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Environmental Modelling & Software 23 (2008) 1250–1261

lable at ScienceDirect

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Environmental Modelling & Software

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

Environmental Modelling & Software

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

Risk assessment modelling of microbiology-related solids separationproblems in activated sludge systems

J. Comas a,*, I. Rodrıguez-Roda a, K.V. Gernaey b, C. Rosen c, U. Jeppsson c, M. Poch a

a Chemical and Environmental Engineering Laboratory (LEQUiA), University of Girona, Campus Montilivi s/n, E-17071 Girona, Catalonia, Spainb Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Kgs. Lyngby, Denmarkc Department of Industrial Electrical Engineering and Automation (IEA), Lund University, P.O. Box 118, SE-22100 Lund, Sweden

a r t i c l e i n f o

Article history:Received 21 May 2007Received in revised form 4 January 2008Accepted 19 February 2008Available online 19 May 2008

Keywords:Risk assessmentActivated sludgeFilamentous bulkingFoamingRising sludgeModellingQualitative knowledgeBenchmark simulation

* Corresponding author. Tel.: þ34 972 418 804; faxE-mail addresses: quim@lequia.udg.cat (J. Comas)

drıguez-Roda), kvg@kt.dtu.dk (K.V. Gernaey), christiaulf.jeppsson@iea.lth.se (U. Jeppsson), manel@lequia.u

1364-8152/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.envsoft.2008.02.013

a b s t r a c t

This paper proposes a risk assessment model for settling problems of microbiological origin in activatedsludge systems (filamentous bulking, foaming and rising sludge). The aim of the model is not to diagnosemicrobiology-related solids separation problems with absolute certainty but to quantify in dynamicscenarios whether simulated operational procedures and control strategies lead to favourable conditionsfor them to arise or not. The rationale behind the model (which integrates the mechanisms of standardactivated sludge models with empirical knowledge), its implementation in a fuzzy rule-based system andthe details of its operation are illustrated in the different sections of the paper. The performance of therisk assessment model is illustrated by evaluating a number of control strategies facing different short-term influent conditions as well as long-term variability using the IWA/COST simulation benchmark. Theresults demonstrate that some control strategies, although performing better regarding operating costsand effluent quality, induce a higher risk for solids separation problems. In view of these results, it issuggested to integrate empirical knowledge into mechanistic models to increase reliability and to allowassessment of potential side-effects when simulating complex processes.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Biological wastewater treatment systems, and specifically acti-vated sludge systems, are complex systems. The complexity isa consequence of the presence of a dynamic multi-species micro-organism population whose composition is changing continuouslyunder the influence of factors such as influent flow rate and com-position, temperature variations and plant operational parameterssuch as the sludge retention time, the ratio between aerobic andanoxic time, etc.

Development of tools to support control and operation of acti-vated sludge systems was primarily focused on introducing andimproving deterministic models until the late 1970s. The consid-erable number of literature references on the topic reveals thatgreat efforts have been made in this research field. Simulation ofmechanistic models has been used for plant design, for control andfor optimization purposes (e.g. Coen et al., 1996; Salem et al., 2002),often leading to more cost-effective solutions. Progress in mecha-nistic modelling has meant a great leap forward by improving

: þ34 972 418 150., ignasi@lequia.udg.cat (I. Ro-n.rosen@vaing.se (C. Rosen),

dg.cat (M. Poch).

All rights reserved.

activated sludge process descriptions during normal operatingconditions. However, there are still some operational problems,mainly microbiology-related issues, which cannot be predictedadequately by deterministic models due to lack of sufficientmechanistic understanding of the underlying microorganismpopulation dynamics.

These common operational problems in activated sludgesystems are phenomena of biological nature, caused by eithermicroorganism population imbalances between filamentous andfloc-forming bacteria leading to filamentous bulking, filamentousfoaming and deflocculation (Wanner, 1998; Jenkins et al., 2003;Martins et al., 2004a), or caused by undesirable operating condi-tions that, for example, can lead to rising sludge (Henze et al., 1993).Different types of process data combined with heuristic knowledgeof the operators are exploited to detect these microbiology-relatedoperational problems on full-scale systems: on-line data, off-lineanalytical data reflecting the water and sludge quality and quantity,off-line microbiological information (floc and sludge aspects, bac-teria species identification, etc.) and in situ visual observations areall taken into account. However, the usefulness for prediction islimited: it only provides a warning when the problem is already ina developed stage.

The presence of heuristics and qualitative knowledge on com-plex phenomena such as filamentous bulking, foaming and rising

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–1261 1251

sludge stands in sharp contrast with the lack of basic mechanisticknowledge on the population dynamics of the microorganismscausing these phenomena. As a consequence, although attemptshave been made to explain the development of filamentous bac-teria by means of mathematical modelling (Chen and Beck, 1993;Hug et al., 2006), none of these attempts has led to a general andexperimentally validated model (Martins et al., 2004b).

As an alternative to deterministic models, the complexity in-volved in the description of microbiology-related activated sludgesolids separation problems can be tackled by means of approachesthat are able to integrate different modelling techniques. Enhanceddescriptions of complex operational problems can be achieved bylinking numerical models and knowledge-based systems into in-tegrated tools, which allow for management and active use of es-sential knowledge related to these problems. Several integratedtools for environmental applications were recently reported in theliterature (e.g. Matthies et al., 2007; Nguyen et al., 2007; Schluterand Ruger, 2007; Cherkassky et al., 2006; Krasnopolsky and Fox-Rabinovitz, 2006; Valdes and Bonham-Carter, 2006). Literatureexamples on the application of knowledge-based tools (e.g. expertsystems, fuzzy logic or qualitative modelling) or data-based models(e.g. neural networks or data-driven models) complementingmechanistic models in activated sludge modelling are also alreadyavailable (e.g. Raduly et al., 2007; Yong et al., 2006; Fiter et al.,2005; Lardon et al., 2004; Poch et al., 2004; Rodriguez-Roda et al.,2002; Steyer et al., 2001; Belanche et al., 1999).

This paper introduces a risk assessment model, which integratesempirical knowledge with the mechanisms of standard de-terministic models to infer solids separation problems of microbi-ological origin in the activated sludge system. The paper isstructured as follows: first the knowledge used for the inference ofthe risk is presented and formalized by means of decision trees.Then, the operation and the implementation of the risk assessmentmodel are described, together with the new set of specific indices toalert on solids separation problems. Finally, the potential of themodule is demonstrated for the simulation-based evaluation ofa number of different control strategies and scenarios, where therisk assessment model has been applied on the IWA/COST bench-mark simulation model (BSM) platforms (e.g. Copp, 2002).

2. Knowledge formalization for risk assessment

This section details the knowledge included in the risk assess-ment model of microbiology-related activated sludge solids sepa-ration problems. The operational problems considered in thisintegrated approach are those process situations caused by im-balances of microorganisms (filamentous bulking and foaming) andby undesirable operating conditions (rising sludge in clarifiers). Thecore of this risk assessment approach is a knowledge base com-posed of heuristic and empirical knowledge acquired from litera-ture and domain experts (i.e. information that cannot beadequately represented by mechanistic models). Interestinglythough, a significant part of this knowledge can be captured bymeans of equations, graphs, relationships, etc. that are based onnumerical data which are directly related to state variables or pa-rameters of the mechanistic models. The central idea behind theresearch work presented in this paper is therefore to select andinterpret those process variables that could be available in real-time in a simulation (e.g. readily biodegradable substrate con-centration (SS), ammonium nitrogen (NH4

þ-N), nitrate nitrogen(NO3

�-N), etc.) and which, albeit following human expert-like pro-cessing, allow for obtaining actionable and meaningful patternsrelated to operational risks. Thus the intention of this paper is topropose a risk assessment model for microbiology-related prob-lems using only information available in the simulation outputs,either directly or after simple data processing. Note that only

a minority of the simulated state variables used as inputs to the riskmodule presented in this paper are monitored on-line in full-scaleWWTPs due to lack of affordable and reliable sensors. Instead, mostvariables are only available sporadically and with some delayfollowing sampling and off-line analysis.

A review of state-of-the-art understanding about filamentousbulking, foaming and rising sludge has led to the identification ofsymptoms, relationships and inference methods. This knowledge,founded on data used by the experts in their reasoning strategies,was organized and formalized by means of three decision trees orknowledge-based flow diagrams (Comas et al., 2003). A data-drivenrisk assessment model was not possible to create in this case be-cause large input–output data sets were required. Such experi-mental data sets related to the risk of settling process disturbancesare usually not available while expert knowledge is. In a simulationthe decision trees explore a set of process state variables related towater and sludge quality at different sampling points (e.g. SS, het-erotrophic biomass concentration (XB,H), biochemical oxygen de-mand (BOD5) or total suspended solids (TSS)) together with theoperational parameters in the activated sludge system (e.g. thewaste activated sludge (WAS) flow rate or the return activatedsludge (RAS) flow rate) and a set of calculated parameters like thefood-to-microorganism ratio (F/M) or the sludge residence time(SRT). These diagrams consist of hierarchical, top-down de-scriptions of the linkages and interactions among pieces ofknowledge used for problem solving. The representation in de-cision trees has allowed an easy interpretation and verification ofthe available knowledge by a panel of internationally recognizedwastewater treatment experts. As a result of that evaluation and ofthe suggestions received during and after the 2nd IWA conference onInstrumentation, Control and Automation, where the decision treeswere first introduced (Comas et al., 2006), the original decisiontrees and the resulting knowledge base have been updated to theversion presented in this paper.

2.1. Filamentous bulking decision tree

The knowledge related to risk of filamentous bulking pro-liferation was synthesized into a decision tree with three branches(Fig. 1). Each branch of the tree evaluates one of the three maincauses: low dissolved oxygen concentration (left), nutrient de-ficiency (middle) and low F/M ratio or substrate limiting conditions(right). The other common causes of filamentous bulking (septicconditions or low pH in the influent) were not considered withinthe current approach since standard mechanistic models do neitherinclude sulphur nor pH modelling.

During conditions of low dissolved oxygen (DO) concentration,the growth of filaments is favoured according to the substrate-diffusion and the kinetic selection theories (Martins et al., 2004b).The left branch of the tree illustrates that the level of occurrence oflimiting DO conditions in the biological reactors is related to thecurrent F/M ratio in a non-linear way (Grady et al., 1999). So, al-though DO control is considered standard for most plants, favour-able conditions for low DO caused bulking might arise if the DO setpoint is not high enough when the WWTP experiences a high F/Mratio. The branch in the middle evaluates whether nitrogen (N) and/or phosphorus (P) limiting conditions occur. Finally, promotingconditions for the growth of low F/M filamentous bacteria can becaused by both readily biodegradable substrate limiting conditions(SS) in the bioreactor and by low or oscillating influent organicloading rate. Thus, up to seven variables can be used by theknowledge-based decision trees as indicators to assess risk of fila-mentous bulking: SRT (measured as biomass present in the systemper biomass removed from the system per day), DO, F/M_removed(measured as kg of removed COD per kg biomass and per day),

High

Low orsignificantoscillations

Not low Not high

F/M_removed F/M_fed SSSRT

SRT

Low

Not low

(kg

CO

D r

emov

ed/ k

g M

LVSS

·d)

Aeration Basin DO conc., mg/L1 5

1

0.2

Bulk

ing o

ccurr

ed

safetyzone

F/M

_ re

mov

ed

due to nutrient

deficiency

Risk of BULKING Risk of BULKING

due to low organic

loading

Risk of BULKING

due to low DO

(Aerobic Bulking)

BOD5/N &/orBOD5/P

Fig. 1. Decision tree developed to evaluate the risk of filamentous bulking.

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–12611252

F/M_fed (measured as kg BOD5 supplied per kg biomass per day),BOD5/N, BOD5/P and SS.

2.2. Foaming decision tree

The set of indicators that were found to be the most useful indetecting favourable conditions for filamentous foaming includedF/M_fed, SRT, DO and the ratio between SS and slowly biodegrad-able substrate (XS) (Fig. 2). Two main branches allow for in-vestigating the operational conditions enhancing the growth ofdifferent filamentous microorganisms that would cause biologicalfoaming problems.

Nocardioforms and Microthrix parvicella, the most common fil-amentous organisms causing foaming (Wanner, 1994; Jenkins et al.,2003; Rossetti et al., 2005), experience better conditions for growththan floc-forming bacteria when the activated sludge system ex-periences low F/M ratios or significant oscillations of F/M ratioscombined with high SRT. In the case of M. parvicella, which also

Ris

bio

High

(M. Parvicella & Nocardioforms)

Risk of FOAMING

due to low F/M ratio

Low orsignificantoscillations

F/M_fed

DO

SRT

Low

Very high

Fig. 2. Decision tree developed to

causes sludge bulking, foam formation is favoured by the two for-mer conditions together with low DO concentrations in the aerobicreactors. The development of biological foams due to growth oftype 1863, although less frequent, is also probable if the F/M ratiofed to the bioreactor is very high (or the SRT is very low) and theinfluent contains a high fraction of readily biodegradable organicmatter (RBOM) (a high SS/XS ratio).

2.3. Rising sludge decision tree

Fig. 3 illustrates the decision tree developed to estimate the riskof rising sludge in activated sludge systems. According to Henzeet al. (1993), rising sludge turns into a problem when the nitrateconcentration in the secondary clarifier influent is higher than thecritical nitrate concentration (8 mg NO3

�-N L�1 at 15 �C). In thissituation, the time required for nitrogen gas production is calcu-lated (based on the denitrification rate and the time delay causedby removal of the remaining oxygen at the bottom of the clarifier),

F/M_fed SRT

k of FOAMING due to high readily

degradable organic matter fraction

(probably Type 1863)

High

SS/XS

Very high Very low

evaluate the risk of foaming.

Denitrification rate, Rdn (g NO3.L-1.d-1) =

NO3 conc. eff. bioreactor

critical concentration

time delay for denitrification(t needed to remove oxygen)

Nitrogen gas production time = timeto denitrify + time delay for denitrification

Yes

clarifier = amount of sludge inclarifier / RAS flow rate

Sludge residence time in

Risk of RISING SLUDGE

Time to denitrify =Nitrate critical value / Denitrif.rate

Nitrogen gas prodution time <=Sludge residence time in clarifier

Fig. 3. Decision tree developed to evaluate the risk of rising sludge.

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–1261 1253

and compared to the sludge residence time in the clarifier (esti-mated as the amount of sludge in the sludge blanket divided by theRAS flow rate). The denitrification rate is calculated as in the ASM1(Henze et al., 1987) but using the active heterotrophic biomassconcentration at the bottom of the clarifier. Whenever the nitrateconcentration is higher than the critical one and the nitrogen gasproduction time is lower or equal to the sludge residence time inthe secondary settler, then favourable conditions for denitrificationare inferred, and consequently the risk for occurrence of solidsseparation problems due to rising sludge increases. Fast dissolvedoxygen consumption is assumed in the settler and therefore thedenitrification rate is always computed assuming no oxygen in-hibition (DO¼ 0 mg O2 L�1). Thus, the variables used as indicatorsfor rising sludge are NO3

�-N, RAS flow rate, sludge blanket depthand denitrification rate.

3. Operation and implementation of the risk assessment model

In this work, the description of heuristic and empirical knowl-edge is tackled using the principles of fuzzy decision theory (Bell-mann and Zadeh, 1970; Pedrycz, 1995). Due to its simplicity andefficiency, this theory has already been widely used in environ-mental modelling applications (see for example Fleming et al.,

CL

AS

SIC

AL

MO

DE

LIN

G

INFLUENT DATA MECHANISTIC MODEL

RIS

K A

SS

ES

SM

EN

T

MO

DE

LIN

G

FUZZIFICATION

FOAMING KB

RISING SLUDGE KB

FILAMENT. BULKING KB

RISK ASSESSMENT MODEL

Fig. 4. Relationship between the mechanistic model and the fuzzy knowledge

2007; Koutroumanidis et al., 2006; Kuncheva et al., 2000; Marsili-Libelli, 2004; Iliadis, 2005) and also in wastewater treatment(Olsson and Newell, 1999).

The model estimates the risk of occurrence of microbiology-related solids separation problems by processing the data used bythe mechanistic model (not only simulation outputs but also in-fluent data and operational parameters, see Fig. 4).

The risk estimation implies three main steps.

(i) Fuzzification, where the crisp values of numerical data areconverted into linguistic/qualitative descriptors or input fuzzysets (i.e. low, high, etc.) by means of corresponding member-ship functions. Membership functions are defined for eachvariable used as risk assessment indicators or symptoms in thedecision trees: F/M_removed ratio, F/M_fed ratio, DO, SRT,BOD5/N ratio, SS, and SS/XS. Triangular or pseudo-trapezoidalfunctions are used to define the membership functions. Fig. 5illustrates an example of the membership functions used inthis approach.

Table 1 summarizes the number of membership functions andthe ranks and shapes for each of the input and output fuzzy setsconsidered in the risk assessment model.

PERFORMANCE CRITERIASIMULATION OUTPUT

DEFUZZIFICATION SETTLING QUALITY CRITERIA

•

••

Effluent quality• Environmental impact

Operational costsController assessment

• risk of filamentous bulking• risk of foaming• risk of rising sludge• overall solids separation risk

0.5 110

20

base to estimate risk of microbiology-related solids separation problems.

0 0.5 1 1.5

0

0.2

0.4

0.6

0.8

1

FtoM_fed (kg BOD

supplied•kg MLVSS-1

•d-1

)

Deg

ree o

f m

em

bersh

ip L N H VH

Input variables

0 5 10 15 20

0

0.2

0.4

0.6

0.8

1

SRT (d)

Deg

ree o

f m

em

bersh

ip VL N HL VH

0 0.5 1

0

0.2

0.4

0.6

0.8

1

Risk of Foaming

due to low F/M ratio

Deg

ree o

f m

em

bersh

ip Low Medium High

Output variable

Fig. 5. Example of membership functions for the input and output variables for the risk of foaming due to low F/M ratio.

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–12611254

The limits of these membership functions as well as their degreeof overlapping can be customized by the user according to theconfiguration and characteristics of the simulated activated sludgeplant.

(ii) Fuzzy inference of the risk through a Mamdani approach, togenerate a fuzzy output from the corresponding input fuzzysets based on implications contained in the fuzzy rule base.All the fuzzy rules in the model are derived from literaturereview, based on the existing empirical knowledge aboutcause–effect relationships of microbiology-related solidsseparation problems in the activated sludge system. The de-cision trees shown in the previous section (Figs. 1–3) onlyrepresent those IF–THEN production rules leading to high riskof each particular microbiology-related separation problem,while the complete rule base of the risk assessment model isshown in Table 2. The Max–Min Mamdani’s fuzzy inference(Mamdani and Assilan, 1975) was proposed as the mecha-nism to concatenate the set of IF–THEN rules. As an example,Fig. 6 illustrates the response surface to infer potentialfoaming problems caused by Nocardioforms and M. parvicella

Table 1Membership functions for each variable considered in the risk assessment model

Variable/modality Very low Low

F/M_removed Shape – TrapezoidalRank – [�0.1429 �0.1429 0.25

F/M_fed Shape TrapezoidalRank – [�0.0536 �0.0536 0.25

DO in reactor 3 Shape Trapezoidal TriangularRank [�0.4488 �0.1164 0 1] [0 1 2]

SRT Shape Trapezoidal TriangularRank [�7.2 �0.8 1 3] [0 3 6]

BOD5/N in the influent Shape – TrapezoidalRank – [�7.145 �7.145 10 20]

BOD5/P in the influent Shape – TrapezoidalRank – [�10 �1 0 10]

SS in reactor 1 Shape – TrapezoidalRank – [�4.645 �4.645 4 14]

SS/XS in the influent Shape – TrapezoidalRank – [�0.08415 �0.02183 0.

SNO in reactor 5 Shape – TrapezoidalRank – [�1.429 �1.429 2 5]

Time for nitrogengas production

Shape – TrapezoidalRank – [�0.135 �0.0437 0.046

Risk of filamentous bulking Shape – TriangularRank – [�0.2 0 0.2]

Risk of foaming Shape – TriangularRank – [�0.2 0 0.2]

Risk of rising sludge Shape – TriangularRank – [�0.2 0 0.2]

a These values work for BSM1 control strategies with a constant sludge recycle flow r

(due to low F/M ratio) by using F/M_fed and SRT as inputvariables.

(iii) Defuzzification of the output variable, where the linguistic fuzzyoutput has to be translated into a numerical value as the out-come of the risk assessment (again, membership functionswere defined for the three output variables of the module: ‘riskof filamentous bulking’, ‘risk of foaming’ and ‘risk of risingsludge’) by means of the Center of Gravity (COG) method (Fiteret al., 2005).

The knowledge used to develop the fuzzy rules is empirical andbased on qualitative assumptions to determine cause–effect re-lationships of microbiology-related solids separation problems inactivated sludge systems (e.g. if SRT is high and DO is low, risk offoaming is high). This is an empirical assumption included in themodel by means of a fuzzy rule but the model uses deterministicvalues, for example, of SRT and DO to infer a deterministic risk offoaming.

The estimation of the risk of filamentous bulking, foaming and/or rising sludge is performed continuously along the simulated

Normal High Very high

Triangular Triangular Trapezoidal0.5] [0.25 0.5 0.75] [0.5 0.75 1] [0.75 1 4.027 4.187]

Triangular Triangular Trapezoidal0.5] [0.25 0.5 0.75] [0.5 0.75 1] [0.75 1 1.51 1.57]

Triangular Triangular Trapezoidal[1 2 3] [2 3.5 5] [3.5 5 8.021 8.261]Triangular Triangular Trapezoidal[3 6 9] [6 9 12] [9 12 20.29 23.4]Triangular Trapezoidal –[10 20 33.33] [20 33.3 201.3 209.3] –Triangular Trapezoidal –[10 50 100] [50 100 200 210] –Triangular Trapezoidal –[9 20 34] [29 40 131.6 143] –Triangular Trapezoidal –

1 0.2] [0.15 0.25 0.35] [0.3 0.45 1.55 1.56] –Triangular Trapezoidal –[2 5 8] [5 8 40.27 41.87] –Triangular Trapezoidal –

0.056]a [0.046 0.056 0.066]a [0.056 0.066 2.205 2.272]a –Triangular Triangular –[0.2 0.5 0.8] [0.8 1 1.2] –Triangular Triangular –[0.2 0.5 0.8] [0.8 1 1.2] –Triangular Triangular –[0.2 0.5 0.8] [0.8 1 1.2] –

ate of 18,446 m3 d�1.

Table 2Complete knowledge base of the risk assessment model (VL: very low, L: low, N:normal, H: high; VH: very high)

Foaming due to low F/M ratio

F/M_fed

L N H VHSRT (d) VL Low Low Low Low

L Low Low Low LowN Moderate Low Low LowH High Moderate Low LowVH High Moderate Low Low

Foaming due to high RBOM fraction

Ss/Xs

L N HSRT VL Low Medium High

L Low Low MediumN Low Low LowH Low Low LowVH Low Low Low

F/M_fed L Low Low LowN Low Low LowH Low Medium MediumVH Low Medium High

Bulking due to low DO

DO (mg L�1)

VL L N H VHF/M_removed L Low Low Low Low Low

N High Medium Low Low LowH High High Medium Low LowVH High High High Medium Low

Bulking due to low organic loading

SRT

VL L N H VHSs L Low Low Medium High High

N Low Low Low Low LowH Low Low Low Low Low

F/M_fed L Low Low High High HighN Low Low Low Medium MediumH Low Low Low Low LowVH Low Low Low Low Low

Bulking due to nutrient deficiency

BOD5/N or BOD5/P L LN LH H

Rising sludge

Nitrogen gas production time

L N HNO3 L Low Low Low

N Medium Low LowH High Medium Low

LN

HVH VL

LN

HVH

L

M

H

SR

T (d)

FtoM-fed (kg/kg·d)

Risk o

f F

oam

in

g

Response surface

Fig. 6. Response surface for foaming due to low F/M ratio (VL: very low, L: low, N:normal, H: high, VH: very high).

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–1261 1255

time period, yielding to a new value at every time step, when a newset of simulation output data becomes available. Note that when-ever the output results of the risk model indicate that conditions foreach specific problem (filamentous bulking, foaming and risingsludge) are satisfied for more than one cause (branch), the worstconditions (highest risk) will be selected for this problem.

The appearance of one of these microbiology-related solidsseparation problems in an activated sludge system requires a highrisk for a long and sustained period of time. Therefore for longsimulation periods the results of the risk assessment model aresmoothed by means of an exponential filter that takes this issueinto account. The exponential filter has a time constant related tothe dynamics of each specific problem (2 h for rising sludge and 3days for filamentous bulking and foaming problems). This filter canbe written as

yfilteredðtÞ ¼ a$yfilteredðt � 1Þ þ ð1� aÞ$yðtÞ

where yfiltered represents the filtered data, y is the raw data and a iscalculated according to

a ¼ 1� 1T$ns

where T represents the time constant in days and ns is the numberof output samples per day in the simulation (here ns¼ 96). Thistype of filter has also been used with a time constant of 3 days tocalculate the SRT in the system. Moreover, the exponential filterfacilitates the visualisation and interpretation of the results.

The risk assessment model has been implemented in-dependently of the existing activated sludge models. Although theMATLAB 6.5 Fuzzy Toolbox (Mathworks, Inc.) was initially used todevelop the membership functions and build the rule base, theproposed risk assessment procedure has been developed to besoftware independent.

3.1. Outcomes of the risk assessment model

The risk assessment model provides new plant performancecriteria related to the risk of occurrence of settling problems inactivated sludge systems. It provides six different vectors corre-sponding to the time series signals for the risks of the followingproblems: filamentous bulking due to low DO, filamentous bulkingdue to nutrient deficiency, filamentous bulking due to low organicloading, foaming due to low F/M ratio, foaming due to high readilybiodegradable organic matter fraction and rising sludge.

The six risk indices for filamentous bulking, foaming and risingsludge can be integrated into a single overall risk index. To do that,an integrated index for filamentous bulking should first be obtainedas the maximum value, at each time step, among the smoothedtime series signals of the three risk indices of filamentous bulkingproblems (caused by low DO concentrations, low F/M ratios ornutrient deficiencies). (Fig. 8 (top) illustrates how the integratedindex for risk of bulking is obtained.) At the same time the maxi-mum value at each time step of the smoothed signals of the risks offoaming due to low F/M ratio on the one hand and due to highRBOM fraction on the other hand provides the integrated foamingindex. The final aggregation simply consists of taking the maximumvalue at every time step among the integrated risk for bulking, theintegrated risk for foaming and the risk for rising sludge to producethe overall risk index. For a specific activated sludge system, theseintegrated values give an idea of the overall risk of occurrence of

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–12611256

solids separation problems as well as indicate which problem toaddress first.

The results from the risk assessment are reported and quantifiedin four different ways for each of the settling problems and causes,for the integrated risks of bulking and foaming and for the overallrisk index: (i) a time series plot (or average data) showing theevolution of the risk occurrence for a specific settling problem (orfor one of the integrated indices) during the evaluation period. Inthis plot 0 means no risk while 1 indicates the highest possible risk;(ii) the percentage of time during which the plant is experiencingrisk for occurrence of a specific settling problem; (iii) the per-centage of time during which the plant is experiencing severe riskof settling problems (an arbitrary but customizable limit value ofrisk �0.8 is used for defining a severe problem); and, (iv) the mostdangerous situation during the evaluation period, computed as thelargest time interval the plant is exposed to an uninterrupted se-vere risk of experiencing a specific settling problem.

These new settling criteria complement the traditional plantperformance criteria such as operating costs, effluent quality andcontroller performance which are traditionally used for comparingthe performance of different control strategies in a simulation en-vironment (see e.g. Copp, 2002).

4. Simulation results and discussion

This section demonstrates the potential of the proposed ap-proach to assess the risk of occurrence of solids separation prob-lems in activated sludge modelling. The performance of the riskassessment has been tested on the IWA/COST benchmark simula-tion model platforms by means of two case studies, which includeshort and long-term influent variability scenarios – benchmarksimulation model no. 1 (BSM1) and BSM1_long term (BSM1_LT) –and different control strategies. The results illustrate how theproposed risk assessment model complements two other evalua-tion indices of the activated sludge system performance (plantoperating costs and effluent quality).

4.1. The IWA/COST simulation benchmark

A standardized simulation protocol (a ‘simulation benchmark’)is used for the objective evaluation and comparison of wastewatertreatment plant control strategies. An early version of the bench-mark protocol, the BSM1, was published by Copp (2002), and en-ables the unbiased comparison and evaluation of activated sludgecontrol strategies related to removal of organics and nitrogen. Itincludes a plant layout, simulation models and parameters, a de-tailed description of the influent disturbances (dry weather, stormand rain events), as well as performance evaluation criteria to de-termine the relative effectiveness of proposed control strategies.The benchmark plant layout consists of five completely mixedreactors in series with a total volume of 5999 m3. The pre-denitrification section occupies 1/3 of this volume. The secondarysettler has a volume of 6000 m3 (Fig. 7). The Activated Sludge

Influent

Reactor 1 Reactor 2 Reactor 3

Internal recycle

Sludge recycle

Anoxic;V =

1000 m3

Anoxic;V =

1000 m3

Aerobic;V =

1333 m3

Fig. 7. Layout of the IWA/C

Model no. 1 (ASM1; Henze et al., 1987) is used to model the bi-ological processes while a non-reactive Takacs one-dimensional 10-layer model (Takacs et al., 1991) is used to describe the settlingprocesses. Kinetic and stoichiometric model parameters as well asbasic operational conditions are also provided within the bench-mark description.

All simulations and performance assessments in BSM1 must becarried out as specified in the benchmark protocol (Copp, 2002), i.e.perform a 150-day steady-state simulation to obtain adequate ini-tial state values, simulate the plant with the dynamic dry weatherscenario for 14 days, and then apply the dynamic dry, rain or storminfluent file conditions for another 14 days. Only the last week ofthe dynamic simulation is used for plant performance evaluation.

The IWA Task Group on Benchmarking of Control Strategies forWWTPs (see www.benchmarkwwtp.org) is currently extending thegoal of the benchmark in order to encompass other processeswithin a wastewater treatment system (mainly primary clarifica-tion, anaerobic digestion and sludge thickening, BSM2; Jeppssonet al., 2007). Moreover, tools that will enable evaluation of theperformance of process monitoring algorithms related to the de-tection of equipment failures and process disturbances are alsounder development (BSM1_LT; Rosen et al., 2004). These extendedbenchmark simulation models will enable plant-wide controlevaluation when confronted with a one-year dynamic influent filecontaining many typical influent-related perturbations, includingseasonal effects (Gernaey et al., 2006). Influence of temperature onthe biological processes is also considered in both BSM1_LT andBSM2 using Arrhenius type equations. Preliminary versions of thelong-term influent data file plus temperature dependency equa-tions have also been used to test the risk assessment model.

Food-to-microorganism (F/M) ratio is calculated in two differentways within this risk model even though the membership func-tions are the same. While F/M_removed is calculated based on thedaily mass flow rate of COD removed from the whole plant per unitof biomass, F/M_fed aims at detecting low organic loading (dailymass flow rate of supplied BOD per unit of biomass). The dissolvedoxygen level is evaluated in reactor 3, the nitrate concentration(SNO) in reactor 5, the readily biodegradable organic matter (SS)concentration in reactor 1 and BOD5/N, BOD5/P and the ratio be-tween SS and slowly biodegradable organic matter (XS) are calcu-lated for the influent wastewater. The sludge residence time (SRT)is calculated as the total mass of heterotrophic (XB,H) and autotro-phic (XB,A) biomass within the five reactors divided by the dailymass of XB,H and XB,A removed from the plant via the waste sludgeand the effluent.

The usefulness of the presented risk assessment model for de-tection of microbiology-related separation problems is illustratedwith two case studies. The first case study provides illustrativeexamples of the risk plots and provides examples of the quantifi-cation of the new set of plant operation quality criteria proposed inthis paper. The second case study illustrates the potential impor-tance and usefulness of the risk assessment model when compar-ing two control strategies.

Reactor 4 Reactor 5

SettlerEffluent

Waste sludge

V = Aerobic; Aerobic;

V = 1333 m3 1333 m3

OST benchmark plant.

Table 3Settling quality criteria for the 3DO control strategy applied to the BSM1_LT

Problem/settling quality criteria Average risk % Timein severe risk

Worst situation

Bulking due to low DO 0.20 5.5 0.5 daysfrom day 354.4

Bulking due to low organic loading 0.65 18.7 19.9 daysfrom day 221.3

Bulking due to nutrient deficiency 0.00 0.0 –Foaming due to low F/M ratio 0.6 27.4 2.5 days

from day 235.9Foaming due to high RBOM fraction 0.01 0.01 –Rising sludge 0.93 86.4 19 days

from day 296.5

Integrated valuesBulking 0.69 24.1 –Foaming 0.60 27.4 –Rising 0.93 86.4 –Overall risk 0.95 90.9 –

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.20.40.60.8

1

time (days)

Risk

Risk of Bulking due to the 3 different causes and integrated value

Risk of Low F/M Filamentous Bulking

00.20.40.60.8

1

time (days)

0 50 100 150 200 250 300 350

Risk

time (days)

Overall Risk

0.20.40.60.8

1

00 50 100 150 200 250 300 350

Risk

Fig. 8. Risk of filamentous bulking due to the three different causes and integrated value (top, dashdot line: due to nutrient deficiency; dotted line: due to low F/M ratio; narrowsolid line: due to low DO; and bold solid line: integrated value) for the 3DO control strategy under long-term influent conditions. Risk of filamentous bulking caused by low F/M ratio(middle, dotted line: raw data; solid line: smoothed data) for the 3DO control strategy under long-term influent conditions; overall risk of settling problems (bottom) for the 3DOcontrol strategy under long-term influent conditions.

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–1261 1257

4.1.1. Case study 1: risk assessment for a control strategyin BSM1_LT

An example of the results obtained after applying the risk as-sessment procedure for the evaluation of control strategies withinthe BSM1_LT configuration is provided. The upper part of Fig. 8 il-lustrates the calculation of the integrated value for risk of fila-mentous bulking from the original values of filamentous bulkingcaused by low F/M ratio, low DO values and nutrient deficiency. Themiddle part of Fig. 8 represents the time series plots of the risk offilamentous bulking caused by low F/M ratio, for one controlstrategy during a one-year plant performance evaluation period.The control strategy tested is based on identical DO control loops inthe three aerobic reactors of the benchmark plant, where DO iscontrolled at a constant set point (2 mg O2 L�1) by manipulating theoxygen transfer rates (KLa) (3DO control strategy).

In Fig. 8, day 1 corresponds to July 1st. The winter period withlow influent temperatures appears in the middle of the evaluationperiod. The dotted line in Fig. 8 (middle) expresses the raw value forthe risk of filamentous bulking caused by low F/M ratio while thesolid line indicates the risk smoothed by the exponential filter. Thefiltered value is considered to be more valid since, although ina simplified way, it considers the dynamics of the settling problems,i.e. the development of filamentous organisms in a sufficient con-centration to produce bulking or foaming problems (or the pro-duction of nitrogen gas in secondary clarifiers) require a sustainedhigh risk for a certain period of time. Therefore the raw or in-stantaneous value is not revealing enough. Risk for filamentousbulking due to low organic loading is severe (�0.8) during about19% of the evaluation period (2.5 months). Higher risks for occur-rence of this problem are experienced between days 125 and 300(winter and spring seasons). Although the influent organic load is

similar all year around, the sludge residence time is higher in thewinter period due to higher biomass concentrations, caused byusing different sludge waste flow rates for winter and summer(lower in winter) to maintain sufficient nitrification activity in theplant. For the same reason there is a severe risk of foaming due tolow F/M ratio during approximately 27% of the evaluation period(detailed time series plot not shown, see Table 3 for a summary).Table 3 also gives the average risk, percentage of time in severe riskand the longest period with severe risk for all the settling problemsand causes considered within the risk model: bulking due to lowDO, bulking due to low organic loading, bulking due to nutrient

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–12611258

deficiency, foaming due to low F/M, foaming due to high readilybiodegradable organic matter (RBOM) fraction (high SS/XS ratio)and rising sludge.

Fig. 8 (bottom) illustrates the time series plot for the overall riskof settling problems. It is obtained by plotting the maximum valuefor the filtered risk of bulking, foaming and rising sludge problemsat each time step. In this case study, the time series plot of theoverall risk illustrates a nearly continuous severe risk of occurrenceof settling problems.

Several conclusions can be drawn from Table 3. One is thataerobic filamentous bulking, foaming due to high RBOM fractionand bulking due to nutrient deficiency are not really a problem atany time of the year for this control strategy. The low risk values forthese problems are due to the continuous high DO level in theaerobic reactors (set point values are 2 mg O2 L�1), the pre-dominance of low values for the SS/XS ratio and the absence ofshortage of nitrogen or phosphorus, respectively. On the otherhand, the risk of experiencing rising sludge is severe for almost thecomplete evaluation period (315 days, 86% of the time). Indeed, dueto the high nitrification capacity as a result of applying the selectedDO control strategy, and due to the low biodegradable substrateconcentration in the influent, the plant experiences incompletedenitrification.

The bottom part of Table 3 provides the integrated values for thesettling quality criteria for the 3DO control strategy. Firstly, theintegrated values for filamentous bulking, foaming and risingsludge are given. The higher percentage of time for severe risk ofrising sludge w.r.t. the risks of bulking and foaming (86.4% versus24.1% and 27.4%, respectively) shows the former problem to be themore risky one for the 3DO control strategy, and thus the one to betackled first. Finally, Table 3 shows the overall risk value of expe-riencing settling problems due to microbiological reasons for thiscontrol strategy.

0

0.2

0.4

0.6

0.8

1Bulking due to low DO

7 8 9 10 11 12 13 14

time (days)

7 8 9 10 11 12 13 14

time (days)

Risk

0

0.2

0.4

0.6

0.8

1

Risk

Foaming due to low F/M ratio

Fig. 9. Risk of settling problems under rain influent conditions. Risk of bulking due to low Dlow F/M ratio (bottom left) and rising sludge (bottom right). In each of them, the solid line

4.1.2. Case study 2: comparison of risk assessment for two controlstrategies in BSM1

Two different control strategies were compared using the BSM1platform. Control strategy-1 consists of two control loops whichcontrol the DO level in the last aerated reactor and the nitrateconcentration in the second anoxic reactor by manipulating the KLaof the last aerated tank and the internal recycle flow rate, re-spectively. Control strategy-2 is equivalent to strategy-1 except thatthe RAS flow rate is not constant (18,446 m3/day) but proportionalto the influent flow rate with a gain of 2. If applied to the BSM1results, the risk assessment model provides an estimate of the riskfor occurrence of microbiology-related problems at each time stepduring the final 7 days of the three weather file simulations. Fig. 9illustrates the time series plots obtained for the risk of both aerobicand substrate limiting filamentous bulking, foaming due to low F/Mratio and rising sludge for both control strategies using the raininfluent scenario of the BSM1.

It can be noticed in Fig. 9 that different control strategies yielddifferent estimates of the risk for the process to experience mi-crobiology-related settling problems. Fig. 9 illustrates that there isnot much difference between the two strategies w.r.t. the risk forexperiencing aerobic bulking (Fig. 9-top left). The risk for thisproblem is almost all the time low since the existing DO controllermaintains an adequate set point according to the F/M ratio expe-rienced by the plant. In contrast, operational conditions imposed bycontrol strategy-2 lead to severe risks for both substrate limiting(low F/M) filamentous bulking and foaming (M. parvicella andNocardioforms) during rainy influent conditions (Fig. 9-top rightand bottom left, respectively). For both problems, the higher RASflow rate of control strategy-2 causes higher biomass concentra-tions in the activated sludge tanks, and thus contributes to very lowsubstrate concentrations in the bioreactors. On the other hand, thiscontrol strategy reduces the risk for experiencing rising sludge by

7 8 9 10 11 12 13 14

time (days)

7 8 9 10 11 12 13 14

time (days)

0

0.2

0.4

0.6

0.8

1

Risk

0

0.2

0.4

0.6

0.8

1

Risk

Bulking due to low organic loading

Rising sludge

O (top left), risk of bulking due to low organic loading (top right), risk of foaming due toindicates control strategy-1 while the dotted line represents strategy-2.

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–1261 1259

33% (by 30% on average, when considering the three different in-fluent scenarios) because the sludge residence time in the clarifieris decreased (Fig. 9-bottom right). Filamentous bulking caused bynutrient deficiency is not plotted, as the risk for occurrence of thistype of problem is never high because the nitrogen load is highthroughout the simulation. Note that phosphorus is not consideredin the ASM1 model. Similarly, foaming caused by a high RBOMfraction is never experienced because conditions favouring thistype of foaming do not occur.

Table 4 shows the values of the settling quality criteria togetherwith more traditional plant performance evaluation criteria relatedto effluent quality (Copp, 2002) and plant operating costs (Vreckoet al., 2006) for both control strategies, and an open-loop controlstrategy with constant actuator settings, exposed to all three in-fluent scenarios. The effluent quality index (EQI, in kg PollutionUnit d�1) is calculated as a weighted averaged sum along theevaluation period of relevant effluent concentrations (TSS, COD,BOD5, total Kjeldahl nitrogen and nitrites and nitrates) while theoperating cost index (OCI) is calculated as a weighted sum of theaeration energy, pumping energy, sludge production for disposaland external carbon addition. Detailed mathematical expressionsfor both indices can be found in Copp (2002) and Vrecko et al.(2006). Consequently, the differences between the different strat-egies can now be quantified in an additional dimension by in-cluding the risk aspects (see below).

From the results in Table 4 it can be concluded that both closed-loop control strategies improve the plant performance compared tothe open-loop strategy when only considering traditional plantperformance evaluation criteria, such as operating costs and ef-fluent quality, for the three influent scenarios (dry, rain and storm).The settling quality criteria for the open-loop case and controlstrategy-1 are very similar while operating costs and the effluentquality index for control strategy-1 are on an average 9% and 5%lower (for the three influent files). Control strategy-2 achievesbetter performance concerning economical and effluent qualitycriteria (improvements with respect to the open-loop case on anaverage 12% and 9%, respectively) although globally the risks for thedevelopment of settling problems increase. Specifically, the risk ofbulking due to low organic loading and the risk of foaming due tolow F/M ratio increase by 26% and 82%, respectively, compared tothe open-loop approach due to the fact that higher recycle flowrates overload the bioreactor. On the other hand, the risk of risingsludge is decreased by approximately 31%, compared to the open-loop case, because nitrate control in control strategy-2 improvesdenitrification efficiency.

Table 4 also illustrates that while control strategy-2 leads tohigher risks for both bulking due to low organic loading and

Table 4Plant performance obtained using three different control strategies for different influent

Performance criterion Dry influent

Operating cost index 16,503a 15,044b 14,597c

Effluent qualityindex (kg d�1)

8069 7556 7225

Settling qualitycriteria(% time in severe risk of)Bulking due to low DO 1.5 1.6 3.3Bulking due to low organic loading 68.8 68.8 84.4Bulking due to nutrient deficiency 0 0 0Foaming due to low F/M ratio 14.1 14.9 68.0Foaming due to high RBOM fraction 0 0 0Rising sludge 60.9 64.0 42.0

a Open-loop (with constant actuator settings, KLa3¼ KLa4¼ KLa5¼ 240; RAS¼Qin avg.b Control strategy-1.c Control strategy-2.

foaming due to low F/M ratio than strategy-1, it reduces the risk ofexperiencing rising sludge for all three influent scenarios. It canalso be pointed out that bulking due to low DO concentration,bulking due to nutrient deficiency and foaming due to high RBOMfraction are never severe for any of the influent scenarios.

The overall risk for occurrence of settling problems of microbi-ological origin is higher for control strategy-2. However, thisstrategy performs better than control strategy-1 from the conven-tional economical and effluent quality perspectives for all weatherconditions (see Table 4).

Fig. 10 illustrates a three-dimensional comparison of the twocontrol strategies during rainy influent conditions. The inclusion ofthe presented risk module provides a ‘third dimension’ of plantperformance assessment in the IWA/COST simulation benchmark.With this new approach, the control strategies can be evaluatedthrough simulation not only based on operational costs and envi-ronmental impacts but also with regard to the risk of experiencingthe most common activated sludge settling problems. In this sense,it enables a leap forward providing more realistic results whenusing modelling and simulation for developing control strategiesfor activated sludge processes. As a consequence of the additionalsimulation data interpretation obtained by applying the risk as-sessment model, the model users can be more confident w.r.t. thesimulation results.

The plane formed by the operational costs and effluent qualityindices proves that control strategy-2 is less costly and more effi-cient than strategy-1. However, taking into account the third di-mension, i.e. when we add the ‘z’ axis in Fig. 10, the overall risk ofexperiencing activated sludge settling problems of microbiologicalorigin is considerably higher for strategy-2 than for strategy-1. Al-though a control strategy may perform very well according to costsand environmental impacts, the results indicate that the in-tegration of numerical modelling and empirical knowledge allowsfor identifying control strategy-2 as more prone to experiencingoperational problems.

As explained previously, the risk model was verified by a panelof experts. However, a validation of the risk model based on realdata from a pilot plant or a full-scale WWTP is very complicateddue to the fact that the model is giving the risk of a particularmicrobiology-related settling problem but this risk is never regis-tered in real plants. Moreover, the risk model was not developed tobe applied in full-scale facilities but to complement dynamic sim-ulations when comparing operational procedures and controlstrategies (and thus, for example, the model uses variables notavailable in typical full-scale databases, such as SS and XS).

An approach such as Bayesian Networks (Jensen, 2001) mighthave been used instead of the fuzzy approach but the former

scenarios

Rain influent Storm influent

16,257a 14,767b 14,238c 17,002a 15,607b 15,104c

9485 9038 8717 8723 8304 7984

2.8 3.4 3.1 3.3 3.6 4.350.9 50.9 81.8 63.1 62.9 81.8

0 0 0 0 0 012.6 13.1 70.7 9.5 8.3 60.4

0 0 0 0 0 043.8 39.6 29.9 56.5 56.8 40.0

and Qintrec¼ 3$Qin avg.).

87008750

88008850

8900 8950 9000 90509100

14200,00

14300,00

14400,00

14500,0014600,00

14700,0014800,00

Overall R

isk

EQI (kg PU/d)

OC

I

Strategy 2

Strategy 1

20

40

60

80

100

Fig. 10. Comparison between effluent quality index (EQI), operating cost index (OCI)and overall risk of experiencing settling problems for two different control strategies.

J. Comas et al. / Environmental Modelling & Software 23 (2008) 1250–12611260

requires estimates of predictive uncertainty in the form of proba-bility distributions, to quantify the conditional relationships to beset up, which is an information not available in this case.

Although the original risk assessment model has been de-veloped for a pre-denitrification plant (the ‘benchmark’ plant), itsapplication to other configurations (SBR, oxidation ditch .) isstraightforward. Some changes are certainly required, e.g. the cal-culation of SRT and F/M ratio must be adequately re-defined and thesampling point for estimating the readily biodegradable substrateor dissolved oxygen level properly located, but the underlyingmechanisms for evaluating the risk of occurrence of microbiology-related solids separation problems are the same.

A sensitivity analysis of the risk assessment model is of highinterest as future work not only to understand and analyse theknowledge embodied in the decision trees, but also to highlight therelevance and linearity of the variables (both for operation – e.g.SRT and design – e.g. volume) and the accuracy of the measure-ments. Also, parameters related to the fuzzy approach (number/shape of the membership functions, overlapping, etc.) should beanalysed.

Potential further extension of the risk assessment procedureentails extending the knowledge base for dealing with other phe-nomena of qualitative nature creating operational problems in ac-tivated sludge plants. Extension of the risk assessment towardsanaerobic digestion processes is also being investigated.

Furthermore, investigating the use of a high and sustained risksignal in a feedback loop to adapt the set points of simulated controlstrategies is another interesting issue. By including the risk as-sessment in the control loop, the identification of unsafe opera-tional conditions would allow avoiding the process being ledtowards a serious destabilization and restoring it back to normaloperation. The current Matlab implementation of the presentedrisk assessment model is available at the benchmark website(www.benchmarkwwtp.org).

The usefulness of the approach has been demonstrated for ob-jective comparison of control strategies. However, this new riskassessment model can also be helpful when using WWTP model-ling and simulation for other purposes, such as design optimization,scenario testing, trouble shooting, learning tool or operationalimprovement.

5. Conclusions

This paper presents a new procedure to integrate numericalmodelling and qualitative aspects to deal with activated sludgesolids separation problems of microbiological origin.

The aim of the risk assessment model is not to diagnose mi-crobiology-related solids separation problems with absolute cer-tainty but to quantify whether the simulated control strategiesbring about a severe risk for leading the process towards a situationwith microbiology-related solids separation problems. As a result,plant performance evaluation criteria based on plant operationalcosts and effluent quality, typically calculated when comparingcontrol strategies by simulation, can now be complemented witha risk assessment for the occurrence of settling problems, in-formation that the state-of-the-art numerical models cannot de-scribe. It is illustrated that adding an additional dimension toclassical evaluation criteria is important when benchmarkingcontrol strategies and can lead to different conclusions with respectto a specific control scheme. Thus control approaches with an in-creased risk for suffering from endemic settling problems can beavoided.

Acknowledgements

This paper was partially written when Dr Joaquim Comas wasa guest researcher at the Department of Industrial Electrical Engi-neering and Automation (IEA), Lund University, Sweden. The re-search work of Dr Krist Gernaey and Dr Joaquim Comas wassupported by the European Community’s Human Potential Pro-gramme under contract HPRN-CT-2001-00200 (WWT&SYSENG).The authors also benefited from the partial support of the SpanishCICyT projects DPI 2003-09392-C02-01 and DPI 2006-15707-C02-01. The authors want to thank the expert panel that reviewed thedecision trees for their collaboration and insightful suggestions.

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