An advanced integrated expert system forwastewater treatment plants control

P.A. Paraskevas* , I.S. Pantelakis, T.D. Lekkas

Department of Environmental Studies, University of the Aegean, 17 Karantoni Str. Mytilini GR81100, Greece

Received 8 December 1998; received in revised form 13 July 1999; accepted 3 August 1999

Abstract

The activated sludge process is a commonly used method for treating wastewater. Due to the biological nature of the process it ischaracterized by poorly understood basic biological behavior mechanisms, a lack of reliable on-line instrumentation, and by controlgoals that are not always clearly stated. It is generally recognized that an Expert System (ES) can cope with many of the common problemsrelative with the operation and control of the activated sludge process. In this work an integrated and distributed ES is developed whichsupervises the control system of the whole treatment plant. The system has the capability to learn from the correct or wrong solutions given toprevious cases. The structure of the suggested ES is analyzed and the supervision of the local controllers is described. In this way, the mainproblems of conventional control strategies and individual knowledge-based systems are overcome.q 1999 Elsevier Science B.V. All rightsreserved.

Keywords:Expert systems; Artificial intelligence; Activated sludge; Wastewater treatment; Automatic control

1. Introduction

During the last decade, the research of wastewater treat-ment has been concentrated mainly on the operation ratherthan the design and construction of the treatment plants.This is due to the fact that the management and operationof the plants is the key step for the efficiency of waterpollution control. The complexity of the processes used inwastewater treatment, the inadequate and unreliable on-lineinstruments, the absence of appropriate performance speci-fications on which to base control, the limited flexibility inplant design, the lack of accurate process models, and theexcess influent variability have showed that the existingcontrol technology has not been applied effectively. It isgenerally believed that techniques other than straightfor-ward application of control theory will be necessary toachieve effective operation of wastewater treatmentprocesses [1].

In this paper the development of an integrated ES isdescribed which it is believed can overcome most of theproblems usually encountered in a typical wastewater treat-ment plant.

2. The wastewater treatment plant

The heart of a typical wastewater treatment plant, espe-cially of domestic sewage, is the activated sludge process. Inthe bioreactor, a mixture of microorganisms, with thepresence of sufficient dissolved oxygen, consume the biode-gradable pollutants (substrate) and transform it into energyand new biomass. Next the water overflows to the settlingtank where the biomass flocks settle. The supernatant cleanwater is disposed properly after being disinfected. A frac-tion of the sludge is returned to the input of the bioreactor inorder to maintain an appropriate level of biomass, allowingthe oxidation of the organic matter. The wastewater feed tothe bioreactor (aeration tank) has passed through pretreat-ment including screening, grit and grease removal andsometimes primary settling for removal of suspended solidsheavier than water. The resulting solids from the primaryand secondary settlers are treated in the sludge line, whichnormally includes the processes of thickening, stabilization(aerobic or anaerobic digestion) and dewatering. The waterand sludge lines are interconnected since liquids from thesludge line are directed back to the water line.

3. Need for an expert system development

Nowadays the emphasis on the wastewater treatment has

Knowledge-Based Systems 12 (1999) 355–361

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* Corresponding author. Tel.:1 30-251-36001; fax:1 30-251-36099.E-mail address:ppar@env.aegean.gr (P.A. Paraskevas)

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been moved from the design and construction to the opera-tion of the plants. Inadequate plant operation is responsiblefor the majority of wastewater treatment plant failures [2].

Conventional control strategies are usually based oncontrol algorithms applied to local control situations e.g.DO control in the aeration tank or sludge wastage ratecontrol. The difficulty with providing supervisory controlover several local controllers (i.e. hierarchical control) isthat treatment plants are operated under conflicting objec-tives that are difficult to compare in such a way as to makeprocess optimization possible [2]. These multiple, non-comparable objectives can be summarized as minimizingtreatment cost (energy, chemical, sludge handling) whileimproving effluent quality.

Compared to other industrial processes, the wastewater ischaracterized by frequent variations in environmentalconditions such as the feed flow rate and the influentsubstrate concentration. These variations affect the processperformance significantly, sometimes even resulting inprocess failure. Therefore, careful design of the controlstrategy becomes necessary to maintain the process opera-tion and to improve the effluent quality [3].

The application of a process control over the wastewatertreatment plant system [2,4–6] has the following difficul-ties:

1. The complexity of the whole process that consists ofseveral operational units where many facts of differentnature take place such as chemical, mechanical andbiological.

2. Many parameters influencing the system cannot becontrolled, for instance flow and organic load variations,toxic loading, water temperature etc.

3. The system is highly dynamic and only very seldom it isworking under steady state conditions. On the other hand,there is not a valid and accurate model of processdynamics.

4. The objective of the wastewater systems, which are tokeep the effluent under the limits defined by the autho-rities and to minimize cost and environmental effects, arenot precisely stated criteria.

5. Most information is neither numeric nor quantified.Qualitative information such as water smell and color,microbiological quality etc., which is essential to theoperator of the plant, cannot be used to conventionalcontrol techniques.

6. Uncertainty or approximate knowledge. The subjectiveinformation, based on local experience, supplied by theplant’s expert is often vague or uncertain. The variablesthat describe the plant are global, a lot of them cannot beobtained on-line, most of the existing on-line sensors arenot reliable and measure mainly macroscopic character-istics.

Furthermore, it can be said that the conventional controlmethods work well during the normal states of the plant,but not in other abnormal situations like toxic inputs and

mechanical faults. The difficulty of the conventional controlsystems to face with such problems has given rise recentlyto considerable research effort in Artificial Intelligence.

4. Expert systems in wastewater treatment

A sub-field of Artificial Intelligence that has receivedmuch attention in wastewater treatment is the area of ExpertSystems (ES). The ES is a computer program that performsdifficult specialized tasks at the level of a human expert.Because of the reliance of these programs on varied typesof knowledge, these programs are also known as knowl-edge-based systems. They differ from conventionalprograms in that they clearly differentiate knowledge ofhow problems are solved from the domain-specific or appli-cations knowledge, and also because they deal with non-algorithmic knowledge, often in the form of heuristics orexperience-based rules-of-thumb [7]. The central feature ofan ES is that it relies on heuristic rules suggested by humanexperts rather than on detailed mathematical models ofcause and effect. The underlying assumption is that humanexperts are able to perform effectively in spite of weak-nesses in data and incomplete understanding of cause andeffect at a detailed level.

The knowledge in ESs is most often represented as rulesin the following form:

IF [a set of conditions is true]THEN [certain conclusions can be made]

The antecedent (IF) may be evidence, symptoms, orobservations that represent a condition, while the conse-quence could be a hypothesis or action. This rather naturalway for human experts to explain how they do their jobprovides a format that can be easily coded for computerinterpretation.

Several expert systems have already been developed inenvironmental engineering of water and wastewater treat-ment problems. Tong et al. [8] were among the first to makeuse of ES type rules for wastewater treatment plant opera-tion and control, although they do not use the term expertsystem. Fuzzy logic was used to provide a qualitative inter-pretation of the quantitative data. Later, Beck [5], combinedthese fuzzy logic rules with a dynamic model to control theprocess. Jenkins and Jowitt (1987) used Beck’s rules todevelop a simple expert system inprolog for the diagnosisof an activated sludge plant while Berthouex et al. (1987)extended Beck’s work by integrating the expert system to adatabase to provide plant operators with a more powerfulsoftware package (cited in Ref. [2]). Barnwell et al. [9]evaluated the application of ES in water quality modelingand concluded that ES will increase the level of sophistica-tion and proficiency of the model user. After them a greatamount of literature followed describing various schemes,which include knowledge for ES, and develop consultationfor diagnosis, design and process optimization. These will

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not be reviewed here but a few interesting and instruc-tive efforts on ES applied to wastewater treatment arementioned, such as those of Herrod [10], Downs [3],Koskinen [11], Maeda [12], Yu et al. [13], Galil andLevinsky [14], Laukkanen and Pursiainen [15], Stover

and Campana [16], Barnett [4], Berthouex [6], and Serraet al. [17].

Although with all these papers and many others as well,significant progress has been made to the improvement inthe operation and efficiency of the ES, there is not an

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USER

USER INTERFACE

SUPERVISORY AGENT

CONTROLLER NUMERICAL KNOWLEDGE

MODULE WATER LINE SUBSYSTEM

SLUDGE LINE

SUBSYSTEM CASE-BASED LEARNING MODULE

DATA BASE MANAGEMENT

SYSTEM

ON LINE SENSORS

OBSERVATIONS SAMPLE COLLECTION LABORATORY

ANALYSIS MICROSCOPE

W A S T E W A T E R T R E A T M E N T P L A N T

SU

PE

RV

ISO

RY

LEV

EL D

IST

RIB

UT

ED

KN

OW

LED

GE

LEV

EL

DA

TA

LEV

EL

Fig. 1. Structure of the integrated distributed ES.

integrated ES which will supervise the operation of thewhole plant, will advice the operator to decision makingor will decide itself, will prevent, by the appropriate actionsprobable process failures, and generally will provide for theproper and economic operation of the plant.

5. Structure of the suggested ES

The suggested integrated ES for the control of the wholewastewater treatment plant is based on the recent works ofSanchez et al. [18] and Serra et al. [17]. They have tried toovercome some of the problems of knowledge-basedsystems and those of classical control systems by proposinga more efficient and robust control system which uses adistributed problem solving architecture [19]. DistributedArtificial Intelligence (DAI) encompasses the research,analysis and development of “intelligent communities”that integrate a coordinated set of knowledge-basedprocesses, usually called modules which interact either bycooperation, by coexistence or by competition, in order toreach common objectives.

The main reasons for choosing an AI system with adistributed problem solving architecture are: geographicdistribution in the domain of application, functional decom-position, faster processing speed by means of parallelexecution, modularity and extendibility, controlling theincreasing complexity of AI systems and increasing thepower of the resulting system [18]. Among the variouskinds of DAI architectures a supervisory integrated systemis chosen because there is a set of fixed abnormal situationsfor wastewater treatment plants such as storm, bulking,organic and flow shock loads etc., which may be solvedwith a predetermined plan or strategy in a more efficientway than with other types of DAI architectures.

The ES we applied, which is a modification of thatsuggested by Sanchez et al. [18], is shown in Fig. 1. It isconsisted of several interacting subsystems (modules) thatcan be executed in parallel processing. The modules belongin three levels, data level, distributed knowledge level, andsupervisory level.

The general knowledge is obtained from interviews withexperienced process engineers and operators of real waste-water treatment plants, and from the international literature[1,2,20–23,35,36].

5.1. Data level

This level receives all the information from the variousunits of the whole plant, the influent, and the effluent. Threecategories of information [24] are received and stored to theData Base Management System (DBMS) from:

1. On-line sensors located in the plant such as inflow,dissolved oxygen (DO) in the bioreactor, turbidity,temperature, pH etc. Recently on-line meters have

become reliable with respect to other parameters suchas ammonia, nitrate, phosphate and redox potential.

2. Off-line analyses, by sampling, carried out in the plantlaboratory such as COD, BOD, SVI, heavy metalconcentrations etc. In addition microscopic observationof the biomass in the aeration basin is advisable becauseit is generally recognized as an effective way of diagnos-ing activated sludge problems.

3. Visual information describing the state of the plant suchas water color, foaming, and odors are critical parametersand must be closely monitored.

The last two categories of information are entered off-lineand stored to the DBMS by the operators. The DBMS keepsrecords of all the monitored variables and sends the valuesto the higher levels, distributed knowledge and supervisory.The transmitting frequency of the various parameter valuesdepends on the category that each parameter belongs, and onthe control needs.

5.2. Distributed knowledge level (DKL)

The DKL consists of five subsystems (modules).

5.2.1. Numerical knowledge module (NKM)The NKM contains the activated sludge model No 1 of

IAWPRC [25] in association with a model of the secondarysettler [26], a dynamic model of the primary settler [27], anda dynamic model of the anaerobic digestion of sewagesludge [28]. These partial modules allow the simulation ofthe whole plant operation.

In addition the value of some parameters which needcalculations, such as OUR (Oxygen Uptake Rate) andSCOUR (Specific Oxygen Uptake Rate), are estimated.By simulation and some measured characteristics of theinfluent and the effluent, through recursive parameter andstate estimation, and the aid of a Kalman filtering algorithm,the values of some parameters are estimated for modelupdating. In this way the values of the parameters can befitted and the control system can be easily applied to eachspecific plant.

The NKM, through a software program, can detect“outliers” or false measurements. Most often they are dueto the problems caused during calibration of the instru-ments, due to a random sensor malfunction, or to a driftof a sensor. Sometimes they are due to human mistakes.The NKM is capable to recognize erroneous sensor read-ings, to compute a more likely value for these cases and tohandle periodically missing data as well.

5.2.2. Water line subsystemThis subsystem contains the modules with the KBSs of

the various local units of the water line [29]. The partialKBSs are the following:

• Preliminary treatment KBS: module supervising thephysical unit operations which remove large solids

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(screening), grit and grease from the inflow of the treat-ment plant in order to protect the following mechanicalequipment (8 rules).

• Primary treatment KBS: module supervising the opera-tion of the primary settler whose purpose is the removalof the settleable and floating solids found in wastewater(14 rules).

• Biological treatment KBS: module supervising theoperation of the biological process, including the biolo-gical reactor (aeration tank), the secondary settling tank,and possibly the anoxic reactor (for denitrification), andthe selector (for the control of filamentous organisms).This is the hard core of the whole process and it isconsisted of 148 rules.

• Disinfection KBS: module supervising the disinfectionprocess (most frequently chlorination) for disinfectingthe treated wastewater prior the discharge to the environ-ment (6 rules).

5.2.3. Sludge line subsystemIt contains the following partial KBSs:

• Thickening KBS: module supervising the process(usually gravity thickening) used to increase the solidscontent of sludge by removing a portion of the liquidfraction (10 rules).

• Stabilization KBS: module supervising the process ofsludge stabilization commonly achieved by aerobic oranaerobic digestion of sludge. The KBS developed isthat of anaerobic digestion process (28 rules).

• Dewatering KBS: module supervising the physical(mechanical) unit operation used to reduce the moisturecontent of sludge. The KBS developed is that of the beltfilter press, one of the predominant sludge-dewateringdevices, which involves the application of chemicalconditioning, gravity drainage, and mechanically appliedpressure to dewater sludge (5 rules).

5.2.4. Case-based learning moduleThe CBLM supervises the operation of the Case Library.

This is supplied by all the abnormal states of the plant andthe solutions given to them either correct or wrong. TheCase Library is updated with the new information and learnsfrom the past cases. In every abnormal state the EScompares the current situation with the recorded ones andaccording to the experience obtained, the operator makes hisdecision. In any case the ES is able to suggest a solution tothe operator.

5.2.5. ControllerController is the module through which the final control

of the plant is realized. Because of its many advantages [30],a combination of feedforward and feedback control systemis implemented for the activated sludge process using thedata of the DBMS. The manipulated variables used are:

1. Airflow rate for the control of dissolved oxygen.2. Waste activated sludge flow rate for the control of the

total sludge mass in the system and the sludge age.3. Return sludge flow for the control of the MLSS in the

biological reactor.4. Mixed liquor return flow from the aerated volume of the

biological reactor to the anoxic zone, for the control ofthe denitrification.

5. Step feed flow distribution, which can redistribute thesludge within the aerator and control the SCOUR,which gives an indication of the DO profile within thefloc. High SCOUR values might mean that conditions arelikely to result in low oxygen concentrations in the centerof the floc. If SCOUR is controlled at low value it shouldbe possible to maintain the DO concentration at a lowerlevel without increasing the risk of adversely affectingthe settling properties [31]. The calculation of OUR isused in several ways: (a) estimation of the overall activ-ity in the aerated part of biological stage and (b) rapiddetection of inhibition of the biological reactions [32].

The activated sludge process requires substantial amountsof energy with the air supply system being the single largest“energy user”. Savings in energy used for aeration are thusessential for reducing operating cost [31]. This is accom-plished by maintaining the DO concentration as low aspossible without negatively affecting the biological reac-tions and therefore the quality of the effluent. Anotherconstraint for the lowest DO concentration limit is set bythe amount of air necessary to provide adequate mixing.Moreover, regulation of DO may improve the plant perfor-mance, not only from an energy point of view, but avoidingincidences which can cause filamentous sludge bulking orpoor sludge settling conditions [33]. A sufficient number ofon-line DO meters assures the same value of DO at all thepoints of the aerated part of the bioreactor. An algorithm isdeveloped, executed by the NKM, which takes into accountall the data and estimates this lowest permitted value eachtime.

5.3. Supervisory level

The supervisory module acts as the manager of the wholecontrol system. It receives information from the distributedknowledge level and the DBMS and diagnoses the state ofthe plant. This is normal if all the following conditions aretrue:

1. There are not divergences from the permitted limits ofthe effluent.

2. The measured and observed values of all the monitoredparameters of the system are within the normal ranges.

3. There are not predictions for some abnormal input to theplant such as storm water or toxic substances loading.

If no problem to the operation of the plant is detected, thenormal situation of the plant is actuated and the conven-tional automatic control system operates the whole plant.

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If an abnormal situation is detected then the supervisoryagent infers the current state of the plant, which is comparedwith the most similar previous case. The experience alreadyobtained, in combination with the rules of the variousknowledge bases, is used to give the same solution or adifferent one. The supervisory module or the operator eithermodify the set points of the control system, or deactivate thecontrol system and give the proper orders to the on-lineactuators. Sometimes manual operation may be requiredfor some actions. The result of the given solution is a newexperience, which is added to the old one.

The user interface module is used for the communicationbetween the user and the supervisory module. In any casethe supervisory module reaches to its own conclusion aboutthe action to be taken but the user may decide and actdifferently. If additional information is required the ESasks this from the operator. Also if an impending upset isidentified the ES may provide an alert to personnel if neces-sary.

An important principle in controlling the treatmentprocess is to avoid over-adjustment, since this reducesrather than improving the stability [34]. Experienced opera-tors seem to understand that and they rely not only on thecurrent values but also on the data from the previous fewdays so as to obtain a view of the trends and a whole pictureof the process. Operators usually make adjustments step-by-step rather than making a large-step change. The effect of acontrol action may last for several days or take several daysto become effective, and knowing that similar action wasrecently taken may affect operator’s decision for the currentday. All these are taken into account to the structuring of thepartial KBS agents and the inference rules.

Another feature of the control system is that the ES mayrecommend more than one-process adjustments and therecan be contradictions. If taking no action seems a safeoption, the operators usually choose to wait until more infor-mation becomes available and the ES is programmed tosuggest the same “no action”. If delay seems risky, theoperators tend to select the action that has the most immedi-ate effect on improving the quality of the effluent or theaction with the least potential harm. Examining trends ofkey variables is often helpful in determining which controlaction is needed [34].

6. Conclusions

In order to control a wastewater treatment plant an inte-grated supervisory ES is currently being developed basedessentially on the recent work of Sanchez et al. [18].

The ES is consisted of several interacting modules thatcan be executed in parallel processing. The modules belongin three levels, data level which receives all the informationfrom the plant, distributed knowledge level which containsthe general knowledge, the conventional controller, theCase Library and the partial control rules, and the

supervisory level which manages the whole control systemand communicates with the operator.

The main features of the suggested control system are thefollowing:

• A conventional feedforward in combination with feed-back controller is used for controlling the process duringnormal conditions.

• If an abnormal situation is detected, the ES eitherchanges the set points of the controller appropriately orthe controller is deactivated and the plant is operated bythe ES or the operator himself.

• The control system has the capability to learn frompreviously solved problems, and a case library is struc-tured where all the abnormal situations with the givensolutions, either correct or wrong, are recorded.

• The fittings of the parameters through a recursive para-meter estimation process, and the learning capability ofthe ES, make it easily implemented to each specific realpart.

• The optimization criterion of the ES is to keep the efflu-ent characteristics within the permitted limits by operat-ing the plant with the least possible cost.

• The operator finally decides for the action to be taken butthe ES can always reach a conclusion and suggest its ownsolution. Sometimes more data may be asked.

When the whole system is tested, calibrated and validatedon a real plant it is believed that it will improve significantlythe efficiency of the wastewater treatment plants.

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