Aquifas Development Team, Mountain View, CA

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1 Applying the Operations Version of the Aquifas+ Model to Simultaneously Control Effluent Quality, Green House Gas (GHG) and Nitric Oxide Emissions and Energy Consumption in Conventional and Advanced Wastewater Treatment Dipankar Sen and Adnan Lodhi Aquifas Development Team, Mountain View, CA Earlier version on GHG Emissions published in Proc: Session 54, Green House Gas Emissions, WEFTEC, New Orleans 2010, p Differences in GHG and Nitric Oxide Emissions for Activated Sludge and Biofilm ENR processes based on Aeration, MCRT, Mixing and Media, and Control of Emissions and Nutrients by Enhancing Process Models in an ENR Operations Simulator (Aquifas) Adnan Lodhi 1, Dipankar Sen 1,3, Clifford Randall 2, Lenny Gold 3, Karen Brandt 3, John Rawlings 4, Kartik Chandran 5 1 Aquifas Dev Team 2 Virginia Tech 3 Maryland Center for Env Training 4 City of Westminster 5 Columbia Univ ABSTRACT Practicing wastewater process engineers use mathematical models when planning, designing, optimizing, evaluating, and performing problem solving work related to proposed or existing municipal and industrial WWTPs. This paper presents the development and results of an extension and enhancement of ASM2d Model to capture GHG and Nitric Oxide Emissions. The model uses a substantially faster algorithm than ASM2d based design models such that it can be applied as a real time emissions controller and energy optimizer. To accomplish this, the authors added several components and processes. N 2 O, NO and NO 2 are introduced in the model to simulate the nitrification and denitrification pathways by the ammonium oxidizing bacteria (AOB), followed by NO 2 oxidation by nitrite oxidizing bacteria (NOB). The denitrification is broken up into four steps for ordinary heterotrophs (OHO) and polyphosphate accumulating bacteria (PAO) to go from nitrate to nitrite to nitric oxide to nitrous oxide and then to nitrogen gas. The physical portion of the model includes equations for diffusion of NO 2 and NO into the atmosphere. These equations are related to the type of aeration and mixing. Unlike ASM2d, which uses the same coefficient for several reactions, Aquifas+ uses a substantially larger number of coefficients, such as the ability to specify different half saturation constants for DO (K DO ) for fermenters, Ordinary Heterotrophs (OHO) and Poly-phosphate Accumulating Organisms (PAO under aerobic conditions, OHO and PAO for inhibition under anoxic conditions, and each step of AOB nitrification. A similar set of equations were constructed for the biofilm models. The mathematical technique used to invert the larger matrix of equations was improved to enable faster and accurate solutions. The faster algorithms were necessary for applying the model as a real time operations simulator and controller inside enhanced nutrient removal plants in Maryland, USA. The simulator can simultaneously optimize the plant operations for GHG emissions and effluent nutrients. Additionally, it is designed to optimize the plant for energy and chemical consumption for the biological treatment and water reuse applications at plants in developed and emerging markets. The model includes optimization techniques for membranes applied as membrane bioreactors, and membranes for tertiary filtration, nano-filtration and RO as used in water reuse (desalination and wastewater treatment for reuse).

2 This white paper focuses on the equations and model enhancement for simulating and controlling GHG emissions. It is part of a series of publications on plant operations for wastewater treatment and water reuse. KEYWORDS Wastewater Treatment, Water Reuse, Green House Gas, Nutrient, Operations, Control, Aquifas, Design INTRODUCTION Modeling and controlling GHG emissions in suspended growth and biofilm systems in wastewater treatment is a significant challenge because of the number of processes involved. The difficulty of converging a multi-cell plant configuration (multiple cells that are anaerobic, anoxic, aerobic, post anoxic and aerobic) and multi-process configuration (activated sludge with denitrification filters) to a solution increases markedly as the number of processes increases. Presently, none of the commercially available WWTP simulators is equipped with GHG emissions model. Also, the computational speeds of the algorithms in those models do not allow for real time operations control. This paper shows how earlier version of Aquifas has now been extended to simulate and control GHG emissions in addition to nutrients in activated sludge and biofilms. It is an extension and modification of the ASM2d (Henze et al., 2000). New components representing N 2 O, NO and NO 2 were introduced in the model to simulate the nitrification processes using two different group of nitrifying bacteria including ammonium oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB). Four denitrification processes for NO 3, NO 2, NO and N 2 O were added in the model for uptake of each of the available substrate for X OHO and X PAO. The earlier versions of Aquifas (Aquifas which was Excel based; AquaNET Aquifas which uses C# interface with Excel Visual Basic Applications) was upgraded using C# and other high level programming tools to develop Aquifas+. A second part of effort involves around conversion of Aquifas+ to a whole plant process optimization and control software in collaboration with Enhanced Nutrient Removal (ENR) plants in Maryland. This enables linkage into the plant SCADA system and other PLCs, including specific monitoring and control systems for membrane bioreactors and denitrification filters. LITERATURE REVIEW WWTP simulators consist of modules that can be configured and connected by flow streams to represent specific processes. The algorithm used in each module can vary greatly. Some modules include empirical removal rates while others may involve complex mathematical models based on detailed modeling of individual substrate removal and biomass growth processes. Examples of the latter include the International Water Association (IWA) Activated Sludge Models (ASMs), namely ASM1, ASM2d, and ASM3 (Henze et al., 2000), and the Anaerobic Digestion Model No. 1 (ADM1) (Batstone et al., 2002). The ASMs have been successfully applied both in research and practice, and serve as the benchmark for new or expanded activated sludge models (Morgenroth et al., 2000). Despite being modified in some manner, the ASMs serve as the basis

3 for suspended growth reactor (SGR) process modules in each of the individually developed and/or commercially available WWTP simulators. Matrix notation made popular by the ASMs is the consensus format for process models simulating biochemical transformation processes in wastewater treatment. The ASMs can be applied to virtually all SGR configurations including extended aeration, separate-sludge, biological nutrient removal, tertiary bioreactors, and membrane bioreactors. Generally speaking, SGR simulation results from similar ASM-type models are accepted as being comparable amongst WWTP simulators when given equivalent influent wastewater characteristics, stoichiometric and biokinetic parameter values (Boltz et al., 2008). METHODOLOGY In wastewater treatment, a set of complex equations are required to describe the various reactions in a suspended growth and biofilm processes. The equations involve several components such as organic substrate (soluble and particulate), inorganic substrate (ammonia, phosphorus), dissolved oxygen and various heterotrophic and autotrophic bacteria. A reaction system, particularly in case of a biological wastewater treatment process, can be viewed in context of mass balance modeling as a set of M biochemical reactions involving N components. The reactions most commonly found in biochemical processes are microbial growth and enzyme catalyzed reactions. Some other reactions however also take place, such as microorganism death, maintenance, etc. The main components of these reactions are microorganism population, enzymes, substrate and products. The mathematical model for biochemical reactions in Aquifas+ consists of same essential set of equations as used in IWA-ASM2d with additional processes for four step nitrification and denitrification processes; additional bacteria such as methanotrophs. There are physical reactions such as the diffusion of NO 2 and NO into the atmosphere; hydrodynamics, shape and surface characteristics of biofilm to determine the thickness of the biofilm. There are chemical reactions to determine the residual concentrations of phosphorus that may remain in various secondary and tertiary systems. The physical, chemical and biochemical reactions are interrelated within the model. Monod specific growth rate kinetics are used in the model to describe the growth of heterotrophic and autotrophic bacteria. The substrate, oxygen and nutrient utilization rates are related to the growth rates by stoichiometric factors. The methodology is broken up into two sections to explain the improvements to: (a) Model Structure; (b) Integration into an Operations Simulator that can function as a real time controller. Model Structure Model Components: The suspended growth model in IWA-ASM2d is based on single step nitrification and denitrification. It neglects the intermediate steps involved in these processes responsible for the production of N 2 O, NO and NO 2 and therefore cannot be used for nitrous oxide and nitric oxide modeling. Nitrous oxide is a component of GHG. The suspended growth model for Aquifas+ is a comprehensive extension of ASM2d in which the number of components has been increased

4 from 20 to more than 42. Some of the components are shown in Table 1. They allow the operator to: a. Simulate and control the four step nitrification and denitrification processes and consequently the nitrous oxide and nitric oxide emissions through diffusion. Three new parameters S N2O, S NO and S NO2 are introduced into the model. In addition, the nitrifying organism has been further divided into two separate group of microorganism including ammonium oxidizing bacteria X AOB and nitrite oxidizing bacteria X NOB. b. The fermentable and unfermentable fractions of readily biodegradable organic substrate (other than methanol) are modeled separately using two different components, S F and S UF. This provides flexibility in modeling the fermentation process in anaerobic bioreactors. Also the hydrolysis of slowly biodegradable substrate X S can be divided between the fermentable and unfermentable fraction of soluble substrate using a new stoichiometric coefficient, f SF. c. The organic nitrogen and orthophosphates are modeled as separate components in both particulate and soluble form and are further categorized as biodegradable and nonbiodegradable fraction. This improves the ability to simulate entrapment of particulates and their subsequent hydrolysis in the suspended solids and the biofilm. It enables much better quantification of removals achieved in MBBRs operating at low MLSS levels. d. Two new group of microorganism, obligate anaerobic fermenters X OAF and obligate anaerobic methanogens X OAM, were added into the model for simulating the fermentation and methanogenesis in anaerobic conditions. e. Two new components for tracking methane S CH4 and carbon dioxide S CO2 are also included in the model. f. Methanotrophs that use methanol, X MET, are tracked separately, together with the COD provided with methanol, S MET. This is important because the model can inform the operator how long it will take to establish the required population of X MET in suspended growth and in the biofilm when methanol is added only during certain months of the year. g. A structure that allows additional customized bacteria to be added through a custom model to accommodate reactions such as annamox types of bacteria in suspended solids and biofilm (X AMX ) Suspended Growth Processes The ASM2d model consists of 21 processes involving COD, biological nitrogen and chemical and biological phosphorus removal. The suspended growth model used in Aquifas+ has more than 74 processes, as shown in mind map provided in Annexure III. Following are the major addition to ASM2d:

5 The anoxic uptake of COD (denitrification) by heterotrophic organisms X OHO utilizing S NO3, S NO2, S NO and S N2O as electron acceptors consuming each of the available organic substrate S F, S A and S UF is modeled as separate simultaneously is modeled as separate processes. The necessitated 4 separate processes for each of the substrate adding up to a total of 12. In addition, the anoxic uptake of S A and storage of X PP by X PAO also required 8 additional processes. The growth of three new groups of bacteria, X OAF, X OAM and X MET The hydrolysis of particulate and soluble organic nitrogen and phosphorus are modeled as individual processes for aerobic, anoxic and anaerobic conditions. The lysis and decay for each of the microorganism (X OHO, X OAF, X OAM, X MET, X PAO, X PHA, X PP, X AOB and X NOB ) is modeled as separate processes for aerobic, anoxic and anaerobic conditions. The nitrification in modeled as four step process including one step oxidation of ammonium (S NH4 S NO2 ) by X AOB, oxidation of nitrite (S NO2 S NO3 ) by X NOB, and reduction of nitrite by (S NO2 S NO S N2 ) by X AOB when subjected to stress. The structure of equations used for multistep nitrification and denitrification by X OHO using S F is given in Annexure I and II. Rest of the equations for anoxic uptake of S A by X OHO and X PAO, S UF by X OHO and storage of X PP by X PAO are structured in similar manner.

6 Table 1. Partial list of Components of Aquifas+ Model Sr. No Description Units Symbol Particulate 1 Ordinary heterotrophic organism mg/l as COD X OHO 2 Obligate anaerobic fermenters mg/l as COD X OAF 3 Obligate anaerobic methanogens mg/l as COD X OAM 4 Phosphorous accumulating organisms mg/l as COD XP AO 5 Internal storage products of PAOs mg/l as COD X PHA 6 Ammonium oxidizing bacteria mg/l as COD X AOB 7 Nitrite oxidizing bacteria mg/l as COD X NOB 8 Organic substrate mg/l as COD X S 9 Inert organic material mg/l as COD X I 10 Polypolyphosphates mg/l as X PP 11 Biodegradable Orthophosphates mg/l as X P 12 Nonbiodegradable Orthophosphates mg/l as X PI 13 Biodegradable organic nitrogen mg/l as nitrogen X N 14 Nonbiodegradable organic nitrogen mg/l as nitrogen X NI 15 Inert mineral material mg/l as TSS X M 16 Metal hydroxide mg/l as TSS X MEOH 17 Metal phosphates mg/l as TSS X MEP Soluble 18 Fermentable organic substrate mg/l as COD S F 19 Fermentation products mg/l as COD S A 20 Unfermentable organic substrate mg/l as COD S UF 21 Inert organic material mg/l as COD S I 22 Dissolved oxygen mg/l as COD S O2 23 Carbon dioxide mg/l as COD S CO2 24 Methane mg/l as COD S CH4 25 Biodegradable organic phosphates mg/l as S P 26 Nonbiodegradable organic phosphates mg/l as S PI 27 Orthophosphates mg/l as S PO4 28 Biodegradable organic nitrogen mg/l as nitrogen S N 29 Nonbiodegradable organic nitrogen mg/l as nitrogen S NI 30 Ammonium nitrogen mg/l as nitrogen S NH4 31 Nitrate nitrogen mg/l as nitrogen S NO3 32 Nitrite nitrogen mg/l as nitrogen S NO2 33 Nitric oxide mg/l as nitrogen S NO 34 Nitrous oxide mg/l as nitrogen S N2O 35 Nitrogen gas mg/l as nitrogen S N2 36 Alkalinity mg/l as CaCO 3 S ALK Note: The following components not shown Methanotrophs, X MET, S Methanol ; Annamox, X AMX

7 Integration into Operations Simulator for a Real Time Plant Control Aquifas+ Simulator Aquifas+ is a Microsoft.NET based application which uses sophisticated numerical analytic technique to solve the system of non-linear model mass balance equations. The software has an advance graphical environment with drag and drop modeling of different process units in form of graphical icons. The software consists of modules that can be configured and connected by flow streams to represent a specific WWTP. The modules denote different unit processes and operations in a WWTP, a boundary condition (e.g. Influent and Effluent) or flow junctions (e.g. flow distributer and combiners). In addition to the liquid flow, each flow stream represents a set of process components as defined in Table 1, expressed in terms of their concentrations. Each flow stream carries a flow containing these process components between these modules. These modules bring about changes in just the flow (e.g. a flow divider), some or all of these process components through physical, chemical or biological processes (e.g. bioreactor), or both in flow and components (e.g. clarifier, membrane separator and combiner). The flow through the system and these components are the variables that the model solves for at each location inside a plant. In order to determine the changes in the flow and the concentration of these components, one must have the flow and mass balance equations for these variables against each node. A node is a process tank, screen or flow splitter/combiner where reactions or removals take place. The nodes are defined at a plant operations level, starting with the process and instrumentation diagram. The set of equations representing flow distribution, physicochemical and biological reactions that are taking place inside a module constitute the mathematical model for that particular module. The mass balance required to compute the concentrations of each component (Table 1) is carried out against the module. This is done by solving the set of equations simultaneously. Technical details: The equations relate input to output and contain two type of elements i.e. variables and parameters. Flow streams and component s concentrations are considered variables whereas all other coefficients are considered as constant parameters. The mass balance equations based on Monod type expression have inherent non linearities. The solution of this system of equations would give the component concentration in the modules for an equilibrium point (steady state). An analytical solution for a system of nonlinear equations is not possible. Instead, a numerical technique for inverting and solving the matrix of equations is used in Aquifas+ to solve these equations. Aquifas+ is designed using strict object oriented programming technique to model the real life plant units as domain objects. The domain objects represents the things that exist (such as the shell of a tank, diffuser, valve) or events that occur in a wastewater treatment plant. A logical view of the system is developed by integrating the domain objects to create modules for the Bioreactor, Clarifier, etc. The modules are hydraulically connected with each other through pipes representing flow streams.

8 The benefit of modeling real life objects in an application environment is several fold. In addition to giving robust architecture to the software, this strategy can be further exploited for creating an enterprise solution with asset management capabilities for WWTPs. The technique goes beyond the capabilities of simulators originally built for design to a simulator for operations that starts at the process and instrumentation diagram. It not only captures the simple control loops such as a DO control loop used in plant systems; it captures the biological and physical processes through equations traditionally used in design simulators that allows one to understand the interaction between different parts of the plant and control the plant accordingly. For example, an upstream secondary system can be simultaneously optimized to optimize the operation of a denitrification filter downstream based on a real time analysis of the true capacity of the denitrification filters, as computed from the influent ortho-phosphorus, temperature, DO and nitrate levels in the secondary effluent. RESULTS At the time of authoring this paper (Sep 2010), the model is being applied at several plants. The results are presented for the Westminster WWTP, MD, for the period when it is operated in the A 2 O configuration with one anaerobic, two pre-anoxic and aerobic reactors in series. Westminster operates in the A 2 O configuration from May to October. The RAS of 0.5Q (Q = influent flow rate) is sent to the first cell (Figure 1). The nitrate recycle of 3Q is sent to the second cell. It switches over to a MLE mode and uses chemical P removal for the coldest months of the year. In the MLE mode, the nitrate recycle is sent to the first cell. The plant inflow averages close to 20,000 m 3 /day. The plant has influent screens. It does not have primary clarifiers. The solids handling system is fairly simple. There is a holding tank followed by dewatering. The plant does take in landfill leachate into a side stream tank, runs it through dewatering, and sends the liquid portion of the leachate with the filtrate from dewatering. The combined bioreactor volume is 8540 m 3. The anaerobic cell (anaerobic section of the bioreactor) has 16.67%, the two pre-anoxic cells have 12.4% and 14.0%, and the aerobic zone has 57% of the total bioreactor volume. The aerobic zone has a length to width ratio of 3:1. There are no intermediate baffles in the aerobic zone. However, there is limited mixing along the length. The aerobic zone was modeled as 1, 2 and 3 cells in series. The screenshot of the simulated plant shown in Figure 1 shows three cells. Additional screen shots of the simulator interface, including emissions of N 2 O and NO from the aerobic zone, are shown in Annexures IV to VI. The influent parameters of the plant are given in Table 2. The kinetic parameters used for the equations for N 2 O and NO computations are shown in Tables 3 and 4. The model allows the user to change these values. The model runs to compute N 2 O and NO emissions were made with one aerobic cell of volume set equal to the combined volume of the actual three cells. This was done to simulate a complete mixed flow condition in the aerobic zone of the plant. Three different DO setpoints at an operating MCRT of 29 days were used to evaluate how decreasing the DO setpoint at this MCRT would impact the GHG emissions (Figure 2). Additionally, for an operating DO of 2.5 mg/l, the MCRT was reduced to evaluate the effect of decreasing the MCRT (Figure 3). The actual MCRT of the plant is between 20 and 30 days. The operating DO in the basin is between 1.5 and 2.5 mg/l.

9 Table 2. Influent parameters Sr. No Symbol Description Unit Value 1 TSS Total suspended solids mg/l as TSS VSS Volatile suspended solids mg/l as TSS Inerts Total inert solid mg/l as TSS 10 4 SCOD Soluble COD mg/l as COD COD Total COD mg/l as COD SCODnbio Nonbiodegradable soluble COD mg/l as COD 15 7 PCODnbio Nonbiodegradable particulate COD mg/l as COD 20 8 SCODfe Fraction of fermentable COD mg/l as COD NH4 Ammonium mg/l as Nitrogen SKN Soluble TKN mg/l as Nitrogen TKN Total Kjaldhal nitrogen mg/l as Nitrogen NO3 Nitrates mg/l as Nitrogen 0 13 NO2 Nitrites mg/l as Nitrogen 0 14 SKNnbio Soluble nonbiodegradable TKN mg/l as Nitrogen PKNnbio Particulate nonbiodegradable TNK mg/l as Nitrogen OP Ortho-Phosphorus mg/l as Phosphorus 2 17 TSP Soluble Ortho-Phosphorus mg/l as Phosphorus TP Total Phosphorus mg/l as Phosphorus TSPnbio Soluble nonbiodegradable Phosphorus mg/l as Phosphorus PPnbio Particulate nonbiodegradable Phosphorus mg/l as Phosphorus Alk Alkalinity mg/l as CaCO 3 164

10 Table 3. Kinetic parameters for X AOB and X NOB Serial Symbol Description Unit Value No AOB 1 K O2 AOB Saturation/inhibition coefficient for oxygen mg/l K NH4 AOB Saturation coefficient for ammonium mg/l K N2O AOB Saturation coefficient for S N2O mg/l K NO AOB Saturation coefficient for S NO mg/l q NO AOB Reduction rate of S NO2 1/day q NO AOB Reduction of S NO 1/day q N2O AOB Reduction of S N2O 1/day 0.6 NOB 8 K O2 NOB Saturation/inhibition coefficient for oxygen mg/l K NO2 NOB Saturation coefficient for nitrite mg/l 0.1 Note: These values were determined through calibration to plant data. The default values for q N2O and q NO are 1/10 th those for oxidation of S NH4 to S NO2 by X AOB (q NO2 = u AOB /Y AOB = 0.72/0.12 = 6). Table 4. Kinetic parameters for X OHO and X PAO Serial Symbol Description Unit Value No OHO 1 K O2 OHO Saturation/inhibition coefficient for oxygen mg/l K NO3 OHO Saturation/inhibition coefficient for NO 3 mg/l K NO2 OHO Saturation/inhibition coefficient for NO 2 mg/l K NO OHO Saturation/inhibition coefficient for NO mg/l K N2O OHO Saturation/inhibition coefficient for N 2 O mg/l μ OHO F NO3 Maximum growth rate on S F using NO 3 1/day μ OHO F NO2 Maximum growth rate on S F using NO 2 1/day μ OHO F NO Maximum growth rate on S F using NO 1/day μ OHO F N2O Maximum growth rate on S F using N 2 O 1/day 0.91 PAO 1 K O2 PAO Saturation/inhibition coefficient for oxygen mg/l K NO3 PAO Saturation/inhibition coefficient for NO 3 mg/l K NO2 PAO Saturation/inhibition coefficient for NO 2 mg/l K NO PAO Saturation/inhibition coefficient for NO mg/l K N2O PAO Saturation/inhibition coefficient for N 2 O mg/l μ PAO F NO3 Maximum growth rate on S F using NO 3 1/day μ PAO F NO2 Maximum growth rate on S F using NO 2 1/day μ PAO F NO Maximum growth rate on S F using NO 1/day μ PAO F N2O Maximum growth rate on S F using N 2 O 1/day 0.12 Note: (a) Same values are used for growth of X OHO on S A and S UF (b) These values were determined through calibration to plant data.

11 Figure 1. Aquifas+ Model in one of the configurations for the Westminster Plant, MD Note: As an operations model, the computer code is written such that the view shown above can be customized to the needs of each facility. Aquifas+ is structured to allow for such customization. Icons, display sizes, SCADA interface, etc, are part of the customization.

12 Table 5. Impact of changing DO levels Description Units Actual Plant Results of changing DO levels Volume m DO Setpoint mg/l Wasting kg/day MCRT day GHG Emission N2O emitted kg/day NO emitted kg/day % of TKN emitted % Plant Effluent OP mg/l NH4-N mg/l N2O-N mg/l NO-N mg/l NO2-N mg/l NO3-N mg/l MLVSS mg/l MLSS mg/l 4231 Figure 2: Graph showing impact of changing DO levels on the GHG emission The effects of changing the DO level while continuing to operate at a high MCRT are shown above. The percent of the plant influent TKN emitted increased from 0.9% to 1.3% as the DO was decreased. While this may not see large, the effect was muted by the high operating MCRT. This effect of MCRT can be seen from the results in Table 6 and Figure 3.

13 The next model runs are carried out using operating DO setpoint of 2.5 mg/l in the aerobic zone but decreasing the MCRT from 29 days to 5 days. The results are given in Table 6 for four different MCRTs and represented graphically in Figure 3. Table 6: Impact on changing MCRT Description Units Results of changing MCRT Volume m DO Setpoint mg/l Wasting kg/day MCRT day GHG Emission N2O emitted kg/day NO emitted kg/day % of TKN emitted % Plant Effluent OP mg/l NH4-N mg/l N2O-N mg/l NO-N mg/l NO2-N mg/l NO3-N mg/l MLVSS mg/l MLSS mg/l Figure 3: Impact of changing MCRT levels on the N2O and NO emissions

14 The results presented above indicate a significant increase in the %TKN emitted when the MCRT of the system is lowered to a range where the effluent ammonium-n (NH 4 N) begins to increase. The effluent data in Table 6 shows the increase in ammonium-n (NH 4 N) with the reduction in MCRT at 20 C. As the population of nitrifiers became insufficient for the ammonium-n load received, the concentrations of N 2 O, NO and NO 2 increased with NH 4 N, and the aeration of the aerobic zone released more of the N 2 O and NO into the atmosphere. It should be noted that this simulation was for the fine bubble diffusers (Parkson panel and membranes) used at the Westminster plant. One can run the model for coarse bubble diffusers. Coarse bubble diffusers will reduce the bubble surface area (which reduces emissions) and increase the air flow (which increases air flow). An analysis of results with coarse bubble diffusers in IFAS and conventional systems for configuration used at the Broomfield, CO WWTP is presented by Sen et al., (2010). The analysis was repeated with additional model runs to simulate the effect of dividing the aerobic zone of the plant into different number of aerobic cells, all of which were operated at the same DO setpoint (2.5 mg/l) the same DO setpoint was used across the length of the aerobic zone. This analysis shows the effect of partial plug flow regime on the N 2 O and NO emissions. Table 5 showed the results for single cell complete mix configuration in which only 0.94% of the influent TKN was emitted as N 2 O and NO. Table 7 shows the results after dividing the zone into two cells 1.5% of the TKN was emitted as N 2 O and NO. Table 8 shows the results after dividing the aerobic zone into three cells with equal volume 1.9% of the influent TKN was emitted. Figure 4 shows the breakdown of emissions between the three aerobic cells. Over 90% of the emission is from the first aerobic cell. Table 7: Impact of dividing the aerobic zone into two cells Description Units Results of Aerobic zone modeled as two cells Cell Aerobic 1 Aerobic 2 Total Volume m DO Setpoint mg/l Wasting kg/day 100 MCRT day 29 GHG Emissions N2O emitted kg/day NO emitted kg/day % of TKN emitted % 1.5% Reactor Effluent OP mg/l 0.27 NH4-N mg/l 0.07 N2O-N mg/l NO-N mg/l NO2-N mg/l NO3-N mg/l 3.04

15 Table 8: Impact of dividing the aerobic cell into three cells Description Units Results of Aerobic zone modeled as three cells Cell Aerobic 1 Aerobic 2 Aerobic 3 Total Volume m DO Setpoint mg/l Wasting kg/day 100 MCRT day 29 GHG Emissions N2O emitted kg/day NO emitted kg/day % of TKN emitted % Reactor Effluent OP mg/l 0.23 NH4-N mg/l 0.06 N2O-N mg/l NO-N mg/l NO2-N mg/l NO3-N mg/l 2.99 Figure 4. Profile of N 2 O and NO Emissions when simulating the Aerobic Zone as three cells in series

16 Table 9 and Figure 5 summarize the results observed when the aerobic zone was simulated as one cell, as two cells and as three cells in series. The emissions increased from 0.9% of the influent TKN to close to close to 1.9% of the influent TKN load when one switched from the complete mix to a plug flow pattern, while operating with a high MCRT and adequate DO for all conditions. This brings up the question what is the most accurate approach to simulate multicell configurations or plug flow patterns in the aerobic zone? Should the designer avoid a very plug flow pattern. How should the operate operate a plug flow basin? These are discussed below. Table 9: Summary of results of dividing the aerobic cell into 1, 2 and 3 cells Description Units Dividing Aerobic Cell in Cell N2O emitted kg/day NO emitted kg/day % of TKN emitted % Plant Effluent OP mg/l NH4-N mg/l N2O-N mg/l NO-N mg/l NO2-N mg/l NO3-N mg/l Figure 5. Change in the Computed Amounts of N2O and NO when switching the same reactor operating condition from complete mix to plug flow

17 DISCUSSION The results presented above show an increase in N 2 O and NO emissions when the aerobic zone was simulated as three aerobic cells in series. This is done to reflect some characteristics of a plug flow within the aerobic zone with a Length/Width ratio of 3:1. The higher emissions were because of the higher concentration of N 2 O and NO in the first aerobic cell and the high rate of air flow per unit volume to the first cell. Even if the DO set point in the first cell was lowered, the plug flow would result in higher emissions compared to a simulation where the entire aerobic zone was modeled as complete mix tank. These results point to the need for two pieces of information to further the simulation of N 2 O and NO emissions from plants: 1. The actual hydraulics within the aerobic zone should be evaluated at prior to modeling N 2 O and NO emissions. One should answer the question is the flow pattern through the aerobic zone modeled better as one or multiple aerobic cells? This question is important if the cell is not baffled at different points along its length, as was the case in Westminster. 2. One should also determine if the kinetics of nitrous oxide and nitric oxide conversion (q N2O and q NO ) for AOB (Table 3) need to be modified based on the operating conditions and measurements of N 2 O and NO emissions, as experienced over a 24 hour period. The model allows the user to change the coefficients. The research by Ahn et al. (2009, 2008) may provide some information as to how to modify the values of q N2O and q NO. Should the system behave in a manner that shows a dominance of the genes that convert ammonia to nitrite-n, relative to the genes that result in intermediate species, the values of q N2O and q NO can be increased to accelerate the formation of NO 2. The values in Table 3 can be increased from 6 to 20 day -1. Additional research and measurements in plants can help researchers understand when this may be implemented. It can also help us understand whether the modeling of nitrous and nitric oxide emissions should be as a complete mix basin prior to their modeling as a plug flow. The complete mix condition may reflect the genetic makeup of the bacteria. The model also generates the magnitude of emissions of N 2 O and NO from the anoxic and anaerobic cells. These emissions were much lower than the emissions from the aerobic cells (Sen et al., 2010). What is confirmed through the model is that periods of starvation of the nitrifiers because of insufficient DO and periods of excessive loading will increase the N 2 O and NO emissions. What is also interesting is that a long MCRT system, such as a MBR, may not generate substantially higher N 2 O and NO emissions when it is operated at DO levels of 0.5 to 1.0 mg/l to optimize the system for energy, as compared to a shorter MCRT system. The MCRT can have a more pronounced effect than DO.

18 CONCLUSIONS 1. An activated sludge model was created based on the structure proposed in IWA-ASM to simulate the emissions of N 2 O and NO in the nitrification and denitrification processes and applied to a full scale plant. 2. The equations for the ammonia-oxidizing bacteria (AOB), ordinary heterotrophs (OHO), polyphosphate accumulating bacteria (PAO) and methanotrophs were modified to allow for reactions mediated with N 2 O and NO in addition to NH 4 and NO Simulation of an actual plant showed that emissions increase with the reduction in MCRT and DO. The increase in more prominent when the operating conditions change over a range in which the effluent ammonia increases and there is partial nitrification. 4. Additional research needs to be completed to improve our understanding of how to best model and aerobic zone for these emissions, especially when switching the simulation of the aerobic zone from a complete mix to multiple cells in series. The latter can result in a higher level of emissions. If the multi-cell configuration is higher than the actual plant, it can be compensated for by changing the values of the kinetic coefficients used in the model; it can also be compensated for by using a complete mix simulation of the aerobic zone as the basis for the quantification of N 2 O and NO emissions. The question can be answered by measuring the emissions at the plant under the two configurations. 5. The enhanced model was incorporated into a plant operations and control software that can operate in real time and simultaneously control the nutrients in the plant effluent and the emissions. REFERENCES Ahn, J.-H.; Kim, S.; Pagilla, K.; Katehis, D.; Chandran, K. (2009). In Spatial and temporal variability in N 2 O generation and emission from wastewater treatment plants, Proceedings of the 2 nd Nutrient Removal Conference, Washington D.C., Water Environment Federation Washington D.C. Ahn, J-H., Yu, R., Chandran, K. (2008). Distinctive microbial ecology and biokinetics of autotrophic ammonia and nitrite oxidation in a partial nitrification bioreactor. Biotechnology and Bioengineering, 100 (6), Batstone, D. J., Keller, J., Angelidaki, I., Kalyuzhnyi, S. V., Pavlostathis, S. G., Rozzi, A., Sanders, W. T. M., Siegrist, H., and Vavilin, V. A. (2002). Anaerobic Digestion Model No.1 (ADM1), IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes. Scientific and Technical Report No. 13. IWA Publishing, London. Boltz, J.P., Morgenroth, E. and Sen, D. (2008). Mathematical modeling of biofilm and biofilm reactors for engineering design, IWA/WEF WWTmod Conference, Mont-Sainte-Anne QC, Canada. Henze, M., Gujer, W., Mino, T., and van Loosdrecht, M. (2000). Scientific and Technical Report No. 9. Activated Sludge Models ASM1, ASM2, ASM 2d and ASM3. IWA Publishing, London.

19 Morgenroth, E., Van Loosdrecht, M.C.M. and Wanner, O. (2000). Biofilm models for the practitioner. Wat. Sci. Tech. 41(4-5): Sen, D.; Lodhi, A.; Randall, C.W.; Gold, L.; Brandt, K.; Copithorn, R. R.; Pehrson, R.; Chandran, K. (2010). Improving our Understanding of the Differences between Fixed and Moving Bed Media Systems for Design, Operations and for Real Time Control of Plants (Aquifas+) to Simultaneously Enhance Nutrient Removal and Minimize GHG Emissions. Proc: WEFTEC Session 61. Biofilm Processes II. New Orleans 2010.

20 Annexure I: Aquifas+ suspended growth model matrix for X AOB and X NOB for N 2 O and NO Emissions (partial matrix shown) Sr. No Process S O2 S NH4 S N2O S NO S NO2 S NO3 X AOB X NOB Equation 1 Growth of X AOB (1) inbm, YAOB Y AOB 2 Growth of X NOB i NBM, (2) 3 Reduction of S NO2 1-1 (3) 4 Reduction of S NO 1-1 (4) 4 Reduction of S N2O 1-1 (5) Y NOB Y NOB Equations: 1 (u_aob)* [S_O2 / (KO2_AOB + S_O2) ] * [S_NH4 / (KNH4_AOB + S_NH4) ] * [S_PO4 / (KPO4_AOB + S_PO4) ] * [Salk / (Kalk_AOB + Salk)] *X_AOB 2 (u_nob)* [S_O2 / (KO2_NOB + S_O2) ] * [S_NO2 / (KNO2_NOB + S_NO2) ] * [S_PO4 / (KPO4_NOB + S_PO4) ] * [Salk / (Kalk_NOB + Salk ]* X_NOB 3 q_no2_aobdn * [KO2_NO2_AOBDN / (KO2_NO2_AOBDN + SO2) ] * [ SNO2 / (SNO2 + KNO2_AOBDN)] * X_AOB 4 q_no_aobdn * [KO2_NO_AOBDN / (KO2_NO_AOBDN + SO2) ] * [ SNO / (SNO+ KNO_AOBDN)] * X_AOB 5 q_n2o_aobdn * [KO2_N2O_AOBDN / (KO2_N2O_AOBDN + SO2) ] * [ SN2O / (SN2O+ KN2O_AOBDN)] * X_AOB Note: The actual model also accounts for the electron acceptors made available for nitrification by the denitrification of NOx by AOB

21 Annexure II: Aquifas+ suspended growth model matrix for X OHO for N 2 O and NO Emissions (partial matrix shown) Sr. No Process S F S NO3 S NO2 S NO S N2O S N2 X OHO Equations 1 Anoxic growth 1 1 YOHO _ ax 1 Y 1 (5) OHO _ ax of X OHO on S F Y _ using S OHO ax 1.14YOHO _ ax 1.14YOHO _ ax NO3 2 Anoxic growth 1 1 YOHO _ ax 1 Y 1 (6) OHO _ ax of X OHO on S F Y _ using S OHO ax 0.57YOHO _ ax 0.57YOHO _ ax NO2 3 Anoxic growth 1 1 YOHO _ ax 1 Y 1 (7) OHO _ ax of X OHO on S F Y _ using S OHO ax 0.57YOHO _ ax 0.57YOHO _ ax NO 4 Anoxic growth 1 1 YOHO _ ax 1 Y 1 (8) OHO _ ax of X OHO on S F Y _ using S OHO ax 0.57YOHO _ ax 0.57YOHO _ ax N2O Equations: μ μ μ μ K S S S S O2 _ OHO NO3 F NH4 PO4 ALK OHO _ F _ NO X 3 OHO KO2 _ OHO + SO K 2 NO3 _ OHO + SNO K 3 F _ OHO _ ax + SF KNH4 _ OHO + SNH K 4 PO4 _ OHO + SPO K 4 ALK _ OHO + SALK K S S S S O2 _ OHO NO2 F NH4 PO4 ALK OHO _ F _ NO X 2 OHO KO2 _ OHO + SO K 2 NO2 _ OHO + SNO K 2 F _ OHO _ ax + SF KNH4 _ OHO + SNH K 4 PO4 _ OHO + SPO K 4 ALK _ OHO + SALK K S S S S S O2 _ OHO NO F NH4 PO4 ALK OHO _ F _ NO X OHO KO2 _ OHO + SO K 2 NO _ OHO + SNO KF _ OHO _ ax + SF KNH4 _ OHO + SNH K 4 PO4 _ OHO + SPO K 4 ALK _ OHO + SALK K S S S S O2 _ OHO N2O F NH4 PO4 ALK OHO _ F _ N2O X OHO KO2 _ OHO + SO K 2 N2O _ OHO + SN2O KF _ OHO _ ax + SF KNH4 _ OHO + SNH K 4 PO4 _ OHO + SPO K 4 ALK _ OHO + SALK S S S (1) (2) (3) (4) Note: Model for anoxic uptake of S A and S UF by X OHO and S A and storage of X PP by X PAO is structured similarly.

22 Annexure III: Mind map of Aquifas+ suspended growth model.

23 Annexure IV. Screen shot of Aquifas+ showing influent properties.

24 Annexure V. Screen shot of Aquifas+ showing aerobic cell properties, N 2 O and NO emissions from the first aerobic cell

25 Annexure VI. Screen shot of Aquifas+ showing flow stream properties in piping between reactors