~ Pergamon

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Waf. Sci. Tech. Vol.37,No. 12,pp. 327-334, 1998.IAWQ

(l) 1998Published by Elsevier ScienceUd.Printedin GreatBritain. Allrightsreserved

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SELECTION OF OPERATIONALSTRATEGIES IN ACTIVATED SLUDGEPROCESSES BASED ON OPTIMIZATIONALGORITHMS

E. Ayesa*, B. Goya**, A. Larrea*, L. Larrea* andA. Rivas*

*Section ofEnvironmental Engineering. CElT, P.O.Box 1555.20009 San Sebastian,Spain**DepartmentofElectrical. Electronicand ControlEngineering, CElT,P.O. Box 1555,20009 San Sebastian. Spain

ABSTRACT

This paper presents a new optimization algorithm for the selection of design and operation parameters incomplex Activated Sludge (AS) processes. The algorithm estimates automatically the dimensions andoperating point of the plant that minimize a global penalty function combining effluent requirements andcosts. The mathematical optimization is based on a direct search algorithm integrated in a previouslydeveloped simulation package.

Some illustrative examples concerning the design and operation of the Alpha process have been included toshow the potential of this kind of mathematical tool when the complexity of plant configuration increases.The results obtained by the optimization procedure generate useful guidelines for the design and operationand suggest a great potential in the application for solving more complex problems when additionalobjectives and costs are included. @ 1998 Published by Elsevier Science Ltd. All rights reserved

KEYWORDS

Activatedsludge; Alpha process; cost criteria;denitrification; nitrification; optimization;simulation.

INTRODUCTION

During the last few years new advanced processes have been developed to improve the efficiency of theremoval of organic matter and nitrogen in wastewater treatment plants. These new processes are usuallybased on complex configurations (includingoxic and anoxic reactors, internal recycles, step-feed, etc.) withhigh flexibilityand many parameters for selectionby the designer or the operator.

Conventional rules for plant design and operation are frequently limited when the number of "independent"variables increase and the selection of their most appropriate values is frequently a very difficult task, evenfor experienced people. The possibility of considering different objectives (effluent requirements, safety,investmentscosts, exploitation cost, etc.) and different external conditions (influent load, temperature, etc.)significantly increases the complexityof the problem.A general frameworkfor the formulation and analysisof an overall decision support index is discussed in Vanrolleghem et al. (1996).

327

328 E. AYESA et al.

Optimum operational strategy means selecting the set-points (dissolved oxygen levels, recirculat ion flows,influent fractioning , etc.) that optimize (by some predefined criterion) the behavior of the plant underdifferent conditions. In many complex WWTP, when reliable mathematical models of the plant areavailable, a mathematical optimization for the estimation of the crucial parameters in plant design andoperation could be the best way to find the most appropriate combination of all the independent parametersto be selected.

This paper shows some practical applications of an optimization algorithm for the design and operation of astep-feed process for nitrogen removal (Alpha process).

METHODS: MATHEMATICAL OPTIMIZATION

The objective of the mathematical optimization algorithm is to estimate automatically the optimumdimensions and operational conditions of Activated Sludge (AS) processes in WWTP in order to minimize aglobal cost function that could include effluent quality , construction and exploitation costs and plantrestrictions .

The first version of this algorithm was oriented to the optimum design and operation of biological ASreactors including nitrification and denitrification and it has already been incorporated in to the software fordynamic simulations DAISY 2.0 developed by CElT for the Spanish engineering company CADAGUA S.A.(Suescun et al., 1994; Rivas and Ayesa, 1997). This first version of the mathematical optimization is basedon the minimization of a global cost criterion that could include some homogeneous penalty functionsassociated with effluent quality (ammonia, nitrate and total nitrogen), construction costs (total HydraulicRetention Time HRT) and exploitation costs (total air flow). The list of independent variables currentlyavailable to be optimized are: reactor volumes, Solids Retention Time (SRT), influent fractioning andinternal recycles . The selection of the most appropriate penalty function clearly influences the optimzationresults. However a complete discussion of this critical point exceeds the scope of this paper.

From an initial estimation of the set of independent variables to be selected, the optimization module makesan automatic search until a "minimum cost" set of independent variables is found. Logically, every stepneeds the prediction of plant characteristics (a new steady-state point) to evaluate the state variables of theplant associated with the new independent variables and the corresponding penalty functions . The predictionof AS process behavior is based on the IAWQ model no I (Henze et al., 1986).

-,,,,,,

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Figure t. Schematicprocedure for 'model-based selectionof operatingpoint using the optimizationalgorithm.

Optimizationalgorithms 329

Figure 1 shows the block diagram of the recursive optimization procedure introduced in the simulator whena operating point with minimum cost is reached for a defined AS process.

The mathematical algorithm used for the automatic optimization is based on a modified pattern searchmethod (Brodyen et al., 1972) that determines the path toward the optimum by evaluating the objectivefunction at several points without calculating derivatives (Rivas and Ayesa, 1997). The optimizationprocedure finishes when the set of independent variables produces a minimum for the previously definedglobal cost criteria. Depending on the selected independent variables and penalty functions, differentlyoptimum points could be reached as the best solution for design or operation.

The real validity of the optimization results when implemented in a real WWTP is clearly conditioned by thecalibration of the mathematical model, the characterization of the influent wastewater and the calibration ofthe specific cost functions.

As an example of the practical possibilities of the optimization algorithms incorporated in the simulationprograms, some illustrative cases relevant to the optimum design and operation of an Alpha process havebeen analyzed.

CASE STUDY: THE ALPHA PROCESS

The Alpha process which includes 3 in-series D-N processes and step-feed to the 3 anoxic reactors (Fig. 2) isa very efficient treatment process for nitrogen removal. However, design and operation of this kind of stepfeeding system is not a simple task.

Figure 2. Schematicdescriptionof the Alpha process.

There are some studies dealing with the Alpha process, especially those related to the evaluation of thenitrogen removal (Miyaji et al., 1980; Lesouef et al., 1992; Gargan et al., 1996) and to the up-grading ofexisting sewage treatment plants in terms of nitrogen removal (Fujii, 1996; Fillos et al., 1996). Otherinvestigations have been oriented to the operation of the process (Kayser et al.; 1992). Identifiability ofmathematical models for the Alpha process has been analyzed in Ayesa et al. (1995).

The CEIT has been studying the Alpha process for three years on a lab-scale plant fed with syntheticwastewater. The results obtained during the experimental verification have shown good predictivecapabilities of IAWQ AS Model no1 (Henze et al., 1986) to describe the biological process. Taking intoaccount the complexity of the configuration and the high number of independent variables to be selected, theoptimization algorithm is currently being applied to the selection of optimum design and optimum operatingpoint of the Alpha process.

RESULTS AND DISCUSSION

Optimization of plant dimensions (minimum temperature)

First applications of the optimization algorithm have been made to select the most appropriate design (6volume fractions, 3 fractions of the influent flow, SRT and HRT) when the plant is at l3°C (critical

330 E. AYESA et al.

conditions for design). The characteristics of the influent wastewater are those typically found in urbanwastewater and the numerical values of model parameters are those commonly used in the bibliography. Theconcentration of dissolved oxygen in the 3 oxic reactors is controlled to DO = 2.0 mgll and the aeration inthe 3 anoxic reactors is null . The required restrictions are:

N-NH4 (effluent) :;:;\.O m.g/IN-N03 (effluent) ~8 m.g/IMLSS (last reactor) S3500 m.g/I

and the objective is to find the configuration that accomplishes the restrictions with minimum total HRT.The high number of parameters to be selected (11 parameters with 3 restrictions implies 8 degrees offreedom) means that the problem is very arduous using conventional methods based on simulat ion and trialand error procedures.

The solution obtained for this example by the mathematical algorithm is presented in Figure 3. The set of IIparameters automatically selected by the optimization procedure minimizes the total volume of the plantwhile maintaining the required limits for ammonia and nitrate concentration in the effluent and suspendedsolids in the last biological reactor. Any other combination of design parameters will overcome therestrictions or will increase the required HRT. So, this is the theoretically "optimum" combination of des ignparameters for the selected objectives , restrictions , wastewater characterization and model coefficients.

The time consumed by the computer in the optimization depends on the initial estimation and step sizes. Fora Pentium at 133 MHz an expected time for this optimization (with 8 free variables) is around 24 hours.

~ () . 5%

GOptimum configuration at 13°C

.H 5%

Anoxi area

D OXI area

Figure 3. Optimum Alpha configuration at t3°e selected by the mathematical algorithm .

Optimization of plant Qperation

The second application of the optimization algorithm for the Alpha process was aimed at finding guidelinesfor the long-term operation of the plant when temperature varies from 13 to 22 ·C. The aim of this exampleis to establish the phys ical configuration that minimizes effluent concentration when the plant is runningwithin this range of temperature. No other perturbations like changes in load or hydraulics have beenconsidered. The total HRT of the plant has been fixed at the previously obtained value for designrequirements at 13·C. The restrictions to be met in this case are:

N-NH4 (effluent) S1.0 m.gllN-N03 (effluent) S8 rn.g/IMLSS (last reactor) S3500 m.gllHRT =9.3 hours (fixed)

Optimization algorithms 331

The additional optimization criterion is to find the configuration that obtains minimum ammonia + nitrate inthe effluent, selecting the numerical values of the independent variables (reactor volumes, influentfractioning and SRD. Some optimization examples have been made with the plant working at differenttemperatures from Boe to 22°e.

I. The first group of optimizations analyzes the behavior of the Alpha process if no facultative areas areavailable and the fixed volumes are those specified in the original design. The influent fractioning has beenoptimized for every temperature . Figure 4 shows the ammonia concentration in the effluent at differenttemperatures and Fig. 5 shows the ammonia + nitrate concentration obtained in the effluent at differenttemperatures and the corresponding optimum influent fractioning to the 3 anoxic reactors (II, 13 and IS).

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Tempera ture roFigure 4. Effluent ammonia (SNH) and volume of the last oxic reactor (V6) as function of temperature when all the

reactor volumes are those specified in the design .

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Figure .5. Effluent ammonia + nitrate (SNH+SNO) and optimum influent fract ioning as function of temperaturewhen all the reactor volumes are those obtained for the design .

The results show that when temperature increases from the critical conditions and no facultative areas areavailable, ammonia in the effluent is lower than the required value, but the ammonia + nitrate concentration

332 E. AYESAet al.

is only reduced from 9.0 mg-NII to 8.3 mg-N/I. It is important to note that when no facultative areas areavailable there is no substantial advantage in modifying the influent fractioning.

2. The second set of examples analyzes the optimum configuration and operational strategy if all thevolumes of the reactors and influent fractioning are free variables. First optimizations with all theseindependent variables have shown two interesting conclusions.

The theoretically optimum volumes of the first 4 reactors are similar when temperature varies insidethe selected range. There is only a significant change in the optimum oxic and anoxic fractions of thelast D-N (15% of total volume is transferred from the oxic to the anoxic area when temperatureincreases from 13°C to 22°C).

Variations in optimum influent fractioning are only associated with changes in the volumes ofreactors and are nearly independent of temperature or SRT.

From the engineering point of view, these first suggestions are crucial for the simplification of plant design .On the one hand facultative areas are then restricted to the last reactor without a significant reduction ofplant performance. On the other hand, when temperature changes, the required variations in influentfractioning are only associated with the discrete changes in the facultative areas.

From the mathematical point of view, the use of only 2 free volumes (last D-N block) as independentvariables in the optimization simplifies the problem and reduces the time significantly. The number ofindependent variables is then reduced to 6 (with 4 degrees of freedom) and the time consumed by a Pentiumat 133 MHz is reduced to 30 min for every optimization.

3. The third set of optimizations analyzes the optimum operation with 2 free volumes (last D-N block). Inorder to reach some practical outlines about the best operational strategy when temperature changes, threefacultative zones (oxic or anoxic) of 5% are proposed in the last reactor V6. Figure 6 shows the previouslydesigned Alpha process where I I is the percentage of the total influent to the first reactor, 13 to the thirdreactor and 15 to the fifth reactor. Volumes VI, V2, V3 and V4 are the fixed volumes obtained in the designfor critical conditions while V5 (anoxic) and V6 (oxic) can be modified by the use of the 3 facultative areasin order to optimize the behavior of the plant at different temperatures.

Figure 7 shows the ammonia concentration in the effluent at different temperatures and the progressivevolume variations of the last reactor V6 from 33% to 18%. Figure 8 shows the ammonia + nitrateconcentration obtained in the effluent at different temperatures and the corresponding optimum influentfractioning to the 3 anoxic reactors when facultative areas are used. The optimum value of the fractions ofinfluent flow at every temperature have been obtained automatically by the optimization algorithmminimizing ammonia + nitrate in the effluent.

Facultauvc15 %1 Zones

Dc,,!:" j l:lr 'IIII Clcrs

Total HkT ='J .2 hours (prcv1011 I~ Iivcd)Total HT m II d;l~ \ (obtmncd b~ opnnu/auon)

A IIOSl e :II C:I

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Figure 6. Generic Alpha configuration with 3 facultative areas in the last reactor .

Optimizationalgorithms 333

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Temperature ("C)

Figure 7. Evolution of ammonia in the effluent (SNH) and volume of the last oxic reactor (V6) when facultativereactors are used as functionof temperature,

10 1011Variab le V5 6 90

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Figure 8. Evolution of effluent ammonia + nitrate (SNH+SNO)and optimum influent fractioningwhen facultativereactorsarc used as functionof temperature.

The results show a great improvement in nitrogen removal (from 9.0 mg-NII to 5.8 mg-NIl) by theappropriate combined manipulation of the facultative areas and influent fractioning. Effluent ammonia andsuspended solids in the last reactor are also maintained below the required levels and no substantialadvantage has been found in modifying the SRT. Then, the suggested operational strategy when tempef~ltureincreases is the progressive reduction in volume of the last oxic reactor Y6, the associated increase involume the last anoxic reactor Y5 and the progressive reduction of influent flow IS to the last oxic reactor.For temperatures higher than 19°e this flow can be completely eliminated and the denitrification is mainlybased on substrate from biomass decay.

These guidelines for plant operation must be better defined taking into consideration the effect of wastewatercharacteristics, kinetic coefficients and effluent requirements. Additionally, other optimization criteria haveto be tested to try to find measurable parameters associated with the suggested operational guidelines and, asa consequence, the development of practical rules for plant control.

334 E. AYESAet al.

CONCLUSIONS ANDFURTHER RESEARCH

Optimization algorithms are a powerful tool for selecting optimum design parameters and operationalstrategies for complex AS processes. Validity of results is clearly conditioned by the validity of modelcalibration and wastewater characterization but the results might always suggest interesting guidelines fordesign and operation. The usefulness of these tools increases when complex configurations or combined costcriteria are considered. Further research is aimed at adding other unit processes to the global model and tointegrating initial investment and exploitation costs in a global cost function.

ACKNOWLEDGMENTS

The authors wish to express their gratitude to CADAGUA S.A. and to the Basque Government for thefinancial support of the project.

REFERENCES

Ayesa, E.• Oyarbide, G., Larrea, L. and Garcfa-Heras, J. L. (1995). Observability of reduced-order models - Application to amodel for control of Alphaprocess. Waf. Sci. Tech., 31(2),161-170.

Brodyen, C. G.. Fletcher, R., Powell, M. J. D., Swann, W. H. and Murray, W. (1972). Numerical methodsfor unconstrainedoptimization. Chapter2, W. Murray (ed), Academic Press.Londonand New York.

Fillos, J., Diyamandoglu, V., Carrie, L. A. and Robinson, L. (1996). Full-scaleevaluationof biological nitrogenremoval in thestep-feedactivatedsludge process.Wat. Env. Res.68(2),132-142.

Fujii, S. (1996). Theoreticalanalysison nitrogenremovalof the step-feedanoxic-oxic activatedsludge processand its applicationfor the optimaloperation.Wat. Sci. Tech. 34(1-2),459-466.

GlIrgiln, E., Artan, N.• Orhon, D. and Sozen, S. (1996). Evaluation of the nitrogen removal by step feeding in large treatmentplants. Wat. Sci. Tech. 34(1-2), 253-260.

Henze, M., Grady,Jr. C. P. L.•Gujer, W.•Marais,G. v R. and Matsuo,T. (1986).ActivatedSludge Modelno1. Technical Reportby IAWPRCTaskGroupon Mathematical Modellingfor DesignandOperation of Biological Wastewater Treatment.

Kayser. R., Stobbe, G. and Werner,M. (1992). Operational resultsof the Wolfsburgwastewater treatmentplant. Wat. Sci. Tech.,25(4-5),203-209.

Lesouef,A., Payraudeau, M., Rogalla,F. and Kleiber,B. (1992). Optimization nitrogenremovalreactorconfigurationsby on-sitecalibrationof the IAWPRCAS model.Wat. Sci. Tech., 25(6),105-123.

Miyaji,Y.,lwasaki, M. and Sekigawa,Y. (1980). Biologicalnitrogenremovalby step-feedprocess.Prog. Wat. Tech..12(6), 193·202.

Rivas, A. and Ayesa, E. (1997). Optimum design of activatedsludge plants using the simulator DAISY 2.0. Measurements andModelling in Environmental Pollution. R. San Jose and C. A. Brebbia (eds). Computational Mechanics Publications.Southampton, Boston.

Suescun, J.• Rivas. A.• Ayesa, B. and Larrea. L. (1994). A new simulation programoriented to the design of complex biologicalprocesses for wastewatertreatment. Computer Techniques in Environmental Studies V. P. Zannetti (ed.). ComputationalMechanics Publications. Southampton, Boston.

Vanrolleghem, P. A.. Jeppsson, U., Carstensen,J., Carlsson,B. and Olsson,G. (1996). Integrationof wastewatertreatment plantdesign and operation- A systematicapproachusingcost functions. Wat. Sci. Tech. 34(3-4), 159-171.