Simulation and Verification of a Cyclic Aeration ...

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SIMULATION AND VERIFICATION OF A CYCLIC AERATION CONTROL SYSTEM by Randal W. Samstag, P.E. Senior Sanitary Engineer Tetra Tech/KCM, Inc. 1917 First Avenue Seattle, WA 98101 Andre Gharagozian, E.I.T. Civil Engineer, TetraTech/KCM, Inc., Seattle, WA Gil Bridges Wastewater Supervisor, Olympus Terrace Sewer District, Mukilteo, WA

ABSTRACT Cyclic aeration has been practiced at several locations for nitrogen removal in activated sludge plants. Little data is available in the literature, however, to demonstrate the performance of actual systems compared to simulations based on process theory. In the current paper, the authors present data from field measurements of process parameters and compare these data to results derived from a computer simulation of the process. A series of control options were evaluated, using timers, oxidation-reduction potential (ORP), and dissolved oxygen (DO). It is concluded that a combined control strategy using all three—timers, ORP and DO—best satisfies the goals of reduced energy consumption and enhanced control of effluent quality. KEYWORDS Process control, simulation, biological nitrogen removal, oxidation-reduction potential, dissolved oxygen, intermittent aeration. BACKGROUND The Olympus Terrace Sewer District (“the District”) operates a wastewater treatment facility in Mukilteo, Washington with a capacity of 10,000 m3/day (2.6 million gallons per day). The plant uses the activated sludge process in an oxidation ditch configuration. Figure 1 is a schematic of the process configuration (see Figure 1 “Plant process schematic”). The process had been operated with a sufficiently long solids residence time to promote conversion of ammonia nitrogen to nitrate. This presented the opportunity to save power and reduce the consumption of alkalinity by recovering nitrate. If anoxic conditions could be created in the reactor, then nitrate could be used as an oxygen source to break down reduced organic materials in the wastewater. This could be accomplished by shutting off the aeration. However, since the rotor aerators also provide mixing in an oxidation ditch, a separate mixer would be required. The District elected to purchase a mixer, which would permit cyclic operation of the rotor aerators for nitrate recovery. With the help of a grant from the Washington State Energy Office (Gray & Osborne, Inc. 1993)

Copyright (c) 2000 Water Environment Federation. All Rights Reserved.

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the District purchased a 3 kW (5.0 hp) propeller mixer. This mixer has proven to have sufficient power to prevent separation of mixed liquor in the aeration tank when all the rotor aerators are turned off. Initially, the mixer was operated on manual control. In 1997, the District undertook a project to install a control system to optimize operation. Goals for process control were as follows: • • •

Reduce energy consumption Control drops in alkalinity and pH Produce the best possible effluent quality

The control system was conceived to turn the aerators and mixers on and off based on process measurements of oxidation-reduction potential (ORP) and dissolved oxygen (DO). The current work was undertaken to assist in configuring the process control system. LITERATURE REVIEW Cyclic aeration is a variation of the activated sludge process in which aerobic and anoxic conditions are created in the same tank by turning on and off the aeration supply. To keep solids in suspension while the aeration is turned off, a separate mixer is required for cyclic aeration systems. In the literature this process is also frequently called “intermittent aeration.” Cyclic aeration has been recognized as an effective tool for nitrate recovery in nitrifying wastewater treatment systems (Schwinn 1977; Samstag, et al. 1988). Cyclic aeration is a form of biological nitrogen removal. There is a wide range of literature on this subject. For a complete description of the biology and chemistry of biological nitrogen removal see Randall, et al. (1992). Cyclic aeration can be controlled by timers or by process measurements. A promising technique is to use readings of ORP and DO to turn aerators and mixers on and off. ORP is a measure of the electron activity in a liquid treatment system. ORP is also called “redox potential,” especially in the chemical literature. Use of ORP for optimization of nutrient removal systems has been recommended in the literature. Several commercial instruments are available that are suitable for use in activated sludge reactor tanks. The ORP measurement has been used as a process control tool in wastewater treatment for many years. Early researchers (Hood 1948; Rohlich 1948; Nussberger 1953) demonstrated correlations between the degree of wastewater treatment and the ORP measurement. It was then understood that well stabilized and aerobic wastewater had higher ORP measurements than less stabilized, overloaded, or anaerobic wastewater. Early researchers suggested ORP measurement as a process control tool for activated sludge and digestion processes. However, some argued that ORP can’t identify the status of any specific reaction in wastewater because it measures the oxidative status of the wastewater as a whole (Stumm 1966; Morris and Stumm 1967). This criticism, along with the commercial development of the DO probe, led to a lack of research interest in ORP for some time (Koch and Oldham 1985). The 1980s brought a resurgence of interest in use of ORP as a process control tool. In Japan, several patents were awarded that included ORP measurement as a method of controlling DO


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and preventing sludge bulking in activated sludge (Koch and Oldham 1985). During this period, the value of ORP for nutrient removal in activated sludge was also demonstrated in a number of studies (Koch and Oldham 1985; Watanabe, et al. 1985; Sekine, et al. 1985; Charpentier, et al. 1989; Heduit and Thevenot 1989). In these studies, relationships were developed between ORP and DO, ORP and nitrate, ORP and pH, and ORP and phosphorus. A significant advantage of an ORP probe over a DO probe is that an ORP measurement can be obtained even when there is no DO. An ORP probe can provide information on the status of the process during the anoxic and anaerobic conditions needed for nutrient removal, whereas a DO probe cannot. A large body of research was published in the 1990s concerning using ORP control for nutrient removal in wastewater treatment plants (Lefevre, et al. 1993; Lie, et al. 1994; Lo, et al. 1994; Peddie, et al. 1990; Stensel, et al. 1995; Wareham, et al. 1993; Zhao, et al. 1999). Much of this research explored how ORP can be used to control and optimize nutrient removal and sludge digestion processes. An ORP probe can identify key transitions in the nutrient removal process, such as when ammonia oxidation and nitrate reduction are complete (Wareham, et al. 1993). The use of only a DO probe for process control will not reveal when nitrate reduction has been completed. Figure 2 presents a typical relationship of ammonia and nitrate concentration in a cyclic aeration system compared to ORP and DO (see Figure 2 “ORP and DO patterns in nitrification and denitrification”). The figure shows a cycle of three hours of aeration followed by three hours with aeration off. Data for this figure were derived from studies of aerobic digestion by Wareham, et al. (1993). In Figure 2, the following phenomena are seen: When the aeration is turned on: • • • • •

DO rises ORP rises and becomes positive as DO becomes positive Ammonia concentrations drop to near zero A drop (deceleration) in the rate of increase of ORP occurs when the ammonia is exhausted Nitrate concentrations rise correspondingly to the fall in the ammonia concentration

When the aeration is turned off: • • • • •

DO drops quickly to zero Ammonia concentrations gradually rise Nitrate concentrations gradually drop ORP gradually falls and becomes negative when DO falls to zero An increase (acceleration) in the rate of decrease of ORP occurs when nitrate is exhausted

These characteristics can be used as a basis for a control system for cyclic aeration. A promising technique is to use ORP in conjunction with DO. ORP setpoints would be used to turn aerators and mixers on and off, while DO setpoints would be used to control the rate of aeration. Caulet, et al. (1999) presented a study where this technique was evaluated at a treatment facility in France.


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Although there is an abundance of research on the use of ORP control for nutrient removal, there appears to have been less work done in the simulation of these mechanisms. Batchelor (1980) presented a dynamic model very similar to the one used in the present work over 20 years ago. Stensel et al. 1995 developed a simpler model for use in analysis of cyclic aeration. Neither of these authors applied their model to optimization of control, however. With the work presented here, the authors hope to contribute to this field. CONTROL STRATEGY The results of previous studies suggested the basis for a control system for cyclic aeration. The control strategy was based on the theory of Koch and Oldham (1985) and Wareham, et al. (1993). It includes an option for turning off the mixer and turning on the aerators using either a setpoint value for ORP or a calculation based on the rate of change of ORP. A second control loop varies rotor immersion in response to DO. In practice, the simple ORP setpoint has proven adequate to obtain good control. The control strategy may be expressed as follows: 1)

Measure and record DO and ORP in the aeration tank

2)

Measure and record the rate of change of ORP

3)

Provide for Hand/Off/Auto operation of aerators and mixer

4)

Turn the mixer off and turn the aerators on sequentially either: • •

5)

Alternately, turn the mixer off and turn the aerators on sequentially when both of two conditions are true: • •

6)

ORP is negative, and The ORP reaches an operator-adjustable lower setpoint (-50 to –200 mV)

To turn on aerators sequentially: • • •

7)

After an operator-adjustable (5 minutes to 12 hours) time period has expired, or When both of two conditions are true: ∗ ORP is negative, and ∗ The rate of decrease of ORP decreases from the trend of previous values by an operator-adjustable difference (20 - 300 millivolt (mV) per hour).

Turn on aerator 1 After an operator-adjustable time period (5 to 15 minutes) turn on aerator 2. Similarly for aerators 3 and 4, until all four are on.

Turn off all aerators and turn on the mixer, either: • • • •

When an operator-adjustable setpoint for DO has been reached (1.5 to 10 mg/L), or When an operator-adjustable time period has elapsed (30 minutes to 12 hours), or When ORP is positive and the rate of change of ORP is less than an operatoradjustable setpoint value (20-300 mV per hour), or When ORP is positive and an operator-adjustable upper limit is reached (+25 to +150 mV)


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8)

An independent control system is maintained to raise and lower the aeration tank effluent weir to vary rotor immersion and oxygen transfer: • •

When the measured DO is greater than an operator-adjustable upper limit setpoint (0.5 to 9 mg/L), decrease the weir height by an amount proportional to the difference between the current DO reading and the setpoint value When the measured DO is less than an operator-adjustable lower limit setpoint (0.2 to 5 mg/L), increase the weir height by an amount proportional to the difference between the current DO reading and the setpoint value

A simplified process and instrumentation diagram for the system is provided in Figure 3 (see Figure 3 “Control system diagram”). SENSORS Implementing the control strategy requires process measurement of ORP and DO. Several manufacturers produce suitable sensors for insertion directly into an aeration tank. ORP sensors are based on the principle of the electrochemical cell. The ORP sensor consists of an electrode of glass or metal and a reference electrode. The electrode produces an electrical potential proportional to the hydrogen ion activity. The reference electrode completes the circuit. A typical unit for use in process industries has a platinum electrode with a silver/silver chloride reference electrode. The probe assembly produces an output in the range of -(1500 to 5000) to +(1500 to 5000) mV. Negative outputs represent reducing conditions. Positive outputs represent oxidizing conditions. Probes are available from several manufacturers, including Yellow Springs Instrument, Foxboro, and Broadley James Corporation. The Broadley James unit has been extensively used by early researchers in the field and has proven to be an effective unit (Koch and Oldham 1985; Stensel, et al. 1995). This unit was purchased for use at the plant. DO sensors are widely used in the wastewater industry. Sensors are available in membrane and galvanic types. The membrane probes have the longest history and a better record for reliability than the galvanic probes. The membrane unit is based on a polarographic analysis. The sensor consists of an oxygen permeable membrane that shields a cathode and anode from contamination by the process liquid. A potential is applied across the anode and cathode. Any oxygen passing through the membrane is reduced at the cathode, causing a current proportional to the dissolved oxygen concentration in the sample. Many firms, including Beckman Instruments, Great Lakes Instruments, and Danfoss, manufacture membrane probes. The District has had good experience with the Danfoss unit, which has a polystyrene ball float to mount the unit in the process liquid. Each instrument was specified to deliver a 4 to 20 milliampere (mAmp) signal for transmission to the system process controller. SIMULATIONS To confirm appropriate strategies for control of cyclic aeration, a series of simulations of the process were undertaken using a computer program written by one of the authors (Samstag 1992). The program includes a simulation of the growth of heterotrophic and autotrophic


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organisms based on a Monod model coupled to a one-dimensional sedimentation model. The model is simplified version of a model developed by Stenstrom, et al. (1989). The structure of the basic model for uptake of reduced carbon and nitrogen materials is illustrated by the following equations: µ q CREM dC/dt

= = = =

µmax* C(2) / (Ks + C(2)) * DO(2) / (KsDO + DO(2)) µ/ Y q*X(2)*Vol/24 1/Vol*(Q(1)*C(1) + Q(5)*C(5) - Q(6)*C(6) - Q(2)*C(2) - CREM)

= = = = = = = = = = = = =

Oxygen demanding substrate for flow stream n (mg/L) Time rate of change of substrate (mg/L/hr) Substrate removal rate (kg substrate removed/kg cells/day) Dissolved oxygen for flow stream n (mg/L) Flow for flow stream n (m3/hr) Volatile suspended solids for flow stream n (mg/L) Flow stream number Reactor volume (m3) Organism growth rate (kg cells produced/kg cells/day) Maximum organism unit growth rate (kg cells produced/kg cells/day) Half-saturation concentration (mg/L) Yield coefficient (kg cells produced/kg substrate consumed) Half-saturation coefficient for oxygen (mg/L)

where, C(n) dC/dt q DO(n) Q(n) X(n) n Vol µ µmax Ks Y KsDO

Aerobic carbonaceous substrate removal, nitrification, and denitrification were all modeled using this basic structure. The reactor was modeled as a single-celled completely mixed tank. The ordinary differential equations were solved using a simple finite difference discretization. The model incorporates a one-dimensional sedimentation tank. Operation of the sedimentation tank did not affect the results for this study and is, therefore, not discussed. A listing of model equations is presented in Table 1 (see Table 1 “Model equations”). Model nomenclature is presented in Table 2 (see Table 2 “Model parameter definitions”). Flow stream indicators are presented in Table 3 (see Table 3 “Model flow streams”). ORP calculations were based on a version of the Nernst Equation: ORP

=

29.57*Log10[DO(2)*Nox(2)/S(2)/Nr(2)]

= = = =

Dissolved oxygen for flow stream 2 (mg/L) Oxidized nitrogen species in solution for flow stream 2 (mg/L) Soluble BOD5 for flow stream 2 (mg/L) Reduced nitrogen species in solution for flow stream 2 (mg/L)

where, DO(2) Nox(2) S(2) Nr(2)


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The model includes a cyclic aeration feature to simulate on/off cycles in the aeration tank. The simulation was configured for an aeration tank volume of 3,800 m3 (1.0 million gallons) and a sedimentation area of 425 m2 (4,600 ft2) with a solids residence time of 7.5 days. Flows were set to vary on a diurnal cycle from approximately 100 to 400 m3/hr (634,000 to 2,539,000 gallons per day). The diurnal flow pattern was taken from plant operating records. Influent concentrations were taken from plant discharge monitoring reports. The diurnal flow pattern used is presented in Figure 4 (see Figure 4 “Diurnal flow pattern from plant records”). Influent flow and concentration data for the simulations are presented in Table 4 (see Table 4 “Influent data for simulations”). The model assumed that influent total Kjeldahl nitrogen (TKN) was entirely converted to ammonium ions in the aeration tank and that any nitrite-nitrogen species produced were immediately converted to nitrate. FIELD VERIFICATION The control system was implemented in the fall of 1996 and soon placed on-line. The system has been in continuous operation since then. Figure 5 presents data from field operation compared to a simulation of the same influent data. (see Figure 5 “Calibration of model to measured plant data”). The field data are in reasonable agreement with the simulation results. The time required for exhaustion of nitrate after the start of the mixer cycle is approximately 3.5 hours in the model and about the same in the field. Ammonia release after shutting down the aerators is predicted well. The model incorporated a factor, Fdn, to represent the fraction of mixed liquor volatile suspended solids that have the capability to use nitrate as an electron acceptor and perform denitrification. This fraction was used as a calibrating factor in the model. A fraction of 0.3 produced an apparent denitrification rate matching the plant data. The model predicted ORP less well. The model under-predicted the ORP values measured in the field at the upper end of the ORP range by about 80 percent. The model also under-predicts measured data at the lower end of the ORP range. The Nernst equation in the model accounted for only the four most common oxidants and reducing materials in the system. Others, including biologically unavailable reduced materials, were not modeled. In a future improvement to the model, chemical oxygen demand (COD) should also modeled in addition to BOD. pH also has a profound effect on ORP. The model accounted for alkalinity changes in the wastewater, but didn’t include a simulation of pH. This could be another cause of the lack of fit of the model to the plant data for ORP. Since the rate of change of ORP at the lower bound is often very fast, lack of accuracy in the model does not adversely affect the general character of the simulation in predicting low ORP setpoints. In the upper range, the under-prediction of ORP by the model has the result of activating upper ORP setpoints sooner than this condition is experienced in the field. This was compensated in the modeling work by setting the upper ORP setpoint relatively lower than the actual setpoint used in the field. SIMULATION RESULTS Simulations were conducted for a number of conditions to estimate the impact of different control strategies on power consumption and effluent quality. The following conditions were simulated:


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• • • • • • •

No control Timer control with 3 hours on and 5 hours off ORP control with ORP setpoints at 105 mV upper bound and -75 mV lower bound DO control with upper DO setpoint at 1.5 mg/L and lower DO setpoint at 1.2 mg/L Combined Timer and ORP control Combined ORP and DO control Combined Timer, ORP, and DO control

Results are shown in Figures 6 through 12. Table 5 presents a compilation of simulation results (see Table 5 “Simulation results”). Results from simulation of the condition of no control are shown in Figure 6 (see Figure 6 “Simulation of operation with no control”). It was assumed that oxygen transfer from the plant’s four rotors at full immersion resulted in a constant KLa of 3.0 kg O2 transferred per kg basin O2 per hour. With no control of aeration, all reduced nitrogen species are converted to oxidized species. DO is high. All parameters (except KLa) vary with diurnal loading. Power consumption is highest with this control strategy. Predicted results from the timer control strategy are illustrated in Figure 7 (see Figure 7 “Simulation with timer control”). In the timer control strategy, aerators and mixers are turned on and off three times per 24-hour day by a timer. With timer control, denitrification is encouraged compared to the condition with no control. Power consumption is one-third of the value for the condition with no control. Timer control results in incomplete nitrification, however. Figure 8 shows results from simulation of an ORP control strategy (see Figure 8 “Simulation of ORP control”). In this strategy, ORP is used to turn aerators and mixers on and off. The program was set to turn the aerators on and the mixer off when the calculated ORP value reached -75 mV and to turn the aerators off and the mixer on when the ORP value reached 105 mV. The ORP signal automatically turns off the aerators and turns on the mixers once per day in the morning after a long period of low loading overnight. This is the actual experience at the plant when this control is used. This strategy of ORP control wastes more nitrate than timer control and, therefore, uses more power. Results from simulation of the DO control strategy are shown in Figure 9 (see Figure 9 “Simulation of DO control”). This control is accomplished at the plant by raising and lowering the mixed liquor weir. For the simulations, a simple control condition was applied aimed at keeping the DO between setpoints of 1.5 and 1.2 mg/L. This strategy results in complete nitrification but uses twice as much power as the timer control strategy since a large amount of nitrate is wasted. Alkalinity is very low with this alternative, as it is with the alternative with no control. Figure 10 shows results for a strategy combining timer and DO control (see Figure 10 “Simulation of combined timer and DO control”). This strategy results in the lowest power consumption of all strategies tested. Nitrate is fully utilized. It has the lowest power consumption of all of the alternative strategies. But TKN (ammonium) levels in the effluent are high.


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Results for the strategy combining ORP and DO control are shown in Figure 11 (see Figure 11 “Simulation of combined ORP and DO control”). This is the strategy currently used at the plant. It results in approximately one on/off cycle per day. To compensate for the model’s underprediction of ORP, the upper ORP control setpoint in the model was set at 75 mV rather than the 115 mV setpoint currently used for control at the plant. Although rate-of-change control was incorporated into the process control system at the plant, it hasn’t been necessary to use it, and it was not modeled in this simulation. The predicted pattern of ORP and DO in Figure 11 is very similar to the typical daily pattern at the plant. This strategy results in low power consumption with moderately good control of nitrification, but with a considerable waste of nitrate to the plant effluent and relatively poor alkalinity control. The last strategy tested is a combination of all three control techniques: timer, ORP, and DO (see Figure 12 “Simulation of combined timer, ORP, and DO control”). In this strategy, mixers and aerators were turned on and off by a timer. The aerator-off/mixer-on cycle was terminated by a low setpoint for ORP and the timer was reset. DO was maintained between a value of 1.2 and 1.5 mg/L. This strategy seems to result in the best combination of results: relatively low power consumption, low effluent concentrations for both ammonia and nitrate, and moderately good alkalinity control. This strategy results in approximately six aerator/mixer start/stops per day, which may be a disadvantage where mechanical equipment is subjected to high stress during starting. CONCLUSIONS Simulations were used to evaluate strategies for control of cyclic aeration at the Olympus Terrace Sewer District Wastewater Treatment Plant using data gathered from DO and ORP probes. In the control system at the plant, up to four aerators and a submersible mixer can be turned on and off using DO and ORP data for control. The control system saves a substantial amount of the power formerly used at the plant for oxidation ditch aeration, maximizes recovery of alkalinity, and controls loss of ammonia and soluble BOD during off-cycles. A series of different control strategies were tested using a simulation program calibrated to plant data. The simulations indicate that a combination of timer, ORP, and DO control best meets the goal of reduced power consumption and excellent effluent quality. REFERENCES Batchelor, Bill. (1982) “Kinetic analysis of alternative configurations for single-stage nitrification/denitrification,” Journal WPCF, Volume 54, Number 11, pp.1493-1504. Caulet, P., Bujon, B., and J.M. Audic. (1999) “Modulated Aeration Management by Combined ORP and DO Control: A Guarantee of Quality and Power Savings for Carbon and Nitrogen Removal in Full Scale Wastewater Treatment Plants,” Presented at the annual conference of the Water Environment Federation. New Orleans, Louisiana. Charpentier, J., Godart, H., Martin, G., and Y. Mogno. (1989) “Oxidation-Reduction Potential (ORP) Regulation as a Way to Optimize Aeration and C, N and P Removal: Experimental Basis and Various Full-Scale Examples,” Water Science and Technology, Vol. 21, 1209-1223.


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Gray & Osborne, Inc., Consulting Engineers, project manager, Tom Coleman, P.E. (1993) “Oxidation Ditch Submersible Mixer Installation Feasibility Analysis Summary Report,” prepared for the Olympus Terrace Sewer District and funded by the Washington State Energy Office. Heduit, A., and D.R. Thevenot. (1989) “Relation Between Redox Potential and Oxygen Levels in Activated-Sludge Reactors,” Water Science and Technology, Vol. 21, 947-956. Hood, J.W. (1948) “Measurement and Control of Sewage Treatment Process Efficiency by Oxidation-Reduction Potential,” Journal Sewage Works, Vol. 20(4), 640-650. Koch, F.A. and Oldham, W.K. (1985) “Oxidation-Reduction Potential - A Tool for Monitoring, Control and Optimization of Biological Nutrient Removal Systems,” Water Science and Technology, Vol. 17, 259-281. Lefevre, F., Audic, J.M., and B. Bujon. (1993) “Automatic Regulation of Activated Sludge Aeration – Single-Tank Nitrification-Denitrification,” Water Science and Technology, Vol. 28, No. 10, 289-298. Lie, E., and T. Welander. (1994) “Influence of Dissolved Oxygen and Oxidation-Reduction Potential on the Denitrification Rate of Activated Sludge,” Water Science and Technology, Vol. 30, No. 6, 91-100. Lo, C.K., Yu, C.W., Tam, N.F.Y., and S. Traynor. (1994) “Enhanced Nutrient Removal by Oxidation-Reduction Potential (ORP) Controlled Aeration in a Laboratory Scale Extended Aeration Treatment System,” Water Research, Vol. 28, No. 10, 2087-2094. Morris, J.C. and Stumm, W. (1967) “Redox Equilibria and Measurement of Potentials in the Aquatic Environment,” Ad. Chem. Ser., 68, 270-285. Nussberger, F.E., (1953) “Applications of Oxidation-Reduction Potentials to the Control of Sewage Treatment Processes,” Sewage and Industrial Wastes, Vol. 25(9), 1003-1014. Peddie, C.C., Mavinic, D.S., and C.J. Jenkins. (May/June 1990) “Use of ORP for Monitoring and Control of Aerobic Sludge Digestion,” Journal of Environmental Engineering, Vol. 116, No. 3, 461-471. Randall, Clifford W., Barnard, James L., and Stensel, H. David. (1992) Design and Retrofit of Wastewater Treatment Plants for Biological Nutrient Removal, Water Quality Management Library, Vol. 5. Technomic Publishing Company, Inc. Lancaster, Pennsylvania. Rohlich, G.A. (1948) “Measurement and Control of Sewage Treatment Process Efficiency by Oxidation-Reduction Potential – A Discussion.” Journal Sewage Works, Vol. 20(4), 650-653. Samstag, Randal W., et al. (1988) “On-Site Disposal of Secondary Effluent and Sludge at Forks, Washington,” Presented at the annual conference of the Pacific Northwest Pollution Control Association.


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Samstag, Randal W. (1992) “CYCLIC.FOR - A FORTRAN 77 program compiled for operation on DOS/Windows computers using Microsoft FORTRAN.” Schwinn, D.E. et al., (1977) “Full-Scale Operation of a Single-Stage Nitrification-Denitrification Plant,” EPA-600/12-77-088, United States Environmental Protection Agency, Washington, D.C. Sekine, T., Iwahork, K., Fujimoto, E., and Y. Inamori. (1985) “Advanced Control Strategies for the Activated Sludge Process,” Instrumentation and Control of Water and Wastewater Treatment and Transport Systems, Proceedings 4th IAWPRC Workshop, Denver, Colorado, 269-280. Stensel, H. David et al. (1995) “Innovative process used to upgrade oxidation ditch for nitrogen removal and SVI control,” Presented at the Water Environment Federation, 68th Annual Conference and Exposition. Miami Beach, Florida. Stenstrom, M.K., Kido, W., Shanks, R.F., and Mulkerin, M.. (1989) “Estimating oxygen transfer capacity of a full-scale pure oxygen activated sludge plant,” Journal WPCF, Volume 61, Number 2, pp. 206-220. Stumm, W. (1966) “Redox potential as an environmental parameter; conceptual significance and operational limitation,” Ad. Wat. Pol. Res., 3, 283-303. Wareham, David G., Hall, Kenneth J., and Mavinic, Donald S. (January/February, 1993) “Realtime Control of Aerobic-Anoxic Sludge Digestion Using ORP,” Journal of Environmental Engineering, Vol. 119, No. 1, 120-136. Watanabe, S., Baba, K., and S. Nogita. (1985) “Basic Studies of an ORP/External Carbon Source Control System for the Biological Denitrification Process,” Instrumentation and Control of Water and Wastewater Treatment and Transport Systems, Proceedings 4th IAWPRC Workshop, Denver, Colorado, 641-648. Zhao, H.W., Mavinic, D.S., Oldham, W.K., and F.A. Koch. (1999) “Controlling Factors for Simultaneous Nitrification and Denitrification in a Two-Stage Intermittent Aeration Process Treating Domestic Sewage,” Water Research, Vol. 33, No. 4, 961-970.


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Table 1 - Model equations

Reaction

Equation

Aerobic BOD Removal

µcarb = µcarbmax* S(2) / (Kscarb + S(2)) * DO(2) / (KsDO + DO(2)) qcarb = µcarb /Ycarb SREM = qcarb*X(2)*Vol/24 dS/dt = 1/Vol*(Q(1)*S(1) + Q(5)*S(5) - Q(6)*S(6) - Q(2)*S(2) - SREM)

Anoxic BOD Removal

µdn = µdnmax* S(2) / (Ksdn + S(2)) qdn = µdn /Ydn SREM = Fdn*qdn*X(2)*Vol/24 dS/dt = 1/Vol*(Q(1)*S(1) + Q(5)*S(5) - Q(6)*S(6) - Q(2)*S(2) - SREM)

Aerobic TKN Removal

Faut = Yaut*Nr(1)/( Yaut*Nr(1) + Ycarb*S(1)) µaut = µautmax* Nr(2) / (Ksaut + Nr(2)) * DO(2) / (KsDO + DO(2)) qnit = µaut /Yaut NrREM = qnit* Faut*X(2)*Vol/24 dNr/dt = 1/Vol*( Q(1)*Nr(1) + Q(5)*Nr(5) - Q(6)*Nr(6) - Q(2)*Nr(2) - NrREM)

Anoxic NOX-N reduction

SREM = FDN*qdn*X(2)*Vol/24 NoxREM = SREM/2.9 WASN = NRATIO*X(4)*Q(4)+NRATIO*Q(6)*X(6) dNox/dt = 1/Vol*(Q(1)*Nox(1)+Q(5)*Nox(5)-Q(6)*Nox(6)-Q(2)*Nox(2) - NoxREM - WASN)

Volatile GROWTH = Ycarb*SREM - kd * X(2)*Vol/24 solids production dX/dt = 1/Vol*(Q(1)*X(1) + Q(5)*X(5) - Q(6)*X(6) - Q(2)*X(2) + GROWTH)


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Table 1 - Model equations (continued)

Reaction

Equation

Dissolved Oxygen

DOREM = [µcarb*X(2)*(1-Faut)*(1- Ycarb)/ Ycarb*YO2 + kd*X(2)*(1- Faut)* YO2]*Vol/24 + YNOX*NRREM DOADD = KLa*(Cs – DO(2))*Vol dDO/dt = 1/Vol*(Q(1)*DO(1) + Q(5)*DO(5) - Q(6)*DO(6) - Q(2)*DO(2) + DOADD - DOREM)

Oxidation- ORP = 29.57*Log10[DO(2)*NOX(2)/S(2)/NR(2)] Reduction Potential

Alkalinity

AlkREM = KNr*NrREM AlkADD = KNox*NoxREM dAlk/dt = 1/Vol*(Q(1)*Alk(1) + Q(5)*Alk(5) - Q(6)*Alk(6) - Q(2)*Alk(2) + AlkADD - AlkREM)


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Table 2 - Model parameter definitions

Parameter

Definition

S(n)

Biologically available oxygen demanding substrate for flow stream n (mg/L)

Nr(n)

Reduced nitrogen species in solution for flow stream n (mg/L)

Nox(n)

Oxidized nitrogen species in solution for flow stream n (mg/L)

X(n)

Volatile suspended solids for flow stream n (mg/L)

DO(n)

Dissolved Oxygen for flow stream n (mg/L)

Alk(n)

Alkalinity for flow stream n (mg/L)

Q(n)

Flow for flow stream n (m3/hr)

n

Flow stream indicator

Vol

Reactor volume (m3)

µcarb

Aerobic carbonaceous organism growth rate (1/day)

µcarbmax

Maximum carbonaceous organism growth rate = 5.0 day-1

Kscarb

Half-saturation concentration for carbonaceous growth = 50 mg/L

Ycarb

Aerobic carbonaceous organism yield coefficient = 0.7 kg cells produced / kg substrate consumed

qcarb

Aerobic substrate removal rate (kg S consumed / kg cells / day)

µdn

Anoxic carbonaceous organism growth rate (day-1)

µdnmax

Maximum anoxic carbonaceous organism growth rate = 5.0 day-1

Ksdn

Half-saturation concentration for anoxic carbonaceous growth = 0.50 mg/L

Ydn

Anoxic organism yield coefficient = 0.40 kg cells produced / kg substrate consumed


WEFTEC 2000

Table 2 - Model parameter definitions (continued)

Parameter

Definition

qdn

Anoxic substrate removal rate (kg S consumed / kg cells / day)

Fdn

Fraction of mixed liquor volatile solids that can denitrify = 0.30

µaut

Autotrophic (nitrifying) organism growth rate (day-1)

µautmax

Maximum autotrophic organism growth rate = 0.30 day-1

Ksaut

Half-saturation concentration for autotrophic organism growth = 1.0 mg/L

Yaut

Autotrophic (nitrifying) organism yield coefficient = 0.15 kg cells produced / kg substrate consumed

qnit

Reduced nitrogen removal rate (kg TKN consumed / kg cells / day)

Faut

Fraction of total mixed liquor volatile solids that are nitrifiers

kd

Decay coefficient = 0.03 kg cells destroyed / kg cells/day

KsDO

Half-saturation coefficient for oxygen = 0.50 mg/L

NRATIO

Nitrogen content of waste sludge = 6%

Cs

Saturation dissolved oxygen concentration at temperature = 9.17 mg/L

KLa

Overall oxygen mass transfer coefficient (hr-1)

KNr

Ratio of alkalinity removed in nitrification = 7.19 kg alkalinity destroyed / kg TKN oxidized

KNox

Ratio of alkalinity produced in denitrification = 2.9 kg alkalinity produced / kg Nox-N reduced

YO2

Oxygen equivalents in cells = 1.42 kg oxygen consumed / kg cells produced or destroyed

YNOX

Oxygen equivalents in nitrification = 4.4 kg oxygen consumed / kg TKN oxidized


WEFTEC 2000

Table 3 - Model flow streams

Indicator

Flow Stream

1

Influent

2

Mixed Liquor

3

Effluent

4

Waste sludge from sedimentation tank underflow

5

Return activated sludge from the sedimentation tank underflow

6

Waste sludge from mixed liquor

Table 4 - Influent data for simulations

Parameter

Value

Flow (m3/hr)

238

BOD5 (mg/L)

163

TSS (mg/L)

180

TKN (mg/L)

28

NOX-N (mg/L)

2

DO (mg/L)

2

Alkalinity (mg/L)

250

Temperature (degrees C)

20


WEFTEC 2000

Table 5 - Simulation results

Parameter

No Timer Control Control

ORP DO Timer and ORP and ORP, DO, control Control DO DO and Timer

SBOD (mg/L)

0.97

0.43

0.38

1.09

0.49

0.54

0.72

TKN (mg/L)

0.58

4.69

2.55

0.68

6.07

2.61

1.79

NOX-N (mg/L)

17.03

0.40

6.74

16.80

0.27

6.05

1.25

30

77

52

31

87

50

49

DO (mg/L)

3.80

1.64

3.44

1.36

0.60

0.93

0.98

KLa (1/hr)

3.00

1.13

2.04

2.04

0.77

1.07

1.30

O2 Transfer (kg/day)

2,549

1,344

1,848

2,517

1,043

1,395

1,687

BOD Loading (kg/day)

927

927

927

927

927

927

927

O2 Transfer (kg/kg BOD)

2.75

1.45

1.99

2.71

1.12

1.51

1.82

90

34

61

61

23

32

39

Alkalinity (mg/L)

Power (kW)


WEFTEC 2000

Figure 1 - Plant process schematic


WEFTEC 2000

Figure 2 - ORP and DO patterns in nitrification and denitrification (after Wareham, et al. 1993)

6

2 1.5

4 2 0

Aeration Off

Aeration On

1

-2

6

5.5

5

4.5

4

3.5

3

2.5

2

-6 1.5

0 1

-4

0.5

0.5

Time, hours


ORP / 30 (mVolts)

DO NH4-N NOX-N ORP

2.5

0

DO, NH4-N, nd NOX-N (mg/L)

3

WEFTEC 2000

Figure 3 - Control system diagram


WEFTEC 2000

Figure 4 - Diurnal flow pattern from plant records

300 250 200 150

Time (hr)


22

19

16

13

10

7

4

2

23

20

17

14

11

9

6

3

100 50 0 0

Flow (m3/hr)

450 400 350

WEFTEC 2000

Figure 5 - Calibration of model to measured plant data

200.00

Simulated Kla

150.00

10.0

Simulated TKN

100.00 8.0

50.00

6.0

0.00 -50.00

4.0

-100.00 2.0

-150.00

19 23

10 15

6

22 2

13 17

4 9

-200.00 0

0.0

Measured NH4-N ORP (mV)

SBOD, TKN, NOX-N, DO (mg/L) and Kla (1/hr)

12.0

Simulated NOX-N Measured NO3-N Simulated DO Measured DO Simulated ORP Measured ORP

Time, hr


WEFTEC 2000

Figure 6 - Simulation of operation with no control

120.00

18.0 16.0 14.0

80.00

12.0 10.0

60.00

8.0 40.00

6.0 4.0

20.00

KLa ORP (mV) and Alkaliinity (mg/L)

100.00

2.0 22

18

14

11

7

3

23

19

15

12

8

0.00 4

0.0 0


20.0

Time, hr


TKN NOX-N DO SBOD ORP Alkalinity

WEFTEC 2000

150.00

10.0

100.00 50.00

8.0

0.00 6.0 -50.00 4.0

-100.00

2.0

-150.00

21

17

13

9

5

0

20

16

12

8

-200.00 4

0.0


12.0

0


Figure 7 - Simulation with timer control

Time, hr



WEFTEC 2000

Figure 8 - Simulation of ORP control

150.00 100.00

14.0 50.00

12.0

0.00

10.0 8.0

-50.00

6.0

-100.00

4.0 -150.00

2.0 21

17

13

9

5

0

20

16

12

8

-200.00 4

0.0


16.0

0


18.0

Time, hr



WEFTEC 2000

90.00

18.0

80.00

16.0

70.00

14.0

60.00

12.0

50.00

10.0

40.00

8.0

30.00

6.0


21

17

13

9

5

0

0.00 20

0.0 16

10.00 12

2.0 8

20.00

4

4.0


20.0

0


Figure 9 - Simulation of DO control

Time, hr


WEFTEC 2000

Figure 10 - Simulation of combined timer and DO control

150.00 100.00

40.0 35.0

50.00

30.0

0.00

25.0 20.0

-50.00

15.0

-100.00

10.0 -150.00

5.0 21

17

13

9

5

0

20

16

12

8

-200.00 4

0.0


45.0

0


50.0

Time, hr



WEFTEC 2000

120.00

16.0

100.00

14.0

80.00 60.00

12.0

40.00

10.0

20.00

8.0

0.00

6.0

-20.00


21

17

13

9

5

0

-80.00 20

0.0 16

-60.00 12

2.0 8

-40.00

4

4.0


18.0

0


Figure 11 - Simulation of combined ORP and DO control

Time, hr


WEFTEC 2000

80.00

40.0

60.00

35.0

40.00

30.0

20.00

25.0 0.00

20.0

-20.00

15.0

-40.00

10.0


21

17

13

9

5

0

20

16

-80.00 12

0.0 8

-60.00

4

5.0


45.0

0


Figure 12 - Simulation of combined timer, ORP, and DO control

Time, hr
