Engineering Applications of Artificial Intelligence 25 (2012) 317–325

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Engineering Applications of Artificial Intelligence

0952-19

doi:10.1

n Corr

neering

17003 G

E-m

journal homepage: www.elsevier.com/locate/engappai

Argumentation-based framework for industrial wastewaterdischarges management

M. Aulinas a,b,n, P. Tolchinsky b, C. Turon c, M. Poch a, U. Cortes b

a Laboratory of Chemical and Environmental Engineering, Parc Cientıfic i Tecnol�ogic, Edifici Jaume Casademont, Pic de Peguera 15, 17003 Girona, Catalonia, Spainb Knowledge Engineering and Machine Learning Group, Technical University of Catalonia, Campus Nord - Edifici C5, Jordi Girona 1-3, 08034 Barcelona, Spainc Consorci per a la Defensa de la Conca del Riu Bes�os, Av. Sant Juli �a 241, Granollers, Spain

a r t i c l e i n f o

Article history:

Received 12 December 2008

Received in revised form

24 November 2010

Accepted 19 September 2011

Keywords:

Agents

Argumentation

River basin management

Urban wastewater system

Industrial discharge management

Wastewater treatment plant (WWTP)

76/$ - see front matter & 2011 Elsevier Ltd. A

016/j.engappai.2011.09.016

esponding author at: Laboratory of Chemic

, Parc Cientıfic i Tecnol �ogic, Edifici Jaume Cas

irona, Catalonia, Spain. Tel.: þ34 972183244

ail address: aulinas@lequia.udg.es (M. Aulinas

a b s t r a c t

The daily operation of wastewater treatment plants (WWTPs) in unitary sewer systems of industria-

lized areas is of special concern. Severe problems can occur due to the characteristics of incoming flow.

In order to avoid decision that leads to hazardous situations, guidelines and regulations exist. However,

there are still no golden standards by which to a priori decide whether a WWTP can cope with critical

discharges. Strict adherence to regulations may not always be convenient, since special circumstances

may motivate operators to accept discharges that are above established thresholds or to reject

discharges that comply with guidelines. Nonetheless, such decisions must be well justified. This paper

proposes an argumentation-based model by which to formulate a flexible decision-making process. An

example of the model application describes how experts deliberate the safety of a discharge and adapt

each decision to the particular characteristics of the industrial discharge and the WWTP.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

In industrialized areas, where industrial discharges are con-nected to sewer systems and are treated, together with domesticwastewater and rainfall, by wastewater treatment plants(WWTPs), industrial discharges represent an important loadcontribution to urban wastewater systems (UWSs). In this con-text, where there is high diversification of industries (e.g., long-and short-term variations), it is difficult to define typical inputoperating conditions at the WWTP and to account for externalfactors. The uncertain and often insufficient knowledge describingthe interrelation between industrial discharges and the treatmentperformance complicate the regulation and management ofindustrial wastewater discharges into the UWS (Butler andSchutze, 2005; Devesa et al., 2009; Vanrolleghem et al., 2005).The characteristics of inflow wastewater and its influence onbiological processes, can cause problems that may have an effecton the WWTP effluent, which in turn may produce an undesirableoutcome on the receiving media where treated wastewater isdischarged (e.g., the river).

ll rights reserved.

al and Environmental Engi-

ademont, Pic de Peguera 15,

; fax: þ34 972418150.

).

Up-to-date approaches to control and prevent hazardoussituations related to industrial discharges are based on theapplication of standards at discharge point sources. These stan-dards use numerical limits of a set of polluting parameters,indicating a concentration and/or load (Tilche and Orhon, 2002;Gabriel and Zessner, 2006), to define the permitted quality ofwastewater discharged.

Strict adherence to conventional guidelines and regulationsmay not always be convenient for both WWTP and water qualityprotection, since each input operating condition is different.Special circumstances may sometimes motivate operators toeither reject discharges that are under legal limits in order toprevent potential complications (e.g., the WWTP is overloaded) oraccept discharges that are above legal thresholds, thereby opti-mizing the infrastructure, because the current state of the WWTPcan deal with them.

However, because these decisions are critical, they need to bewell justified. This task implies successfully adapting the WWTPoperation to influent variability and avoiding or mitigatingoperational problems in the WWTP. These decisions should bebased on whether, in the current circumstances and accountingfor possible complementary courses of action (e.g., adjustment ofoperational WWTP parameters, preventive or mitigating actions,etc.), the discharge will cause an undesirable side effect thatjustifies not performing it. For this reason, we propose a flexibledecision-making process in which decisions can be adapted to

Aeration tank Secondary settler(clarifier)

ACTIVATED SLUDGE SYSTEM

Recycle (RAS) Waste (WAS)

PrimaryEffluent Effluent

DO

Fig. 1. Diagram of a typical activated sludge (AS) system. Primary effluent comes

from a primary treatment and enters the aeration tank containing the micro-

organisms population: part of the activated sludge is recycled (RAS) whereas

another part is purged (WAS). DO stands for the content of dissolved oxygen at the

aeration tank.

M. Aulinas et al. / Engineering Applications of Artificial Intelligence 25 (2012) 317–325318

each circumstance depending on the particular situation. Thedifferent stakeholders involved in wastewater management canpose arguments to justify or counter a previously made argument.In our proposal, guidelines and thresholds are taken as perspec-tives by which to evaluate the arguments for and against thedischarge safety. Other relevant perspectives are expert opinionand empirical evidence. These perspectives make it possible toconsider of accepting the discharge or not because it is belowthresholds because experts claim it will not cause an undesirableside effect.

This paper proposes a method, based on artificial intelligencetools, that provides a procedural framework for deciding whetheror not an industrial discharge can safely be handled by the WWTP.This method defines an argumentative process for eliciting therelevant factors from the decision makers (experts in the domain).This results in a network of interacting arguments in favor oragainst the industrial discharge. These arguments are then eval-uated, taking into account the domain’s guidelines and thresholds,the available empirical evidence and the decision makers’ opi-nions. The result of such an evaluation is a justification as to whythe proposed industrial spill is or is not environmentally safe.Moreover, the proposed method, being of a computational nature,allows partial automation of the process. The following subsectioncontains the background literature on the argumentation-basedmethod applied. In Section 3, we introduce ProCLAIM (see Cortesand Poch, 2009 for a more detailed explanation).

The rest of the paper is organized as follows. Section 2describes the context in which the decision making process takesplace, motivating a need for an alternative representation of theproblem. This alternative approach will enable deliberation on thesafety of an industrial discharge. Section 3 explores the differentlines of reasoning relevant for deliberating the safety of adischarge beyond the existing standards, and it presents theoverall framework to evaluate the arguments. Section 4 intro-duces a simplified but significant wastewater scenario in which atoxic substance is discharged into the system and a critical safetydecision must be made. This is an example from a collection usedto show the power of the tools presented and to study theirfeasibility and viability for deployment in a real scenario. Finally,Section 5 gives the main conclusions of the paper and plannedfuture work.

1.1. Background

Argumentation theory has recently emerged from an inter-disciplinary area of research as one of the most promisingparadigms for conflict resolution and as an important method ofreasoning and human interaction (Bench-Capon and Dunne,2007). The practical benefits of using argumentation in contextsas diverse as inference, decision-making and dialog has drawnincreasing attention from the computational research commu-nity. As a result, in recent years formal models have emerged toestablish argumentation on a rigorous computational basis andenable the development of automated computational capabilitiesbased on argumentation (Tolchinsky et al., 2009).

It is worth emphasizing that argumentation has already beenproposed in safety-critical domains, particularly in medical sce-narios. In Tolchinksy and Cortes (2006) the ProCLAIM model isproposed to allow experts to efficiently deliberate on whether aproposed action is safe or not, accounting for common consentedknowledge, evidence and participants’ degree of expertise. Theseauthors report that the family of scenarios for which ProCLAIM

may be useful naturally falls into safety-critical domains. Forexample, in environmental scenarios, in the same way as in themedical domain, the performance of wrong actions may causecatastrophic effects on the environment. In many of these

situations, decisions are made by experts with the help of variousguidelines to indicate the most appropriate, commonly agreedupon solutions. Furthermore, in such contexts empirical evidenceplays an important role in the decision making.

Briefly, for model evaluation, two safety-critical scenarios arenow being explored. One scenario is related to human organtransplants (Tolchinsky et al., 2008) while the other is related tothe environmental impact of industrial wastewater discharges inriver basins (Aulinas et al., 2007; Aulinas, 2009). The latter is thescenario investigated in this work.

2. Using arguments to reason about problems in municipalWWTPs

The aim of the decision-making process we present is todetermine whether an industrial spill can be safely discharged.The most common treatment method at municipal WWTPs is theactivated sludge system, based on a dynamic multi-speciesmicroorganism population capable of oxidizing organic matterunder special operating conditions (Tchobanoglous et al., 2003).The microorganisms’ metabolism and capacity to remove organicmatter depend on wastewater composition as well as the capacityto adapt the management of WWTP operational parameters (e.g.,returned activated sludge (RAS), dissolved oxygen (DO), wasteactivated sludge (WAS)) to wastewater influent variations inorder to meet effluent safety standards. A scheme of the activatedsludge process at a biological WWTP is shown in Fig. 1. Industrialdischarges of various types, with different characteristics, canaffect the growth of microorganisms in municipal WWTPs work-ing with activated sludge systems (e.g., content of organic matter,nutrients and/or presence of pollutants), and by extension of thetreatment and the final result. A spill is safe if it does not causeany undesirable side effects to the UWS. Otherwise, the spill isconsidered to be unsafe.

Several studies have been carried out to improve and increasethe knowledge of WWTP operational problems related to influentcharacteristics (Ayesa et al., 1998; Comas et al., 2003, 2008;Henze et al., 1993; Jenkins et al., 2003). This knowledge, basedon on-line and off-line data as well as on the experts’ heuristics, ismainly represented by means of decision trees and/or knowledge-based flow diagrams (Cortes et al., 2003; Poch et al., 2004;Rodrıguez-Roda et al., 2002; Serra et al., 1994, 1997). The knowl-edge is organized in a hierarchical manner: top-down descrip-tions of interactions between different parameters and factorsused to solve a problem. This allows for the easy interpretation ofthe available knowledge, mainly in terms of cause–effect relationsfor a specific problem.

These decision trees do not account for different possibleperspectives on the decision that may be in conflict. For instance,while guidelines may suggest that an industrial discharge cannot

M. Aulinas et al. / Engineering Applications of Artificial Intelligence 25 (2012) 317–325 319

exceed the threshold of1500 mg/l of chemical oxygen demand (COD),an expert (e.g., the WWTP manager) may believe that this is notthe case in all situations. Moreover, empirical evidence can havean influence on the decision (e.g., the WWTP is overloaded at thatmoment).

This paper addresses the question of how to make decisions, asan argumentative process, in which the knowledge available inthe decision-making trees can also be accounted for and repre-sented as interacting arguments. The added value is that, in theargumentative process, alternative proposals or the identificationof a potential complication caused by the interaction amongdiverse factors can naturally be integrated into the decisionmaking via the submission of arguments and counter-arguments.In particular, this approach facilitates the active participation ofexperts in the decision-making process.

Many aspects must be accounted for to allow such argumenta-tive processes to take place in an efficient and effective way. In thissection we focus on the first obvious question: what is to be argued

about? Later, in Section 3, we discuss how to argue. Concerning thequestion when to argue, the answer is: whenever there are poten-tially conflicting arguments; an argumentation process can be usefulfor reaching consistent conclusions (Maudet et al., 2007).

Table 1Sets of information used to construct arguments.

Argument

components

Notation Descriptio

Initial states (R) r1:ind_ww(COD) Industrial

r2:ind_ww(BOD) Industrial

r3:ind_ww(N, P) Industrial

r4:ind_ww(Cd) Industrial

r5:ind_ww(Cr) Industrial

r6:fungi Presence o

sp., Asperg

^ ^r10:WWTP_design WWTP de

capacity, t

r11: WWTP_setpoints Setpoints

rn: y y

Actions (A) a1:increase_(DO, RAS, WAS) Increase e

a2:decrease_(DO, RAS, WAS) Decrease e

a3:add_(N, P, NaOH, CHLy) Add nutrie

effects

a4:primary.treatment N/D_(sets) Avoid or e

an:y y

Final states (S) s1:fil.B(type) Inhibition

s2:FFB(type) Inhibition

s3:EPS Absence, i

s4:overaeration Excess of

s5:hydraulic.shock Hydraulic

s6:overdose Overdose

s7:reduction(HM) Reduction

sn: y y

Undesirable

goals (G�)

g1:fil.bulking Overgrow

g2:viscous.bulking Excessive

settle and

g3:bio.foaming Overgrow

g4:dispersed.growth The absen

g5:rising Denitrifica

g6:pin-point floc The absen

g7:biomass.loss Washout o

g8:aquatic.toxicity Toxicity to

flocculant

g9:charge.reversal Overdose

the colloid

g10:sludge.toxicity Accumula

(e.g., comp

gn:y y

Note: COD (chemical oxygen demand), BOD (biological oxygen demand), N (nitrogen),

RAS (recycle activated sludge), DO (dissolved oxygen), CHL (chlorine), EPS (extracellula

Whether or not an industrial discharge is expected to causeundesirable side effects depends on the wastewater’s content, thereceiving media characteristics (e.g., characteristics of theWWTP), external factors such as the weather conditions and,finally, any complementary courses of actions that can be per-formed to prevent or mitigate the undesirable spill’s side effects.

Let us consider the following four sets:

�

n

was

was

was

was

was

f fu

illus

sign

ype

of o

ithe

ithe

nts

nha

of fi

of f

nsu

DO,

sho

app

of a

th o

prod

bec

ing

ce o

tion

ce o

f bi

aq

s

of c

co

tion

ost

P (p

r p

R: the set of facts in the current circumstances.

� A: the set of possible actions. � S: the set of side effects that an industrial discharge may cause. � G�: the set of undesirable goals that may be achieved because

of an industrial discharge.

R is the set of facts constituting the circumstances in which thespill is intended to be discharged. R would thus contain all theknown characteristics of the wastewater, the characteristics of thereceiving media and other known environmental conditions.A contains the actions relevant to decision making. A would thuscontain the discharge action and other alternative actions such asdecrease the waste activated sludge (WAS) or add chlorine. S is theset of side effects of the discharge and G� contains the undesirable

tewater concentration of COD

tewater concentration of BOD

tewater concentration of nutrients (N and P)

tewater concentration of cadmium (Cd)

tewater concentration of chromium (Cr)

ngi spp. in the active biomass (e.g., Pseudomonas

sp., Candida maltosa, etc.)

parameters (desirable maximum flow, N/D

of reactory)

peration variables: DO, WAS, RAS, HRT, etc.

r DO, RAS or WAS to modify WWTP performance

r DO, RAS or WAS to modify WWTP performance

, caustic soda, chlorine, coagulants/flocculants to prevent or mitigate negative

nce the process of N/D through a set of interrelated actions

lamentous bacteria

oam-foaming filamentous bacteria

fficient or overloading of EPS

undesirable bubbles

ck at the WWTP due to a heavy rain or storm

lication of coagulants/flocculants

heavy metal (e.g., CrVI to CrIII)

f filamentous bacteria

uctions of EPS by the floc-forming bacteria (viscous sludge is difficult to

ome compact)

of foam-forming filamentous bacteria

f EPS hinders the formation of flocs

occurs in clarifiers (instead of in reactors)

f filaments hinders the formation of large flocs

omass hence loss of active microorganisms

uatic organisms of WWTP effluent water with overdose of coagulants/

oagulants/flocculants can cause a complete charge reversal and re-stabilize

mplex, thus settling problems

of toxic substances in the sludge, making them unavailable for posterior uses

for agriculture)

hosphorous), N/D (nitrification/denitrification), WAS (waste activated sludge),

olymeric substances).

M. Aulinas et al. / Engineering Applications of Artificial Intelligence 25 (2012) 317–325320

goals that these side effects may have. Possible side effects are, forinstance, undesired quantity of filamentous bacteria and overdose

application of chlorine, which may, respectively, realize the pre-sences of the undesirable goals of filamentous bulking and chlorine

organic compounds (COCs) in the effluent. Table 1 illustrates asubset of possible values of R, A, S and G� .

We can now reformulate the problem of deciding whether aspill is environmentally safe as a process of identifying therelevant facts in the current circumstances (r1,y,rn in R) becauseof which the wastewater discharge, along with other comple-mentary actions (a1,y,an in A), may or may not cause any sideeffect (s1 in S), that realizes an undesirable goal g1 which justifiesnot performing the course of actions a1,y,an. Fig. 2 graphicallydepicts an example of the formation of an argument by linkingthe pieces of information organized as R, A, S and G� . Thesearguments can announce either an undesirable goal (argument

con) or a favorable one (argument pro). One example of how it isdone computationally is given by Tolchinsky et al. (2008).

Thus, to argue against an industrial discharge means toindicate that there is a subset of R from which the proposedactions will cause a side effect that produces some undesirablegoal. For example, the argument ‘‘The industrial discharge con-tains a concentration of readily biodegradable organic matter –rbCOD – that will cause an overgrowth of filamentous bacteriacausing filamentous bulking’’.

An argument defending the discharge’s safety will contradictsuch a statement: ‘‘The industrial discharge that contains aconcentration of rbCOD will not cause the side effect overgrowthof filamentous bacteria achieving the undesirable goal filamen-tous bulking since the action add nutrients can be performed toavoid the side effect overgrowth of filamentous bacteria and thus,prevent filamentous bulking’’.

Typical problems such as filamentous bulking can therefore berephrased in terms of the interaction of these arguments con-structed to instantiate the tuple R, A, S and G�. This tuple, in fact,defines an argument scheme (AS). As described by Walton (1996),AS are used to classify different types of arguments that embodystereotypical patterns of reasoning, i.e., to identify the premisesand conclusion of an argument. In this case, the scheme embodiesconventional patterns of reasoning over the safety of an action(Atkinson et al., 2005). An instantiated scheme (what we term anargument) can be questioned (attacked) by posing critical ques-

tions associated with the scheme. A critical question (CQ) chal-lenges the validity of the given argument. The asking of a CQ

Fig. 2. Argument formation using as a basis a set of R, A, S and G� .

shifts the weight of presumption back to the arguer so that his/her argument is defeated unless the question is answered. EachCQ can itself be posed as an attacking argument instantiating aparticular scheme. This scheme is then itself subjected to criticalquestioning. Thus, CQ is used to evaluate the argument byprobing into its potentially weak points. How the revisionprocedure can be handled is established by defining the set ofattached CQs (Walton, 2005).

AS and CQ together provide a natural basis for the definition ofa protocol-based exchange of arguments. Indeed, such anapproach is taken in Tolchinsky et al. (2007), where a protocol-based exchange of arguments is defined to argue about the safetyof a proposed action. In the following section the model is brieflydescribed.

3. The ProCLAIM model

ProCLAIM defines a setting in which the different agents (e.g.,those involved in the UWS management) can effectively deliber-ate the safety of the proposed actions. Broadly construed, theProCLAIM model consists of a mediator agent (MA) directingproponent agents (e.g., industries, WWTP manager, etc.) in anargument-based collaborative decision-making dialog, in whichthe final action should comply with certain domain-dependentguidelines. However, the arguments submitted by the proponentagents may also persuade the MA to accept decisions that deviatefrom the guidelines. For example, the MA may able to reason thatthe submitted arguments in support of an alternative decisionhave proven to be correct in previous, similar deliberations.

Thus, the MA has three main tasks to accomplish

1.

Guide the proponent agents in the arguments they can submitat each stage of the deliberation.

2.

Ensure that the submitted arguments are relevant (e.g., in thesense that instantiations of schemes are relevant in terms ofthe domain of discourse).

3.

Evaluate the submitted arguments in order to identify thewinning arguments and thus determine whether a proposedaction is appropriate. This last task may require the MA toassign a preference relation to the submitted arguments thatare in conflict and, possibly, to submit additional arguments.

In order to carry out these tasks, the MA employs four knowl-edge resources that are part of the model – the argument schemerepository (ASR), the guideline knowledge (GK) base, the casebased reasoning engine (CBRe) and the argument source manager(ASM) component – depicted in Fig. 3.

The ASR encodes the full space of argumentation, i.e., all thepossible lines of reasoning, or the argument schemes, that shouldbe pursued with respect to a given issue. The repository isstructured in such a way that it defines the protocol-basedexchange of arguments. Associated with these argument schemesare CQs that enable agents to attack the validity of the variouselements of the argument scheme and the connections betweenthem. Each CQ can itself be posed as an attacking argumentinstantiating a particular scheme.

The GK enables the MA to check whether the arguments complywith the domain knowledge and, in particular, whether the argu-ments are valid instantiations of abstract arguments in the ASR.CBRe assigns strengths to the submitted arguments on the basis oftheir associated evidence gathered from past deliberations, andprovides additional arguments deemed relevant in previous similarsituations. Finally, the ASM component enables us to readjust thestrengths of these arguments depending on the source—fromwhom, or where, the arguments have been submitted. Hence, this

Fig. 3. ProCLAIM’s architecture. Shaded boxes identify the model’s constituent parts specialized for the basin scenario. Shaded ovals identify the participant agents in the

presented case (see Section 4.1).

M. Aulinas et al. / Engineering Applications of Artificial Intelligence 25 (2012) 317–325 321

latter component manages the knowledge related to the agent’sroles and/or reputations, and/or types of certificates or referencesthat may empower agents to undertake some exceptional actions.

The agents’ submitted arguments shape a graph of interactingarguments based on the attack relation. The arguments used tomake these graphs are those available at the ASR that areconstructed as explained in Section 2.

The construction of arguments is done in two steps. The firststep consists of building the ASR. In this phase the particularschemes too deliberate on a question are built. In the second step,these schemes have to be instantiated according to a fewconstrained variables. So, once these schemes are delivered tothe agents, it is necessary to instantiate some variables. Oneexample of how it is done computationally can be found inTolchinsky et al. (2008).

4. Deliberating in the river basin scenario

This section presents the river basin context in which indus-trial discharges are released. In recent years, water governancehas undergone a remarkable paradigm shift. Old notions of waterresource management dominated by a supply-orientation andreliance on civil engineering science and technical solutions towater problems have been discarded in favor of a softer govern-ance regime that embraces stakeholder participatory processes(Guimar~aes and Corral, 2002). The Water Framework Directivesupports this approach and other important policy principles forEuropean member states (CEC, 2000).

This scenario comprises, as the more relevant components, theland with its uses, rural and urban areas, and groundwater andsurface water. Everyone lives in a river basin. People who live faraway from natural water resources may also affect water qualityand quantity, since everything in a river basin is interconnectedand interdependent. For that reason, in the context of a riverbasin, a decision made affects all of its components. The partici-pation of the different agents in the river basin managementscenario emerges as key to reaching a consensus.

In Section 4.1 the basin scenario is simplified to facilitateunderstanding of the argument-based method when dealing with

critical decisions in the system, focusing on the safety of anindustrial discharge containing a polluting substance. There aremany smaller catchments or watersheds within a river basin(i.e., a smaller part of the basin that drains water to a stream). Thismakes it possible, in a first stage, to build reduced prototypes andthen amplify their scale.

In the following sections, the agents used to illustrate anexample are described (Section 4.1), and the process of argumentsubmission, validation and preference assignment is exemplified(Section 4.2).

4.1. Urban wastewater system agents

As mentioned in Section 1, an urban wastewater system(UWS) is constituted by an urban catchment that comprises asewer system, a WWTP and an urban river stretch, which drainswastewater from human activity (i.e., communities and indus-tries) as well as water from rainfall.

Industries often deal with the wastewater they produce byconnecting to the sewer system. Therefore, industries can beconsidered part of the UWS, whose main components are shownin Fig. 4. We consider every relevant element as a software agent,i.e., an autonomous entity that can interact with other entities toachieve an individual or common goal (Russell and Norvig, 2010;Wooldridge, 2001). In this example, we consider the followingproponent agents that can participate in the argument-baseddeliberation when dealing with an industrial wastewater dis-charge into a UWS

�

Industry agents (IA) represent individual industries and/orgroups of industries that need to manage their producedwastewater as a result of their production process. IA dis-charge their produced wastewater into the sewer system,where it is collected together with other inflows and trans-ported to the WWTP (from here on this course of action iscalled a0). � Wastewater treatment agents (WTA) represent the manager of

WWTP. Their main function is to keep track of wastewaterflow arriving at the WWTP as well as to supervise and controlthe treatment process. They sound convenient alarms when

Fig. 4. Urban wastewater system (UWS). In bold the normal path followed by an industrial discharge. CSO: combined Sewer overflow.

Fig. 5. Acquaintance model for the proposed case (i.e., industrial discharge

containing a heavy metal). Arrows indicate agent acquaintances, whereas num-

bers inside the boxes indicate agents’ reputation, i.e., the priority order of their

decisions.

M. Aulinas et al. / Engineering Applications of Artificial Intelligence 25 (2012) 317–325322

necessary and give orders to change the operational set points.This responsibility is shared between the managers of theWWTP (WTAM) and the operators (WTAO).

� River consortium agents (RCA) represent the maximum author-

ity in the catchment, and their main objective is to preservethe river quality. Their main functions are to manage andcoordinate a group of WWTPs in the river catchment as well asto monitor river quality and prevent possible hazardouscontamination by supervising IA and WTA.

All of these agents, representing experts in the wastewater,having different degrees of responsibility, and making use of theirexpertise in front of safety critical decisions treatment domain,can take part in the deliberation process. Fig. 3 shows all theseagents in the context of ProCLAIM for this scenario. To illustratethe evaluation of the arguments posed by these experts it isimportant to know the acquaintances among them and the degreeof confidence in their arguments. Fig. 5 shows the main relation-ships among the agents considered in this specific example. Thenumber in the box indicates the order of the agents’ level ofexpertise specific to each type of discharge. WTAM knows theoperation of the WWTP and thus the degree of confidence in theirarguments is high (in fact, since the problem of concern involves apriority contaminant, WTAM responsibility g WTAO, consequentlyWTAM arguments will have more strength). However, the figureshows that when dealing with discharges containing prioritypolluting substances, the RCA is more reliable, so their argumentswill be deemed stronger.

All of these acquaintances will be taken into consideration bythe MA to evaluate the arguments. The deliberation processes areposed and it is decided whether or not to accept the participant

submitted arguments. If accepted, they will be part of theargument graph for a specific deliberation process, such as theone depicted in Fig. 6.

4.2. An industrial discharge with a toxic substance

Let us suppose that an industry, represented by its IA, proposesa wastewater discharge claiming that no undesirable effects willoccur as a result of it. Accordingly, the IA poses argument Arg1

Arg1: In the current circumstances (i.e., a wastewater dis-charge and a WWTP) industry Indi will effectuate the dis-charge (action a0) claiming that this action (a0) will not causeany side effect S so any undesirable goal g to the treatmentsystem.

Generally speaking, when an IA claims to discharge its waste-water because no negative effects occur (e.g., Arg1), a CQ that willnaturally arise is ‘‘AS1_CQ1: Is there a contraindication for under-

taking the proposed action?’’ This will help the MA to check if thefollowing dialog move is relevant. Assuming that the WTA knows

that the discharge contains Chromium VI and believes Chromium VI

is a contraindication for the treatment process because there isevidence it can provoke both the inhibition of nitrification(significantly decreasing the ammonia removal efficiency) andthe reduction of filament abundance, causing the appearance ofpin-point floc and free-dispersed bacteria (Alkan et al., 2008;Samaras et al., 2009). The WTA reports the latter possibility bysubmitting Arg2

Arg2: If in current circumstances industry Indi effectuates thedischarge (a0) containing Chromium VI (r5), this will reducefilaments abundance (s1) and provoke the appearance of pin-point flocs (g6).

Arg2 introduces new important information about the dis-charge (i.e., the discharge contains chromium that can cause theinhibition of filamentous bacteria). Different experts in thedomain can start a dialog of attacking and supporting arguments,seeking more information, alternative actions, etc. before finallydeciding on the possible actions to be taken to prevent WWTPproblems.

Until now, it has been documented that the degree of inhibi-tion in activated sludge is influenced by several factors such aspH, the concentration of inhibitors, the species present, theconcentration of suspended solids, the sludge age, the solubilityof the inhibitor, and the concentration of other cations and

Fig. 6. Argument graph that captures the moves in a dialog over the acceptability of a toxic industrial discharge into the WWTP. Each node of the tree holds one argument

described in the table. Each new introduced factor is highlighted in bold.

M. Aulinas et al. / Engineering Applications of Artificial Intelligence 25 (2012) 317–325 323

molecules present (Alkan et al., 2008; Samaras et al., 2009).According to this, other possible counterarguments are raised bythree new CQ (they are meant to limit the possible counter-arguments, discarding ones that are not relevant for the discus-sion and looking for key information).

AS2_CQ1: Are the current circumstances such that the stated

effect will be achieved? That is equivalent, in the presentedexample, to questioning if the concentration of chromium,given the current circumstances, is enough to produce theundesirable effect (i.e., filamentous bacteria inhibition) even ifit is under legal thresholds.

AS2_CQ2: Are the current circumstances such that the achieved

effect will realize the stated negative goal? That is, to exploreother relevant circumstances in which no undesirable goal isrealized (e.g., synergetic effects with other pollutants, precipi-tation of this heavy metal due to the presence of a specificcation, etc.).

AS2_CQ3: Is there a course of action that prevents the achieve-

ment of the stated effect, i.e., to explore the possible actions thatcan prevent or mitigate the negative effect (e.g., try toprecipitate the heavy metal, increase the capacity of theactivated sludge to adsorb heavy metals by means of someadded adsorbent, etc.).

Fig. 6 shows some of the possible lines of reasoning whendealing with the industrial discharge containing a heavy metal(e.g., Chromium VI). Following the example, AS2_CQ1, AS2_CQ2and AS2_CQ3 attack Arg2; consequently Arg3, Arg4 and Arg5 (seethe table in Fig. 6) attack Arg2 (e.g., they are instances with newinformation about the discharge, possible synergetic effects of thecurrent circumstances or an alternative action, respectively).

Arg3: If in current circumstances industry Indi effectuates thedischarge (a0) containing Chromium VI (r5), it will not causeas much inhibition of filamentous bacteria (s1) as necessary,hence it does not provoke pin-point flocs (g6).

Arg4: If in current circumstances industry Indi effectuates thedischarge (a0) containing Chromium VI (r5), it will not causeinhibition of filamentous bacteria (s1), hence it does notprovoke pin-point flocs (g6) due to the positive presentcondition of activated biomass to reduce Chromium VI (i.e.,the presence of several fungi (r6) species capable of reducingChromium VI to a less unsafe form of this heavy metal –

Chromium III – together with the availability of organicmatter).

Arg5: If in current circumstances industry Indi effectuates thedischarge (a0) containing Chromium (r5), it will not causeinhibition of filamentous bacteria (s1), hence it does notprovoke pin-point flocs (g6) since an ion (a3) (e.g., ferrous)can be added to precipitate Chromium VI.

As mentioned before, Arg3, Arg4 and Arg5 are instantiations ofAS3,4,5 that introduce new facts, new information about thecurrent situation or alternative/preventive actions, respectively.These may in turn warrant or cause some undesirable secondaryeffect(s). Consequently, associated with these arguments animportant new CQ arises leading to Arg6 and Arg7 instances

AS3,4,5_CQ1: Will the introduced factor cause some undesirable

side effects?

Arg6: If in current circumstances industry Indi effectuates thedischarge (a0) containing Chromium VI (r5), the presence ofspecific active biomass (r6) can reduce its toxicity and preventpin-point flocs (g6); however, the new form of chromium(Chromium III) will remain in the sludge.

Arg7: If in current circumstances industry Indi effectuates thedischarge (a0) containing Chromium VI (r5), the enhancementof chromium precipitation will prevent pin-point flocs (g6);however, the precipitate will remain in settled sludge, afterbeing processed in the sludge line, making it unavailable forother uses, such as in agriculture (g10).

In this fashion all possible lines of reasoning with regard to thedischarge and its consequences can be effectively studied, if notquestioned.

Once the argument graph is constructed, the MA has todetermine the winning arguments. In this example (see Fig. 6)we are going to consider the following: there is no evidence posedby any of the participant agents, that the stated Chromium VI loadis safe, thus the line of reasoning on the left of the argumentgraph is discarded (no IA challenges the discharged load; this isdepicted by a in Fig. 7). Therefore, the conflict between Arg4 andArg6 needs to be resolved, i.e., whether the current state ofthe plant causes positive synergetic effects to mitigate theproblem must be clarified (b1 or b2 in Fig. 7). A similar procedureshould be started to resolve the conflict between Arg5 and Arg7(c1 or c2 in Fig. 7). On the basis of the domain-consented

Fig. 7. Graph of interacting arguments. Detail of reasoning lines w.r.t. Fig. 6. Within dashed squares the two possible final solutions of the particular reasoning line (b or c)

is authorized. The double square frames the final evaluated reasoning line of the presented example.

M. Aulinas et al. / Engineering Applications of Artificial Intelligence 25 (2012) 317–325324

knowledge (articulated in terms of R, A, S and G) and thereputation of the agents involved, different strengths can be givento each of the arguments in order to finally decide which is thewinner and which course of action is safest for the actual WWTPperformance.

Accordingly, an expert operator of the WWTP (WTAO) stayswith Arg4 since it reports the presence of a specific biomass thatcan reduce the most toxic form of chromium to a less unsafe form.However, as depicted in Fig. 5, in critical safety decisions WTAM

have higher reputations and their arguments are ranked betterthan those of WTAO. So the reasoning line containing Arg5 (c ofFig. 7) is preferable.

For this specific case, since the weight of the argument posedby the RCA (Arg7) is greater than the argument of WTAM

defending Arg5, the discharge is considered unsafe. Although amitigating action can avoid operational problems at the WWTP(e.g., sludge settling problems due to pin-point flocs), Arg7 attacksArg5, finally supporting Arg2, and certifying the danger of thedischarge given in the present conditions.

From now, since the discharge proposed by the IA should berejected due to the present circumstances, another course of actionneeds to be considered to manage the discharge (e.g., specific pre-treatment at industry, storing the discharge – if storage tanks areavailable – until the system is in proper condition to holdthe discharge, and/or any other possible action that could increasethe argument graph for this specific problem). Moreover, since theaction proposed by the IA, after being considered safe, is rejected,its reliability (in terms of reputation) will diminish.

The aim of the overall system is to preserve the river waterquality by allowing the agents to participate in the deliberationand to finally make the safest environmental decision. In a way,this is a Group Decision Support System that is based on anargumentation framework (Karacapilidis and Papadias, 2001).

5. Conclusions and future work

Industrial wastewater discharges represent a major concernfor WWTP managers due to their potential impact on WWTPprocesses and the environment. The variability of possible indus-trial discharges, complex and often uncertain knowledge, andinformation related to activated sludge-based processes to treat

wastewater make the management of industrial discharges both achallenge and a problem. Using timely and precise information tounderstand and make decisions about problems, and developingcriteria for evaluating the possible solutions for each situation, isof special importance.

We have presented the application of an agents’ deliberationframework based on argumentation to support decision-makingprocesses. In particular, we are using the illustrated schema (seeFig. 2) to identify the ways in which evidence supports non-polluting actions against the receiving media, with safety as themain goal of the decision-making process. The tool we presentenables us to understand the problem focus on the undesirableside-effects, in order to prevent or mitigate them, easing theexploration of the current context and a possible set of actions,and thus articulating the problem beyond numerical thresholds(conflict resolution mechanisms, quantifiable metrics and algo-rithms have already been treated in previous contributions suchas Tolchinsky et al., 2008; Fox et al., 2006).

The argumentative approach in decision support systems tosolve environmental problems is becoming more widespreadand challenging other more traditional knowledge-basedapproaches. This paper proposes a different way to conceptualizethe decision-making process, and offers a reliable way to under-stand the problems together with possible solutions (e.g., alter-native actions).

The use of answer set programming (ASP) – based on the workof Nieves et al. (2005) – is envisioned as the agent’s reasoningmodel. The pros and cons of applying such a reasoning frameworkare largely discussed in Aulinas (2009).

As a result of this work, we offer some promising lines offuture research

�

Constructing an ontology to manage the knowledge related toindustry types, pollutants, polluting potential, etc. A clusteringand categorization could be done to provide the basic knowl-edge to build the declarative rules and hence the arguments. � Extending the argumentation framework to allow the devel-

opment of plans of actions automatically (such as the onesachieved with the diagnosis phase).

� Performing an evaluation phase based on the execution of

existing and well documented cases of discharges that couldbe used as gold standards.

M. Aulinas et al. / Engineering Applications of Artificial Intelligence 25 (2012) 317–325 325

Acknowledgements

The authors would like to acknowledge the project funded bythe Spanish Government ASISTIR TEC2008-06734-C02-02. Wealso want to acknowledge the support provided by the Novedar_Consolider project (CSD2007-00055), and funded by the SpanishMinistry of Education and Science. The views expressed in thispaper are not necessarily those of either the ASISTIR or theNovedar_Consolider consortiums.

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