T U D Y ............. .. ...............57................. .........

E x p erim mental M eth od s........................................................................... ...............58
Process Configurations and M odeling...................................... ............... 58
Wastewater Composition and Model Parameters..........................................61
Diurnally Varying Flow and Component Concentrations in Influent
W a stew after ................................................... ............ ................ 6 2
R results and D iscu ssion .............................. ........................ .. ...... .... ...... ...... 64
Simulations of Fed-Batch Process............................ .... ...............64
Simulations of BDP Process................................................................... ......... 65
Optimum Cycle Length as a Function of UVF .............................................67
C o n clu sio n s..................................................... ................ 6 8

5 O B JE C T IV E S ........ .. ...... .. .. .. .... .... ............ ........................................ ..

6 GENERAL MATERIALS AND METHODS................................. ...... ...............70

B bacterial C cultivation .......7.. ....... ............ ............ ..... .................. ... 70
Reviving Freeze-Dried Bacteria and Deep-Freezing of Bacterial Cultures........70
R eviving of Frozen B acteria.................................................................. ... ... .. 71
Preculture Procedure ......................... ..... ... .. ...... ............ 71
R e a cto rs ..............................................................................7 1
O overall L ayout................................................... 72
F erm entor A ssem b ly ........................................ .............................................72
F eed R eservoir A ssem bly ............................................................... ............... .... 75
Autoclaving Procedure and Aseptic Connection of Feed Reservoir Assembly
to Ferm entor A ssem bly.................................................. ............. ................ 75
Inoculation of Fermentor and Initiation of Startup Phase .............................. 76
Initiation of Continuous Flow Phase ........................................................77
Sampling from Fermentor ........................ ............................ 77
Monitoring of Contamination of Pure Culture ......... ........... ..................78
A nalytical M easurem ents ........................................ .............................................78
B iom ass A bsorbance ......................... .. ........................... ................78
Chem ical Oxygen D em and........................................... ........................... 79
N itrate and N itrite .................. .................................................. 79
N itrate R eductase A activity ............................................................................ 79









7 METHOD FOR ACHIEVING REPRODUCIBLE INITIAL CULTURE STATES
IN STUDY OF BACTERIAL DENITRIFICATION KINETICS .............................81

In tro d u ctio n ...................................... ............................................... .. 8 1
M materials and M methods ....................................................................... ..................82
suParacoccusts...........pantotrophus ....................................................................................85
Paracoccus pantotrophus......................... ..........85
Pseudomonas denitrificans..........................................95
D isc u ssio n ................................................... ................. ................ 1 0 1

8 DIAUXIC LAG OF DENITRIFYING BACTERIA IN A CONTINUOUS FLOW
REACTOR-I. SINGLE SWITCH FROM OXIC TO ANOXIC CONDITIONS .... 110

In tro du ctio n ................................................................................................ ..... 1 10
M materials and M methods ................................... ................................... ............... 111
Experim ental Procedures.............................................................. .... ........... 111
M odeling .............................................. 114
R esu lts .................. .. ........................ ..................................... .................... 115
Determination of Diauxic Lag under Continuous Flow Conditions ...............15
E x p erim ental R esu lts............................................ ....................................... 12 1
M modeling R results .................................................. ...... .. .......... .. 131
D discussion ....................................................................... .......... 136

9 DIAUXIC LAG OF DENITRIFYING BACTERIA IN A CONTINUOUS FLOW
REACTOR-II. ALTERNATING OXIC/ANOXIC CONDITIONS........................147

In tro du ctio n ................................................................................................ ..... 14 7
M materials and M methods ...........................................................................................148
Alternating Oxic/Anoxic Cycling under Continuous Flow Conditions ............148
M odeling .............................................. 149
R e su lts ................................................................................................................. 1 5 1
Prelim inary Sim ulations ............................................................................ 151
E xperim ents ...................................... ........................................... ............... 154
Short cycle length (12 hours) ........................................ ............... 157
Long cycle length (24 hours) ....................................................................160
Simulations to Predict Experimental Results using eASMlm..........................166
D iscu ssion .......................................................... ....... ....... ........................ 170
Effect of Alternating Cycling on Growth of P. denitrificans Predicted by
eA SM m ............. ................... ............. ....... .... .. ........ ....... ............. 170
Growth Dynamics ofP. denitrificans in Alternating Oxic/Anoxic Cycling
under Continuous Flow Condition-Experiment............... .............. ........ 171
Suggestion of a Preliminary Modeling Concept to Show the Effect of Growth
Patterns during Oxic Phase on Diauxic Lag of P. denitrificans .................... 175
Fast Decrease of Biomass Absorbance during Lag Period ............................176

10 SUMMARY AND CONCLUSIONS ............... ............ ................................179









11 F U T U R E W O R K ............................................................................. .................... 182

APPENDIX

A SIMULATIONS OF RESULTS FROM A PREVIOUS STUDY............................185

B MEASUREMENTS USING HACH TEST TUBES.............................................186

L IST O F R E FE R E N C E S ......................................................................... ................... 187

BIOGRAPH ICAL SKETCH .............................................................. ............... 191












































viii
















LIST OF TABLES


Table pge

2-1. Factors affecting the diauxic lag of denitrifiers.....................................................13

3-1. Process rates and stoichiometric coefficients of eASMlc............... ... ...............27

3-2. Process rates and stoichiometric coefficients of eASMlm. ...................................30

3-3. Characteristics of growth medium in batch simulations. ........................................34

3-4. Parameters of ASM1 for the batch simulation. ................................. ...............34

4-1. Design of UF BDP water reclamation facility (train 1 of two parallel trains). .........60

4-2. Sequence of phases in the fed-batch and BDP processes ....................................61

4-3. 24-hour flow-weighted average wastewater composition ..................................62

4-4. Stoichiometric and kinetic parameters in the ASM1 and eASM1 models ..............63

7-1. Composition of nutrient solution for P. pantotrophus. .............................................83

7-2. Composition of nutrient solution for P. denitrificans. ............................................83

7-3. Amount of carbon substrate in feed solutions. ............ ...................................83

7-4. N utrients in tw o feed solutions ....................................................... ...................84

7-5. Summary of experimental results of anoxic batch phases................................... 103

7-6. Summary of experimental results of oxic continuous flow phases .......................104

7-7. Com prison of experim mental data................................. ................ ............... 109

8-1. Experim mental conditions. ......................................................... ............... 113

8-2. Composition of nutrient solution for P. denitrificans. ..........................113

8-3. Amount of carbon substrate, ammonia in nutrient solution of each stage. ...........13

8-4. N utrients in tw o feed solutions ....................................................... .............. 113









8-5. Calculation procedures of virtual batch curve method ................ ...............119

8-6. Parameters of eASMlm for simulation........................................................134

8-7. Initial conditions for eASM lm simulations. ................................... ..................... 134

8-8. Parameters of eASMlm after calibration. ..................................... ............... 137

8-9. Summary of experimental results. ................................. 142

9-1. Parameters ofeASMlm for simulations. ................................... ...............169















LIST OF FIGURES


Figure page

2-1. Schematics of Mechanistic Denitrification Model ................ ............................24

3-1. Simulation of experimentally observed diauxic lag ofPseudomonas denitrificans,
predicted by eASMlc................... .............................32

3-2. Simulation of experimentally observed diauxic lag ofPseudomonas denitrificans,
predicted by eA SM m ................................................. ................................ 32

3-3. Growth of heterotrophic biomass during cyclic simulations with 8 mg/L of DO
during oxic phase, predicted by A SM 1 ........................................ ............... 35

3-4. Mass specific and volumetric denitrification rate during cyclic simulation,
predicted by A SM 1 ......................... .... .................. ... ........ ........... 36

3-5. Growth of heterotrophic biomass during cyclic simulations with 4 mg/L of DO
during oxic phase, predicted by A SM 1 ........................................ ............... 37

3-6. Growth of heterotrophic biomass during cyclic simulations with 2 mg/L of DO
during oxic phase, predicted by A SM 1 ........................................ ............... 38

3-7. Growth of heterotrophic biomass during cyclic simulations with 1 mg/L of DO
during oxic phase, predicted by A SM 1 ........................................ ............... 39

3-8. Growth of heterotrophic biomass under oxic/anoxic switch................................41

3-9. Specific nitrate reductase level and activity of heterotrophic biomass under
oxic/anoxic sw itch, predicted by eA SM lc.................................. .....................42

3-10. Growth of heterotrophic biomass during cyclic simulations with 8 mg/L of DO
during oxic phase, predicted by eA SM c ..................................... .................43

3-11. Mass specific and volumetric denitrification rate during cyclic simulation,
predicted by eA SM c ............. .... ......................................................... .. .... .....44

3-12. Growth of heterotrophic biomass during cyclic simulations with 4 mg/L of DO
during oxic phase, predicted by eA SM c ..................................... .................45

3-13. Growth of heterotrophic biomass during cyclic simulations with 2 mg/L of DO
during oxic phase, predicted by eA SM c ..................................... .................46









3-14. Growth of heterotrophic biomass during cyclic simulations with 1 mg/L of DO
during oxic phase, predicted by eA SM c ..................................... .................47

3-15. Growth of heterotrophic biomass during cyclic simulations with 8 mg/L of DO
during oxic phase, predicted by eA SM c ..................................... .................49

3-16. Specific nitrate reductase level and specific intracellular nitrate level of
heterotrophic biomass under oxic/anoxic switch, predicted by eASMIc ...............50

3-17. Growth of heterotrophic biomass during cyclic simulations with 1 mg/L of DO
during oxic phase, predicted by eASM m .................................... ............... 52

3-18. Growth of heterotrophic biomass during cyclic simulations with 0.5 mg/L of
DO during oxic phase, predicted by eASM m ..................... .................. ........... 53

3-19. Growth of heterotrophic biomass during cyclic simulations with 0.1 mg/L of
DO during oxic phase, predicted by eASMlm ....................................................... 54

3-20. Simulation of experimental results from a previous study .............. ...................56

4-1. Process schematics of fed-batch process (top) and BDP process (bottom)
showing the fraction of the cycle length or hydraulic residence time occupied by
each phase or part of the processes. ................................... .................................... 59

4-2. Sequence of phases in the BDP oxidation ditches..................................................59

4-3. Effects of anoxic volume fraction and cycle length on performance of fed-batch
process predicted by ASM 1 and eASM c .................................... ............... 65

4-4. Effect ofunaerated volume fraction (UVF) and cycle length on performance of
B D P p ro c e ss ....................................................... ................ 6 6

4-5. Optimum cycle lengths of fed-batch and BDP processes as a function of
unaerated volum e fraction (U VF) ........................................ ........................ 67

6-1. Overall layout of experimental configuration. .................................. .................73

6-2. Side view of New Brunswick Bioflo 2000 Fermentor. ............................................73

6-3. F erm entor assem bly ................. .............. ................. ................... ..... .............. ......74

6-4. Feed reservoir assem bly. ................................................ ............................... 76

7-1. Biomass absorbance profile of Experimental 1................................. .................86

7-2. Biomass absorbance during anoxic batch phase (Trial 1, Experimental 1)...............88

7-3. Biomass absorbance during anoxic batch phase (Trial 2, Experimental 1)...............89









7-4. Biomass absorbance profile of Experimental 2..................................................... 90

7-5. Biomass absorbance during anoxic batch phase (Trial 1, Experimental 2):. ............92

7-6. Biomass absorbance during anoxic batch phase (Trial 2, Experimental 2):. ............93

7-7. Biomass absorbance profile of Experimental 3 .....................................................94

7-8. Biomass absorbance during anoxic batch phase (Trial 1, Experimental 3)...............96

7-9. Biomass absorbance during anoxic batch phase (Trial 2, Experimental 3)...............97

7-10. Biomass absorbance profile of Experimental 4....................................... .......... 98

7-11. Biomass absorbance during anoxic batch phase (Trial 1, Experimental 4).............99

7-12. Biomass absorbance during anoxic batch phase (Trial 2, Experimental 4)...........100

8-1. F eed inlet configurations. ............................................................. ..................... 114

8-2. Flow and components around CSTR in simulation ...........................................116

8-3. Determination of diauxic lag under continuous flow condition using virtual batch
curve m ethod. .................................................................... ..........118

8-4. Biomass absorbance profile (Trial 1). ........................................ ............... 122

8-5. Biomass absorbance profile during anoxic continuous flow phase (Trial 1). .........122

8-6. Determination of diauxic lag (Trial 1)............... ........................ ............... 123

8-7. Biomass absorbance profile (Trial 2). ........................................ ............... 124

8-8. Biomass absorbance profile during anoxic continuous flow phase (Trial 2). .........124

8-9. Biomass absorbance profile (Trial 3). ........................................ ............... 125

8-10. Biomass absorbance profile during anoxic continuous flow phase (Trial 3). .......125

8-11. Determination of diauxic lag (Trial 3)...................................... ............... 127

8-12. Biomass absorbance profile (Trial 4). ....................................... ............... 127

8-13. Biomass absorbance profile during anoxic continuous flow phase (Trial 4). .......128

8-14. Determination of diauxic lag (Trial 4)...................................... ..................128

8-15. Biomass absorbance profile (Trial 5). ....................................... ............... 129

8-16. Biomass absorbance profile during anoxic continuous flow phase (Trial 5). .......130









8-17. Determination of diauxic lag (Trial 5)...................................... ..................130

8-18. Biomass absorbance profile (Trial 6). ....................................... ............... 131

8-19. Biomass absorbance profile during anoxic continuous flow phase (Trial 6). .......132

8-20. Determination of diauxic lag (Trial 6)...................................... ..................132

8-21. Simulation of experimental result (Trial 6). ............................... ............... 135

8-22. Simulation of experimental result with calibrated parameters (Trial 6)..............137

8-23. Simulation of experimental result with calibrated parameters (Trial 5).............. 138

8-24. Simulation of experimental result (short oxic continuous flow phase).................138

8-25. Change of biomass absorbance and carbon substrate concentration during
diauxic lag and recovery of growth, predicted by eASMm. .............................140

9-1. Schematic view of gas supply system. ....................................... ............... 150

9-2. Biomass absorbance profile from a typical simulation. ........................................152

9-3. Biomass absorbance profile during ultimate state in alternating oxic/anoxic
cycling (6-hour cycle length). ........................................................................... 153

9-4. Biomass absorbance profile during ultimate state in alternating oxic/anoxic
cycling (12-hour cycle length). ........................................ .......................... 153

9-5. Biomass absorbance profile during ultimate state in alternating oxic/anoxic
cycling (24-hour cycle length). ........................................ .......................... 155

9-6. Biomass absorbance profile during ultimate state in alternating oxic/anoxic
cycling (48-hour cycle length). ........................................ .......................... 155

9-7. Biomass absorbance profile in alternating oxic/anoxic cycling (180-hour cycle
length) ............................................................... .. ... ...... ........ 156

9-8. Overall biomass absorbance profile of short cycle length experiment (12 hour-
cy cle length). ...................................................................... 158

9-9. Biomass absorbance profile in alternating cycling (12 hour-cycle length). ............159

9-10. Overall biomass absorbance profile of long cycle length experiment (24-hour
cy cle length). ...................................................................... 16 1

9-11. Biomass absorbance profile in alternating cycling (24-hour cycle length). ..........162

9-12. Component concentrations during ultimate state (24-hour cycle length)............163









9-13. Component concentrations during the final anoxic phase ..................................165

9-14. Simulation of experimental results (12 hour cycle length)...............................168

9-15. Simulation of experimental results (24 hour cycle length)...............................168

9-16. Simulation of experimental results (final anoxic phase, 24 hour cycle length) ....169















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

DIAUXIC LAG OF DENITRIFYING BACTERIA IN OXIC/ANOXIC CYCLING
UNDER CONTINUOUS FLOW CONDITIONS

By

Dong-Uk Lee

August 2005

Chair: Ben L. Koopman
Cochair: Spyros A. Svoronos
Major Department: Environmental Engineering Sciences

The present study was conducted to investigate diauxic lag of denitrifying bacteria

under an ultimate state of oxic/anoxic cycling under continuous flow conditions. As

preliminary steps, the industry standard Activated Sludge Model No. 1 was extended with

denitrification models and a simulation study was conducted to compare predictions of

the conventional and an extended version.

An experimental system was developed to implement bacterial pure culture growth

under continuous flow conditions. The performance of the system was verified by

determining the reproducibility of experimental results. Using the experimental system,

diauxic lag of denitrifying bacteria was then studied under oxic/anoxic cycling conditions.

The experimental system developed in the present study was capable of achieving

pure culture of denitrifying bacteria without contamination up to the desirable length of

time for experiments. The reproducibility of the length of diauxic lag and the highest









anoxic observed specific growth rates were significantly improved by achieving steady

state growth of bacteria as a preliminary stage.

Diauxic lag ofPseudomonas denitrificans under continuous flow conditions could

be characterized by the virtual batch curve method developed in the present study. The

eASMlm was able to predict the observed diauxic lag under the continuous flow

conditions with slight modification of parameters. The experimental results were

significantly influenced by the magnitude of biomass accumulation at the feed inlet.

Growth patterns in preceding oxic phase were likely to have an effect on length of

diauxic lag during consecutive anoxic phase, which could not be predicted by eASMlm.

Predictions of eASMlm on growth of Pseudomonas denitrificans in alternating

oxic/anoxic cycling under continuous flow conditions were consistent with results from a

previous study. It has been found that Pseudomonas denitrificans could not establish

significant anoxic growth during alternating oxic/anoxic cycling under continuous flow

conditions, with up to 24 hour cycle length. The eASMlm could not fit the growth

behaviors with the previous parameters. Furthermore, the diauxic lag after the cycling

was significantly longer than the initial lag, which was additional evidence explaining

that growth patterns of bacteria in the preceding oxic phases may influence diauxic lag of

bacteria in the following anoxic phases.














CHAPTER 1
INTRODUCTION

Nitrogen removal from wastewater has become more and more important because

of a number of reasons, including pollution, eutrophication of receiving water bodies and

increasing needs for reuse of reclaimed wastewater. Biological nitrogen removal using

activated sludge is a popular method to remove nitrogen from wastewater.

Ammonia nitrogen is first oxidized to nitrite and nitrate nitrogen by nitrifying

bacteria (nitrification) in activated sludge under oxic conditions in a typical wastewater

treatment process utilizing biological nitrogen removal. Nitrite and nitrate nitrogen are

then reduced to dinitrogen or other gaseous nitrogen compounds by denitrifying bacteria

(denitrification) under anoxic condition. Since the two major reactions take place under

different growth conditions in a single sludge biological nitrogen removal process, it is

inevitable that bacteria in activated sludge are exposed to cycling oxic/anoxic conditions.

Growth dynamics of bacteria can occur in such conditions if bacteria cannot adjust their

growth capabilities to repetitive change of growth conditions. Therefore, it is very

important to study growth dynamics of bacteria that have important roles in biological

nitrogen removal from wastewater.

The phenomenon of diauxic lag for bacteria switching between electron donors was

discovered at least 60 years ago (Monod, 1942). Subsequently, Kodama et al. (1969)

observed a similar lag for bacteria switching between electron acceptors. Experiments

with activated sludge and pure culture ofPseudomonas denitrificans have established

that the diauxic lag of bacteria switching between oxygen and nitrate as electron









acceptors can last for several hours and depend on the preculture environment, length of

aeration period, and dissolved oxygen concentration during the aeration period that

precedes anoxic conditions (Liu et al., 1998a, b; Gouw et al., 2001; Lisbon et al., 2002).

Effects of aeration period length and dissolved oxygen concentration on diauxic lag of

bacteria switching between oxygen and nitrate were successfully modeled (Liu et al.,

1998a, b; Casasus-Zambrana, 2001). However, the popular Activated Sludge Models No

1, 2, 2d, and 3 (Henze et al., 2000) cannot portray the diauxic lag phenomenon. This

deficiency could result in sub-optimal operational strategies or designs and lead to

needless environmental impact on receiving waters or waste of economic resources.

Recently, Lee et al. (2004) compared the predictions of an extended version of

ASM1 (eASMIc) with enzyme kinetics to the predictions of ASM1 for periodically

operated nitrogen removing processes (fed-batch and BioDenipho). ASM1 and eASMIc

gave similar predictions of optimal unaerated volume fraction (UVF) that were consistent

with operation of the BioDenipho process at the University of Florida. However, the

eASMIc predicted substantially longer optimal cycle lengths, which are more consistent

with the BioDenipho process operation at the University of Florida than those predicted

by ASM1. Furthermore, eASMIc predicted a critical cycle length, below which

denitrification would cease.

The growth dynamics of denitrifying bacteria under alternating oxic/anoxic cycling

found by Lee et al. (2004) has never been investigated by experimental work. In early

studies of diauxic lag of denitrifying bacteria, growth responses of those bacteria were

investigated within few switches between oxic and anoxic conditions (Liu et al., 1998a,

b; Gouw et al., 2001; Lisbon et al., 2002). Moreover, the experiments were performed









under batch conditions where carbon substrates were provided with non-limiting amounts.

In such conditions, growth dynamics of denitrifiers could be different from those taking

place in real wastewater treatment plants, where concentrations of organic substrates are

relatively low.

With these needs, the present study was conducted to investigate growth dynamics

of denitrifying bacteria with better understandings. Efforts were made to develop a

proper experimental setup to implement a bacterial pure culture system under continuous

flow conditions. Diauxic lag ofPseudomonas denitrificans in single switch and

alternating cycling between oxic and anoxic conditions was studied under continuous

flow conditions. The capabilities of an extended version of ASM1 were evaluated with

respect to prediction of experimental results.














CHAPTER 2
REVIEW OF LITERATURE

The phenomenon of diauxic bacterial growth was extensively studied by Monod

(1942, 1949). He mentioned that diauxie is characterized by a dual growth cycle which

consists of two exponential growth phases separated by a period during which the growth

rate reaches a minimum, or becomes negative. He reported that a diauxie could occur

when bacteria grew on media where the organic substrate is limiting and consists of

mixtures of two carbohydrates. He reported that this phenomenon indicated that each of

two exponential growth cycles corresponded to the exclusive utilization of one of the

substrates, due to an inhibitory effect of one of the substrates on formation of the enzyme

for the other substrate. It has also been found that bacteria may experience lag when they

switch between electron acceptors (Kodama et al., 1964, Liu et al., 1998a, b). In the

following sections, the diauxic lag of bacteria switching between electron acceptors will

be reviewed. The discussion will be focused on diauxic lag of denitrifying bacteria

switching their electron acceptors from nitrate to oxygen. Furthermore, several

mathematical models to predict the denitrification and related enzyme dynamics will be

reviewed.

Dynamics of Heterotrophic, Denitrifying Bacteria Switching between Electron
Acceptors

A number of studies have been performed to investigate the dynamics of

denitrification under conditions in which the electron acceptors switch. A general

discussion of the effect of alternating electron acceptor on denitrification and growth of









bacteria will be given and the diauxic lag of denitrifying bacteria switching between

electron acceptors between oxygen and nitrate will be discussed.

Effects of Alternating Electron Acceptors

Several investigators have examined the effect of alternating electron acceptors on

the dynamics of bacteria. Simpkin and Boyle (1985) investigated variations of nitrate

and nitrite reductase activities of activated sludge exposed to alternating aerobic/anoxic

conditions. In laboratory sequencing batch reactors (SBRs), anoxic phases were provided

during part of the reaction phase or during settling. The highest nitrate reductase activity

was found when the feed had a high level of nitrate (> 30 mg/L) and the anoxic period

included 4 hours out of a total 6-hour reaction phase, plus one-half hour of anoxic

conditions during settling. An intermediate nitrate reductase level was found in a reactor

with high nitrate (16 mg/L) but only one-half hour of anoxic conditions. The lowest

nitrate reductase activities were found in SBRs with low nitrate and only one-half hour of

anoxic conditions (during settling).

O'Neil and Horan (1995) investigated the effect of oxic/anoxic cycling on

nitrification and denitrification in a chemostat that was inoculated with nitrifying

activated sludge. They first cycled the growth conditions between 4 hours of aerobic

phase and 20 hours of anaerobic phase for 15 days. They performed two experiments

involving the same length of oxic and anoxic periods and different feeding patterns.

There was no indication of a growth lag in denitrification after the switch from oxic to

anoxic conditions. In the third experiment they provided 14 hours of oxic growth

conditions. After aeration stopped and dissolved oxygen dropped to zero, denitrification

activity remained very low for the remainder of the time monitored, which was 20 hours.









Baumann et al. (1996) studied the response of Paracoccus denitrificans to changes

between aerobic and anaerobic growth conditions in a continuous culture. They first ran

the reactor until an aerobic steady state was reached. They then stopped aeration and ran

the reactor until an anaerobic steady state was reached. Finally, they restarted aeration

and ran the reactor to another aerobic steady state. When the growth conditions were

changed from aerobic to anaerobic, the culture did not immediately establish complete

denitrification. Nitrite started accumulating immediately after the switch and nitric oxide

production began somewhat later. Dinitrogen became the major denitrification product

after the intermediates disappeared and the culture established a new steady state. The

mRNA levels for nitrate reductase and nitrous oxide reductase started increasing

immediately after the switch whereas the mRNA level for nitrite reductase started

increasing somewhat later. Biosynthesis of nitrite reductase was started about 30 minutes

after the increase of the mRNA level of the enzyme and gradually built up over a period

of 30 hours.

Baumann et al. (1997a) investigated the effect of repeated alternating aerobic-

anaerobic conditions on denitrification in continuously-fed cultures ofParacoccus

denitrificans and activated sludge. In the case of a Paracoccus denitrificans growth

reactor with alternating aerobic (24 h) and anoxic (24 h) phases, the authors grew the

bacteria for three cycles of aerobic-anoxic phases. The authors showed measurements of

nitrate versus time over a span of 120 hours. Vertical lines in the graph indicated

switches between external electron acceptors. In the first cycle, it is apparent that nitrate

levels began to decrease immediately after oxygen supply was stopped. In the second

cycle, decrease of nitrate levels lagged exhaustion of dissolved oxygen by one hour. In









the third cycle, the lag lasted two hours. According to the authors, nitrate consumption

began immediately after dissolved oxygen was depleted. However, it seems doubtful that

one or two hours would be required for oxygen depletion in cycles 2 and 3. These data

thus suggest the occurrence of a diauxic lag with the long phase lengths. Denitrification

intermediates (nitrite, nitric oxide, nitrous oxide) accumulated during the anoxic phase of

the first cycle but not during the anoxic phases of the second or third cycles.

The authors performed another experiment with shorter phase lengths, 1.5 h

anaerobic and 2.5 h aerobic, through a total of four cycles. With the shorter phase

lengths, nitrite accumulated during the anoxic phases and there was negligible nitrous

oxide production. The nitrite reductase level increased throughout the experiment, during

aerobic as well as anoxic phases. The authors attributed nitrite accumulation to

insufficient time for the bacteria to adjust their enzyme synthesis system. The authors

also measured mRNA for nitrate reductase during the second experiment. The mRNA

for nitrate reductase decreased during the aerobic phases and increased in the anoxic

phases after approximately 0.5 hour of lag period. The characteristics of behaviors of

activated sludge under longer alternating aerobic-anaerobic conditions (24 h aerobic and

24 h anaerobic) were similar to those ofParacoccus denitrificans (i.e., denitrification

intermediates (e.g., nitrite and nitrous oxide) were accumulated during the first anoxic

phase but later disappeared). Upon change to anoxic conditions, nitrate consumption

lagged for about 4 hours.

Baumann et al. (1997b) studied the effect of change from aerobic to anaerobic

growth conditions on the denitrification of a continuous culture of Paracoccus

denitrificans at a suboptimal pH. The biomass concentration started decreasing









immediately after the switch and continued decreasing for 50 hours. This trend

approximately followed a dilution and decay curve with a near-zero specific growth rate

(simulation not shown). Nitrite and nitric oxide started accumulating almost immediately

after the switch. Accumulation of nitrous oxide and dinitrogen started somewhat later.

Increases in levels of the mRNAs for nitrate reductase and nitric oxide reductase were

observed immediately after the switch, whereas levels of the mRNA for nitrite reductase

began increasing one hour later. However, even though mRNA levels for nitrite

reductase increased, the amount of nitrite reductase synthesized was low. The authors

suggested that biosynthesis of nitrite reductase was inhibited by higher free nitrous acid

concentration due to lower pH. Cultures grown under cycling aerobic/anaerobic

conditions or strictly anaerobic conditions were less affected by the low pH, indicating

that they may have accumulated nitrite reductase over time.

Oh and Silverstein (1999) studied the effect of feeding pattern on the mass specific

denitrification rate of activated sludge in sequencing batch reactors. They found that the

mass specific denitrification rate during anoxic phases decreased and the oxygen uptake

rate of the sludge increased as the length of time that the substrate was present during

aerobic phases was increased. The lengths of aeration period in the absence of substrate

did not influence the mass specific denitrification rate. They concluded that feeding

during the aerobic phase led to growth of aerobic (non-denitrifying) bacteria.

Diauxic Lag of Bacteria Switching between Electron Acceptors

Diauxic lag of bacteria switching between electron acceptors was first reported by

Kodama et al. (1969). The authors examined the growth of Pseudomonas stutzeri in the

presence of various concentrations of nitrate. The authors reported that the initial growth

continued until all nitrate in the culture was consumed. Nitrite was accumulated while









nitrate was consumed. The lag of growth began after exhaustion of the nitrate. After a

period of time, bacterial growth resumed, along with consumption of nitrite. The length

of the lag period depended on the initial nitrate concentration (i.e., the higher the

concentration, the longer the lag). The authors gave two possible mechanisms to explain

this effect: (1) repression of development of the nitrite reducing machinery by nitrate, and

(2) competition between nitrate and nitrite for electrons.

Since the original description of a lag experienced by bacteria switching between

electron acceptors, a number of other investigators have studied this phenomenon. Waki

et al. (1980) investigated the effect of aerobic-anaerobic condition change on the growth,

carbon source and nitrate consumption, and nitrate and nitrite reductase activity of

Paracoccus denitrificans. They reported that the carbon source consumption and the

growth of the bacteria stopped for a few hours when the condition was changed from

aerobic to anoxic (oxygen absent, nitrate present). During this lag period, incomplete

denitrification occurred (i.e., a rapid nitrate consumption was observed, but with a high

level of nitrite accumulation). A careful glance at the profile of the bacterial growth in

the reference reveals a second lag period that begins after the bacteria stop accumulating

nitrite. However, there was still nitrate available at the beginning of this lag period. The

specific nitrate reductase activity began to increase after the transition from aerobic to

anoxic conditions. About 6 hours were required for the bacteria to reach the maximum

nitrate reductase concentration. In comparison, the nitrite reductase activity remained

constant for 2.5 h after the transition from aerobic to anoxic conditions and then started to

increase as the bacteria started reducing nitrite.









Robertson and Kuenen (1984) tested denitrification of Thiosphaera pantotropha

and Thiobacillus A2 switching their electron acceptor from oxygen to nitrate.

Aerobically grown bacteria were exposed to anaerobic conditions with nitrate present as

the electron acceptor. Acetate and thiosulphate or a mixture of both was provided as

electron donor. Thiosphaerapantotropha produced a gaseous product immediately after

the switch, regardless of electron donor. Thiobacillus A2 began to produce gas 3 hours

after the switch when the electron donor was acetate, 4 hours after the switch when the

electron donor was thiosulphate, and 2 hours in the presence of mixed electron donor.

Bonin et al. (1989) examined growth and nitrate and nitrite reductase activity of

bacteria exposed to alternating aerobic and anoxic environments. They found that nitrate

reductase activity declined under aerobic conditions but was regained under anoxic

conditions, once bacteria ended the lag phase and began to grow again.

Liu et al. (1998a) exposed samples of activated sludge from a wastewater treatment

plant to aerobic and anoxic conditions. They were the first investigators to observe

diauxic lag of activated sludge and pointed out that this phenomenon could have

significant engineering and economic implications for nitrogen-removing, single-sludge

activated sludge processes. The authors reported that both activated sludge and nitrate

enrichment denitrifying culture did not grow or grew very slowly for a while during

anoxic conditions that followed oxic conditions. The authors modeled the phenomenon

using a cybernetic approach. They noted that conventional models of single-sludge

wastewater treatment process (e.g., Activated Sludge Model 1; Henze et al., 2000) could

not depict the phenomenon of diauxic lag when bacteria switched between electron

acceptors. Liu et al. (1998a) suggested that the reason for the onset of diauxic lag during









denitrification was the lack of enzyme that was required for reduction of nitrate. They

hypothesized that the lack of enzyme was due to decay and dilution of the enzyme when

the bacteria grew exponentially under aerobic conditions without the synthesis of the

enzyme. They suggested that more than one hour of average length of the diauxic lags

under conditions of their experiment was quite surprising, because the length of the lags

was similar to the length of anoxic phase in a typical BioDenipho process, which is the

nutrient removal process utilized at the facility where the samples were obtained.

Liu et al. (1998b) studied the growth characteristics of a facultative denitrifying

bacterium, Pseudomonas denitrificans. The authors observed that length of aerobic

period and presence of nitrate during aerobic periods could affect the length of diauxic

lag under subsequent anoxic conditions and successfully modeled these effects using a

modified cybernetic approach.

In the study of Baumann et al. (1997b), the authors observed that the biosynthesis

of nitrite reductase was less inhibited by the low pH when the cultures were grown under

cycling aerobic/anaerobic conditions or strictly anaerobic conditions, indicating that they

may have accumulated nitrite reductase over time. Hence, it would be interesting to see

whether alternating oxic/anoxic conditions results in development of a stable denitrifying

continuous culture due to building up of denitrifying abilities over time or failure of

denitrification in continuous culture due to diauxic lag. In this point of view, the cycle

length of alternating oxic/anoxic conditions will be very important to the continuous

denitrifying culture because insufficient length of anoxic condition would result in

difficulties in developing denitrifying abilities, such as reductase enzymes.









Factors Affecting the Diauxic Lag of Heterotrophic, Denitrifying Bacteria

Investigations of the dynamics of bacteria switching between oxygen and nitrate

have identified several factors that can affect the length of the diauxic lag. These include

bacterial species, length of the aerobic phase, dissolved oxygen concentration in the

aerobic phase, and nitrate exposure history of the preceding culture. These factors are

summarized in Table 2-1.

Bacterial species

There is some evidence in the literature that diauxic lag of denitrifiers under cyclic

oxic/anoxic conditions differs according to bacterial species. For example, Pseudomonas

denitrificans have relatively long diauxic lag when they experience an oxic/anoxic switch

(Liu et al., 1998b; Lisbon et al., 2001; Casasus-Zambrana, 2002) whereas Paracoccus

denitrificans exhibit little or no lag following oxic/anoxic switches (Baumann et al., 1996,

1997a, b).

Length of aerobic phase

Bonin et al. (1989) examined the effect of alternating changes from aerobic to

anoxic conditions on an enzyme level of denitrifying bacteria, Pseudomonas nautica617.

The authors reported that, in case of a short aerobic phase, both nitrate and nitrite

reduction activities, which were depleted under aerobic conditions, recovered quickly in

the following anoxic phase. However, after a long aerobic phase, the start of nitrate

reduction activity was delayed for four hours, and the nitrite reduction rate reached only

20% of the original rate before aerobic conditions.

Liu et al. (1998b) reported that a pure culture ofPseudomonas denitrificans aerated

for a longer period experienced a longer lag than the same culture aerated for a shorter

time. This result can be explained using dilution and decay of a denitrifying enzyme, as









Table 2-1. Factors affecting the diauxic lag of denitrifiers.
Factors Effects on length of lag

Length of aerobic phase Positive effect on length of lag


Dissolved oxygen concentration in Positive effect on length of lag
aerobic phase

Nitrate exposure history of preceding Presence of nitrate in preceding aerobic
conditions phase has negative effect on length of lag


suggested by Liu et al. (1998a). A longer period of the aerobic phase provides a higher

amount of dilution and decay of the denitrifying enzyme due to the suppression effect of

dissolved oxygen on synthesis of denitrifying enzyme during aeration.

Dissolved oxygen concentration in aerobic phase

Lisbon et al. (2001) investigated the effect of dissolved oxygen concentration

during the aerobic phase on the length of diauxic lag during the following anoxic phase.

The authors reported that the average length of diauxic lags in the case of the high

dissolved oxygen runs was longer than that in the case of the low dissolved oxygen runs.

The average specific growth rates in the anoxic phases following low dissolved oxygen

aerobic phases were significantly higher than those in the anoxic phases following high

dissolved oxygen aerobic phases. The authors computed the ratio of biomass

concentration at the end of an aerobic phase to the biomass concentration at the beginning

of an aerobic phase. Higher values of the aerobic biomass ratio indicate higher levels of

new biomass formed under aerobic conditions. The specific growth rate during the

anoxic phase was inversely correlated with the biomass ratio for the preceding aerobic

phase, whereas the diauxic lag of bacteria switching between oxygen and nitrate was

directly correlated to the aerobic biomass ratio. This is consistent with a mechanism of









nitrate reductase dilution by growth under aerobic conditions and indicates that the effect

of dissolved oxygen was to influence the rate of aerobic growth and, hence, enzyme

dilution.

Nitrate exposure history of preceding culture

Gouw et al. (2001) examined the effect of nitrate exposure history on the

oxygen/nitrate diauxic growth ofPseudomonas denitrificans. Their culturing sequence

consisted of a pre-culture (bacterial growth in nutrient media that were inoculated from

agar plates), an aerobic phase, and an anoxic phase. Three different pre-culture

conditions were investigated: (1) anoxic with nitrate present, (2) aerobic with nitrate

present, and (3) aerobic with nitrate absent. The effect of presence or absence of nitrate

during the aerobic phase was also examined.

In the case of aerobic pre-culture, the diauxic lag was long (4.0-9.5 h) if nitrate was

absent in pre-culture, whereas the presence of nitrate in pre-culture resulted in shorter

lags (1.0-4.5 h). The presence of nitrate in pre-culture partially compensated for absence

of nitrate in subsequent long aerobic phases. (The combination of aerobic pre-culture and

aerobic phase, both without nitrate, gave the longest lags.) In the case of anoxic pre-

culture (with nitrate present), presence of nitrate during the following aerobic phase

resulted in relatively short diauxic lags or no lags whereas there were always diauxic lags

if nitrate was absent during the aerobic phases. The authors hypothesized that key

denitrification enzymes might be synthesized under aerobic conditions if nitrate is present.

Nitrate concentration in anoxic phase

Kodama et al. (1969) investigated the effect of nitrate concentration on the diauxic

lag of Pseudomonas stutzeri switching their electron between acceptors although they did

not focus on diauxic lag switching between oxygen and nitrate. As the bacteria reduced









nitrate, nitrite accumulated until all nitrate was removed. The growth was then lagged

until the bacteria started reducing nitrite. The authors found that the length of the lag

period depended on the initial nitrate concentration (i.e., the higher the nitrate

concentration, the longer the lag.) The authors gave two possible mechanisms for this

effect: (1) repressed development of the nitrite reducing system by nitrate, and (2)

competition between nitrate and nitrite for electrons.

Modeling of Denitrification in Activated Sludge

Several mathematical models have been developed to predict denitrification of

activated sludge. Prediction of denitrification of the activated sludge models will be

discussed and two new models capable of depicting diauxic lag of denitrification will be

introduced.

Activated Sludge Model No. 1 (ASM1)

Activated Sludge Models No. 1, 2, 2d and 3 (Henze et al., 2000) were created by

the task group on mathematical modeling for design and operation of biological

wastewater treatment of the International Water Association. They have become well

accepted for modeling of single-sludge biological wastewater treatment processes. The

four models have similar expressions for growth of heterotrophic biomass on oxygen and

nitrate and control the respective rates using the same switching functionality. ASM1

will be discussed in the present literature review because it is the oldest of the four

models and thus has the longest experience base.

The complete matrix representation of ASM1 is given in Table 2-2. In ASM1, the

process rate for growth of heterotrophic biomass on oxygen is expressed by


p1 /H + S K X S B, (2-1)
Ks +SS KOH +So









where p, is the process rate for growth of heterotrophic biomass on oxygen, /-H is the

maximum specific growth rate of heterotrophic biomass, Ss is the concentration of readily

biodegradable substrate, So is the dissolved oxygen concentration, Ks and KO,H are the

half saturation coefficients for a readily degradable substrate and dissolved oxygen,

respectively, and XB,H is the concentration of heterotrophic biomass.

The process rate for growth of heterotrophic biomass on nitrate (p2) is expressed

by


P2 H SN O,H O ,XBH (2-2)
KS +Ss KOH +So0 KNO +SNO)

where SNO is nitrate plus nitrite nitrogen concentration, KNO is the half saturation

coefficient of nitrate nitrogen, and r/ is the correction factor for anoxic growth of

heterotrophic biomass. In equation (2-1), the effect of oxygen on the rate of growth of

heterotrophic biomass on oxygen is portrayed by the following switching function:

S- (2-3)
KO,H +So

The term approaches 1.0 when dissolved oxygen concentration is high and approaches

zero as the dissolved oxygen concentration approaches zero. The effect of dissolved

oxygen on the rate of growth of heterotrophic biomass on nitrate is depicted by the

following switching function:

KO,H (2-4)
So + KO,H

The term approaches zero when dissolved oxygen concentration is high and approaches

1.0 as the dissolved oxygen concentration approaches zero. Thus, it has the effect of









slowing the growth of heterotrophic biomass on nitrate when dissolved oxygen is present

in the medium.

Modeling of Denitrification with a Cybernetic Approach for Denitrifying Enzyme
Kinetics

Liu et al. (1998a) proposed a model of denitrification that relied on a cybernetic

approach analogous to that of Kompala et al. (1986) to predict the extent of utilization of

two alternative electron acceptors (oxygen, nitrate). The proposed model includes the

concentrations of two enzymes, Eo and ENO, which stand for concentration of oxygenase

and nitrate reductase, respectively. Both the specific levels and activities of these

enzymes regulate the growth rate of heterotrophic biomass. The process rate expressions

for growth of heterotrophic biomass on oxygen (p, ) and on nitrate (p2) from ASM1

were modified and process rate expressions for enzyme synthesis and decay (p9 p2 )

were developed. Since the model was subject to be incorporated into ASM1, the order

numbers were assigned to the process rates in a manner consistent with that of ASM1.

The #1 and #2 were assigned to the process rate for growth of heterotrophic biomass on

oxygen and nitrate, respectively, as in ASM1 and #9 through #12 were assigned to the

four additional process rates.

The effects of oxygenase level and activity on the process rate for growth of

heterotrophic biomass on oxygen (p,) are expressed in the model of Liu et al. (1998a) by

multiplying the ASM1 expression by the term eov /eo, m, as follows:

i eovo S()
P, = ^H O S XB,H (2-5)
eO,max KO,H +So









where eo represents the specific level ofoxygenase (i.e., eo = Eo /XB,H ), v is activity

of oxygenase (ranging from 0 to 1), and eo max is the maximum specific level of

oxygenase. The effect of nitrate reductase level on the process rate for growth of

heterotrophic biomass on nitrate (p2) is expressed by


I i eNOVNO S NO
P2 =H NOmax N -SNO ) qrXB,H (2-6)
NOmax KN o +Sno

where the enzyme variables eNO and VNo and parameters eo max are analogous to those in

equation (2-6). The process rate for synthesis of oxygenase (p9,) can be expressed by the

following:



KO,H +So

where ao represents a synthesis rate coefficient for oxygenase and uo is "cybernetic

variable" ranging from 0 to 1, which governs the specific oxygenase synthesis rate. The

process rate for synthesis of nitrate reductase (p/o1) can be expressed by the following:


APo = aNOouNo KSN XB,H (2-8)
o 1 NO(2-8)

where parameter aNO is analogous to that in Equation (2-8) and UNO = 1 -u. The

process rate for decay of oxygenase (p, i) was assumed to be first order with respect to

oxygenase concentration with the same manner that expresses biomass decay (p4), as

follows:

P11 = P oE (2-9)









where 8fo is oxygenase the decay coefficient. The process rate for decay of nitrate

reductase (p2) is described as follows:

A,2 = NOENo (2-10)

where p8N is the nitrate reductase decay coefficient.

The variables uo, uNo, vo, and vNo in the above formulation represent the control

actions of the cellular regulatory process of repression-induction and inhibition-action.

The cybernetic modeling approach postulates that the bacteria adjust the values of these

variables, as well as the values of eo max and eo max, to maximize their instantaneous

growth rate. Kompala et al. (1986) showed the solution of the optimization problem to

be


uo = / vo (2-11)
A1 / Vo + P2 / NO


V = p / vo (2-12)
max (p / vo)


o = 2 /NO (2-13)
max(p2 /No)


,max (2-14)
o 1 H + 8O


NOmax NO (2-15)
Hg + NO

Liu et al. (1998a) used the above model to successfully simulate the diauxic lags

observed in their experiments.

In a second paper, Liu et al. (1998b) pointed out the fact that the new model still

could not depict longer lags or the effect of length of aerobic phase on the length of









diauxic lag. They modified the process rate expressions for enzyme synthesis and

enzyme activity. Although the cybernetic variable u is retained in the new model, the

overall skim for regulating denitrification is no longer analogous to that of Kompala et al.

(1986), hence the model was referred to as a modified cybernetic approach.

The process rate for synthesis of oxygenase (p9) was modified by adding a second

synthesis rate coefficient, a,,2 as follows:


p9 O, O,2 e OH S XB,H}UO (2-16)
eo,max KOH +So

The process rate for synthesis of nitrate reductase (p12) was modified as follows:


,Po = aNoI + 0a,2 O XB,HNO (2-17)
eNOmax KNO + SN

The expression for oxygenase activity ( vo) was changed to provide a sharper transition

from inactive to active enzyme. This was accomplished utilizing a logistic function of the

ratio eo / ,max as follows:


Vo = (2-18)

1+e eo.ma

where r,,o is the value of eo / emax at which the oxygenase activity is 0.5 and s is the

sharpness parameter, which is the slope of the curve at eo / eomax = r,o Similarly, the

expression of nitrate reductase activity (vNo) was modified as follows:

1
VNo = e (2-19)
1+NO
l+e [r e rNO-

where parameters s and re,'o are analogous to those in Equation (2-18).









The maximum specific level of oxygenase (e max ) can be calculated using the

material balance on Eo, when the oxygen is non-limiting and nitrate is absent (uo = 1),

as follows:


eOmax = 01 --',2 (2-20)
o 1H + 0 bH

Similarly, the maximum specific level of nitrate reductase (eoma, x) can be calculated

using the material balance on Eo, when the nitrate is non-limiting and oxygen is absent

(uN = 1), as follows:


eNmax aNO, + NO,2 (2-21)
"HO g + NO bH

Liu et al. (1998b) reported that the modified model efficiently depicted the influence of

aerobic phase length on growth ofPseudomonas denitrificans and length of diauxic lag.

Limitations of the model of Liu et al. (1998b) were its inability to predict the effect

of organic substrate limitation on bacterial growth and its inability to simulate growth of

denitrifying bacteria when switching from anoxic to aerobic conditions. Casasus-

Zambrana (2001) modified the model of Liu et al. (1998b) by changing the growth rate

and enzyme synthesis rate expressions, the enzyme activity terms in the growth rate

expressions, and the nitrate reductase activity expression.

Casasus -Zambrana (2001) gave the following expression for the process rate for

growth of heterotrophic biomass on oxygen (p, )


p, = H e0vo Ss SO XB,H (2-22)
eO.maxVO.max Ks + Ss KO.H +So









In this expression, a switching function has been added to account for the effect of

organic substrate limitation on growth rate. Furthermore, the enzyme activity is

expressed relative to its maximum value, vomax (VOmax = 1).

Analogously, the process rate for growth of heterotrophic biomass on nitrate ( p,) was

given as:

eNv,, NO S
P2 = H e NOVN Ss77gXBH (2-23)
eN max NO ,max Ks + Ss KNo + SNO

where VN omax is the maximum nitrate reductase activity (vo max 1).

The process rate for oxygenase synthesis was modified by adding the switching

function of the organic substrate as follows:


p9 O= O,2 Ss O e0 max XBH2O (2-24)
SeO,max Ks Ss KO,H +So

Analogously, the process rate for nitrate reductase synthesis was modified as follows:



NOmax Ks + S KNO + SNO

A switching term was added to the expression for nitrate reductase activity to

depict the inactivation effect of dissolved oxygen.


vNO = K (2-26)
4s rNo eNo K, + S
1 +e eNo.max

where K is the inactivation coefficient. The switching term turns down the enzyme


activity in the presence of oxygen.









Modeling of Denitrification with Mechanistic Approach for Denitrifying Enzyme
Kinetics

Hamilton et al. (2005) introduced a mechanistic biochemical model that depicts

denitrifying enzyme dynamics of heterotrophic bacteria. In the model, nitrate enters the

cell by active transport mediated by a transport protein (Figure 2-1). The model assumes

that the genes for the nitrate reductase and the transport protein are part of the same

operon, thus the two genes are induced together. In the absence of intracellular nitrate,

the operator is repressed by the binding of a protein (repressor). Nitrate that is

transported into the cell can bind with the repressor protein, causing its release from the

operator and allowing transcription of the genes for the nitrate reductase and the transport

protein to take place. The activity of the nitrate transport protein is inhibited by the

presence of dissolved oxygen in the bulk medium.

In the following, the process rate expressions that appear in the model of Hamilton

et al. (2005) are explained. The strategy for the numbering of process rates (e.g., p2 for

growth of heterotrophic bacteria on nitrate) follows that of ASM1. Expressions for

process rates not included in ASM1 are numbered, beginning with 9.

The process rate for growth of heterotrophic biomass on nitrate is described as

follows:

EN SNo,, Ss
2 EN SNO S gXBH (2-27)
N,max NO,,,max Ks + S (2-27)

where E max is the maximum level of nitrate reductase concentration and S, max is the

maximum level of intracellular nitrate concentration. The process rate of synthesis of

nitrate reductase (p9) is expressed as follows:











(NO3,
O3 Transport Represso Operator



Repressor


Nar !Operator




Figure 2-1. Schematics of Mechanistic Denitrification Model

( XBH +KSol, Ss
Ip9 --.,N BXB,H (2-28)
K2XB,H + KSN,, Ks +S )

where aN is the specific rate of nitrate reductase synthesis, K, is the equilibrium

constant for the binding of repressor to an inducer molecule intracellularr nitrate), K, is

the equilibrium constant for the binding of repressor to the operator. The process rate for

uptake of nitrate (p/,1) is shown as follows:


o10 = VS N N NO N KO H SS XBH (2-29)
No EN,max KSN + SNo KO,H + So KS + SS

where VsN, is the specific rate of uptake of nitrate, EN max is the maximum level of nitrate

reductase and Kno and KO,H are half saturation coefficients for nitrate and oxygen,









respectively. The process rate for decay of nitrate reductase (p,, ) was assumed to be

first order with respect to nitrate reductase concentration as follows:

Ap1 = bEEN (2-30)

where bE is decay rate of nitrate reductase. The process rate for decay of intracellular

nitrate (p1 ) is described as follows:

P12 = bSNO, (2-31)

EN max and SNO max can be expected when bacteria are growing exponentially in a

batch reactor with non-limiting organic substrate and nitrate. In that case the time

derivatives of those variables are zero, and the following maximum expressions of the

two components are generated.

VsNO, (I YH )
SNO, a NO (1-Y) (2-32)
Hmax H7g 2.86YH H


EN,max a XB,H + KSNO,,max XBH (2-33)
bE, + g b KXB, + KSNo,,,max














CHAPTER 3
EXTENSION OF ACTIVATED SLUDGE MODEL NO. 1 TO INCORPORATE
DENITRIFYING ENZYME KINETICS

In the present study, the denitrification modeling approaches discussed in Chapter 2

were integrated with ASM1 to generate extended versions of ASM1. These are eASMIc,

which incorporates a modified cybernetic approach for modeling denitrification, and

eASMlm, which incorporates a mechanistic approach for modeling denitrification. The

extended models are discussed in the following sections.

Extension with Cybernetic Approach (eASM1c)

The extended model is presented in Table 3-1. The major differences between the

extended model and the original version of ASM1 are:

* Process rate expressions for heterotrophic growth on oxygen and heterotrophic
growth on nitrate were changed.

* New expressions were added for synthesis of oxygenase, synthesis of nitrate
reductase, decay of oxygenase and decay of nitrate reductase.

* Auxiliary expressions for enzyme activities, maximum enzyme levels, and
cybernetic variables were added. (These do not appear in the model matrix.)

Process rates for aerobic growth of heterotrophic biomass [equation (2-22)], anoxic

growth of heterotrophic biomass [equation (2-23)], synthesis of oxygenase [equation (2-

24)], and synthesis of nitrate reductase [equation (2-25)] were taken from Casasus -

Zambrana (2001). Process rates for decay of oxygenase [equation (2-9)] and decay of

nitrate reductase [equation (2-10)] were taken from Liu et al. (1998a). These were

integrated with the process rates for decay of heterotrophic biomass, aerobic growth of














Table 3-1. Process rates and stoichiometric coefficients of eASMlc.

Component i 1 2 3 4 5 6 7 8 9 10 11 12

j Process I SI Ss X1 Xs XB,H XB,A Xp So SNO SN SD XD
1 1-Y
1 Aerobic growth of heterotrophs 1 -1XB
Y" Y,
1 1-r,
2 Anoxic growth of heterotrophs 1 2.8 --/
H, 2.86Y, -02
4.57-Y 1 1
3 Aerobic growth of autotrophs .-- -lxB

4 Decay of heterotrophs 1- f -1 fp i1 -fpiB

5 Decay of autotrophs 1- f -1 fp i -flP

6 Ammonification of soluble organic nitrogen 1 -1

7 Hydrolysis of entrapped organic 1 -1

8 Hydrolysis of entrapped organic nitrogen 1 -1

9 Synthesis of oxygenase

10 Synthesis of nitrate reductase

11 Decay of oxygenase

12 Decay of ntrate reductase

Observed Conversion Rates, ML-T-1 r = v1 p,
J













Table 3-1. Process rates and stoichiometric coefficients of eASMIc (continued).
Component--* i 13 14 15
Com t i 13 14 15 Process rate, pj, ML-3T1
j Process S SALK Eo ENO

1 Aerobic growth of heterotrophs iXB eOvO SK So X-B,
14 eo,mxVo.,m. Ks+S KO.H +So

2 Anoxic growth of heterotrophs 1- 8YH eNOVNO Ss K-SNI XB
14-2.86YH 14 eNo,maxVNo,max Ks +SsKN+So

3 Aerobic growth of autotrophs -14- A \ SNH K S XBA
14 A A NHNH & .K0A+ SO2

4 Decay of heterotrophs bHXB,H

5 Decay of autotrophs bAXB,
1
6 Ammonification of soluble organic nitrogen kaSNDXB,A
14
X,/X,, SK K S+,
7 Hydrolysis of entrapped organic kh XBH So ( KoH NO + XBH
KX+(XS /XB,H) K,,H +S0 a KOH +So Ko +SN

8 Hydrolysis of entrapped organic nitrogen p7 (XND /Xs)

( __ ___ Ss V( So .< X
9 Synthesis of oxygenase 1 qao, +aO,2 eo S )KOH XB,H O
Io,m. Ks +Ss KO,'H + SO

10 Synthesis of nitrate reductase 1 aNo, +aNO,2 eNO KS SN K XBH (1 uo)
'Nom ~s+S K +s +SNo)

11 Decay of oxygenase -1 foE
12 Decay of ntrate reductase -1 6NOENO

Observed Conversion Rates, ML-3T-1 = p









nitrifiers, decay of nitrifiers, hydrolysis, and ammonification from ASM1. A fit of the

model to experimental data from Casasus-Zambrana (2001) is shown in Figure 3-1.

Extension with Mechanistic Approach (eASMlm)

The extended model is presented in Table 3-2. The major differences between the

extended model and the original version of ASM1 are:

The process rate expression for heterotrophic growth on nitrate was changed.

New expressions were added for transport of nitrate across the cell membrane,
synthesis of nitrate reductase, and decay of nitrate reductase.

Auxiliary expressions for enzyme activities, maximum enzyme levels, and
cybernetic variables were added. (These do not appear in the model matrix.)

Process rates for anoxic growth of heterotrophic biomass [equation (2-27)],

synthesis of nitrate reductase [equation (2-28)], uptake of nitrate [equation (2-29)], decay

of nitrate reductase [equation (2-30)], and decay of intracellular nitrate [equation (2-31)]

from Hamilton et al. (2005) were integrated with the process rates for aerobic growth of

heterotrophic biomass, nitrification, hydrolysis, and ammonification from ASM1. A fit

of the model to experimental data from Hamilton et al. (2005) is shown in Figure 3-2.

Comparison of Extended Versions of ASM1 to the Original Version of ASM1

Modeling performances of ASM1 and extended versions of ASM1 (eASMIc,

eASMlm) will be evaluated. This will be done by comparing their predictions of

denitrification of bacteria switching electron acceptors between oxygen and nitrate in

various situations. Since the simulations will be done assuming non-limiting organic

substrate and other nutrients (e.g., ammonia nitrogen), the process rates associated with

nitrification, ammonification of organic nitrogen and hydrolysis of particulate matter will

not be taken into account.














Table 3-2. Process rates and stoichiometric coefficients of eASMlm.

Component i 1 2 3 4 5 6 7 8 9 10 11 12

j Process I SI Ss XI Xs XB,H XBA Xp So SNO SH SD XAD
1 1-YH
1 Aerobic growth of heterotrophs 1 Y- --X
Y, Y
1
2 Anoxic growth of heterotrophs 1 -iX

4.57 -Y 1 1
3 Aerobic growth of autotrophs 1 Y -XB A


4 Decay of heterotrophs 1- fp -1 fp XB fpXB

5 Decay of autotrophs 1- fp -1 f, iXB fp'XB

6 Ammonification of soluble organic nitrogen 1 -1

7 Hydrolysis of entrapped organic 1 -1

8 Hydrolysis of entrapped organic nitrogen 1 -1

9 Synthesis of intracellular nitrate -1

10 Synthesis of nitrate reductase

11 Decay of intracellular nitrate

12 Decay of ntrate reductase

Observed Conversion Rates, ML-3T-1 r= Pj













Table 3-2. Process rates and stoichiometric coefficients of eASMlm (continued).
Component -* 13 14 15
Com t i 13 14 15 Process rate, pj, ML-3T-
j Process { SALK SNO, EN

1 Aerobic growth of heterotrophs -14 ,PH S )7 so XB
14 Ks +Ss KO,H +So

2 Anoxic growth of heterotrophs 14 Y XB H PH E N, Sm Noz S XBH
14-2.86Y, 14 2.86Y, ENmx SO,,max Ks +Ss

3 Aerobic growth of autotrophs ixB 1 V SNH 2 So XB.A
14 7YA AKN +SNH)KOA +So

4 Decay of heterotrophs bHXB,H

5 Decay of autotrophs bAX,A
1
6 Ammonification of soluble organic nitrogen k-aSNDXB,
14
XsX S (K S
7 Hydrolysis of entrapped organic kh Kx BO S +h OKH IXBH
K+(Xs XBH) O,H +S KO,H +SO KNO +SNO

8 Hydrolysis of entrapped organic nitrogen p,(XD / Xs)

9 Synthesis of intracellular nitrate 1 VS EN +SNO O KOH SS XB,H
E' E KNmax Ko. 0 +SNo KO,H +So Ks +Ss

( XBH +KSN, S
10 Synthesis of nitrate reductase 1 +Ka1SNO,r] +S )XBH
K2XB,H +KSNO,, Ks +Ss

11 Decay of intracellular nitrate -1 bSNo,

12 Decay of ntrate reductase -1 bEEN

Observed Conversion Rates, ML-3T-1 = ,J
j










350

300
Anoxic Oxic Anoxic
S 250

S200 eASMIc prediction
(u /

S150 -- Experimental result
0
100 Diauxic lag

50

0 ----
5 10 15 20
Time(h)

Figure 3-1. Simulation of experimentally observed diauxic lag of Pseudomonas
denitrificans, predicted by eASMlc

350
Oxic Anoxic
300
Diauxic lag
250

200
S- eASM m prediction
E 150
o Experimental result
100


50

0
0 I ---------------------

0 5 10 15 20
Time (h)

Figure 3-2. Simulation of experimentally observed diauxic lag of Pseudomonas
denitrificans, predicted by eASM1m









Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by
ASM1

A series of simulations of an intermittently aerated batch reactor was carried out to

demonstrate the ASM1 prediction. Table 3-3 shows the characteristics of growth

medium used in the simulations, whereas Table 3-4 presents the model parameters of

ASM1 used in the simulations. Alternating oxic/anoxic conditions were provided to the

reactor. At the end of the each oxic period, the condition was changed instantaneously to

anoxic. This could be achieved experimentally by stopping aeration and then stripping

oxygen with nitrogen gas. The anoxic phase was held at 1.5 times of the oxic phase. At

the end of each anoxic phase the biomass concentration was diluted to 5 mg cell/L. An

ultimate state was always reached within 100 cycles for the given reactor characteristics

and model parameters. The results shown always begin with cycle 101.

Three different aeration periods, 2 h, 4 h, and 8 h, were tested to evaluate the model

prediction of the effect of oxic phase length on the denitrification. Figure 3-3 shows the

biomass concentration during cyclic simulations As shown in the figures, ASM1 cannot

predict diauxic lag under cyclic oxic/anoxic change. We define volumetricc

denitrification rate (VDR)" as the change in nitrate concentration per unit time and "mass

specific denitrification rate (MSDR)" as the VDR divided by the biomass concentration.

Figure 3-4 shows the MSDR and VDR during the cyclic simulation with 4 h of oxic

phase. The model could not predict the effect of aeration on either MSDR or VDR

during the anoxic phases. The MSDR was constant throughout the anoxic phases and

VDR started to increase immediately at the beginning of each anoxic phase.

To investigate the model prediction of the effect of dissolved oxygen (DO) level on

the denitrification of heterotrophic biomass, the simulation of alternating oxic/anoxic










Table 3-3. Characteristics of growth medium in batch simulations.
Components Initial conditions Units


Ss
SNO
So
XB,H


60
4000

8 (oxic, constant)
0 anoxicc, constant)


Table 3-4. Parameters of ASM1 for the batch
Common Parameters Values
^aH 0.6
q9g 0.58


0.002
80
0.025
0.065
0.77


eASMIc parameters
ao,
aO, ,2
aONO,
a'NO,2
Po
PNO
rc,o
rc,NO
S
K,

eASMlm parameters
Vs~


1.00E-02

0.00E-02
1.00E-04
1.00E-04
0.002
0.0002
0.1
0.7
10.9
0.2


4.66E-02
2.45E-08
9.86E+04
1.96E+04


KNo, 5.61E-04
Ko,, 3.31E-04
bNo 0.4


mmol glutamic acid/L
mg NO3--N/L
mg 02/
mg cell/L


simulation.
Units
h-1


h-1

mg dry cells / mmol glutamic acid
mmol glutamic acid / L
mg 02/L
mg NO3-N / L


mg N03-N /mg dry cells/sec
kat/mg dry cells/sec
(mg N03-N/L)-1
(mg N03-N/L)-1

mg NO3-N / L
mg 02/L
h-1


bH
YH
Ks
KO,H
KNO












2 h oxic/ 3 h anoxic


495 500 505 510 515 52

Time (h)


4h oxic 16 h anoxic
I I I I I
II I
) I Oxic Aoxic Oxic Aoxic Oxic
I I
II I
I I

I I
II I
I I I
II I
II I

II I
I I I
) I /
I I
SI/ __ __ _
I





11


995 1000 1005 1010


1015


4500
4000
: 3500
S3000
0 2500
E
S2000
S1500
1000
500
0
1980


Time (h)

8 h oxic/12 h anoxic


1985 1990 1995 2000 2005


Time (h)


Figure 3-3. Growth of heterotrophic biomass during cyclic simulations with 8 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by ASM1











0.04 16
Oxic i Anoxic Oxic I Anoxic i Oxic
0.035 I 14

S0.03 -_ MSDR 12


z I
0 02 VDR 10

z 0.015 6 z



cn 0.005 2 >

0 -- 0
990 995 1000 1005 1010 1015
Time (h)

Figure 3-4. Mass specific and volumetric denitrification rate during cyclic simulation,
predicted by ASM1

cycling was repeated with various levels of DO concentration. Three additional DO

levels (1, 2 and 4 mg/L) were tested. Figures 3-5, 3-6, and 3-7 show the biomass profile

from the repetitive cycling simulations with respect to 4, 2, and 1 mg/L of DO

concentration. As shown in the figures, DO concentrations during the oxic phase did not

affect either the simulated growth of heterotrophic biomass, the MSDR or the VDR

during anoxic phases.

Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by
eASM1c

Growth of heterotrophic biomass switching between oxygen and nitrate as an

electron acceptor in a batch reactor at non-limiting substrate concentrations was

simulated to illustrate the differences between the predictions of ASM1 and eASMIc and

the effects of culture variables on diauxic lag. Characteristics of the growth medium










2 h oxic 13 h anoxic


500 505 510 515


520


Time (h)

4 h oxic 16 h anoxic


995 1000 1005 1010 1015
Time (h)


4500
4000
S3500
3000
S2500
S2000
S1500
1000
500
0
1980


8h oxic 12 h anoxic


1985 1990 1995 2000 2005


Time (h)


Figure 3-5. Growth of heterotrophic biomass during cyclic simulations with 4 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by ASM1










2 h oxic 13 h anoxic


500 505 510 515


520


Time (h)

4 h oxic 16 h anoxic


995 1000 1005 1010 1015
Time (h)


4500
4000
3500
3000
2500
E 2000
1500
1000
500
0
1980


8h oxic 12 h anoxic


1985 1990 1995 2000 2005


Time (h)


Figure 3-6. Growth of heterotrophic biomass during cyclic simulations with 2 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by ASM1










2 h oxic 13 h anoxic


500 505 510 515


Time (h)

4 h oxic 16 h anoxic


520


995 1000 1005 1010 1015
Time (h)


4500
4000
2 3500
i 3000
o 2500
S2000
S1500
1000
500
0
1980


8 h oxic/12 h anoxic


1985 1990 1995 2000 2005


Time (h)


Figure 3-7. Growth of heterotrophic biomass during cyclic simulations with 1 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by ASM1









are given in Table 3-3, whereas parameters for eASMIc are given in Table 3-4. With the

exception of the kinetic and stoichiometric parameters for eASMIc, all simulation

conditions were identical to those for the oxic/anoxic cycling simulations using ASM1

that were discussed in the previous section.

Figure 3-8 shows biomass profiles simulated by ASM1 and eASMIc. These were

generated by setting eo/eo,mx to 1.0 and eNo/eNo,max to 0 in eASMIc at the beginning of

the oxic phase. As apparent from the figure, eASMIc can predict diauxic lag of

denitrifiers switching between oxygen and nitrate, whereas ASM1 cannot predict diauxic

lag. Figure 3-9 presents nitrate reductase specific level and activity during the diauxic

growth of heterotrophic biomass, as predicted by eASMIc. The specific nitrate reductase

level started increasing immediately after the switch. In contrast, the nitrate reductase

activity remained low for some time and then began increasing. Noticeable biomass

growth resumed shortly after the increase of enzyme activity.

Model runs for a simple batch reactor with alternating oxic/anoxic cycling were

carried out to demonstrate eASMIc predictions of the effect of oxic phase length on

dynamics of denitrification in the subsequent anoxic phases. Three different aerobic

phase lengths (2 h, 4 h, and 8 h) with oxic phase DO of 8 mg/L were tested to investigate

the effect of aerobic phase length on the diauxic lag predicted by eASMIc. Anoxic phase

lengths were varied in proportion to the oxic phase lengths, giving cycle lengths of 5, 10,

and 20 hours, respectively. In each case, the simulation was run to an ultimate state. The

eASMIc model predicted longer diauxic lags as oxic phase length increased (Fig. 3-10).

This reflects the ability of eASMIc to depict dilution of nitrate reductase in the biomass

during oxic phases. According to the model, the rate of nitrate reductase synthesis is low









800
ASM1 Prediction
700
600
0) 500 -
400 a.

300 -
3oxic anoxic
200 -
100 switch
100 -

800 eASMIc Prediction
700 diauxic lag
600
U 500 -
400 b.
.400 -oxic anoxic .
300 -
200 -- switch
100
0
0 5 10 15
Time (h)
Figure 3-8. Growth of heterotrophic biomass under oxic/anoxic switch: a) ASM1
prediction, b) eASMIc prediction

relative to the rate of enzyme decay under oxic conditions. Figure 3-11 presents the

MSDR and VDR during the cyclic simulations. The MSDR of biomass at the beginning

of the each anoxic phase was zero and then increased rapidly once biomass started

growing at the end of the diauxic lag period. The VDR at the beginning of the each

anoxic phase was also zero and started to increase along with the increase of MSDR.

To investigate the model prediction of the effect of DO level on the length of

diauxic lag, the simulations of oxic/anoxic cycling with three different cycle lengths













800
biomass
700 diauxic lag
600

Sa500

400
Soxic anoxic
300
200 witch
200

100

0.0006
e NO
o 0.0004 b.
0.0002
0
1
0 NO
o0.5 C.


e eNO VNO
Se 0.5 e NO,maxv No,maxf

0
0 5 10 15
Time (h)


Figure 3-9. Specific nitrate reductase level and activity of heterotrophic biomass under
oxic/anoxic switch: a) Biomass, b) specific nitrate reductase level, c) nitrate
---- ._. L 1-* _.1 \ e f .. _. A i -1


er ductase activity, )


d etciderp, by eASM1 c


NO.max NO.max











2 h oxic 13 h anoxic

,= = 0 0_/O/, 0
1 1 h 1 '0,

S I I I I I O
I I I I I I I I I


I I I I I I I I
m/ / i/ m m


510


520


Time (t)

4 h oxic 16 h anoxic


995 1000 1005 1010 1015


Time (t)


4500
4000
S3500
S3000
0 2500
E
7 2000
d 1500
1000
500
0
1980


8 h oxic 112 h anoxic


1985 1990 1995 2000 2005

Time (t)


Figure 3-10. Growth of heterotrophic biomass during cyclic simulations with 8 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by eASMIc


30

r 25

S 20
0)
E 15

, 10

5

0


495


120

100
-J
- 80

60
E
3 40
A


0 -
990











0.04 5
Oxic Anoxic Oxic Anoxic Oxic
0.035 -"
"5 4
S0.03 -
: -MSDR
0.025 VDR 3

0.02 -
o0
2 z
S0.015
E
W 0.01 E

0.005 >

0 0
990 995 1000 1005 1010 1015
Time (h)

Figure 3-11. Mass specific and volumetric denitrification rate during cyclic simulation,
predicted by eASM c

(5, 10, 20 hours) were repeated with oxic phase DO concentrations of 4, 2 and 1 mg/L

(Figs. 3-12, 3-13, 3-14, respectively). Shorter diauxic lags were predicted as DO

concentrations decreased. This happens because eASMIc predicts a lower rate of

dilution of ENo due to the lower rate of oxic growth of bacteria as DO is decreased.

Two of the terms that affect the process rate for growth of heterotrophic biomass

under oxic conditions in eASMIc are the switching function for DO and the ratio of

specific oxygenase level to the maximum specific oxygenase level (eo/eo,max). In order to

portray the effect of eo/eo,max on oxic phase growth rates, data from the simulations for 1

mg/L and 8 mg/L oxic phase DO and cycle length of 20 hours were re-examined.

The ultimate state biomass profiles from these simulations are shown in Figure 3-

15a. The specific growth rates were extracted from these profiles, as shown in Figure 3-

15b. For a given oxic phase DO, the specific growth rate changes with respect to time






















































6000

5000

S4000
u
S3000
E
2000

1000


0 J-
1980


2 h oxic /3 h anoxic


0 c *O 0 0 c0

I I I I I I I I I I
I I 0 I I I I I
I I I I I
1 I I I I I


I I I l I l
I I I I I I I I
I I I I I I
I I I gI


Time (h)

4 h oxic /6 h anoxic


995 1000 1005 1010 1015

Time (h)

8 h oxic/ 12 h anoxic


1985 1990 1995 2000 2005


Time (h)


Figure 3-12. Growth of heterotrophic biomass during cyclic simulations with 4 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by eASMIc











2 h oxic 13 h anoxic


30

-. 25

| 20
0)
E 15

S10

5

0
49


Time (h)

4 h oxic 6 h anoxic


6000

. 5000
-J
S4000

E 3000

| 2000

1000

0
1980


995 1000 1005 1010 1015

Time (h)

8 h oxic 112 h anoxic


1985 1990 1995 2000 2005

Time (h)


Figure 3-13. Growth of heterotrophic biomass during cyclic simulations with 2 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by eASMIc


505


I I I I I



I I I I I I
I I I I I
.0 c 0 c 0 or_ 0 C O C


S II I I I
I I g I I



I I I I I I I I I I


)5


a.






20


5;
5


120

- 100

i 80
0)
E 60

>, 40

20

0











2 h oxic 3 h anoxic

x o x o x o x o x o
1 I I I i I I I I
I I I I I
I I i i



l I I i i
1l 1 1 I i I I
O cm O c O c O/ c O c
<


505


510 515


Time (h)

4 h oxic 6 h anoxic


995 1000 1005 1010 1015


Time (h)

8 h oxic 112 h anoxic


1985 1990 1995 2000 2005


Time (h)


Figure 3-14. Growth ofheterotrophic biomass during cyclic simulations with 1 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by eASMIc


30

S25

20

E 15

10

5

0


495


100

5 80

S60
E
3; 40

20

0


6000

5000

S4000

S3000

2000

1000


0 -
1980


i









throughout the oxic phase, even though the DO is constant and substrate is non-limiting.

This is due to values of eo/eo,max (Fig. 3-15c) that change throughout the oxic phase as a

consequence of oxygenase biosynthesis. Profiles of eo/eo,ax were generally higher at

higher oxic phase DO.

Simulation of Dynamics of Bacteria Switching between Oxygen and Nitrate by
eASMlm

Simulations were carried out to compare prediction trends of the mechanistic model

(eASMlm) to the cybernetic model (eASMIc). Specifically, the effect of length of

aerobic phase in alternating oxic/anoxic growth and the effect of DO concentration on

the predictions of eASMlm were examined by repeating the series of simulations

previously performed with eASMIc, as described in the previous section. The

experimental conditions (substrate concentration, nitrate concentration, phase lengths)

were same as reported in the previous sections. Model parameters used for eASMlm are

compared to those used for eASMIc in Table 3-4.

It is useful to first examine a single cycle consisting of an oxic phase followed by

an anoxic phase in terms of biomass concentration, enzyme concentration, and

intracellular nitrate concentration (Fig 3-16). The eASMlm predicted a diauxic lag of

denitrifiers switching between oxygen and nitrate. Nitrate reductase and intracellular

nitrate concentrations increased immediately from the switch, but the magnitude of the

increases were small for several hours. The increase of nitrate reductase and intracellular

nitrate concentration then became significant about four hours after the switch. The

biomass started growing as the product of the nitrate reductase level and intracellular

nitrate concentration divided by their maximum values started increasing. This term thus

regulates the rate of growth of heterotrophic biomass under anoxic condition.











600

500 Oxic Anoxic
8 mg/L DO
Z 400
S40 1 mg/L DO
E 300 a

S200

100

0
2000 2005 2010 2015 2020
Time (h)


1
0.9
0.8
0.7
0.6
0 0.5 -8 mgL Do b.
0.4
S0.4 1 mgioo
031 mgL DO
0 0.3
0.2
0.1
0
2000 2005 2010 2015 2020
Time (h)


1
0.9
0.8
0.7 8 mg/L DO
0.6 --1 mg/L DO
0.5 Model KO,H C*
0.4
w 0.3
0.2
0.1 --.-.-.-. -. -.Q
0.1
2000 2005 2010 2015 2020
Time (h)

Figure 3-15. Growth of heterotrophic biomass during cyclic simulations with 8 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by eASMlc












800
Biomass
700
Diauxic lag
j 600 --,

S500

E 400 Oxic Anoxic

m 300 -
x
Swit h
200 -
I
100 -

0.00001 e



0





e NO Snoi
(o (/ 0,5 e NO,max S noi,max d

30 5 ,
2elo.max 5 eN xmax

S0 5 10 15 20
Time (h)


Figure 3-16. Specific nitrate reductase level and specific intracellular nitrate level of
heterotrophic biomass under oxic/anoxic switch: a) Biomass, b) Nitrate
O V.
reductase level, c) Intracellular nitrate level, d) NO VN-, predicted by
eNO,maxV NO,max
eASMlc

The first comparison made was for the case of long-term cyclic oxic/anoxic

operations. In this case, eASMlm predicted longer diauxic lag as the cycle length was

increased (data not shown). This is qualitatively similar to the predictions made by









eASMlc. This reflects the ability of eASMlm to depict dilution of nitrate reductase and

intracellular nitrate in the biomass while the rates of synthesis of nitrate reductase and

transfer of nitrate into the cell are low relative to the rate of decay of enzyme and

intracellular nitrate. The mass specific denitrification rate (MSDR) and volumetric

denitrification rate (VDR) during the cyclic simulations showed similar trends of those of

eASMlc (data not shown).

The second comparison made was in terms of DO concentration of the oxic phases.

Unlike the prediction of eASMlc, the length of lag was not significantly influenced by

DO concentrations from 1 mg/L to 8 mg/L during the oxic phases (data not shown).

Since eASMlm is not capable of predicting the dynamics of oxygenase synthesis, the

lower rate of aerobic growth associated with low specific oxygenase level predicted by

eASMlc in the previous section could not be the case, which means that DO

concentration may affect the specific growth rate only by changing the switching function

for DO concentration in the model prediction of eASMlm. This leads to the fact that the

1 mg/L of DO concentration tested in the simulation was not low enough to result in

lower rate of aerobic growth than that of aerobic growth with 8 mg/L. Hence, the

simulations were repeated with much lower DO concentrations. Figure 3-17, 3-18, and

3-19 show the biomass profile from the alternating cycling simulation with respect to 1,

0.5 and 0.1 mg/L of DO concentration. The eASMlm predicted shorter diauxic lag as

DO concentrations during the oxic phase decreases. This is because low DO

concentration results in lower rate of aerobic growth of heterotrophic biomass and lower

rate of dilution of nitrate reductase (ENo).







52


2 h oxic 3 h anoxic


505 510 515


Time (h)

4 h oxic 6 h anoxic


1005 1010 1015


Time (h)


4500
- 4000
-J
S3500
S3000
E 2500
2000
1500
1000
500
0-
2000


8 h oxic 112 h anoxic


2005 2010 2015


Time (h)

Figure 3-17. Growth of heterotrophic biomass during cyclic simulations with 1 mg/L of
DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length, predicted
by eASMlm


500
500


140
120
100
80
60
40
20
0 -
1000


1020


2020







53


2 h oxic 3 h anoxic


505 510 515


Time (h)

4 h oxic 6 h anoxic


1005 1010 1015


Time (h)


4500
-J
_4000
S3500
0 3000
E 2500
I 2000
1500
1000
500
0
2000


8 h oxic 112 h anoxic


2005 2010 2015


Time (h)

Figure 3-18. Growth of heterotrophic biomass during cyclic simulations with 0.5 mg/L
of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length,
predicted by eASMlm


500
500


100

80

60

40


0 --
1000


1020


2020







54


2 h oxic 3 h anoxic


505 510 515


Time (h)

4 h oxic/ 6 h anoxic


1005 1010 1015


1020


Time (h)

8 h oxic 12 h anoxic


2005 2010 2015


2020


Time (h)


Figure 3-19. Growth of heterotrophic biomass during cyclic simulations with 0.1 mg/L
of DO during oxic phase: a) 2 h, b) 4 h and c) 8 h of oxic phase length,
predicted by eASMlm.


5J ,
0 t
500


80
70
60
50
40
30
20
10
0-
1000


2000


S1500


S1000
I
Snn


0 -
2000









Re-examination of Results from a Previous Study

It would be interesting to revisit the studies about the effect of change of electron

acceptor to the denitrification with a view point of diauxic lag. Baumann et al. (1996)

investigated the effect of switching of electron acceptor from oxygen to nitrate in a

continuous culture ofParacoccus denitrificans as discussed in the previous chapter. In

their study, the biomass concentration slightly decreased over the 20-hour period as the

growth condition was changed from aerobic to anaerobic. It was not clear from these

data if a growth lag occurred after the switch. A simulation was therefore performed

using a denitrification model capable of predicting diauxic lag to investigate if the slight

change of biomass concentration was due to diauxic lag.

The experimental conditions for the simulations are given in Appendix A. The data

of Baumann et al. (1996) are shown in Figure 3-20, along with three simulation scenarios.

In the first scenario, growth stops immediately after the switch from aerobic to anaerobic

conditions. In this case, the biomass concentration would then begin to follow the wash-

out curve until growth resumed. In the second scenario, there is no lag, rather, the growth

yield decreases because less energy is obtained from oxidation of substrate using nitrate

instead of oxygen. In the third scenario, the maximum specific growth rate is decreased,

again with no lag. As can be seen in the figure the trend of biomass did not follow wash-

out curve. Therefore, it can be concluded that the growth lag did not occur in the

experiment. Either the second scenario (lower growth yield) or the third scenario (lower

maximum specific growth rate) could simulate the trend of biomass in the experimental

result well.
























0.2

0 -
200


210 220 230
Time (h)


Figure 3-20. Simulation of experimental results from a previous study.


240














CHAPTER 4
SIGNIFICANCE OF DENITRIFYING ENZYME DYNAMICS IN BIOLOGICAL
NITROGEN REMOVAL PROCESSES: A SIMULATION STUDY

The phenomenon of diauxic lag for bacteria switching between electron donors was

discovered at least 60 years ago (Monod, 1942). Subsequently, Kodama et al. (1969)

observed an analogous lag for bacteria switching between electron acceptors.

Experiments with activated sludge and Pseudomonas denitrificans pure culture have

established that the diauxic lag of bacteria switching between oxygen and nitrate as

electron acceptors can last up to several hours and depends on the length of aeration

period, DO concentration during the aeration period, and the preculture environment that

precede anoxic conditions (Liu et al., 1998a, b; Gouw et al., 2001; Lisbon et al., 2002).

Effects of aeration period and DO concentration on diauxic lag of bacteria

switching between oxygen and nitrate have been successfully modeled (Liu et al., 1998a,

b, Casasus-Zambrana, 2001). However, the widely used ASM 1, 2, 2d, and 3 (Henze et

al., 2000) are unable to portray the diauxic lag phenomenon. This deficiency could result

in sub-optimal operational strategies or designs and lead to needless environmental

impact or waste of economic resources. The purpose of the present study was to compare

predictions of the widely used ASM1 to a extended version of ASM1 that incorporates

enzyme kinetic expressions and can portray diauxic lag. Both models were applied to

activated sludge process configurations for nitrogen removal involving periodic

operation.









Experimental Methods

Process configurations and operational conditions for simulations will be provided.

The wastewater composition and model parameters will also be discussed.

Process Configurations and Modeling

We used two different process configurations to illustrate how predictions of

eASMIc differ from those of ASMI: fed-batch, and BioDenipho (BDP). The fed-batch

reactor had a minimum volume of 4495 m3 at the beginning of a cycle and a maximum

volume of up to 6567 m3 at the end of a cycle. The BDP process consisted of an

anaerobic reactor, two parallel oxidation ditches, a final anoxic reactor, and a final

aerated reactor (Fig. 4-1, bottom). Reactor volumes and the corresponding hydraulic

residence times are given in Table 4-1. The sequence and lengths of anoxic and oxic

phases in the fed-batch process (Fig. 4-1, top) were comparable to the sequence of

unaerated and aerated environments through which mixed liquor passes in the BDP

process (Fig. 4-1, bottom). In the fed-batch process, the reactor was unaerated for the

initial 18.0% of the cycle. Similarly, in the BDP process, the mixed liquor initially

passed through an unaerated (anaerobic) reactor that comprised 18.0% of the total

residence time. Subsequently, aeration in the fed-batch process was turned on and off to

create alternating oxic and anoxic conditions. Mixed liquor was exposed to three oxic

and three anoxic phases during this time period, which lasted 64.2% of the cycle. In the

BDP process, the residence time of the oxidation ditches was 64.2% of the total. While

in the oxidation ditches, the mixed liquor was exposed to a sequence of six phases (Fig.

4-2). The last two phases of the fed-batch process were anoxic (14.3% of cycle) and oxic

(3.5% of cycle), respectively. In comparison, the last two reactors of the BDP process

were anoxic (14.3% of residence time) and oxic (3.5% of residence time), respectively.












Fraction of cycle


Figure 4-1. Process schematics of fed-batch process (top) and BDP process (bottom)
showing the fraction of the cycle length or hydraulic residence time occupied
by each phase or part of the processes.


A 4 B 4 C D I E F 4




R-1 R-2 R-1 R-2 R- R2 R-1 R-2 R-1 R-2 R-I R-2
DN N DN N N N N DN N DN I N N


Figure 4-2. Sequence of phases in the BDP oxidation ditches









Table 4-1. Design of UF BDP water reclamation facility (train 1 of two parallel trains).
Unit Volume, % of total reactor Hydraulic residence time, h
m3 volume At current At design
ADFa ADFb
Anaerobic reactor 808 18.0 6.0 3.4
Oxidation ditch 1 1443 32.1 10.7 6.1
Oxidation ditch 2 1443 32.1 10.7 6.1
Final anoxic 643 14.3 4.8 2.7
Final aeration 158 3.5 1.2 0.7
TOTAL 4495 100.0 33.4 18.9
a Current average daily flow is 3230 m3/d to one train
bDesign ADF is 5700 m3/d to one train

Biomass was wasted at the end of each cycle of the fed-batch process to achieve a

solids retention time (SRT) of 25 days. Delays for settling of sludge and decanting of

supernatant were neglected. Sludge was wasted from the BDP secondary settling tank

underflow to achieve an SRT of 25 days. The secondary settling tank was modeled as a

perfect settler with a sludge recycle ratio of 1.0. Mixing and delays resulting from the

secondary settler were neglected. The oxygen mass transfer coefficient was adjusted

under aerated conditions in both processes by a proportional-integral-derivative velocity

controller, up to a maximum of 100 d1. The proportional gain and reset time for the DO

controller were tuned using the closed loop method of Ziegler and Nicholes (1942). The

oxygen mass transfer coefficient was set to zero to simulate unaerated conditions. DO in

the reactors was determined by mass balance. All reactors were modeled as completely

mixed tanks. Effluent concentrations of soluble components were taken as the

concentrations in the final React phase in the fed-batch process and as the concentrations

in the final oxic reactor in the BDP process. Daily effluent concentrations of the fed-

batch reactor and BDP process were reported as effluent, 24-hour flow-weighted average

concentrations. Base values in simulations were the following: average daily influent









flow = 5700 m3/d, DO =1.0 mg/L during oxic phases, fed-batch reactor DO set point

during final oxic phase = 2.0 mg/L, BDP final oxic reactor DO set point = 2.0 mg/L,

temperature = 230C, and pH = 6.8.

We define the unaerated volume fraction (UVF) as the ratio of the reaction time

under unaerated conditions to the total reaction time. This parameter gives an indication

of the reaction time available for denitrification. In the fed-batch process, the relative

lengths of the React-Fill sub-phases were adjusted in order to manipulate the UVF,

whereas in the BDP process, the relative lengths of phases in the oxidation ditch cycle

were varied in order to manipulate UVF (Table 4-2). Simulations were run until an

ultimate state (i.e., constant component concentrations at any given time of day) was

reached.

Table 4-2. Sequence of phases in the fed-batch and BDP processes.
Fed-batch process BDP process
% of Total React-Fill % of Oxidation Ditch
Phases Phases
Phase Cycle Length
Oxic React Fill 1 22.3 13.7 A 13.3- 18.8
Anoxic React Fill 1 11.0 19.7 B 20.0 28.2
Oxic React Fill 2 22.3 13.7 C 16.7 3.0
Anoxic React Fill 2 11.0 19.7 D 13.3- 18.8
Oxic React Fill 3 22.3 13.7 E 20.0 28.2
Anoxic React Fill 3 11.0 19.7 F 16.7 3.0

Wastewater Composition and Model Parameters

Values for the 24-hour flow-weighted average wastewater composition (Table 4-3)

and kinetic and stoichiometric parameters for ASM1 (Table 4-4) were taken from a study

of a local nitrogen removal activated sludge plant (Antoniou, 1989; Antoniou et al., 1990;

Hamilton et al., 1992). Temperature and pH dependencies reported by these were used









Table 4-3. 24-hour flow-weighted average wastewater composition.
Wastewater Components Average value Units
Soluble inert substrate (Si) 20.8 g COD/m3
Particulate inert substrate (XI) 102.6 g COD/m3
Readily degradable substrate (Ss) 96.7 g COD/m3
Slowly degradable (particulate) substrate (Xs) 127.4 g COD/m3
Soluble inert organic N (SNI) 1.5 g N/m3
Particulate inert organic N (XNI) 2.7 g N/m3
Nitrate plus nitrite N (SNo) 0.0 g N/m3
Ammonia N (SNH) 22.3 g N/m3
Soluble degradable organic N (SND) 8.0 g N/m3
Particulate degradable organic N (XND) 3.6 g N/m3
Active heterotrophic biomass (XB,H) 0.0 g COD/m3
Active autotrophic biomass (XB,A) 0.0 g COD/m3
Inert products from decay (Xp) 0.0 g COD/m3
Alkalinity (SAlk) 6.0 mol HCO3-/m3
DO (So) 0.0 g COD/m3

for the following model parameters: heterotrophic and autotrophic maximum specific

growth rates (/'H, /A, ), heterotrophic decay rate (bH), hydrolysis rate (kh),

ammonification rate (ks), and hydrolysis half- saturation coefficient (Kx). All other

parameters were assumed to not change with temperature or pH. The value used for the

ammonia half-saturation coefficient for autotrophs (KNH) was that found by Antoniou

(1989) in experiments using the same wastewater that the influent composition was based

on. The remaining parameter values for ASM1 were taken from Henze et al. (1986).

New parameters for the eASMic model, as well as the maximum specific growth rate of

heterotrophs used in eASM c, were taken from Casasus-Zambrana (2001).

Diurnally Varying Flow and Component Concentrations in Influent Wastewater

Typical diurnal flow and concentration patterns from Metcalf and Eddy (1991)

were normalized. Influent flow was varied according to the normalized flow variation

curve, soluble components of the influent wastewater were varied according to the

normalized biochemical oxygen demand variation curve, and particulate components










Table 4-4. Stoichiometric and kinetic parameters in the ASM1 and eASM1 models.


Parameter
Yields
Heterotrophic
Autotrophic
Biomass fraction yielding particulates


Composition
Mass N/mass COD in biomass
Mass N/mass COD in biomass products
Maximum Specific Rates
Heterotrophic growth for ASM1
Heterotrophic growth for eASM1
Autotrophic growth
Hydrolysis
Rate Coefficients
Ammonification
Heterotrophic decay
Autotrophic decay

Half Saturation Coefficients
Substrate coeff for heterotrophs
Nitrate coeff for heterotrophs
Ammonia coeff for autotrophs
Oxygen coeff. for heterotrophs
Oxygen coeff for autotrophs
Hydrolysis of particulate organic

Anoxic Correction Factors
Growth
Hydrolysis
Enzyme rate coefficients eASMlcc
Oxygenase synthesis
Nitrate reductase synthesis
Nitrate reductase synthesis
Oxygenase decay
Nitrate reductase decay
Enzyme activity coefficients
eceo ,max at which the activity is 50%
eNo/eNomax at which the activity is 50%
Sharpness parameter
Oxvaen inactivation coefficient


Symbol Valuea Typical Units


0.67
0.24
0.08


g cell COD/g COD
utilized
g cell COD/g N utilized
dimensionless


0.086 g N/g COD
0.06 g N/g COD


ka
bH
bA


Ks
KNo
KNH
Ko,H
KO,HA
Ko,A
Kx


7iG
17H

ao,;
ao,2
CaNO,2


PNO

re.o
rc, o
rc,No
s
K,


7.4 b
14.4c
0.644'd
4.171b

0.098b
0.871b
0.1


20
0.5
0.2e
0.2
0.4
0.042b


0.7f
0.4

0.24
0
0.0024
0.0024
0.0871
0.0871

0.1
0.7
10.9
0.2


day-'
day-'
day-'
g particulate COD /(g
cell COD d)

m3/(g cell COD day)
day-'
day-1

g COD/m3
g NO3-N/m3
g NH4-N/m3
g COD/m3
g COD/m3
g particulate COD /(g
cell COD)

dimensionless
dimensionless

day'
day'
day-'
day-'
day-'
day-'

dimensionless
dimensionless
dimensionless
g COD/m3


aAt 200C and neutral pH unless otherwise specified; assumed not to change with temperature or
pH
bAt T = 230C (Hamilton et al., 1992)
cCasasis-Zambrana (2001)
dAntoniou et al. (1990); T = 230C and pH = 6.8
eAntoniou (1989)
fFor wastewaters from anaerobic sewers. According to Henze et al. (1986), rqG falls
in the range 0.6-1.0, the lower value being for waters from anaerobic sewers.









were varied according to the normalized suspended solids variation curve. The resulting

patterns were shown in Potter et al. (1996).

Results and Discussion

Results from the simulations will be shown and discussed. The results from the

fed-batch process will be discussed first followed by those from the BDP process.

Simulations of Fed-Batch Process

Simulations of the fed-batch process with ASM1 indicated that the optimum UVF

was in the range of 63-66% (Fig. 4-3a). Exceeding this optimum lead to decreased

performance because of insufficient nitrification, whereas operation below the optimum

led to insufficient denitrification. Figure 4-3 also shows that the cycle length impacts the

performance of the fed-batch process. At UVF below the optimum, ASM1 predicts that

shorter cycle lengths cause performance to deteriorate. This is due to an increase of the

number of switches between oxic and anoxic phases per day as the cycle length is

decreased. At each switch, residual DO must be consumed before denitrification can

proceed, thus limiting the time available for denitrification. Furthermore, some organic

matter is consumed to reduce the oxygen, decreasing the quantity available for reducing

nitrate. Excessively long cycles at UVF below its optimum also lead to poor

performance. This is because nitrate can be exhausted before the end of an anoxic phase,

so that the remainder of the phase is not effectively utilized. At UVF above the optimum,

performance deteriorates as cycle length is increased (Fig. Fig. 4-3a), due to failure of

nitrification.

The eASMIc model (Fig. 4-3b) predicts an optimum for UVF at a somewhat

higher range (65-68%) than found with ASM1. At UVF below the optimum,

performance improves as the cycle length is increased. This is because bacteria have










12
a.
10
lh

68
E 8h
2h
M 6


4
3h

40
b.
35 1h
1h
30 -

625 2 h
-20
z 3h
n 15
10 8h

5
0
50 55 60 65 70
UVF(%)
Figure 4-3. Effects of anoxic volume fraction and cycle length on performance of fed-
batch process predicted by ASM1 and eASMIc: a). as predicted by ASM1; b).
as predicted by eASMIc. (SbN is the sum of ammonia, nitrate, and soluble
organic nitrogen.)

more time after the end of the lag period to carry out denitrification. At UVF above the

optimum, performance declines due to failure of nitrification.

Simulations of BDP Process

Our simulation of the BDP process with ASM1 (Fig. 4-4a) gave results that were

comparable to those of Potter et al. (1996). An optimum UVF in the range of 55-57%

was predicted. In comparison, the UVF used for the University of Florida BDP process

is 61%. Cycle length had only moderate impact on performance at UVF below the










8
a. 3h

7 2 2h

1h

E 6 / 0.5 h
z

5



16
b.
14
0.5 h

S12-
E 1h
z 10
Cn 2h
8 3h


6
52 54 56 58 60 62 64
UVF (%)
Figure 4-4. Effect of unaerated volume fraction (UVF) and cycle length on performance
of BDP process: a). Predicted by ASM1; b). Predicted by eASMlc. (SbN is
the sum of ammonia, nitrate, and soluble organic nitrogen.)

optimum. At UVF above the optimum, performance deteriorated with increasing cycle

length, due to impairment of nitrification. An optimum UVF in the range of 57-61% was

predicted by the eASMlc model (Fig. 4-4b). Cycle length had significant impact on

performance when UVF was below the optimum. In this operational regime, increasing

cycle length improved performance. This was consistent with the prediction of the

eASMlc applied to the fed-batch process. Above the optimum UVF, cycle length had

only moderate impact on performance.







67


Optimum Cycle Length as a Function of UVF

Both the ASM1 and eASMIc models predict that the optimal cycle length is a

function of unaerated volume fraction (Fig. 4-5). However, optimal cycle lengths based

on eASMIc are considerably longer than optimal cycle lengths based on ASM1. For

example, at an UVF of 65% in the fed-batch process, the best cycle length according to

eASMIc was about 7 hours, whereas the optimal cycle length according to ASM1 was


6
C-1



4
E

0
2


0

.3



2

E
E

0


52 54 56 58 60 62 64
UVF (%)

Figure 4-5. Optimum cycle lengths of fed-batch and BDP processes as a function of
unaerated volume fraction (UVF): a). Fed-batch process; b). BDP process.
(Dashed lines indicate range of UVF and cycle length tested.)


a.:



eASMIc



ASM1







b.



SseASMI





ASM1
. . . . . . . . .









less than 2 hours (Fig. 4-5a). A similar dichotomy was noted for the BDP process (Fig 4-

5b). For example, at a UVF of 57%, an optimum cycle length of 0.6 h was predicted by

ASM1, whereas an optimum of 2.6 h was predicted by eASMlc. In comparison, the

cycle length employed at the University of Florida BDP process is 1.8 h.

Conclusions

A biochemical model (eASMlc) capable of simulating the diauxic lag of

denitrifying bacteria switching between oxygen and nitrate as electron acceptors was

applied to two different process configurations that are commonly used for nitrogen

removal from wastewater and involve periodic operation: fed-batch and BDP. Its

predictions were compared to those of the industry-standard ASM1, which cannot portray

diauxic lags. In simulations of the fed-batch process, the eASMlc model predicted

slightly higher optimal values for the unaerated volume fraction (UVF) and substantially

higher optimal cycle lengths. Similar results were obtained in simulation of the BDP

process. The eASMlc model predictions of optimal BDP UVF and cycle length are more

consistent with operation of the University of Florida BDP process than are the ASM1

predictions.














CHAPTER 5
OBJECTIVES

A number of researchers (Liu et al., 1998a, b; Gouw et al., 2001; Casasus-

Zambrana, 2002; Lisbon et al., 2002) have investigated the phenomenon of diauxic lag of

bacteria switching between oxygen and nitrate. The observed dynamics could be

successfully modeled by the proposed extended version of ASM1 (eASMIc, eASMlm).

However, these studies were performed after only one or two switches between oxic and

anoxic conditions under non-limiting carbon substrate conditions.

Observation of growth and denitrification dynamics of cultures that have reached

an ultimate state after repeated oxic/anoxic cycling is needed to obtain representative

experimental data and verify model prediction. The objective of present study is,

therefore, to investigate growth dynamic of model bacterium, Pseudomonas denitrificans

in oxic/anoxic cycling conditions under continuous flow reactor.

Major objectives of the present study are to

1. Develop an experimental system capable of achieving bacterial pure culture
under continuous flow conditions.

2. Verify performance of the experimental system in terms of reproducibility of
experimental data after steady state growth.

3. Study diauxic lag ofPseudomonas denitrificans under continuous flow
conditions and verify eASMlm predictions on experimental results.

4. Study diauxic lag ofPseudomonas. denitrificans in oxic/anoxic cycling
conditions and verify eASMlm prediction on experimental results.

More specific objectives of each topic will be introduced in each corresponding chapter.














CHAPTER 6
GENERAL MATERIALS AND METHODS

An experimental system was developed to achieve pure culture in a continuous

flow system. The system was used for all continuous experiments in the following

chapters. In this chapter, the continuous flow reactor system will be discussed along with

general experimental protocols such as preparation of bacteria and methods for analytical

measurements. Specific experimental protocols and conditions pertinent to each type of

experiment will be given in each corresponding chapter.

Bacterial Cultivation

Experimental protocols for preservation and culturing of bacteria will be discussed.

Reviving of freeze-dried bacteria from ATCC, preservation of bacteria by deep-freezing,

reviving of bacteria from preserved stock and preparation of preculture will be discussed

in detail.

Reviving Freeze-Dried Bacteria and Deep-Freezing of Bacterial Cultures

Freeze-dried Pseudomonas denitrificans (P. denitrificans, ATCC 13867) or

Paracoccuspantotrophus (P. pantotrophus, ATCC 35512) were revived in 250 mL

Erlenmeyer flasks containing 125 mL of 8 g/L nutrient broth containing beef extract and

peptone (Sigma N7519) and agitated at 250 rev/min in an incubator-shaker (New

Brunswick Scientific, Model C24) at 350C for two days. The revived bacteria were

cultivated on Tryptic soy agar in Petri dishes at 350C for three days. Bacteria from the

agar plates were used to inoculate 250 mL Erlenmeyer flasks containing 125 mL of 8 g/L

nutrient broth and grown in the incubator-shaker at 250 rev/min and 350C for a day. A









volume of 0.5 mL of bacterial suspension was combined with 0.5 mL of solution

consisting of 60% nutrient solution and 40% glycerol (v/v) in 1.5 mL microcentrifuge

tubes. The microcentrifuge tubes were flash-frozen in liquid nitrogen and then stored at -

850C. Solutions were prepared using deionized (D.I.) water and were autoclaved at

1300C and 1.5 kg/cm2 for 20 minutes before use.

Reviving of Frozen Bacteria

A portion of frozen material from a microcentrifuge tube was scratched off and

allowed to fall in a 250 mL Erlenmeyer flask containing 125 mL of 8 g/L nutrient broth.

The flask was placed in the incubator-shaker and agitated at 250 rev/min and 350C for

one to two days. The revived bacteria were cultivated on Tryptic soy agar plates at 350C

for one to three days. The agar plates were preserved in a refrigerator at 40C for up to

two weeks.

Preculture Procedure

Bacteria from the refrigerated agar plates were grown in preculture before they

were used in subsequent experiments. The bacteria were inoculated into 250 mL

Erlenmeyer flasks containing 125 mL of growth medium containing proper carbon source

and ammonia as the nitrogen source. Aerobic preculture was achieved by agitating the

bacterial suspensions at 250 rev/min and 350C in the incubator-shaker. Preculture

periods were 12 hours to 24 hours.

Reactors

The reactor system for continuous flow experiments will be discussed. The main

fermentor and its ancillary components, feed reservoir and its ancillary components,









autoclaving procedure and installation of experimental system, and initiation of

continuous reactor runs will be discussed in detail.

Overall Layout

Experiments were performed using a 2 L glass fermentor modified for continuous

feeding and withdrawal, electron acceptor switching and sampling. Figure 3-1 presents

the overall experimental configuration, including the fermentor, feeding and effluent

system, gas supply system, sampling system, and computerized operational system for

liquid and gas flow control. Feed solution was stored in an aseptic feed reservoir and was

fed to the fermentor by a peristaltic pump. The medium was pumped from the fermentor

through an effluent line.

Fermentor Assembly

A New Brunswick Scientific Bioflo 2000 fermentor or Bioflo 110 fermentor was

used for all experiments. Figure 3-2 shows a side view of Bioflo 2000 fermentor. The

fermentor consists of a 2 L glass jar, a headplate assembly and a baffle system. The

headplate assembly includes vessel mounting arms to be mounted to the main frame and

impeller assembly to be connected to the main controller. Gas was input to the fermentor

liquid through a gas sparger. The main controller installed in the main frame controlled

temperature and agitation of the fermentor. Temperature of the fermentor was

maintained at 350C and bacterial suspension in the fermentor was agitated at 200 rev/min.

A simplified drawing of the fermentor and ancillary equipment (fermentor

assembly) is shown in Figure 3-3. The assembly includes the fermentor, gas supply, gas

outflow, feed, and effluent. The fermentor was fed by a peristaltic pump (MasterFlex,

Cole-Parmer) using Tygon tubing (6409-13). The solution from the feed reservoir was

filtered by two 0.2 num Domnick Hunter Propor PES Capsule filters (model No. FEMSE-


















Effluent pump


Feed reservoir
assembly
Figure 6-1. Overall layout of experimental configuration.



Vessel mounting HeadPlate assembly
arms




S- Gas sparger




S-Impeller assembly


/Baffle system


Figure 6-2. Side view of New Brunswick Bioflo 2000 Fermentor.










Gas in
"- 0.3 4m Hepa-
/Vent filter 0.2 4m Capsule
filter
Effluent Fer
--- Feed

Peristaltic
I pump

From gas 0.2 tm Capsule
tanks asout filter




Peritaltic -- -
pump I -
O o
o o o
oo 0
1 o
Gas humidifying Gas outlet Feed reservoir
flask Fermenter flask assembly

Figure 6-3. Fermentor assembly.

020GG-PSX, filter material: polyethersulphone) in series to protect both the fermentor

and the feed reservoir from contamination. Liquid level in the fermentor was maintained

by pumping out of a length of tubing that extended from the top of the fermentor to the

desired liquid level. The working volume of the fermentor was set at 1 L. The potential

pumping rate of the effluent line was set to exceed the influent pumping rate. Input gas

was humidified by being passed through an Erlenmeyer flask containing D.I. water and

supplied to the fermentor through three 0.3 num Whatman Hepa-Vent Glass Microfiber

filters in series. The vent gas from the fermentor was passed through a gas outlet flask

(Erlenmeyer flask containing 6,000 mg/L sodium hypochlorite solution) to prevent back

contamination. The sodium hypochlorite solution was prepared by adding 30 mL of

commercial bleach solution containing 6% of sodium hypochlorite to 270 mL of D.I.

water.









Feed Reservoir Assembly

A 12 L glass jar was used as a feed reservoir (Fig. 3-4). Feed solution was

pumped into the reservoir through silicone tubing using a peristaltic pump (MasterFlex,

Cole-Parmer). Two 0.2 /m Domnick Hunter Propor Capsule filters in series were

present in the filling line to remove contamination. Air was forced out of the feed

reservoir during the filling operation and sucked into the feed reservoir while its contents

were pumped to the fermentor. This air was passed through three 0.3 /m Whatman

Hepa-Vent Glass Microfiber filters in series. When nitrate was present in the feed,

nutrients in the feed solution were divided into two groups and prepared in two separate

feed solutions in two feed reservoir assemblies. This was because significant biomass

accumulation was observed at the space between two capsule filters connected to

fermentor and the feed reservoir, resulting in significant pH increase due to high level of

denitrification at the biomass accumulation. Another capsule filter and necessary tubing

were installed to the fermentor and two pump heads were installed to the fermentor

feeding pump for two feed solutions.

Autoclaving Procedure and Aseptic Connection of Feed Reservoir Assembly to
Fermentor Assembly

All components of the feed reservoir assembly were autoclaved together. All

components of the fermentor assembly (one capsule filter on the feed line, the gas filters,

and the gas outlet flask) were also autoclaved together. A volume of 2 L of D.I. water

was added to the feed reservoir before autoclaving to ensure that proper temperature and

pressure were reached inside of the reservoir. (This was not necessary for the fermentor

because of its small size.) Autoclaving was performed at 1300C and 1.5 kg/cm2 for 20

minutes. After being autoclaved and allowed to be cool down to room temperature, D.I.











0.2 jim Capsule
filter

0.2 jim Capsule
filter


Figure 6-4. Feed reservoir assembly.

water was removed from the reservoir with a peristaltic pump (MasterFlex, Cole-Parmer)

using the outlet line.

Feed solution was prepared with D.I. water in a 20 L plastic container. After the

feed reservoir was filled with feed solution, the outlet line to the fermentor was connected

to the filter on the fermentor inlet. The outlet line of the gas humidifying flask was

connected to the outermost gas filter on the gas inlet line of the fermentor assembly.

Finally, an effluent pump was attached to the effluent line of the fermentor.

Inoculation of Fermentor and Initiation of Startup Phase

The fermentor was inoculated with precultured bacteria in a Labconco Purifier

Class 2 Safety Cabinet. A funnel was inserted into an opening on the headplate of the

fermentor before the fermentor assembly was autoclaved. A volume of 2 L of the

appropriate autoclaved nutrient solution was first added through the funnel; then









precultured bacteria were poured into the fermentor through the funnel. Since direct

sampling from the reactor medium could result in contamination, the initial biomass

absorbance was not measured. The amount of preculture added was sufficient to impart a

slight cloudiness (0.05 to 0.1 absorbance at 550 nm). The funnel was then removed from

the fermentor and the opening was closed. Before turning on the feed, cells were grown

under startup phase to approximately 0.4-0.6, depending on the experiment. Air flow rate

was set to the maximum value allowed to the fermentor to prevent oxygen limitation

because of level of carbon substrate during startup phase.

Initiation of Continuous Flow Phase

Continuous mode was initiated by starting feed and effluent flows. Feed and

effluent lines and corresponding inlet and outlet were thoroughly rinsed with ethanol

before they were connected, to avoid contamination. Connections were made

immediately after rinsing. Steady state experiments were run for at least six hydraulic

residence times or until biomass absorbance becomes stable. Air flow rates in all oxic

continuous flow phases were set to 3.0 L/min, which resulted in 70% of the saturation

DO concentration at the given temperature, in a typical oxic continuous flow phase. DO

concentrations during all other oxic continuous flow phases were assumed to be 70% of

the saturation concentration because oxygen requirement from substrate consumption rate

was quite consistent for all experiments. Nitrogen flow rate was set to the same level as

that of air during all anoxic continuous flow phase which resulted in zero DO

concentration.

Sampling from Fermentor

Samples for COD, NO2--N, NO3-N, and biomass were manually taken from the

effluent line of the fermentor. A biomass sample for contamination monitoring was taken









at the end of each experiment. This sample was obtained using a syringe connected to a

sampling port on the headplate of the fermentor. Samples for biomass absorbance

measurement were taken either from the effluent line the absorbance sampling line.

Monitoring of Contamination of Pure Culture

During the continuous experiments, bacterial suspensions from reactor effluent

were observed under microscope. The bacterial suspensions were Gram-stained and

observed under microscope. Morphologies of cells such as shape and size and motility of

cells were observed. The bacterial suspensions were inoculated on Tryptic soy agar

plates and incubated at 350C for three days, and then the bacterial colonies were

compared to the known colonies with respect to their shape, color and merging patterns.

Reactor suspensions were considered as being contaminated if an indication of

contamination was detected with respect to microscopy, Gram-stain or colony

observation.

Analytical Measurements

Protocols for analytical measurement will be discussed. Biomass absorbance, COD,

nitrate, nitrite, and nitrate reductase enzyme activity were measured as needed.

Biomass Absorbance

Cell concentration of a bacterial suspension was estimated by measuring

absorbance of the bacterial suspension at 550 nm using Spectronic Unicam Genesys 10

series spectrophotometers. A plastic cuvette or a quartz cuvette a with path length of 1

cm was used for manual measurements and a quartz flow cell (Spectronic Genesys 10

series spectrophotometers flowcell) with a path length of 1 cm was used in case of

automatic measurement.









Chemical Oxygen Demand

Chemical oxygen demand (COD) of feed was measured using HACH low-range

COD tubes and COD of reactor effluent was measured using HACG ultra-low COD

tubes. Effluent was filtered through a 0.2 yum cellulose acetate membrane before analysis.

Nitrate and Nitrite

Nitrate was measured using the HACH chromotropic acid method (NitraVer

Test'N Tube 26053-45). Nitrite was measured using the HACH diazotization method

(NitriVer Test'N Tube 26083-45). Effluent was filtered through a 0.2 Pum cellulose

acetate membrane before analysis.

Nitrate Reductase Activity

Nitrate reductase level was measured by a nitrate reductase enzyme assay using

benzyl viologen (Jones et al., 1976). Cells in the fermentor medium were harvested by

centrifugation (10,000 xg for 10 minutes at 40C) and washed with 20 mM Tris buffer

solution (pH 7). The harvesting was repeated once and cells were resuspended in the

buffer solution. These operations were carried out within 20 minutes.

The assay method was modified from the nitrate reductase enzyme assay presented

by Jones and Garland (1976). The reaction was performed in a 1 cm optical path

borosilicate cuvette. Solution containing 0.3 mM benzyl viologen and 20 mM Tris buffer

was added to fill the cuvette approximately to one half of the volume; then 200 uL of the

resuspended biomass was added, followed by 20 /L of 20 mM dithionite. A few 3 mm

glass beads were added to enhance mixing and the cuvette was filled with solution

containing 0.3 mM benzyle viologen and 20 mM Tris buffer to the top. Finally the

cuvette was sealed with a Wheaton seal, leaving no headspace.









The final concentration of benzyl viologen was 0.3 mM and the absorbance was

approximately 1.8 at 550 nm. The final biomass concentration was approximately 55 mg

dry cell/L. Washing, resuspension, and preparation of the cuvette were performed in an

anaerobic chamber (Coy Laboratory Type 'A'). After 3 minutes of absorbance

monitoring using a Spectronic Unicam Genesys 10 series spectrophotometer at 550 nm,

the enzyme assay reaction was initiated by injecting 35 ,uL of 20 mM nitrate solution into

the cuvette to give a final concentration of 6 mM nitrate. The cuvette was then inverted

twice and the initial rate of decolorization was measured.

To measure only periplasmic nitrate reductase, sodium azide, which inhibits

membrane-bound nitrate reductase (Craske and Ferguson, 1986; Sears et al., 1993), was

added to the assay to give 100 mM NaN3 in the final assay solution. The unit of nitrate

reductase enzyme activity is mol benzyl viologen/mg biomass/L/sec. In later chapters,

this unit will be specified as "units".

Membrane-bound nitrate reductase activity was calculated by subtracting

periplasmic nitrate reductase activity from total nitrate reductase activity. If an enzyme

activity value was less than 10-13 units, below which has been known as a base line

activity from previous studies (data not published), the value was considered to be zero.














CHAPTER 7
METHOD FOR ACHIEVING REPRODUCIBLE INITIAL CULTURE STATES IN
STUDY OF BACTERIAL DENITRIFICATION KINETICS

Introduction

Diauxic lag of bacteria switching between oxygen and nitrate has been reported in a

number of studies (Liu et al., 1998a, b; Gouw et al., 2001; Casasus-Zambrana, 2002;

Lisbon et al., 2002). In those studies, which consisted of a short-term (less than 0.5 days)

batch aerobic phase followed by a batch anoxic phase, length of diauxic lag and specific

growth rates of pure cultures of bacteria varied significantly, even with identical

experimental procedures and previous culture histories. For example, lengths of diauxic

lag following an anoxic preculture and aerobic batch phase varied from as short as 2

hours to as long as 10 hours (Gouw et al., 2001). Since those experiments were carried

out with direct inoculation from bacteria preserved on agar plates (Casasus-Zambrana,

2002) or after batch preculture phase (Gouw et al., 2001; Casasus-Zambrana 2002;

Lisbon et al., 2002) and the aerobic batch phases were relatively short (0.5 days or less),

there might be no opportunity for the bacterial populations to reach a consistent

physiological state before the anoxic batch phases. This inconsistency complicated the

study of diauxic lag because significant variation in experimental results led to a

significant amount of trials and efforts for a single set of experiments. For example, as

many as 11 trials were necessary to identify an experimental trend in the study of nitrate

exposure history.









Theoretically, growing bacteria in a chemostat until a steady state is reached would

provide reproducible initial conditions in terms of physiological state of bacteria. In such

steady state growth conditions, physiological conditions of bacteria are expected to be

consistent regardless of their previous culture history. The objective of the present study

was, therefore, to determine whether achieving a steady state during the aerobic growth

phase would lead to more reproducible results during the subsequent anoxic batch phase,

including length of diauxic lag and specific growth rate of bacteria.

Materials and Methods

Experimental procedures specific to this set of research will be discussed. Other

general procedures related to continuous growth of bacteria were consistent with the

procedures described in Chapter 6.

Four experiments were carried out, with two duplicate trials per experiment. Each

trial consisted of an oxic preculture phase and oxic continuous flow phase, starting with a

startup phase, that enabled the bacteria to reach constant growth conditions, and an

anoxic batch phase that allowed measurement of diauxic lag. The P. denitrificans were

grown with malate as a carbon substrate, whereas P. pantotrophus were grown with two

alternative carbon substrates: malate or acetate. These substrates were chosen to

represent differing redox states, with malate being highest and acetate lowest. The

nutrient media used with P. denitrificans were made up as recommended by Kornaros et

al. (1996) and the media used for P. pantotrophus were made up as recommended by

ATCC. The compositions of nutrient media for P. pantotrophus and P. denitrificans are

shown in Tables 7-1 and 7-2, respectively. The carbon substrates in feed solutions were

varied depending on the experiments as shown in Table 7-3. With exception of Trial









Table 7-1. Composition of nutrient solution for P. pantotrophus.
Chemicals
Inorganic salts NH4C1
MgSO4 7H20
Vishniac and Santer Trace Element Solution
Phosphates Na2HPO4
KH2PO4
Carbon source -D,L-malic acid
-Sodium acetate
Nitrateb KNO3
aVaried in feed solutions
bOnly in anoxic batch phase.
cmg NO3- -N/L


g/L in D.I. water
0.3
0.1
2 mL
4.2
1.5
1.297a
1.36a
2.88b (400c)


Table 7-2. Composition of nutrient solution for P. denitrificans.
Chemicals g/L in D.I. water
Inorganic salts NaCl 1
NH4C1 1
MgSO4 7H20 0.2
CaCl2 2H20 0.0264
Trace metals 1 drop
Phosphates K2HPO4 5
KH2PO4 1.5
Carbon source -D,L-malic acid 6.45b
Nitratec KNO3 2.88 (400d)
aTrace metal solutions containing 0.5%(w/v) each of CuSO4, FeC13, MnCl2, and
Na2MoO4.2H20.
bVaried in feed solutions
cOnly in anoxic batch phases and feed solution in Trial 2, Experiment 4.
dmg NO3- -N/L


Table 7-3. Amount of carbon substrate in feed solutions.


Experiment 1
Experiment 2
Experiment 3
Experiment 4
amg COD/L


Carbon substrate
Malic Acid
Sodium Acetate
Sodium Acetate
Malic Acid


g/L in D.I. water
0.42 (300)a
0.38 (300)a
1.28 (1000)a
1.39 (1000)a


2 of Experiment 4, nitrate was present in the feed solution and nutrients in the feed

solution were divided into two groups and prepared in two separate feed solutions as

discussed in Chapter 6 (Table 7-4). The dilution rate was set at either 0.1 h-1 or 0.03 h1.