Knowledge Extraction during the Design of Activated Sludge Systems Xavier Floresa, Manel Pocha, Ignasi Rodríguez-Rodaa, Laureano Jiménezb and René Bañares-Alcántarac aLaboratori d’Enginyeria Química i Ambiental, Universitat de Girona, Campus Montilivi s/n, 17071 Girona, Spain. bDepartament d’Enginyeria Química, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain. cDepartment of Engineering Science, University of Oxford, Parks Roads, OX1 3PJ Oxford, United Kingdom.

Abstract This paper presents a support module that extracts and reuses the knowledge generated during the sensitivity analyses for design of activated sludge plants. This support module, consists of three steps: 1) Data handling and knowledge extraction, where all the data generated by numerical simulators during the sensitivity analyses is processed in order to extract qualitative knowledge, 2) Knowledge codification, where the extracted knowledge is codified as IF-THEN rules, and 3) Knowledge reutilization, where numerical and qualitative methods are integrated to guide the designer through the design process. The usefulness of the support module is demonstrated in a case study, where the bioreactor of a denitrifying activated sludge plant is retrofitted to achieve simultaneous carbon, nitrogen and phosphorous removal. Keywords: Activated sludge, Process design, Nutrient removal, Knowledge extraction, Multicriteria analysis.

1. Introduction. The design of activated sludge systems is particularly complex because it includes biological multiphase reaction, solid-liquid separation and several types of recycles and purges (Metcalf & Eddy, 2003). Moreover, activated sludge systems must take simultaneously into account a variety of objectives with different relevance; as a result, the evaluation process becomes a multicriteria problem (Flores et al., 2005). Nowadays, the design of activated sludge systems is supported by the use of numerical simulators (Henze et al, 2002) and by (iterative) sensitivity analyses near a base case to account for the inherent uncertainty of the design variables. Large amounts of data are generated during these analyses which, conveniently processed, could be transformed into knowledge and used by the designer during the rest of the design process. The objective of this paper is to present a design support module that extracts and reuses the knowledge generated during the sensitivity analysis process.

2. Methodology. First, a base case is defined. The relative desirability of the base case and of any alternative case can be evaluated through a set of different evaluation criteria [X = {X1,...,Xy}] that measure the satisfaction of several design objectives [OBJ = {OBJ1,...,OBJx}].

and 9th International Symposium on Process Systems EngineeringW. Marquardt, C. Pantelides (Editors) © 2006 Published by Elsevier B.V.

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The importance of each objective is set by its weight [w = {w1,...,wx}] which reflects the preferences of the stakeholders. Weights are distributed among the evaluation criteria

[X] and normalized to 1 in order to avoid unbiased comparisons 1w·wy

1jj

x

1ii ==∑∑

==

Thus, a design can be measured by a score profile: [A = (X1,…, Xy)]. Then, value functions of the form [v (Xj)] map the score profile of the proposed option [v(A) =( v(X1),…, v( Xy))] into a normalized value from 0 to 1, where the 0 and 1 values are associated with the worst (Xj*) and the best (Xj

*) situation, whilst a mathematical function is used to evaluate the intermediate situations. Finally, the weighted sum is computed according to equation 1, using the normalized criterion v (Xj) and its corresponding weight (wj).

∑=

=++++=y

1jjjyyjj11 )X(v·w)X(v·w...)X(v·w...)X(v·w)A(s (1)

The score in the weighted sum indicates the degree of satisfaction of an alternative design with respect to the set of design objectives [OBJ]. A value of 1 for v(A) would denote that design objectives are fully satisfied. A set of analyses using numerical simulators is then performed to test the sensitivity of the base case design with respect to the variables that rule the design [V ={Vi, …, Vz}]. In these sensitivity analyses, evaluation criteria [X] are recalculated varying the value of the selected variables [V] by a percentage (± 5-10 %) with respect to the base case. All the data generated during these analyses is processed by the proposed design support module. This support module extracts and reuses the knowledge generated during the sensitivity analyses thorough a three step systematic procedure: In Step 1, all the data is handled and qualitative knowledge is extracted by the proposed design support module. The objective of the first step is to classify the relationship between the effect on the evaluation criteria [v(X)] and the analyzed variables [V] in four different categories: (a) directly proportional, (b) indirectly proportional, (c) constant and (d) no monotonic when the value of the analyzed is moved. In Step 2, this knowledge is codified as IF-THEN rules. This set of rules [R = (R1,1,…,Rz,y)] conforms a knowledge base that contains the information from the analysis performed at step 1. For example, IF Vk is increased THEN Xj decreases. Next, these rules are used as the input of an inference engine that keeps a record of design decisions together with the influence of design variables on the behaviour of the evaluation criteria. Finally, in Step 3, the codified qualitative knowledge extracted from the detailed models can be reused guiding the designer through the design process. Thus, the designer can be supported by the system suggesting which actions [Ac] should be implemented to improve the design base case in terms of a specific objective OBJi or criteria Xj. Additionally, the system also informs the designer which criteria worsen in the event of the application of the proposed actions.

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3. Case study.

3.1. Plant Redesign and Sensitivity Analysis. This paper is illustrated with a case study, where a bioreactor of the benchmark denitrifying activated sludge plant (Copp 2003) is retrofitted to achieve simultaneous carbon, nitrogen and phosphorus removal. The plant has a modified Ludzack-Ettinger configuration with five reactors in series (tanks 1 and 2 are anoxic with a total volume of 2000 m3, while tanks 3, 4 and 5 are aerobic with a total volume of 4000 m3) linked with an internal recirculation from the 3rd aerobic tank to the 1st anoxic tank, a 10-layer secondary settling tank (with a total volume of 6000 m3) and two PI control loops. The first loop (OD) controls the dissolved oxygen in the 3rd aerobic tank by the manipulation of the aeration flow, and the second loop (NO) controls the nitrate in the 2nd anoxic tank by manipulating the internal recycle flowrate. For this new plant (A), five variables (z = 5) were selected for the sensitivity analyses: anaerobic volume (V1), waste flow (V2), recycle flow (V3), OD set-point (V4) and NO set-point (V5). All these variables were varied a maximum of ± 10 % considering 4 points above and 4 points below the base case (i.e. every ± 2.5 %). We studied fifteen criteria (y = 14) to measure the accomplishment of the four redesign objectives (x = 4): minimize environmental impact (OBJ1), minimize economic costs (OBJ2), maximize technical reliability (OBJ3) and comply with the limits fixed by the 91/271/ECC Directive (OBJ4). In this case study we assume equal importance for all four redesign objectives, wi= 0.25. Thus, for OBJ1, criterion X1 (global cleaning efficiency) is proposed (Gernaey and Jorgensen, 2004). Construction costs (X2, U.S EPA, 1982) and operation costs (X3, Vanrolleghem and Gillot, 2002) are used for OBJ2. Plant robustness (X4) and flexibility (X5), nitrate control performance (X6) and sensitivity to separation problems (X7-1= foaming risk; X7-2 = bulking risk and X7-3 = rising risk) measure the satisfaction of OBJ3. A detailed description of X4 and X5 can be found in Flores et al., (2005) while X6 and X7 are reported in Copp (2003) and Comas et al., (2005) respectively. Finally, X8-X12 reflect the percentage of time that the concentration of the pollutant exceeds the legal limits or time plant in violation (TIV) for TSS, COD, BOD5, TN and TP (Copp 2003). X2 is calculated by model based estimation with the CAPDET model (U.S EPA, 1982) and the rest of criteria through dynamic simulation. The IWA Activated Sludge Model # 2d was chosen as the biological process model (Henze et al. 2002) and the double-exponential settling velocity function of Takács et al. (1991) was chosen as a fair representation of the settling process Once the criteria are quantified a score profile [A] for the proposed redesign is obtained. Hence, the score of the base case is [A = (82.03 %, 7.7·105 €, 9.1·105 €·year-1, 14.70, 10.27, 0.78 (gN/m3)2·d, 0 %, 61.46 %, 31.10 %; 0 %, 0 %, 0 %, 90.18 %, 29.32 %).] The score profile [A] is normalized from 0 to 1 using value functions: v (A) = (0.82, 0.38, 0.91, 0.74, 0.51, 0.22, 1.00, 0.39, 0.69, 1.00, 1.00, 1.00, 0.10, 0.71). As an example, criterion X1 has the value function v(X1) = 0.01·X1 associating the best (1) and the worst (0) situation with 100% and 0 % respectively. Finally, the weighted sum (eq. 1) for the base case is calculated, with a result of 0.69. The fifteen evaluation criteria are recalculated every 2.5 % for the five variables analyzed. Figure 1 shows an example of two evaluation criteria (X11 and X12) during the sensitivity analysis of variable V1.

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t (days)

8 10 12 14

g N

·m-3

12

14

16

18

20

22

24-10%-5%0+5%+10%

t (days)

8 10 12 14

gP·m

-3

0

1

2

3

4

5

6

7

8-10%-5%0+5%+10%

Figure 1. Evolution of TIV for the sensitivity analysis of V1. (a) TN (X11); (b) TP (X12.).

3.2. Knowledge Extraction and Reutilization Step 1. All the data generated in the previous analyses is used for the extraction of qualitative knowledge. The objective is to find the correlations between the five analyzed variables with the fourteen criteria. Trends in the evaluation criteria are computed and then classified in the four categories summarized in Table 1. Step 2. Once all this knowledge is extracted from the data is codified in form of IF-THEN rules. The set of rules [R= (R1,1,…R5-12)] conforms a knowledge base containing the relationship between 5 variables and 15 evaluation criteria. Finally, these rules are used as input to the inference motor (e.g. CLIPS). As example, the syntaxis of a couple of rules extracted from Table 1 are shown:. R1-12 = IF V1 (anaerobic volume) increases THEN TIV for P (X 12) decreases. R5-6 = IF V5 (NO setpoint) increases THEN control perfomance (X6) increases

Table 1. Evolution of TIV for the sensitivity analysis of V1. (a) TN (X11); (b) TP (X12.).

Indirectly proportional

Directly proportional

Constant Non monotonic

V1 v(X6), v(X7-3), v(X12)

v(X2), v(X4), v(X5), v(X7-2), v(X11)

v(X7-1), v(X8), v(X9), v(X10)

v(X1), v(X3)

V2 v(X7-1), v(X7-2), v(X7-3), v(X12)

v(X1), v(X3), v(X5), v(X6), v(X11)

v(X2), v(X8), v(X9), v(X10)

v(X4)

V3 v(X3), v(X4), v(X5), v(X6), v(X11)

v(X7-2), v(X7-3), v(X12)

v(X2), v(X7-1), v(X8), v(X9), v(X10)

v(X1)

V4 v(X4), v(X5), v(X6), v(X11)

v(X7-2),v(X7-3), v(X12)

v(X2), v(X7-1), v(X8), v(X9), v(X10)

v(X1) , v(X3)

V5 v(X1), v(X4), v(X5), v(X7-3), v(X11)

v(X3), v(X6), v(X12) v(X2), v(X7-1), v(X7-2), v(X8), v(X9), v(X10)

-

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Step 3. In this section is shown, how the rules codified in Step 2 can be reused to identify the most suitable design variables to improve the achievement of a particular criterion (Xj). The proposed scenario proposes a tight compliance in the limits fixed by the European Directive concerning nitrogen concentration (X11). This scenario is relevant because for the base case 90.18 % of the time there is a violation of the nitrogen, while the design removes all other pollutants (TSS, COD, BOD and TP)(see Figure 1a for more details). To analyze any scenario, the system can search automatically all the design variables that are implied to improve this new requirement. At the same time, the system highlights the negative effects of the proposed actions in other criteria. The following examples clarify the situation. If criterion X11 (TIV for nitrogen) has to be improved, the positive effects of the proposed actions had to be balanced with the negative associated. The proposed actions are summarised in Table 2. Table 2. Proposed action to improve criteria X11 and adverse effects of this modification Action 1 Description and adverse effects Ac1 Criterion X11 can be improved decreasing V1; but

When V1-1 is decreased, X6, X7-3 and X12 decrease Ac2 Criterion X11 can be improved decreasing V2, but

When V1-2 is decreased, X7-1, X7-2 , X7-3 and X12 decrease Ac3 Criterion X11, can be improved increasing V3 but

When V1-3 is increased X7-2, X7-3 and X12 decreases Ac4 Criterion X11, can be improved increasing V4, but

When V1-4 is increased X7-2, X7-3 and X12 decreases Ac5 Criterion X11, can be improved increasing V5 but

When V1-5 is increased X3 , X6 and X12 decreases. Then, numerical and qualitative methods are integrated to evaluate the convenience of the proposed actions in different ways. For example, ordering the actions according to the highest decrease in X11 (without considering the rest of criteria), the recommended actions would be arranged as follows: Ac3 (-12.38%), Ac1 (-8.09%), Ac2 (-7.59%), Ac4 (-1.98%) and finally Ac5 (-0.66%). Another approach is evaluating the actions considering their overall effect on the redesign objectives [OBJ], selecting only those actions that suppose a net gain in the weighted sum (eq1). Thus, from the above mentioned actions, only two (Ac2 and Ac3) are recommended, as they achieve a gain in the objective function of 0.66% and 0.08% (i.e. lower time plant in violation). Another approach could be screening of actions that suppose a decrease in the effect of a certain criteria Xj , or a combination of several criteria simultaneously. As in many other tools, the inference system indicates which actions should be revisited, but the final decision remains on the designer. A positive side effect of this procedure is that it keeps a record of the design process in form of rules. On the one hand, because enables the understanding of the whole design process and the designer is conscious of both weak and strong points of any possible action. On the other, because improves the communication both among designers and stakeholders, such as present and future inspectors. Furthermore, plant modifications are made easier in the event of changes in legislation, technologies or plant capacity.

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4. Conclusions This paper presents a design support module that extracts and reuses the knowledge generated in the sensitivity analyses carried out during the design of activated sludge plants. The significance of this approach resides in the advantages of integrating numerical and qualitative methods for decision making. Detailed numerical models are used to recalculate the evaluation criteria around the base case, from which qualitative knowledge is extracted and used in turn, to guide the designer thorough the design process. As a result, it is possible to: (a) reduce the cognitive load on the designer and enhance his/her understanding of the process; (b) improve decision making, e.g. decreasing the number of design iterations, by the concurrent manipulation of multiple criteria –more than a dozen in the case study- highlighting both strong and weak points of the proposed actions and (c) reuse the knowledge (geographically and temporally) that otherwise would be tacit and only usable by a single designer.

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the IWA/COST simulation benchmark to include expert reasoning for system performance evaluation. 2nd IWA Conference on Instrumentation, Control and Automation for Water and Wastewater Treatment and Transport System (ICA).

Gernaey, K.V. and Jørgensen, S.B. 2004. Benchmarking Combined Biological Phosphorus and Nitrogen Removal Wastewater Treatment Processes. Control Eng. Pract., 12, 357-373.

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Henze M., Gujer W., Mino T., and van Loosdrecht M.C.M. 2002. Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Scientific and Technical Report No. 9 IWA. London

Metcalf & Eddy. 2003. Wastewater Engineering: Treatment, Disposal and Reuse. McGraw-Hill: New York.

U.S. EPA - Office of Water Program Operations. 1982. Process Design and Cost Estimating Algorithms for the Computer Assisted Procedure for Design and Evaluation of Wastewater Treatment Systems (CAPDET), Ed. Harris R.W., Cullinane M.J.Jr. and Sun P.T., Vicksburg, Mississippi (EPA ref: PB82-190455).

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Acknowledgements The authors gratefully acknowledge financial support from Spanish “Ministerio de Ciencia y Tecnología” (DPI2003-09392-C02-01). The authors also wish to thank Krist V. Gernaey (CAPEC, Technical University of Denmark) for providing the ASM2d benchmark MatLab/Simulink© code to carry out the simulations and sensitivity analyses.

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