Wastewater treatment improvement through an intelligent … · 2014. 5. 6. · Wastewater treatment...

CONTRIBUTIONS to SCIENCE, 1 (4): 451-462 (2000)Institut d’Estudis Catalans, Barcelona

Abstract

This paper shows the result of years of work by a coopera-tive research group including chemical engineers, environ-mental scientists and computer scientists. This research hasbeen focused on the development and implementation ofnew techniques for the optimisation of complex processmanagement, mainly related to wastewater treatment plants(WWTP). The experience obtained indicates that the bestapproach is a Supervisory System that combines and inte-grates classical control of WWTP (automatic controller formaintaining a fixed dissolved oxygen level in the aerationtank, use of mathematical models to describe the process...)with the application of knowledge-based systems (mainlyexpert systems and case-based systems). The first part isan introduction to wastewater treatment processes and anexplanation of the complexity of the management and con-trol of such complex processes. The next section illustratesthe architecture of the supervisory system and the work car-ried out to develop and build the expert system, the case-based system and the simulation model for implementationin a real plant (the Granollers WWTP). Finally, some results ofthe field validation phase of the Supervisory System whendealing with real situations in the plant are described.

Resum

Aquest article mostra el resultat de la col·laboració portadaa terme durant els darrers anys entre grups d’enginyeriaquímica, enginyeria ambiental i intel·ligència artificial. El tre-ball se centra en el desenvolupament de tècniques per a lamillora i supervisió de processos complexos, especialmentdel tractament biològic d’aigües residuals. L’experiènciademostra que la millor opció requereix desenvolupar un sis-tema supervisor que combini i integri tècniques de controlclàssic (controlador automàtic del nivell d’oxigen dissolt enel reactor biològic, ús de models descriptius del procés,etc.) amb sistemes basats en el coneixement (concretamentsistemes experts i sistemes basats en casos). El present ar-ticle descriu la complexitat de la gestió del procés de tracta-ment de les aigües residuals, l’arquitectura integrada que esproposa i el desenvolupament i la construcció de cadascundels mòduls d’aquesta proposta per a la implementació reala l’estació depuradora d’aigües residuals de Granollers. Fi-nalment, es detallen alguns resultats del procés de validaciódel seu funcionament enfront de situacions quotidianes dela planta.

The survival of the human species and our quality of life de-pend upon our ability to manage the Earth’s natural re-sources on scales ranging from local to global. This requiresan assessment of the extent of these resources, including anunderstanding of their variation in time and space and of

what causes these variations. The concentration of popula-tion at specific locations and the industrialization of our soci-ety (both caused by human activity) are responsible for non-sustainable exploitation of natural resources and for thebreakdown of equilibrium in different natural ecosystems ofour planet. It has resulted in environmental pollution, whichnegatively affects the quality of water, air, soil and therefore,animal, vegetal and human life. The increasing degradationof the environment has forced society to consider changesin human behaviour in order to ensure the essential condi-tions for life on Earth. This consideration has encouraged re-

* Author for correspondence: Manel Poch, Laboratori d’Enginye-ria Química i Ambiental, Dept. Enginyeria Química, Agrària i Tec-nologia Agro-Alimentària, Universitat de Girona. Campus Montilivi,s/n. 17071 Girona, Catalonia (Spain). Tel. 34 972 418 161. Fax: 34972 418 150. Email: [email protected].

Wastewater treatment improvement through an intelligentintegrated supervisory system

M. Poch1*, I.R. Roda1, J. Comas1, J. Baeza2, J. Lafuente2, M. Sànchez-Marrè3 and U. Cortés3

1 Laboratori d’Enginyeria Química i Ambiental (LEQUIA), Institut de Medi Ambient. Universitat de Girona

2 Grup d’Enginyeria Ambiental, Departament d’Enginyeria Química. Universitat Autònoma de Barcelona

3 Secció Intel·ligència Artificial, Departament de Llenguatges i Sistemes Informàtics. Universitat Politècnica de Catalunya

Key words: Biological wastewater treatment,activated sludge, knowledge-based systems,supervision, environmental decision supportsystems.

search and great effort has been put into understanding,preventing and correcting environmental degradation.

In this sense, the treatment of water and wastewater hasbecome one of the most important environmental issues.Wastewater treatment is fundamental for keeping water nat-ural resources (rivers, lakes and seas) as high quality aspossible. More and more restrictive social regulations haveappeared due to these environmental reasons and the cor-rect management of wastewater treatment facilities has be-come a very important topic during the last 20 years. For ex-ample, European directive 91/271 establishes that every cityor village with a population of more than 2000 inhabitantsmust treat its wastewater before the year 2005, at least forthe Suspended Solids (SS) and the Biological Oxygen De-mand (BOD) contained in the wastewater. This criterion at-tempts to provide a regulated water effluent with low conta-minant load in order to cause minimum environmentalimpact (including energy use) on the quality of the receivingwater ecosystem. This concept has been enlarged with thenew water Framework Directive 2000/60/EEC. In Catalonia,the rapid implementation of Directive 91/271 by the General-itat de Catalunya, through its Plà de Sanejament, has led tothe building of several Wastewater Treatment Plants(WWTPs) during the last few years.

By February 2000, there were 225 WWTPs operating inCatalonia (plus another 102 under construction or in an up-grading phase), being their management cost over 60 mil-lion Euros during the year 1999. However, more importantthan economics are the maintenance of quality criteria es-tablished by environmental regulations for treated effluent.In this sense, optimal WWTP management is concerned withtrying to obtain good wastewater treatment efficiency andmaintain maximum process stability while avoiding opera-tional problems.

A typical wastewater treatment plant usually includes aprimary treatment and a secondary treatment to remove or-ganic matter and suspended solids from wastewater. Prima-ry treatment is designed to physically remove solid materialfrom the incoming wastewater. Coarse particles are re-moved by screens or reduced in size by grinding devices.Inorganic solids are removed in grit channels and many ofthe organic suspended solids are removed by sedimenta-tion. Overall, the primary treatment removes almost one-halfof the suspended solids in the raw wastewater. The waste-water flowing to the secondary treatment is called the prima-ry effluent [14]. Secondary treatment usually consists of abiological conversion of dissolved and colloidal organiccompounds into stabilized, low-energy compounds andnew biomass cells, caused by a very diversified group of mi-croorganisms, in the presence of oxygen. This mixture of mi-croorganisms (living biomass) together with inorganic aswell as organic particles contained in the suspended solidsconstitutes what is known as activated sludge. This mixtureis kept moving in wastewater by stirring done by aerators,turbines or rotators, which simultaneously supply the re-quired oxygen for the biological reactions. Some of the or-ganic particles can be degraded by subjecting them to hy-

drolysis whereas others are non-degradable (inert). Thismixture of microorganisms and particles has the ability tobioflocculate, that is, to form an aggregation called activatedsludge floc if there exist a balance in population betweenfloc-formers and filamentous bacteria. The activated sludgefloc gives to the sludge the capacity to settle and separatefrom treated water in the clarifier. A biological reactor fol-lowed by a secondary settler or clarifier constitutes the acti-vated sludge process, which is the most well known processof secondary treatment because it is also the most widelyused (see Figure 1).

Like other environmental and biotechnological process-es, WWTP are complex systems, involving many interactionsbetween physical, chemical and biological processes, e.g.chemical or biological reactions, kinetics, catalysis, trans-port phenomena, separations, etc..The successful manage-ment of these systems requires multi-disciplinary approach-es and expertise from different social and scientific fields.Some of the special and problematic features of environ-mental processes are:

• Intrinsic instability: most of the chemical and physicalproperties as well as the population of microorganisms(both in total quantity and number of species) involvedin environmental processes do not remain constantover time.

• Many facts and principles underlying environmentaldomain cannot be characterized precisely only interms of a mathematical theory or a deterministic modelwith clearly understood properties.

• Uncertainty and imprecision of data or approximateknowledge and vagueness: these processes generatea considerable amount of qualitative information. More-over, on-line data is not sufficient to monitor and diag-nose the process successfully since the use of on-lineanalysers is still rare and they are often unreliable. It isnecessary to be able to access analytical information.Most of this information is acquired with global vari-ables that cannot be obtained on-line but only with adelay of some hours or days.

• Huge quantity of data/information: the application ofcurrent computer technology to the control and super-

452 M. Poch, I.R. Roda, J. Comas, J. Baeza, J. Lafuente, M. Sànchez-Marrè and U. Cortés

Figure 1. Primary and secondary treatment of a typical WWTP.

vision of these environmental systems has led to a sig-nificant increase in the amount of data acquired.

• Heterogeneity and scale: because the media in whichenvironmental processes take place are not homoge-neous and cannot easily be characterised by measur-able parameters, data are often heterogeneous. Focus-ing on wastewater treatment processes, four differenttypes of information can be identified in WWTPs: on-line quantitative data, off-line quantitative data, qualita-tive observations of plant operators and microscopicobservations.

Due to the complexity of wastewater treatment processcontrol, even the most advanced conventional hard controlsystems have encountered limitations when dealing withproblem situations that require qualitative information andheuristic reasoning for their resolution (e.g. presence and in-terrelations among different microorganisms, substrate andoperational conditions in filamentous bulking problems,foaming...). Indeed, in order to describe these qualitativephenomena or to evaluate circumstances that might call fora change in the control action, some kind of linguistic repre-sentation built on the concepts and methods of human rea-soning, such as intelligent systems, is necessary. And thisis the reason why human operators have, until now, consti-

tuted the final step in closed-loop plant control [18]. A deep-er approach is necessary to overcome the limited capabili-ties of conventional automatic control techniques whendealing with abnormal situations in complex systems, and toprovide the level and quality of control necessary to consis-tently meet environmental specifications.

For these reasons, the field of intelligent control systemsbegan to look promising a few years ago in terms of solu-tion to these problems. A reasonable, distributed proposaloutlines the scope for the integration of tools like patternrecognition, knowledge-based systems, fuzzy logic, neuralnetworks, or inductive decision trees, which handle the par-ticular characteristics of complex processes (e.g., environ-mental problems), with numerical and conventional compu-tational techniques (statistical methods, advanced androbust control algorithms and system identification tech-niques).

In this paper we present the results of cooperative re-search conducted over the last years by the Environmentaland Chemical Engineering Laboratory of the Universitat deGirona in close association with the Chemical EngineeringDepartment of the Universitat Autònoma de Barcelona(UAB) and the Knowledge Engineering and Machine Learn-ing Group (KEML) in the Software Department of the Univer-sitat Politècnica de Catalunya (UPC) to develop an intelli-

Wastewater treatment improvement through an intelligent integrated supervisory system 453

Figure 2. Integrated modules of the computing approach.

gent supervisory system for an activated sludge process.This Supervisory System integrates classical control sys-tems with two knowledge-based systems (Expert System -ES- and Case-Based System -CBS-) and has already beenimplemented in the Granollers WWTP. The development, im-plementation and evaluation of the Supervisory System forthe Granollers WWTP can be found in [4]. This real imple-mentation has been called BIOMASS, that is, Besòs Intelli-gent Operation & Management for the Activated Sludge Sys-tem.

The multi level-architecture

The architecture developed provides an integrated frame-work for easy access to three modules: data gathering (in-cluding database management); advanced tools of reason-ing (diagnosis module); and the decision support module(simulation models to implement predictive and supervisorytasks in the plant). This multi-level architecture guaranteesthe useful and successful supervision of wastewater treat-ment processes ([21] and [24]). Figure 2 shows the structureof this computing approach to supervise the GranollersWWTP. This integrated architecture can be used to super-vise any environmental process as an Environmental Deci-sion Support System (EDSS) [5].

The first module is the level that supports the data gath-ering and updating processes. This level is composed ofthe acquisition systems for the on-line data (coming fromsensors and equipment) and the off-line data (biological,chemical and physical analyses of water and sludge quali-ty and other qualitative observations of the process, i.e.,presence of bubbles on the settler surface...). Moreover,this level implements data filtering, validation and manage-ment processes over the temporal evolving (real-time)database where on-line, off-line and data calculated by thesystem are stored.

The second module of this architecture includes two arti-ficial intelligence techniques (expert system and case-based reasoning); and numeric models, overcoming thecaveats and limitations in the use of each single technique,which constitutes the reasoning module for situation as-sessment. This second level entails cooperation betweenknowledge-based control and automatic control for the su-pervision of this complex process. This level implementsreasoning tasks to diagnose its state and behaviour and topropose the action to maintain or return the process to itsnormal operation.

The third level of the Supervisory System (the decisionsupport module) implements a supervisory and predictivetask over the WWTP. The supervisory task of this module isto seek a consensus on the diagnosis and actions to be tak-en proposed by the different reasoning tools. Meanwhile, thepredictive task evaluates the possible alternatives of actionby means of a dynamic model, which predicts the future be-haviour of the plant and, finally, infers and suggests the mostsuitable action strategy to be considered. The decision-

maker should consider the different alternatives in relation tosocio-economic conditions and applicable legislative frame-works. This module also increases the interaction of theusers with the computer system throughout an interactive,graphic user-machine interface (the user may query the sys-tem for justifications and explanations about suggested de-cisions, to consult certain values, etc.). A commercial shellfor developing real-time knowledge-based systems with auser-friendly interface (G2, [11]) was selected as the suit-able platform on which the supervisory system could bebuilt. The next sections describe the ES, CBS and simulationmodel development and their implementation for the Gra-nollers WWTP.

Expert System

An Expert System (ES) is defined as an interactive computerprogram that attempts to emulate the reasoning process ofexperts in a given domain over which the expert makes deci-sions. The ES has two main modules: the Knowledge Base(KB) and the inference engine. The knowledge base in-cludes the overall knowledge of the process as a collectionof facts, methods and heuristics, which are usually codifiedby means of production rules. The inference engine is thesoftware that controls the reasoning operation of the ES bychaining the knowledge contained in the knowledge base inthe best possible way.

The acquisition of the knowledge included in the knowl-edge base is the core and also the bottleneck of the ES de-velopment. It involves eliciting, analysing and interpretingthe knowledge that experts use to solve a particular prob-lem. This knowledge should be represented in an easy,structured way (e.g., in tables, graphs, frames or decisiontrees).

Knowledge included in the KB can be obtained from sev-eral sources. These sources can be divided into two types:documented (based on existing literature about the topicand on the WWTP database) and undocumented (that is, ex-periences or expertise from experts on the process). Infor-mation can be identified and collected using any of the hu-man senses (i.e. through interviews with the experts orreading books, journals, flow diagrams, etc.) or with the helpof machines (the use of machine learning tools to acquireknowledge from historical database). Therefore, the differ-ent knowledge acquisition methods can be classified asmanual or automatic (Figure 3).

In the knowledge base development of this study, differ-ent methods from both types have been used. Convention-al knowledge acquisition methods (literature review, inter-views, etc.) were used first. To overcome the limitations ofconventional methods, they were complemented with theuse of different automatic knowledge acquisition methods.These latter methods can be either supervised (mainly in-ductive learning techniques, CN2, C4.5 and k-NN [3]) orunsupervised (essentially Linneo+ [26], which obtains infor-mation directly from the database [23]). Figure 3 illustrates


the main sources and methods that were used to acquireboth kinds of knowledge on the wastewater treatment pro-cesses.

Among the different possibilities to represent the wholeknowledge elicited (tables, decision trees or knowledge dia-grams and frames), decision trees were selected as themost suitable representation. All symptoms, facts, proce-dures and relationships used for problem diagnosis can becast into a set of decision trees. These trees consist of hier-archical, top-down descriptions of the linkages and interac-tions among any kind of knowledge utilized to describe factsand reasoning strategies for problem solving (objects,events, performance and meta-knowledge). The translationof the knowledge contained in a branch of decision treesinto a production rule is direct. The trees designed includediagnosis, cause identification, and action strategies for awide range of WWTP troubleshooting, avoiding contradic-tions and redundancies. These logic trees serve as a recordof the expert’s step-by-step information processing and de-cision-making activity. Some branches are specific and con-tain some peculiarities of the plant, while others are moregeneral and can be applied to any plant. As an example ofthe developed trees, Figure 4 shows the tree for diagnosing

and solving filamentous bulking problems. The inferencecan start from any kind of data, both quantitative (chemicalor biological), related to the water and sludge characteris-tics, and qualitative referring to sludge settleability and in-situ observations (supernatant in the settling test, presenceof foams on the clarifier surface...). Whenever a symptom offilamentous bulking is presented, the expert system acti-vates an intermediate alarm, signalling the risk of the prob-lem occurring. The diagnosis rules then evaluate the fila-mentous bulking problem according to the activity ofmicrofauna, the settleability of the sludge (Sludge VolumeIndex, SVI) and the presence of filamentous bacteria. Oncethe expert system has concluded a situation, it tries to detectits specific cause. With the situation and cause diagnosed,the expert system sends its conclusions to the third level ofBIOMASS, which conducts the processes of supervisionand prediction and proposes an action plan.

Table 1 shows the list of all the situations contemplated.The list covers primary and secondary treatment, distin-guishing between the non-biological and the biological ori-gins of the problems. The latter origin causes a decrease inthe biological reactor performance or dysfunctions in thesecondary settler.


Figure 3. Classification of the sources and methods used to acquire knowledge from WWTP.

Secondary treatment

Primary treatment problems Non-biological Origin Biological Origin

Old sludge White foams Filamentous bulkingSeptic sludge Overloading ( and Organic shock) Foaming (Actynomicetes)Sludge removal systems breakdown Nitrogen shock Deflocculation (pinpoint)Clogged pumps or pipes Conductivity shock Deflocculation (disperse growth)Low efficiency of grit removal Storms Slime viscous bulkingPrimary high sludge density Hydraulic shock Toxic shockInadequate sludge purges Underloading Nitrification/denitrificationHydraulics shock Aeration problems (include rising sludge)High solids loading Clarifier problemsOther mechanical problems Mechanical and electrical problems

Transition state to some of these problems

Table 1. List of decision trees developed for detecting and solving WWTP problems

The use of expert systems offers a number of advantagesthat overcome the limitations of other techniques: ES facili-tate the inclusion and retention of heuristic knowledge fromexperts and allow qualitative information processing; knowl-edge is represented in an easily understandable form(rules); a well-validated ES offers potentially optimal answersbecause action plans are systematised for each problematicsituation; in addition, ES make possible the acquisition of alarge general knowledge base, with flexible use for anyWWTP management. Finally, ES facilitate objective acquisi-tion of specific knowledge throughout the use of machinelearning techniques. The first results in ES developmentwere published in [19] and [29]. Expert systems also showsome limitations: most of the knowledge acquired is generalknowledge for managing any WWTP (coming from a reviewof the literature), in which there is a lack of specificity, mainlyin the repertory of actions proposed. In addition, this insuffi-cient specific knowledge comes from interviews with plantmanagers and workers (thus involving bias, discrepanciesand imprecision) and from database study and classifica-tion, which is often incomplete and almost never containsqualitative information. People tend to remember their pastendeavours as being successful, regardless of whether theyactually were or not. Complex problems require many (hun-dreds of) rules, involving long development time and maycreate problems in both using the system and maintaining it.And, perhaps most importantly, the knowledge base is stat-

ic. Once developed, it is not an easy task, at least for the ex-pert or final user, either to modify rules or to adapt the knowl-edge base to new specifications, and the system is unableto learn from new experiences. This last fact could provokethe systematic repetition of errors in diagnosis and pro-posed actions. All these limitations indicate that ES shouldbe complemented with other approximations to managecomplex processes optimally.

Case-Based System

The proposed Case-Based System (CBS) methodologypermits the use of past experiences to solve new problemsthat arise in the process. It is based on the idea that thesecond time we solve a problem it is usually easier than thefirst time because we remember and repeat the previoussolution or recall our mistakes and try to avoid them. Thebasic idea is to adapt solutions that were used on previousparticular problems affecting process performance anduse them for solving similar new problems with less effortthan with other methods that start from scratch. A case isdescribed as a conceptualised piece of knowledge repre-senting an experience that teaches a fundamental lessonabout how to achieve the reasoner’s goals [16]. The set ofspecific cases is stored in a structured memory in a case-base (the case-library) and initialised with a set of typical


Figure 4. Decision tree for bulking filamentous problems.

cases in the plant. The CBS development includes the casedefinition, the case-library definition, and the selection ofthe initial seed.

The case is the codified description of each specific stateor experience of the WWTP studied. Codification should bein storable form in order to be easily retrieved in the future.As an example, table 2 shows a real case for the GranollersWWTP. Each case must contain:

• An identifier or label. In this study, we have used thedate of the day preceded by the word «Case-».

• The description of the situation, based on the values ofthe relevant variables that characterize the wastewatertreatment process itself (corresponding set of observa-tions related to that situation).

• The diagnosis implicit in each situation, indicating theexpert evaluation of the «state» of the process.

• Action plan proposed or recommended to keep theprocess under control.

• The solution-result, with the evaluation of the applica-tion of the proposed action, specifying whether the re-sult has been a success or a failure.

• The similarity or distance between the case retrievedfrom the library and the current case.

The CBS requires a library of cases to broadly cover theset of problems that may arise from the process. These cas-

es are indexed in memory so that they are retrievable whentheir experiences can be used advantageously to contributeto achieving the goals of the process. Both successful casesand failures must be included in the library. The structureand organization of this case library is different from a nor-mal database, where full match among variables is required.Case library definition in CBS is a key point because it has agreat effect on the efficiency of the system both on responsetime and on success in finding a suitable stored case tomatch the new situation.

It is recommendable to initialize the library using a set ofcommon situations (or cases) obtained from technical booksor provided by experts on the process. Thus, the CBS isready from the very start to propose solutions to problemssimilar to those considered in the initial «seed». Otherwise,the CBS would have to increase its knowledge from eachnew experience, increasing significantly the «set up» timenecessary for the CBS to be used successful. The initialseed at Granollers included 74 real cases from the historicaldatabase, which covered a broad range of the main prob-lems in the process and normal situations.

The library is updated with new cases as the knowledgeabout the process progresses; so the CBS evolves into abetter reasoner and system accuracy benefits from thesenew acquisitions. However, because the library can becomeovercrowded with large amounts of information, it is crucialto include only the most relevant cases.


:identifier (( Case-29-11-99 ))

:description of (( Influent flow rate (Flow-I) 19380 )the situation ( Influent-COD (COD-I) 727 )

( Influent-Suspended Solids (SS-I) 254 )( Influent-Total kjeldhal Nitrogen (TKN-I) 110 )( Primary effluent-COD (COD-P) 693 )( Primary effluent-SS (COD-P) 140 )( Secondary effluent-COD (COD-E) 61 )( Secondary effluent-Suspended Solids (SS-E) 15 )( Secondary effluent -Total Nitrogen (TN-E) 80 )( Biomass Concentration (MLSS-AS) 4271 )( Sludge Volumetric Index (SVI-AS) 111 )( Waste Activated sludge Rate (WAS) 663 )( Recycle Activated sludge Rate (RAS) 66 % )( Dissolved Oxygen_line1 (DO1) 2.5 )( Dissolved Oxygen_line2 (DO2) 2.3 )( Sludge Residence Time (SRT) 6 )( Food to Microorganism ratio (F/M) 0.18 )( Filamentous_organism_Predominant (Filam) Microthrix Parvicella )( V30-setttling test observations (V30-settling test) Good but foams on supernatant ))

:diagnosis Foaming caused by Microthrix Parvicella )

:action plan (( 1. Cause Identification: Low F/M ratio )( 2. Physical aeration tank foam and clarifier foam removal )( 3. High wasting activated sludge flow rate )( 4. Low Recycle activated sludge flow rate to facilitate good compaction )( Check also for Nocardia trends during a week )

:evaluation (( Success. In five days, Nocardia population starts to decrease ))

:similarity (( 94.1 % ))

Table 2. Example of a real case stored in the case-library of the Granollers WWTP

When a new case occurs, CBS start a new cyclic process(figure 5), which consists of the following steps:

• Gathering and processing data from the process in or-der to define the current case.

• Searching for and retrieving from the case library thecase that best fits the current case. This step is accom-plished by using a suitable algorithm, which is basedon a numerical similarity comparison between the cas-es.

• Adapting the solution proposed by the retrieved case ifit does not perfectly match with the current case. Tocarry out this step a simple parameter adjustment algo-rithm is usually applied, under the assumption that thedistance between the case retrieved from the libraryand the current case is small enough so that only a lin-ear interpolation adjustment of the parameter involvedin the solution is needed.

• Applying the adapted solution to the process, and eval-uating its consequences. There are different ways toevaluate these results: by asking the operator directly,by simulating the proposed actions, or by directly get-ting feedback on the results of the proposed solution.

• Learning each new experience to update the knowl-edge contained in the case library. When the evalua-tion has been successful, the case is incorporated intothe library so that the CBS can benefit from it in the fu-ture when similar cases arise. This stage is known aslearning from success, and, to avoid the introduction ofredundant information in the library, it must be consid-ered only when this new case offers complementaryknowledge. Alternatively, the proposed solution maybe a failure. In this case the learning process is knownas learning from failure, and the system should record it

to prevent continued mistakes in trying to solve futureproblems. From the beginning of the CBS implementa-tion in BIOMASS and during the five-month field valida-tion phase, the case-library was enlarged by more than100 complete new cases, registering diagnosis andaction plan carried out. More details about our CBS ap-proach for complex process supervision can be foundin [20] and, specially for WWTP, in [22], [25], [27] and[28].

Simulation Model

According to the Fishwick definition, to model is to abstractfrom reality a formal description of a dynamic system [17].The characterisation of the behaviour of a system is reachedby simulations. Thus, simulation explains the process of con-ducting experiments with this model for the purpose of en-abling decision makers to either: (1) understand the behav-iour of the system, (2) evaluate various strategies for theoperation of the system, or (3) predict possible future condi-tions [17]. Mechanistic models describe whole processestaking place in the biological organic and/or nutrient removalprocesses, including substrate and biomass evolution. Toaccomplish this objective, different kinds of substrates andbiomass are established. The relationships (stoichiometricparameters) between all these state variables are obtainedby applying the mass balances to the process. The ratesproposed by the International Water Association (IWA) TaskGroup on Mathematical Modelling for Design and Operationof Biological Wastewater Treatment to model the organicmatter, nitrogen and phosphorous biological removal arebased on Monod kinetics or modifications of them. Usuallythese models are represented in a matrix fashion (see Table


Figure 5. Case-Based System working cycle.

3), which should include all the components and processesused to define the model. Many years ago the IWA groupproposed Activated Sludge Model nr. 1 (ASM nr. 1, [12]),which has been internationally accepted as the model of ref-erence for carbon and nitrogen removal. These models re-quire a large number of kinetic and stoichiometric parame-ters, which should be previously adjusted, by means of acalibration phase with experimental data. The validity of themodel developed will largely depend on the reliability of thekinetic and stoichiometric parameters selected.

The first simulation software using mechanistic models fornutrient removal was developed in the late 80s [6]. Nowa-days, there exist several commercial simulators for waste-water treatment processes. Among them, the GPS-X soft-ware, developed by [15], includes all the models developedby the IWA group and some modifications of them.

A mechanistic model of the Granollers wastewater treat-ment plant was developed using the GPS-X commercialsoftware [15]. The biological reactor was modelled as fourContinuous Stirred Tank Reactors, while primary and sec-ondary settlers use a model that consists of a one-dimen-sional tank with 10 layers of solids flux without biological re-action. A calibration process has been carried out to adjustthe kinetic, stoichiometric and settling parameters of theASM2 model [13] of this plant. The standard values for theseparameters given by the GPS-X were used in the first step ofthe calibration procedure being changed manually alongthe calibration process. A mechanistic model enables tosimulate several off-line scenarios with different operationalconditions, changes in the influent characteristics (under-loading, overloading, storms...), and alternative actions pro-posed by the Supervisory System. In spite of these capabili-ties, these models present limitations when dealing withproblematic situations of biological origin (filamentous bulk-ing, foaming, rising...) as well as with situations for whichthey have not been calibrated. In this sense, the utilization ofsoft-computing techniques to build a non-mechanistic mod-el to simulate the behaviour of the plant in any situation isalso being studied.

In addition, the group has also explored the field of softcomputing techniques, in particular, by experimenting withneural networks and fuzzy approaches. The final aim is todevelop a non-mechanistic or black-box model to predictthe evolution of activated sludge processes in short-term un-der any situation and to integrate it into BIOMASS. Specifi-

cally, system identification and real-time pattern recognitionby neural networks were studied to estimate key parametersin the activated sludge process ([7], [8], [9] and [10]). In re-search that is still going on, heterogeneous time-delay neur-al networks are being experimented to study the influence ofqualitative variables coming from microscopic examinationsand subjective remarks of the operators on the prediction ofthe sludge settleability. This model, in which inputs are amixture of continuous (crisp, rough or fuzzy) and discretevalues, performs effectively even when dealing with missingvalues ([1] and [2]).

The supervisory cycle

The different tasks of the third level of BIOMASS are per-formed cyclically, using a supervisory cycle (see Figure 6).Each cycle is composed of six steps: data gathering and up-date; diagnosis; supervision; prediction; user-validation andaction phase; and evaluation phase.

Every time the supervisory cycle is launched, the firsttask to be carried out is data gathering and updating thecurrent data for the inference process, in other words, gath-ering the most recent, both quantitative and qualitative datafrom the evolving database. According to the expert, thereare minimal essential variables - basic information - thatmust be updated in order to make a reliable diagnosis of thecurrent state of the process. In the Granollers WWTP, theseare the influent flow rate and the Chemical Oxygen Demandof the biological influent.

Once the information has been collected, it is sent to thediagnosis module where the knowledge-based systems (ESand CBS) are executed concurrently without any kind of in-teraction between them. The current state of the process willbe diagnosed through a reasoning task carried out accord-ing to both the expert rules and the most similar cases re-trieved. If a problem is detected or suspected, the diagnosismodule will also try to identify the specific cause. The solu-tion of the most similar case is modified to adapt it to the newsituation. The conclusions of diagnosis phase are sent to thedecision support module. This upper module infers a globalsituation of the WWTP and suggests a proper action plan asa result of the supervision and prediction tasks integratingthe expert recommendations sent by the ES and the experi-ence retrieved by the CBS, while evaluating any possibleconflict. The final result of BIOMASS is sent through the com-puter interface to the operator who will finally decide on theaction to be taken (user-validation and action). The expertcan use the dynamic model implemented in the GPS-X shellto support the selection of an action plan by simulating thepossible consequences of applying different alternatives. Fi-nally, the evaluation of the results of the application of theaction plan to solve the problem in the process allows thesystem to close the CBS cycle, in other words, to learn fromfailure or successful experiences and to upgrade the case-library. Thus, if the current case is quite different from all his-torical cases from the case-library, it should be stored as a


Table 3. Matrix representation of models

new experience with the diagnosis and comments of theprocess state, the action carried out and the evaluation of itsapplication to the plant. These features can be detected bythe Supervisory System itself (unless a manual operation iscarried out), but it is essential to provide the confirmation bythe plant manager who will have the opportunity to changemisleading or add missing information. On the other hand,the Supervisory System can also extend the knowledgebase by acquiring new knowledge from new sources (newexperts or new automatic data classification).

Experimental System

The wastewater treatment plant selected to develop and ap-ply our proposed Supervisory System prototype is located inGranollers, in the Besòs river basin (Catalonia, NE of Spain).This plant initially included preliminary and physical-chemi-cal treatment for organic matter and suspended solids re-moval (built in 1992). In April 1998, the plant was expandedto include biological treatment, and physical-chemical treat-ment was completely replaced. Therefore, nowadays, thisfacility provides preliminary, primary and secondary treat-ment to remove the organic matter, suspended solids and,under some conditions, nitrogen contained in the raw waterof about 130,000 inhabitant-equivalents. The raw influentcomes from a sewer that collects the urban and industrialwastewater together. A current plan of the Granollers WWTPis shown in Figure 1.

The Granollers WWTP has several particular characteris-tics that increase the potential advantages of the develop-ment and application of an intelligent supervisory system to

control and supervise the wastewater treatment process.Among these characteristics, we emphasize the following:

• Availability of a significant amount of historical recordsdescribing plant operation. These records include ei-ther quantitative information (e.g., analytic determina-tions of sludge and water quality at different locations inthe plant, and on-line signals from different sensors)and qualitative information (e.g., microscopic observa-tions of mixed liquor twice or three times a week, bio-logical foam presence, filamentous bulking sludgewhich interfere with the settleability, sludge floating inclarifiers and, in general, any abnormality).

• This plant has a high level of automation centralised ina computer that collects on-line data and controls mostof the plant operations.

• The Granollers WWTP is a highly variable system . Con-tinuous change occurs in hourly loading because theplant is located in an area of the Besòs river basin withan important contribution of industrial activities.

• A wide range of different situations taking placethroughout the year (storms, overloading, nitrification inhot periods, uncontrolled industrial spills...), causingsignificant changes in the influent characteristics,which affect standard process operation.

• High level of specialization of plant experts who havebeen working in the plant from the beginning of its op-eration. They are perfectly acquainted with all sorts ofdetails that make up the heuristic knowledge of theplant.


Figure 6. Supervisory cycle.

Validation of the Supervisory System

Field testing was considered to be the most effective validitytest. The main objective of field validation was to test the useof the overall Supervisory System in situ with actual real cas-es. We were attempting to test the system within its real envi-ronment and to identify needs for further modifications. TheSystem performance was tested in its actual operating envi-ronment working as a real-time decision support system formore than 10 months. During this period of exhaustive vali-dation of the Supervisory System, it was able to successfullyidentify 123 different problem situations, suggesting suitableaction strategies. Nowadays, the Supervisory System isused as a complementary tool of diagnosis for the usualmanagement of the activated sludge process.

Conclusions

An integrated intelligent Supervisory System for the supervi-sion of WWTP management has been developed, imple-mented and tested in a real plant (Granollers WWTP). Thesystem integrates an expert system and a case-based sys-tem with classical control in a three-level architecture: datagathering, diagnosis module and decision support module.We think that the research presented in this paper can beconsidered as a good example of a successful applicationof a multidisciplinary approach to tackle complex problems,starting from a basic research and finishing to its implemen-tation at an industrial scale, in order to contribute to the ame-lioration of our environment.

Acknowledgements

This research received support from the CICyT, CIRIT,Agència Catalana de l’Aigua (ACA) of the Generalitat deCatalunya and Consorci per la Defensa de la Conca del RiuBesòs.

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About the authors

The Laboratori d’Enginyeria Química i Ambiental (LEQUIA, http://lequia.udg.es) is a research group at the Universitat deGirona, which was set up ten years ago as a dynamic framework in which different academic staff could combine their re-search areas, from different points of view, all of them focused on environmental aspects. Recently, the LEQUIA has become amember of the Xarxa de Centres de Suport a la Innovació Tecnològica from CIDEM (Generalitat de Catalunya). LEQUIA haswide experience in the field of wastewater treatment plant control and supervision, mainly in the application of computing tech-niques to improve management of real plants. Though we started with a classical approach, applying our previous experiencein the control of lab bioreactors, we soon realised that classical control methods show some limitations when dealing with com-plex environmental processes. Based on this idea, LEQUIA has been working in close association with several researchgroups in Environmental Engineering, Chemical Engineering and Artificial Intelligence, mainly with the KEML group, conclud-ing that it is necessary to integrate an array of specific supervisory intelligent systems and numerical computations for detailedengineering when trying to optimally manage complex environmental problems. In this sense, our research covers different ap-proaches such as classical modelling, knowledge-based systems (mainly expert systems and case-based reasoning) and softcomputing.

The Environmental Engineering Group (http://eq3.uab.es /depuradoras/) is part of the Chemical Engineering Department ofthe Universitat Autònoma de Barcelona. This group started its activities 20 years ago, and during this time it has been workingin several fields, such as the design and implementation of anaerobic processes, detoxification processes, nutrient removalprocesses (as nitrification-denitrification and enhanced biological phosphorus removal) and wastewater treatment plant auto-matic control. This last topic includes development and implementation of real-time knowledge-based expert systems for dif-ferent full-scale wastewater treatment systems, done in co-operation with the LEQUIA and KEML groups.

The Knowledge Engineering and Machine Learning group (KEML, http://www.lsi.upc.es/~webia/gr-SBC/ KEMLG.html) is aresearch group of the Artificial Intelligence Section of the Software Department at the Universitat Politècnica de Catalunya(UPC). The group has been active in the Artificial Intelligence field since 1988. Main research areas of KEML group are Knowl-edge-Based Systems, Data Mining, Knowledge Engineering and Management, Case-Based Reasoning, Machine Learning,Autonomous Agents and Integrated AI architectures. KEML has a special interest on the application of AI techniques to Envi-ronmental Decision and Design Support Systems, and has been working intensely with several research groups, especiallywith LEQUIA.

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