Process Optimization of a Petroleum Refinery Wastewater Treatment Facility Using Process Modeling and Site Specific Biokinetic Constants

Transcription

1 Process Optimization of a Petroleum Refinery Wastewater Treatment Facility Using Process Modeling and Site Specific Biokinetic Constants Hank W. Andres 1, David G. Kujawski 2, Oliver J. Schraa 1, Che-Jen Lin 2 and Arthur D. Wong 2 1 Hydromantis Environmental Software Solutions (ESS), Inc., 1 James Street S., Suite 1601, Hamilton, ON, Canada L8P 4R5 2 Refinery Water Engineering & Associates (RWEA) Inc., 3312 Hwy. 365, Suite 213, Nederland, TX, U.S.A ABSTRACT Correspondence andres@hydromantis-software.com In this project, an old, out-of-service secondary clarifier was converted to a Complete Mix Batch Reactor (CMBR) and was designed to provide maximum deployment flexibility so that it could be operated in a number of modes. Simulation analysis with the MantisIW model in GPS-X TM was used to determine the optimal number of batch runs to treat a high strength COD and inhibitory waste stream to meet predefined effluent criteria for discharge to the Activated Sludge Unit (ASU). The CMBR was also deployed in another instance as an isolated sidestream chemostat reactor to treat a large quantity of floating free oil that was released to the wastewater treatment plant. Simulation analysis of the chemostat reactor was used to determine the optimal incubation period for the microbes to consume the free oil under aerobic conditions. A total of four CMBR batches were required to consume the high levels of free oil. After 24 hours immediately following the first CMBR effluent blend with the oxidation ditch influent, the clarifier effluent oil and grease levels were consistently below 15 mg/l. After an MCRT process control strategy had been effectively implemented at the WWTP, a full-scale Lawrence and McCarty (1970) model field biokinetic study was executed to determine the site-specific biokinetic constants of the existing wastewater treatment facility. The site-specific biokinetic constants were determined by fitting the operating data as a function of the microbial growth rate using statistical regression analysis. For the first time since it began operation, the true organic loading capacity of the existing facility was conclusively quantified. The calibrated GPS-X TM model utilizing the site-specific biokinetic constants is a fully predictive model that can be used to analyze and optimize the wastewater treatment facility operations and accurately predict the plant performance under hypothetical operating conditions. KEYWORDS Process, Modeling, Simulation, Biokinetic Constants, Activated Sludge, Petroleum Refinery, Industrial Wastewater Treatment. INTRODUCTION Process modeling can be used to analyze and optimize industrial wastewater treatment plants (WWTPs) (e.g. Bury et al., 2002; Goodfellow et al., 2004; Schraa et al., 2004; Stricker and Racault, 2005; Takács et al., 1998), but model customization may be required to handle pollutants not commonly found in domestic wastewater. In 2007, Hydromantis ESS, Inc. developed the MantisIW activated sludge model which provides a general modeling framework for industrial activated sludge plants and allows for flexibility in handling a wide variety of

2 industrial wastewaters including those found in the petroleum, petrochemical, organic chemical, pulp and paper, pharmaceutical, and food and beverage industries (Schraa et al., 2007). The objective of the current paper is to demonstrate how the MantisIW model, as implemented in the GPS-X TM software package, can be used to evaluate the capacity of a petroleum refinery wastewater treatment facility by presenting a successful case study. This case study involves using the model to quantify the impact of deploying a sidestream Complete Mix Batch Reactor (CMBR) to partially treat high-strength wastewater offline from the main wastewater treatment facility. This paper will also discuss how site-specific biokinetic constants were determined for the wastewater treatment facility from a full-scale field study that took place from October 1, 2010 to December 15, MantisIW MODEL DESCRIPTION The MantisIW industrial activated sludge model provides sufficient detail in terms of the wastewater characterization and the biological and physical processes modeled to allow for application of the model in a wide variety of industries. The model utilizes aspects of the ASM1 (Henze et al., 2000), ASM2d (Henze et al., 2000), Mantis (Hydromantis, 2010), and Baker (1994) biological models along with a revised categorization of the influent chemical oxygen demand (COD) and added biological and physical transformations. The user has flexibility in choosing which aspects of the model to apply in a given situation by using the appropriate influent categorization and the available model components. The inclusion of multiple model components allows for tracking of a variety of industrial pollutants. The main features of the MantisIW model include the following: Industrial-specific classification of organic influent COD Multiple transformation processes for COD components: volatilization, adsorption, and biological growth. Certain COD components can undergo volatilization and biological oxidation simultaneously. Description of volatilization: current activated sludge models do not separately account for contributions from surface volatilization and air stripping due to aeration. Impact of nutrient and alkalinity requirements on all biological growth processes in model: all processes can be limited if nutrient or alkalinity concentrations are low. The impact of sulfur oxidation on alkalinity is included. The model also includes the following features: Simultaneous nitrification/denitrification Inhibited biological oxidation of toxic compounds modeled using the Haldane equation Biological oxidation of reduced sulfur compounds Modeling the fate of colloidal material

3 In addition to the existing ASM1 influent COD state variables (i.e. readily biodegradable substrate, slowly biodegradable substrate, soluble inert organic material and particulate inert organic material), the MantisIW industrial-specific COD model components or state variables have been categorized by considering the physical, chemical and biological properties (biodegradability, hydrophobicity and volatility) of the different organic compounds. Consideration was also given to the classes of compounds that are normally tracked in industrial WWTPs due to health, safety and environmental concerns. The MantisIW influent COD variables are defined below in Table 1, with accompanying typical and representative compounds in terms of biodegradability, hydrophobicity and volatility. Table 1 Definition of MantisIW Influent COD Variables Influent COD variable Typical compounds Representative compound Oxygenated organic solvents Alcohols Methyl ethyl ketone Aldehydes and ketones Carboxylic acids Esters Ethers Halogenated organic solvents Alkyl halides Chloroform e.g. chloroform, carbon tetrachloride, methyl iodide Short-chain aliphatic compounds Hydrocarbons with less Methyl cyclohexane than 18 carbon atoms including: alkanes, alkenes, alkynes, fats and oils Long-chain aliphatic compounds Hydrocarbons with 18 or Eicosane (C 20 H 42 ) more carbon atoms including: alkanes, alkenes, alkynes, fats, oils Monocyclic aromatic compounds Aromatic compounds with Ethyl benzene single benzene ring e.g. benzene, toluene, styrene, xylene Polycyclic aromatic compounds Aromatic compounds with Fluoranthene multiple benzene rings e.g. naphthalene, phenanthrene, benzo[a]pyrene Halogenated aromatic compounds Aryl halides 1,2,4-Trichlorobenzene e.g. bromobenzene, chlorobenzene) Toxic or inhibitory compounds Phenolic compounds Phenol Colloidal material Lignins Lignins

4 The MantisIW model includes the following transformation processes: Aerobic and anoxic growth of heterotrophs on biodegradable substrate Inhibited aerobic and anoxic growth of heterotrophs on toxic compounds Adsorption and subsequent aerobic/anoxic heterotrophic growth on adsorbed substrate Volatilization due to surface evaporation and air stripping Hydrolysis of slowly biodegradable substrate and particulate organic nitrogen Decay of heterotrophs Ammonification of soluble organic nitrogen to ammonia Aerobic growth and decay of nitrifiers Aerobic growth and decay of sulfur oxidizing organisms The influent COD components can undergo one or more transformation processes depending on their individual characteristics. The possible transformations for each influent COD component in the model are described in Table 2. Table 2 Possible Transformations for Influent COD Components in MantisIW Model Type of Organic Compound Readily biodegradable, soluble, non-volatile Slowly biodegradable, particulate Readily biodegradable, soluble, volatile Biodegradable toxic or inhibitory compounds Partially miscible, nonvolatile Partially miscible, volatile COD Components Included Possible Transformations Readily biodegradable substrate Aerobic/anoxic heterotrophic growth Slowly biodegradable Hydrolysis to readily substrate biodegradable substrate Oxygenated solvents Aerobic/anoxic heterotrophic Halogenated solvents growth Short-chain aliphatic Volatilization compounds Mono-cyclic aromatic compounds Phenol Inhibited aerobic/anoxic heterotrophic growth Volatilization Long-chain aliphatic Adsorption compounds Aerobic/anoxic heterotrophic Poly-cyclic aromatic growth on adsorbed material compounds Colloidal material Halogenated aromatic Adsorption compounds Aerobic/anoxic heterotrophic growth on adsorbed material Volatilization The COD transformation processes in MantisIW are presented in Figure 1 (overview of COD transformations) and Figure 2 (overview of biological growth processes). The transformations are shown along with the required electron acceptors and nutrients.

5 Legend Soluble COD Long-chain aliphatic Particulate COD Transformation Processes Poly-cyclic aromatic Adsorption Adsorbed compounds Electron Acceptors Nutrients/Alkalinity Colloidal Halogenated aromatic Oxygen or nitrate Oxygenated solvents Volatilization Ammonium Soluble phosphorus Alkalinity Halogenated solvents Mono-cyclic aromatic Oxygen or nitrate Inert Decay Products Short-chain aliphatic Phenolic Oxygen or nitrate Biomass Growth Heterotrophic biomass Decay Readily biodegradable Hydrolysis Slowly biodegradable Figure 1 Overview of COD Transformation Processes in MantisIW (Adsorption, Volatilization, and Heterotrophic Growth/Decay; Schraa et al., 2007) Legend Soluble Substrate Particulate Substrate Transformation Processes Oxygen or nitrate Nutrients Alkalinity Electron Acceptors Nutrients/Alkalinity Substrate COD Biomass Growth Heterotrophic biomass Inert Decay Products Oxygen Nutrients & alkalinity Sulfate Soluble phosphorus Reduced Sulfur Biomass Growth Sulfur oxidizer biomass Decay Ammonium Oxygen Nutrients & alkalinity Biomass Growth Nitrate Nitrifier biomass Particulate organic N Slowly biodegradable Ammonification Soluble organic N Hydrolysis Figure 2 Overview of Biological Growth Processes in MantisIW (Schraa et al., 2007)

6 PROCESS OPTIMIZATION CASE STUDY PETROLEUM REFINERY WASTEWATER TREATMENT FACILITY The MantisIW model was used to optimize a petroleum refinery WWTP located in the Midwestern United States with an overall mandate to evaluate the wastewater treatment plant capacity and improve compliance reliability by using innovative process control strategies. Another goal of this study was to establish site-specific biokinetic constants and quantify the true operational capacity of the existing wastewater treatment facility. Several opportunities for improved WWTP performance were identified and evaluated using the MantisIW model as implemented in the GPS-X TM software package, which is considered one of the most advanced dynamic modeling software packages available (Lee et al., 2010). The petroleum refinery WWTP treats the wastewater from a refinery which produces 85,000 barrels per day (BPD) of sour crude oil. The refinery has the typical challenges of profitable operations while meeting permit levels for wastewater discharge and sludge disposal. The wastewater treatment facility operates under a National Pollutant Discharge Elimination System (NPDES) permit and treats an average wastewater flow of approximately 0.8 MGD. The WWTP process design consists of a single train API separator, equalization tank, storm water tank, diversion tank, an activated sludge unit (ASU) with an oxidation ditch configuration and brush aerator oxygen supply system, a secondary clarifier, and 3 non-aerated polishing lagoons in series. The ASU dissolved oxygen (DO) levels are typically the limiting factor for plant loading capacity, and the plant has often been forced to operate with low DO residuals. Since there is no Dissolved Air Flotation unit (DAF) between the API Separator and the ASU, there is little buffering capacity when high levels of free oil enter the process sewer. Refinery Water Engineering and Associates, Inc. (RWEA) partnered with Hydromantis Environmental Software Solutions, Inc. (HESSI) with the goal of creating a calibrated process model of the petroleum refinery WWTP to provide process insight and assist with maximizing the available plant capacity. A schematic of the plant, as represented in GPS-X TM, is shown in Figure 3. Figure 3 Petroleum Refinery WWTP GPS-X TM Process Model

7 The main scope of work for the petroleum refinery WWTP optimization is outlined below: 1. Maximize the capacity of the existing wastewater treatment facility and infrastructure. 2. Determine the maximum plant loading capacity and establish site-specific biokinetic constants for the existing wastewater treatment facility. 3. Implement operational changes and process control strategies to improve oxygen transfer efficiency and oxygen utilization rates. Complete Mix Batch Reactor Implementation In May 2010, to maximize the wastewater treatment capacity while utilizing existing infrastructure, a project was completed to convert an old, out-of-service secondary clarifier into a Complete Mix Batch Reactor (CMBR), equipped with a new fine bubble diffused air system. The CMBR was designed to provide maximum deployment flexibility so that it could be operated in a number of modes, including: 1. In batch or continuous mode of operation. 2. As an isolated reactor in series or parallel configuration with the ASU. 3. As a continuous feed reactor for an increased capacity extension of the ASU. 4. For isolated treatment of stored high-strength influent wastewater. Some of this wastewater was formerly deemed untreatable at full strength, and was slowly blended with influent to the ASU. 5. As an isolated sidestream chemostat reactor for the treatment of intermittently generated liquid waste streams which were formerly shipped offsite for disposal, such as amine solutions, phenolic caustics, sulfidic caustics, undesirable slop oils etc. 6. For onsite microbial population incubation or controlled adaptation of the ASU microbes for specific types of influent contaminant treatment. The CMBR effluent can be discharged to multiple locations in the plug-flow ASU (i.e. step feed) or directly to the polishing lagoons. A GPS-X TM model of the CMBR, shown in Figure 4, was created to evaluate the performance of different CMBR operating modes under different influent loading conditions. Figure 4 Complete Mix Batch Reactor (CMBR) GPS-X TM Process Model

8 NH3-N Concentration (mg/l) COD Concentration (mg/l) The first mode of operation that was evaluated using the model was operating the CMBR as a sidestream chemostat reactor for the treatment of intermittently generated high-strength COD and inhibitory waste streams which also contain significant amounts of amine solutions, phenolic caustics, sulfidic caustics, undesirable slop oils, hydrogen sulfide, and spent catalysts which are known to be inhibitory. The model was calibrated with available operating data from previous batch reactor runs in this mode of operation. Influent concentrations of the high-strength waste included influent COD concentrations ranging from 2,500 mg/l to 4,000 mg/l, influent NH 3 -N concentrations ranging from 275 mg/l to 350 mg/l, and influent phenol concentrations ranging from 250 mg/l to 400 mg/l. Effluent criteria for discharge to the ASU was an effluent COD concentration below 400 mg/l, an effluent NH 3 - N concentration below 100 mg/l, and an effluent phenol concentration below 40 mg/l. The CMBR reactor was initially seeded with 75% mixed liquor from the ASU and 25% was filled with the high-strength wastewater. The results from the simulation analysis, shown in Figure 5, indicated that with each subsequent batch, the contaminant removal efficiencies decreased and the effluent concentrations increased due to biomass growth inhibition from the high-strength influent wastewater. The analysis indicated that after four six-hour batch runs, the CMBR would need to be dumped and reseeded to meet the effluent criteria. Without this valuable insight from the simulation analysis, a trial and error approach would have been applied in the field and the effluent concentrations from the CMBR would have been significantly higher Effluent COD Limit Time (h) Effluent NH 3 -N Limit Time (h) Figure 5 CMBR Chemostat Effluent for High-Strength Wastewater Treatment

9 Oxygen Uptake Rate (mg/(l.h) A second modeling application was executed during a wastewater plant upset which occurred on July 15, A combination of inadvertent sewer dumps, malfunctioning WWTP equipment, and rain resulted in an extremely difficult to treat influent stream with greatly reduced diversion tank availability. The API separator and equalization tank were overloaded and functioning at a rate well below their design capacity. The ASU (i.e. the oxidation ditch and secondary clarifier) and the first lagoon became coated with 1.5 inches of floating free oil. NPDES permit compliance became threatened for a number of contaminants. The oxidation ditch oxygen uptake rates (OUR) decreased below 15 mg/(l. h) during the plant upset. The CMBR was deployed as an isolated sidestream chemostat reactor to treat the floating free oil that was released to the wastewater treatment plant. Floating free oil was collected and removed from the oxidation ditch, secondary clarifier and lagoon surfaces by a vacuum truck for input to the chemostat reactor. Simulation analysis of the chemostat reactor was used to determine the optimal batch run length for the microbes to consume the free oil under aerobic conditions. The OUR in the CMBR was plotted to track the microbial activity and is shown in Figure 6. The results showed that after a four hour period, the microbial activity under aerobic conditions was minimal, hence a four hour batch run length was chosen for the chemostat reactor so that the free oil could be treated as efficiently as possible Time (h) Figure 6 CMBR Chemostat Oxygen Uptake Rate of Microbes for Free Oil Treatment The CMBR was initially seeded with 75% mixed liquor from the ASU, while the remaining 25% of the tank was filled with the floating free oil from the vacuum truck discharge. After the first four hour batch, 50% of CMBR batch was blended into the oxidation ditch influent. The CMBR was then refilled with more vacuum truck discharge. This batching procedure was repeated a total of four times. There were two main benefits to the batch procedure, the first was that the microbes were able to consume the high levels of free oil and the second was that biomass in the main treatment system was augmented with CMBR biomass that was conditioned to consume the free oil in the ASU. After four hours of CMBR operation in a chemostat mode (i.e. after the first batch and during batches #2 to #4), there were no free oil observed in the CMBR. After 14 hours of oxidation ditch operation immediately following the first CMBR effluent blend with the ASU influent,

10 there were no remains of free oil on the surface of the oxidation ditch or the secondary clarifier. During the peak periods of the floating free oil upset, the oxidation ditch bulk water oil and grease levels near the influent point of the ASU ranged from 1,000 mg/l to 5,000 mg/l. The oxidation ditch float oil and grease levels near the influent point of the ASU ranged from 10,000 mg/l to 100,000 mg/l. However, after 24 hours immediately following the first CMBR effluent blend with the oxidation ditch influent, the clarifier effluent oil and grease levels were consistently below 15 mg/l. Mean Cell Retention Time (MCRT) Process Control Implementation In an effort to further maximize the treatment capacity of the existing wastewater treatment facility, the plant operational control approach was switched from a Food-to-Mass Ratio (F:M) strategy to a Mean Cell Retention Time (MCRT) strategy. This modification to the process control strategy began in July In some wastewater applications, the use of the F:M strategy for control of the activated sludge process is sufficient. However, in many types of industrial wastewater treatment applications, this strategy is deficient due to: 1. Wide ranges of variability in the influent. 2. Wide ranges of relative biodegradability of the substrate in the influent. 3. The intermittent presence of biologically toxic and inhibitory compounds in the influent. Inherently, the actual calculation of F:M has several pitfalls, including: 1. In petroleum refinery wastewater, there is no efficient and representative test for the substrate. A Biochemical Oxygen Demand (BOD 5 ) test would be representative, but does not have a quick enough turnaround time for the process information to be applicable. In addition, it requires that the bacteria used in the test are acclimated to the wastewater of interest. Conversely, the use of the MCRT strategy does not depend on measuring the substrate. 2. Unlike the use of the MCRT strategy, an F:M strategy cannot be directly related mathematically to the microbial growth rates and site-specific biokinetic constants. 3. Unlike the MCRT strategy, the process for determination of the optimum target control ranges for F:M is not practical under the operating conditions of a petroleum refinery activated sludge process. As such, the optimum target F:M ranges are typically based on another plant s design specifications, which may not match the specific plant s process considerations. 4. Adjustment of the waste activated sludge (WAS) flow rate to control the F:M ratio is a trial and error process. With the use of the MCRT strategy, the WAS flow rate is calculated directly and administered to hit the target control range. Since an MCRT process control strategy has been implemented at the WWTP, increased ASU contaminant removal efficiencies and process stability have been observed.

11 Determination of the Maximum Plant Capacity and Site-Specific Biokinetic Constants Process control and operation of a petroleum refinery WWTP is not always straightforward due to the high number of process variables involved in biological wastewater treatment. Also, there are many other non-biological chemical mechanisms occurring simultaneously in a bioreactor, which may appear to be the result of biological treatment, but may in fact be facilitated by enzymatic reactions, chemical oxidation, precipitation, adsorption, ligand complex formation reactions, sludge entrapment and air stripping. To quantitatively define the true performance of the specific microbiological population functioning in a given plant, the site-specific biokinetic constants must be determined. Effective process control is dependent on being able to quantify the actual kinetic and metabolic reactions of microbial growth in the system. This knowledge culminates in maximizing the true operational plant capacity, starting with maximization at the individual microbial cell level. Once the MCRT process control strategy had been effectively implemented at the WWTP, a fullscale Lawrence and McCarty model field biokinetic study (Lawrence and McCarty, 1970) was executed from October 1, 2010 to December 15, 2010 to determine the site specific biokinetic constants of the existing wastewater treatment facility. The Lawrence and McCarty model, which can incorporate the Monod equation for relatively non-inhibitory activated biological systems and the Haldane equation for severely inhibitory biological systems, is the most flexible and accepted model for the field determination of biokinetic constants for industrial wastewater facilities. The 3 MCRT s that were targeted during the study, which is the minimum requirement for curve fitting, were 15 days, 21 days and 7 days respectively. During the biokinetic study, the CMBR was operated in continuous mode in parallel with the main ASU and the CMBR was not operated in a sidestream chemostat mode for the entire duration of the study. Once the operating data from the biokinetic study was collected, a rigorous data screening process was completed using information recorded in the operator logs, supplemental lab data and statistical analysis to remove outliers from the data set. After the data set had been screened and the outliers removed, the biological kinetic expressions were obtained by fitting the operating data as a function of the microbial growth rate using statistical regression analysis. The determination of site-specific biokinetic constants, shown in Table 3, conclusively quantified the true organic loading capacity of the existing facility for the first time since it began operation. Table 3 Site-Specific Biokinetic Constants for the Existing Wastewater Treatment Facility Biokinetic Constant Value k Maximum Substrate Utilization Rate mg COD mg VSS -1 d -1 K s Half Saturation Constant mg COD L -1 K Specific Substrate Utilization Coefficient L mg COD -1 d -1 Y Cell Yield mg VSS mg COD -1 K d Decay Rate Coefficient 0.01 d -1

12 Concentration (mg/l) Suspended Solids (mg/l) Once the site-specific biokinetic constants were determined, they were integrated into the GPS- X TM model of the wastewater treatment facility and the model was calibrated using operating data from the field biokinetic study period. Calibration of biological wastewater treatment process models involves characterizing the influent waste streams, specifying operational variables, and adjusting certain key model parameters to minimize the error between the measured and predicted data. The model calibration results using the site-specific biokinetic constants are shown in Figure 7, Figure 8 and Figure /01/10 10/11/10 10/21/10 10/31/10 11/10/10 11/20/10 11/30/10 12/10/10 MLSS - Measured MLSS - Simulated MLVSS - Measured MLVSS - Simulated Figure 7 GPS-X TM Model Calibration Results Oxidation Ditch Activated Sludge Unit /01/10 10/11/10 10/21/10 10/31/10 11/10/10 11/20/10 11/30/10 12/10/10 COD - Measured TSS - Measured COD - Simulated TSS - Simulated Figure 8 GPS-X TM Model Calibration Results Secondary Clarifier Effluent TSS and COD

13 Oil & Grease (mg/l) NH3-N (mg/l) /01/10 10/11/10 10/21/10 10/31/10 11/10/10 11/20/10 11/30/10 12/10/ O&G - Measured NH3-N - Measured O&G - Simulated NH3-N - Simulated Figure 9 GPS-X TM Model Calibration Results Clarifier Effluent Oil & Grease and NH 3 -N Each plot shows the simulated results (solid lines) and the measured data (data points) over time. When reviewing the calibration results, it should be noted that typical wastewater measurements contain error due to analytical variability, sampling location, and normal random process variability. Therefore it is not possible to match the data exactly. Overall the model is matching the plant s performance and response to influent variability reasonably well and the model is a satisfactory representation of the process. The biomass (MLSS and MLVSS) and effluent concentrations are consistent between the model and the sampling results. The calibrated GPS-X TM model utilizing the site-specific biokinetic constants is a fully predictive model that can be used to analyze and optimize the wastewater treatment facility operations and accurately predict the plant performance under hypothetical operating conditions. Predictive control of complex biological wastewater treatment systems is the next stage to achieve maximum utilization of refinery treatment assets as well as maintaining effluent quality. Just as chemical reaction kinetics are used in every upstream refinery unit operation to optimize the process, biological kinetic modeling can be utilized to achieve a greater control over environmental stewardship and provide a return on investment. FUTURE WORK Now that the maximum plant loading capacity has been determined and site-specific biokinetic constants for the wastewater treatment facility have been established and incorporated into the process model, the calibrated GPS-X TM model could be used the evaluate various operational and process control strategies to improve the oxygen transfer efficiency and the oxygen utilization rates of the existing WWTP. Algorithms could also be added to the model to quantify the operating costs that are related to process control modifications and compare plant performance and predicted effluent quality with the associated costs.

14 CONCLUSIONS The MantisIW activated sludge model provides a general modeling framework for industrial activated sludge plants and allows for flexibility in handling a wide variety of industrial wastewaters including those found in the petroleum refinery industry. The model utilizes aspects of several published biological models along with a revised categorization of the industrialspecific influent COD state variables and biological and physical process transformations. The model can be used to optimize and evaluate process operation alternatives of industrial wastewater treatment facilities. An old, out-of-service secondary clarifier was converted into a Complete Mix Batch Reactor (CMBR) to maximize the WWTP capacity while utilizing existing infrastructure. The CMBR was designed to provide maximum deployment flexibility so that it could be operated in a number of modes. In a case where an intermittently generated high-strength COD and inhibitory waste stream required in the CMBR, simulation analysis indicated that with each subsequent batch, the contaminant removal efficiencies decreased and the effluent concentrations increased due to biomass growth inhibition from the high-strength influent wastewater. The analysis indicated that after four six-hour batch runs, the CMBR would need to be dumped and reseeded to meet the effluent criteria. Without this valuable insight from the simulation analysis, a trial and error approach would have been applied in the field and the effluent concentrations from the CMBR would have been significantly higher. The CMBR was also deployed as an isolated sidestream chemostat reactor to treat a large quantity of floating free oil that was released to the wastewater treatment plant. Simulation analysis of the chemostat reactor was used to determine the optimal batch run length for the microbes to consume the free oil under aerobic conditions. The results showed that after a four hour period, the microbial activity under aerobic conditions was minimal, hence a four hour batch run length was chosen for the chemostat reactor so that the free oil could be treated as efficiently as possible. A total of four CMBR batches were required to consume the high levels of free oil. During the peak periods of the floating free oil upset, the oxidation ditch bulk water oil and grease levels near the influent point of the ASU ranged from 1,000 mg/l to 5,000 mg/l. However, after 24 hours immediately following the first CMBR effluent blend with the oxidation ditch influent, the clarifier effluent oil and grease levels were consistently below 15 mg/l. In an effort to further maximize the treatment capacity of the existing wastewater treatment facility, the plant operational control approach was switched from a Food-to-Mass Ratio (F:M) strategy to a Mean Cell Retention Time (MCRT) strategy. Since an MCRT process control strategy has been implemented at the WWTP, increased ASU contaminant removal efficiencies and process stability have been observed. Once the MCRT process control strategy had been effectively implemented at the WWTP, a fullscale Lawrence and McCarty model field biokinetic study was executed to determine the sitespecific biokinetic constants of the existing wastewater treatment facility. Once the operating data from the biokinetic study was collected and the outliers were removed from the data set, the biological kinetic expressions were obtained by fitting the operating data as a function of the microbial growth rate using statistical regression analysis methods. The determination of sitespecific biokinetic constants conclusively quantified the true organic loading capacity of the existing facility for the first time since it began operation.

15 Once the site-specific biokinetic constants were determined, they were integrated into the GPS- X TM model of the wastewater treatment. The calibrated GPS-X TM model utilizing the sitespecific biokinetic constants is a fully predictive model that can be used to analyze and optimize the wastewater treatment facility operations and accurately predict the plant performance under hypothetical operating conditions. Process models with site-specific biokinetic constants are a tool that can be accepted by regulatory agencies to quantify the maximum plant capacity of a wastewater treatment facility and determine if the existing facility is adequate to treat the anticipated wastewater load or if capital improvements must be made to the wastewater treatment facility to provide an adequate level of treatment. REFERENCES Baker, A.J. (1994) Modeling Activated Sludge Treatment of Petroleum and Petrochemical Wastes. PhD. Thesis, McMaster University, Hamilton, Ontario, Canada. Bury, S.J.; Groot, C.K.; Huth, C.; Hardt, N. (2002) Dynamic simulation of chemical industry wastewater treatment plants. Wat. Sci. Technol., 45 (4-5), 355. Goodfellow, J., Malyk, B., Hahn, D., Johnson, D. (2004) Evaluation of Biological Phosphorous Removal Versus Chemical Phosphorous Removal at a Potato Processing Facility. Proceedings of the WEF/A&WMA 10th Annual Industrial Wastes Technical and Regulatory Conference, August 22-25, Philadelphia, Pennsylvania. Henze, M.; Gujer, W.; Mino, T.; van Loossdrecht, M. (2000) Activated Sludge Models ASM1, ASM2, ASM2d and ASM3; Scientific and Technical Report No 9; IWA Publishing, London, England. Hydromantis ESS, Inc. (2010) GPS-X 6.0 Technical Reference Manual. Hamilton, ON, Canada. Lawrence, A.W.; McCarty, P.L. (1970) Unified Basis for Biological Treatment Design and Operation. J. Sanitary Engineering Division, ASCE, 96, 757. Lee, J.S.; Umble, A.K.; Nichols, G.; Lopez, G. (2010) The Control of Effluent Nitrate Spikes and Biomass Loss from Foaming in the Membrane Bioreactor BNR Process at Kyrene WRF, Tempe, AZ. Proceedings of the Water Environment Federation s 83rd Annual Technical Exhibition and Conference, October 2-6, New Orleans, Louisiana. Schraa, O.; Belia, E.; Churn, C.C. (2004) The Use of Process Modeling to Investigate the Performance of a Large Industrial Wastewater Treatment Plant. Proceedings of the Water Environment Federation s 77th Annual Technical Exhibition and Conference, October 2-6, New Orleans, Louisiana. Schraa, O; Monteith, H.; Snowling, S; Andres, H. (2007) A New Activated Sludge Model for Industrial Wastewater Treatment Facilities. Proceedings of the Water Environment Federation s Industrial Water Quality Conference, July 29-August 1, Providence, Rhode Island. Stricker, A.E.; Racault, Y. (2005) Application of Activated Sludge Model No. 1 to biological treatment of pure winery effluents: case studies. Wat. Sci. Technol, 51 (1), 121. Takács, I.; Lockwood, S.; Caplis, J.R. (1998) Simulation model helps manufacturing facility maintain regulatory compliance, optimize treatment processes, and train operators. Industrial Wastewater, May/June 1998.