Selection of the Activated Sludge Configuration during the Conceptual

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Ind. Eng. Chem. Res. 2005, 44, 3556-3566

Selection of the Activated Sludge Configuration during the Conceptual Design of Activated Sludge Plants Using Multicriteria Analysis Xavier Flores, August Bonmatı´, Manel Poch, and Ignasi Rodrı´guez-Roda* Laboratory of Chemical and Environmental Engineering, University of Girona, Montilivi Campus s/n, 17071 Girona, Spain

Rene´ Ban ˜ ares-Alca´ ntara Department of Engineering Science, Oxford University, Parks Road OX1 3PJ. Oxford, United Kingdom

This paper presents an extension of an existing systematic conceptual design methodology for urban wastewater treatment plants (WWTP) that combines a hierarchical decision process with mathematical modeling (Vidal et al. Ind. Eng. Chem. Res. 2002, 41, 4992-5005). The main contributions of this new approach are (i) to refine criteria selection and quantification used to compare the competing options with different sources of information (dynamic simulation, modelbased cost estimations, literature and expert knowledge); (ii) to propose different value functions to normalize the effect of the criteria used; and (iii) to include a visual representation (grayscale approach) that eases the visualization of the objectives that were not accomplished and of the main handicaps of the evaluated options. Finally a case study, where an activated sludge configuration for carbon and nitrogen removal is selected, and a sensitivity analysis, to show the influence of the context in the final decision, are also presented. Introduction From an economic and environmental point of view, the early design stages of any (bio)chemical process, that is, its conceptual design, are worthy of very careful consideration. The decisions made at this early stage have a major impact on later stages. McGuire and Jones1 reported that up to 80% of the capital costs of a plant are determined during the conceptual process design. Conceptual design involves a sequence of decisions that arise from predefined objectives about the technologies, equipment, dimensions, configuration, and operating conditions that make up a process. The decisions have to take into account the environmental, economic, technical, legal, and social contexts, all of which limit the number of potential solutions. Conceptual design is complex and ill-defined because there is a very large number of potential solutions that we might consider in order to accomplish the same goal.2 For this reason, hierarchical decision processes have been proposed,2,3 where the often very time-consuming and complex conceptual design is broken down into several tasks that are much simpler to analyze by successively introducing new levels of detail. In view of the inherent complexity of conceptual design, there is a major need for technologies that provide systematic support to the designer to achieve an appropriate selection of processes that operate close to their desired performance limits. There are some of these applications in the field of (bio)chemical processes.4,5 The activated sludge process, widely used all over the world to biologically treat wastewater contaminants, is * To whom correspondence should be addressed. Telephone: +34-972-418281. Fax: +34-972-418150. E-mail: [email protected].

a clear example of a process where designers need support. The activated sludge process is a suspended growth, biological treatment method to treat wastewater. It uses the metabolic reactions of microorganisms to produce a high quality effluent by converting and removing substances that have an oxygen demand.6 The traditional mode of activated sludge process synthesis has relied exclusively on the use of heuristic knowledge and numerical correlations focused on the evaluation of the economically optimal process configuration from among many possible alternatives. Once the configuration is selected, the process parameters and a stationary operating state are evaluated by means of steady-state models of the process. In this procedure, there is no consideration of the dynamic operability and controllability of the processes under design. Recent years have seen the introduction of new and varied designs of activated sludge plants.7 The increasing need for reducing environmental impacts and achieving lower costs and more effective plant operations have led to the development of tools that can be used to evaluate and compare different options at the early design stage. Literature describes some examples of tools that support the design and retrofit of activated sludge plants.8,9 Along this line, Vidal et al.10 presented a design support system methodology that combined the hierarchical decision process with mathematical modeling. The methodology was applied to a WWTP case study where different alternative designs were evaluated with respect to a set of environmental, social, economic, legal, and technical criteria. Although the results of applying this methodology showed some clear advantages, there are some key aspects in the quantification and normalization process that should be improved to guarantee a consistent decision procedure.

10.1021/ie040278q CCC: $30.25 © 2005 American Chemical Society Published on Web 04/19/2005

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Figure 1. Flow diagram of the conceptual design methodology.

This paper presents an extension of this methodology to support the conceptual design of activated sludge processes. The hierarchical decision process, expressed as a multicriteria problem, determines the direction of the design process and the evaluation procedure to resolve the different issues that arise. The quantification procedure to compare the competing options is systematized using different sources of information such as dynamic simulation (to quantify effluent quality, sludge production, aeration energy, etc.), model-based cost estimations (investment costs, personnel for operation and maintenance costs), and some parameters retrieved from the literature for criteria that cannot be evaluated numerically (e.g., sensitivity to filamentous growth). Standard value functions are proposed to normalize the effect of the quantified criteria. Also, the gray-scale approach is adopted as a visual representation of the final results to identify the weak features of each option.11 Finally, a context weighted sensitivity analysis is carried out to show the influence of the different design objectives in the final decision. This paper is organized as follows: first, a brief description of the extended methodology is presented; then, the methodology is applied to a case study, where the decision procedure is developed to select an activated sludge configuration for organic matter and nitrogen removal; and, finally, a sensitivity analysis of the final results is presented and discussed. Methodology This section details the extended conceptual design methodology for WWTPs, which combines the hierarchical decision process with multicriteria analysis. The flow diagram of the methodology, shown in Figure 1, is subdivided in three main blocks. The first block corresponds to the collection and analysis of all the available information. The goal is to define the context within which the activated sludge

process will be designed. The initial plant information includes the location site of the facility, the composition of the wastewater that has to be treated, the applicable legislation, and finally, any restriction affecting the design process (e.g., economic budget or land occupation). The second block includes the definition of the design objectives and criteria that will be used to measure the accomplishment of the objectives (see Table 1). The initial design objectives are defined to minimize economic cost (OBJ1), environmental impact (OBJ2), and social impact (OBJ3), while maximizing technical reliability (OBJ4) and meeting the quality limits fixed by the Wastewater European Directive (OBJ5). For example, investment cost (C1) and operation and maintenance costs (C2) are the two criteria proposed to minimize OBJ1 (i.e., economic cost). The effects of these criteria are quantified by means of the following 8 indexes: construction costs (X1), which are measured in euros; and aeration costs (X2); pumping costs (X3); sludge disposal costs (X4); chemical costs (X5); mixing costs (X6); maintenance personnel costs (X7); and operation personnel costs (X8), which are all measured in euro‚yr-1. The same approach is followed for the rest of objectives listed in Table 1. Also, different weighting factors are assigned to determine the importance of each objective according to the design context. To avoid unbiased comparisons, the sum of weighting factors should be 1. The third block corresponds to the decision procedure. It includes the identification of the issues to be solved, the generation of options (i.e., potential solutions), the selection of the set of criteria in the context of the particular issue, the evaluation of the proposed options, and the selection of the best option. The evaluation procedure is presented as a multicriteria problem, which includes the quantification of the criteria based on different sources of information (dynamic simulation,

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Table 1. Design Objectives, Criteria, Index, and Index Scalesa criteria

indexes

OBJ1. Minimize Economic Costs X1: construction cost X2: aeration cost X3: pumping cost X4: sludge disposal cost C2, minimize operation and maintenance costs X5: chemical cost X6: mixing cost X7: maintenance personnel costs X8: operation personnel costs C1, minimize investment costs

C3, minimize effect on water

OBJ2. Minimize Environmental Impact X9: impact on water

C4, minimize soil occupation C5, minimize odor C6, minimize noise

OBJ3. Minimize Social Impact X10: soil occupation X11: odor X12: noise

C7, maximize robustness C8, maximize flexibility C9, avoid separation problems C10, maximize controllability C11, meet the limit for COD C12, meet the limit for BOD C13, meet the limit for TSS C14, meet the limit for TN C15, meet the limit for TP a

OBJ4. Maximize Technical Reliability X13: robustness X14: flexibility X15: sensitivity to filamentous problems X16: integral squared error OBJ5. Meet the European Directive X17: time in violation (TIV) COD X18: time in violation (TIV) BOD X19: time in violation (TIV) TSS X20: time in violation (TIV) TN X21: time in violation (TIV) TP

index scales euro euro‚yr-1 euro‚yr-1 euro‚yr-1 euro‚yr-1 euro‚yr-1 euro‚yr-1 euro‚yr-1 % ha qualitative (A-E) qualitative (A-E) % % qualitative (A-E)

% % % % %

Qualitative implies a 5 point scale (A ) best and E ) worst).

model cost estimations, and literature or heuristic knowledge), the normalization of the effect of the quantified criteria by means of value functions, the grayscale approach, and the weighted sum. The weights of the objectives are equally distributed among all the selected criteria. Finally, the options are ranked according to the overall score. The option with the highest score is the one recommended by the process design methodology, but the final decision rests with the designer. The same methodology is applied to solve each new issue that arises until the conceptual design of the activated sludge process is completed. Objectives are not fixed but can evolve through time, thus allowing for the initial design objectives to be refined. This decision procedure follows the logical order based on the hierarchical decision process. The whole design process is organized as a decision network where the evaluated issues represent the nodes and the evaluated options represent the branches. Case Study The case study shows the application of the extended methodology for the conceptual design of a new activated sludge plant. Each one of the blocks of the methodology (see Figure 1), together with numerical details of the process, will be described. The full decision procedure is developed to solve a single issue related to the reactor configuration. Once solved, the design process would continue identifying and successively solving new issues (such as the inclusion of a biological selector, the implementation of an automatic control strategy, etc.) until the whole conceptual design is achieved. Initial Plant Information. Several aspects of the initial plant information are available to define the design context. Council Directive 91/271/EEC of May 21, 1991, concerning urban wastewater treatment is the reference water legislation. The plant is to be located

Figure 2. Influent wastewater flow profile.

in an ecologically sensitive area. According to the reference law, the plant has to remove organic matter, suspended solids, and nitrogen before the treated water is discharged into the environment (to avoid eutrophication). The influent wastewater composition is the same proposed in the COST simulation benchmark. The COST simulation benchmark is a standardized simulation and evaluation procedure including plant layout, simulation models, and model parameters; a detailed description of the disturbances to be applied during testing; and evaluating criteria for testing the relative effectiveness of simulated control strategies in activated sludge plants.12 The average daily flow of the plant is about 18 500 m3‚d-1 (see the profile in Figure 2) with an organic and nitrogen load of 6500 kg of COD‚d-1 and 680 kg of N‚d-1, respectively (see Figure 3). Finally, the budget is restricted to 5 × 106 euro for investment and 8 × 105 euro‚yr-1 for operation and maintenance. Maximum land occupation allowed for the reactor section is 10 ha. Definition of Design Objectives and Criteria. Design objectives and criteria defined in the second

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Figure 3. Influent wastewater components profile.

stage of the methodology (see Figure 1) are used throughout the case study. Different weighting factors are assigned to each objective according to the design context defined within the initial plant information. In this case study legal, technical, and economic objectives are prioritized above environmental and social objectives. Thus, OBJ5 (meet the European Directive), OBJ4 (maximize technical reliability), and OBJ1 (minimize economic cost) have a weight of 0.35, 0.30, and 0.25, respectively. On the other hand, OBJ2 (minimize environmental impact) and OBJ3 (minimize social impact) are weighted with values of 0.075 and 0.025. These weights will be equally distributed for all the criteria used in the decision procedure. Decision Procedure. (a) Identification of the Issue To Be Solved. The decision procedure starts with the identification of the issues to be solved. The only issue addressed in this paper is the determination of the configuration of the activated sludge section for simultaneous organic matter and nitrogen removal. All of these configurations include an aerobic zone in which organic matter and ammonia are oxidized to carbon dioxide and nitrate. At the same time, some anoxic volume or time must also be included to reduce the nitrate to nitrogen gas (denitrification). It is important to emphasize that this reduction requires an electron donor, which can be supplied in the form of influent wastewater BOD, endogenous respiration, or an external carbon source.13 (b) Generation of the Options. Three activated sludge configurations (options) are considered to solve this issue: (i) modified Ludzack-Ettinger (MLE), (ii) double stage (DS), and (iii) oxidation ditch (OxD). In MLE the influent wastewater is fed into an anoxic zone, followed by an aerobic zone. This configuration is provided with an internal recirculation to the anoxic zone directly from the aerobic zone. In DS there are two reactors separated by an intermediate settler; the first reactor is aerobic, and the second one is anoxic with exogenous carbon addition (methanol in this case). OxD is based on an oval-shaped channel with aerobic and anoxic zones established in the same tank. Although there are other possible options to consider (e.g. Bardenpho, Wuhrmann, etc.), we proposed those that are more representative to cover the broad range of existing possibilities. Different design equations based on reduced steadystate models7 are used to size the volumes of the three compared options. Aerobic zone volume in MLE and DS configurations is sized on the basis of the net specific

growth rate of nitrifying organisms, the amount of mixed liquor suspended solids, and the total mass of solids that has to be removed to maintain the required sludge residence time. The anoxic volume in the MLE configuration is designed determining the internal recycle ratio, the nitrate produced in the aerobic zone, and the sludge denitrification rate. The anoxic zone in the DS configuration is designed on the basis of the solids retention time (SRT), the solids production, and the methanol dose. Finally, the volume of the OxD configuration is sized on the basis of the applied hydraulic load and the subsequent determination of the anoxic time to denitrify all the nitrate produced in the aerobic zone. The secondary settling area is calculated on the basis of the solids loading rate. The sludge recirculation and waste flow rates are determined through a mass balance around the aeration tank and the secondary settler. The main design characteristics of the studied configurations are summarized in Table 2, and the schematic representations in Figure 4. (c) Selection of the Criteria. A set of criteria with their corresponding quantification indexes are selected (from Table 1) to evaluate the three options generated: X1 (construction cost), X2-X8 (operation and maintenance costs), X9 (impact on water), X10 (land occupation), X13 (robustness), X14 (flexibility), X15 (sensitivity to filamentous growth), and X17-X20 (time in violation, TIV). Criteria X2-X6, X9, X13, X14, and X17-X20 are quantified by dynamic simulation with the commercial software GPS-X.14 CAPDETworks15 software package provides the information to quantify X1, X7, X8, and X10. Evaluation of index X15 is based on information from the literature. (d) Evaluation of the Options (i) Criteria Quantification. Next, the quantification of criteria is described, starting with the criteria quantified by dynamic simulation, then the criteria quantified by economic model estimations, and finally, the criteria based on a review of the literature. Simulations are performed with the GPS-X, a modeling and simulation environment for municipal and industrial wastewater treatment plants. Reactor hydraulics is modeled using a conventional plug flow pattern (plug flow reactors are modeled in GPS-X as a series of continuous stirred tanks reactors (CSTR)). For the OxD option, a large recycle has been added from the last CSTR of the series to the first. The IAWQ’s activated sludge model 1 (ASM1) is chosen as the biological process model,16 and the double-exponential settling velocity function of Taka´cs et al.17 is chosen as a fair representation of the settling process in a 10-layer pattern. A PI control loop for the dissolved oxygen in the aerobic zone with a set-point of 1 g‚m-3 is included. All the dynamic simulations follow a steady-state simulation. This ensures a consistent starting point and eliminates the influence of starting conditions on the generated dynamic output. Only the data generated during the last 7 d of the dynamic simulation are used to quantify the criteria. All the criteria are quantified through dynamic simulation. The Aeration cost index (X2) is calculated multiplying the aeration energy (AE) expressed in units of kW‚h‚d-1 (see eq A1 in the Appendix) by the energy price (0.07 euro‚kW-1‚h-1) and by 365 d‚yr-1 to obtain the result in terms of euro‚yr-1. The pumping cost index (X3) is calculated multiplying the pumping energy (PE) expressed in units of kW‚h‚d-1

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Table 2. Design Characteristics of Three Options Generated To Solve the Issue: Type of Activated Sludge Configuration units design MLSS hydraulic load aerobic volume aeration anoxic volume settling area waste flow external recirculation flow internal recirculation flow methanol flow a

From Metcalf &

Ludzack-Ettinger

g of MLSS‚m-3 g of BOD‚m-3‚d-1 m3 type m3 m2 m3‚d-1 m3‚d-1 m3‚d-1 m3‚d-1

double stage

4000a

4000a

4800 diffused 2000 900 340 18 446 65 000

4800 diffused 1200 900-900 340-107 18 446-11 000

oxidation ditch 6000a 345a 5300 diffused 5440 1200 214 12 835

1.5

Eddy.7

(see eq A2 in the Appendix) by the energy price (0.07 euro‚kW-1‚h-1) and by 365 d‚yr-1 to obtain the result in terms of euro‚yr-1. The sludge disposal cost index (X4) is calculated multiplying the quantity of sludge for disposal (PSLUDGE) expressed in units of kg of TSS‚d-1 (see eqs A3-A6 in the Appendix) by the sludge disposal cost (0.1079 euro‚ kg of TSS-1) and by 365 d‚yr-1. The chemical cost index (X5) is calculated multiplying the amount of chemicals expressed in units of m3‚d-1 (see eq A7 in the Appendix) by the cost of the chemical product (methanol in our case 440.8 euro‚m-3) and by 365 d‚yr-1 to obtain the result in units of euro‚yr-1. The mixing cost index (X6) is calculated multiplying the mixing energy (ME) expressed units of kWh‚d-1 (see eq A8 in the Appendix) by the energy price (0.07 euro‚kW-1‚h-1) and by 365 d‚yr-1 to obtain the result in terms of euro‚yr-1. The impact on water index (X9) is defined as the reduction percentage of the wastewater contaminant load entering the plant. The impact on water is calculated as expressed in eq 1:

X9 )

IQ - EQ × 100 IQ

(1)

This criterion relates the effluent (EQ) to the influent quality index (IQ) (see eqs A9-A11 in the Appendix). Robustness is defined as the degree to which the process can handle short-term disturbances that affect the dynamics of the process.18 The short-term disturbances used in this case study are the rain and storm influent events defined in the COST simulation benchmark.12 To quantify robustness (X13), it is necessary to calculate the effluent quality index (eq 10) for rain (EQrain) and storm (EQstorm) events and to evaluate the average loss in terms of effluent pollution load. The robustness index (X13) is expressed as a percentage and is calculated as shown in eqs 2-4:

X13,storm + X13,rain 2

(2)

X13,storm )

EQstorm - EQ × 100 EQ

(3)

X13,rain )

EQrain - EQ × 100 EQ

(4)

X13 )

Flexibility is defined as the degree to which the process can handle long-term changes to steady state.18 The long-term change used in this case study is a step

increase of 25% in the influent flow rate as well as in each one of the organic and nitrogen compounds. To quantify the flexibility index (X14), it is necessary to calculate the effluent quality index (eq 10) for this new situation (EQ25%) and to evaluate the loss in terms of effluent pollution load. The flexibility index (X14) is also expressed as a percentage and is calculated with eq 5:

X14 )

EQ25% - EQ × 100 EQ

(5)

The accomplishment of the limits fixed by the European Directive (91/271/EEC) is calculated based on time in violation index.12 This index is a measure of the percentage of the time that the plant is in violation of the effluent constrains. In our case study, TIV is calculated for COD (X17), BOD (X18), TSS (X19), and TN (X20), expressed as shown in eq 6:

TIV )

N0 × 100 NT

(6)

where N0 is the time when the plant is in violation with the legal limits, and NT is the total simulation time. On the other hand, the estimation of the other costs and land occupation indexes is carried out with CAPDETworks, a preliminary design and costing program, which is based on the CAPDET model.19 The construction cost (X1), expressed in euros, includes the cost of earthwork, the cost of putting the wall and slab in place, the cost of the aeration system (diffused or mechanical), the cost of handrails, and the cost of installation of the items of equipments. The personnel costs for maintenance (X7) and operation (X8), in units of euro‚yr-1, include the labor cost required for mechanics, electricians, painters, custodians, supervisors, operators, etc. The land occupation (X10), expressed in ha, is calculated based on the design volumes and areas of the different units that make up the flow diagram of the facility. Finally, sensitivity to filamentous growth (X15) is the only qualitative index used in this case study. Five categories have been defined in Table 3 according to the increasing potential proliferation of filamentous organism population in the bioreactor.20 To quantify this index, the competing activated sludge configurations are assigned to one of these categories according to different design manuals.6,7 Table 4 summarizes the results of the quantification of the 17 indexes used to evaluate the competing options. To compare these effects it is necessary to normalize the results. Note that, for this case study,

Ind. Eng. Chem. Res., Vol. 44, No. 10, 2005 3561 Table 3. Sensitivity to Filamentous Growth Categories category

sensitiviy

process conditions

A

very low

B C D E

low medium high very high

simultaneous phosphate precipitation with aluminum salts plug flow pattern predenitrification systems alternating process conditions complete mix reactors with intermittent aeration or simultaneous denitrification

X17-X19 have the same values; thus, they are not useful in discriminating the competing design alternatives. (ii) Criteria Normalization. Once quantified, the effect of each criterion in the competing options is normalized between 0 and 1 by means of value functions Y(Ci). The 0 and 1 values are associated with the worst (Ci*) and the best (Ci*) situations considered, while a mathematical function is proposed to evaluate the intermediate effect. The difference between the best (Ci*) and the worst (Ci*) is defined as the evaluation domain or range (RI). To normalize C2, for instance, it is necessary to sum up the corresponding operation and maintenance indexes (X2-X8). The worst situation (C2*) is reaching the operation and maintenance budget specified in the initial plant information (8 × 105 euro‚yr-1 for this case study), while the best situation (C2*) is defined as spending half of that budget (4 × 105 euro‚yr-1). A linear function is proposed to evaluate the range (R2) between C2* and C2*. Another example is the normalization of the impact on water criterion (C3) where the evaluation domain (R3) comprises two hypothetical situations, a total (C3* ) 100%) and a null (C3* ) 0%) reduction of the polluted load entering the plant. As in the last case, a linear function is used to evaluate the intermediate situations. The collection of the best and worst effects for all the criteria determines the worst and the best profiles of the option: C* (C1*, C2*, ..., Cn*) and C* ) (C1*, C2*, ..., Cn*). Thus, for the criteria used in this case study, the corresponding extreme profiles are,

Y(C*) ) Y[(C1 ) 5 × 106, C2 ) 8 × 105, C3 ) 0, C4 ) 8.52, C7 ) 100, C8 ) 200, C9 ) E, C11 ) 100, C12 ) 100, C13 ) 100, C14 ) 100)] ) Y(C1*, C2*, C3*, C4*, C7*, C8*, C9*, C11*, C12*, C13*, C14*) ) 0 and

Y(C*) ) Y[(C1 ) 2.5 × 106, C2 ) 4 × 105, C3 ) 100, C4 ) 8.05, C7 ) 0, C8 ) 0, C9 ) A, C11 ) 0, C12 ) 0, C13 ) 0, C14 ) 0)] ) Y(C1*, C2*, C3*, C4*, C7*, C8*, C9*, C11*, C12*, C13*, C14*) ) 1 respectively. Once the extreme profiles are obtained, the mathematical models to evaluate the range for all the criteria are proposed. Table 5 shows the extreme profiles and the value functions used in this case study. (iii) Gray-Scale Evaluation and Weighted Sum. The next step in the decision procedure is to evaluate the normalized criteria with the gray-scale approach to identify the weak features of each option.11 Gray-scale evaluation is a visual representation of the results (the lighter the shade, the more positive the effect). For every criterion, shades from 0% to 100% black are associated

Table 4. Quantified Index for the Three Competing Options index

units

LudzackEttinger

double stage

oxidation ditch

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X13 X14 X15 X17 X18 X19 X20

euro euro‚yr-1 euro‚yr-1 euro‚yr-1 euro‚yr-1 euro‚yr-1 euro‚yr-1 euro‚yr-1 % ha % % qualitative % % % %

4 614 700 183 699 85 629 82 556 0 12 337 75 700 36 530 82 8.05 18 106 C 0 0 0 73

3 670 700 215 735 30 513 119 810 241 338 7370 104 500 49 720 82 8.22 8 83 B 0 0 0 59

3 462 700 114 032 19 015 64 370 0 31 320 30 200 23 700 89 8.52 27 130 C 0 0 0 0

Table 5. Extreme Values and Functions Proposed To Normalize Effect of Selected Criteria in the Case Study best value (C*)

criteria

worst value (C*)

C1 C2 C3 C4 C7 C8 C9

5× 8 × 105 0 8.52 100 200 E

2.5 × 4 × 105 100 8.05 0 0 A

C11 C12 C13 C14

100 100 100 100

0 0 0 0

106

106

value function Y(C1) ) -4 × 10-7C1 + 2 Y(C2) ) -2.5 × 10-6C2 + 2 Y(C3) ) 0.01C3 Y(C4) ) -2.12C4 + 18.12 Y(C7) ) - 0.01C7 + 1 Y(C8) ) - 5 × 10-3C8 + 1 A ) 1, B ) 0.75, C ) 0.5, D ) 0.25, E ) 0 Y(C11) ) -0.01C11 + 1 Y(C12) ) -0.01C12 + 1 Y(C13) ) -0.01C13 + 1 Y(C14) ) -0.01C14 + 1

with the best and the worst criterion values, respectively, while gray levels of the other options are determined by linear interpolation between these values. Finally, a weighted sum is made to obtain a single value for each option. The weighted sum is calculated by adding the product of each normalized criterion multiplied by its corresponding weight. The options are ranked according to the score obtained. The option with the highest score is the one recommended, but the final decision rests with the process designer. The results of the gray-scale evaluation and the weighted sums are presented in Table 6. (e) Selection of the Best Option. At first sight, the results of the weighted sum in Table 6 lead to the following conclusion: according to the design objectives defined, the recommended option is OxD with a score of 0.78, and the rejected options are DS in the second position (score ) 0.68) and MLE in the third position (score ) 0.67). Notice that the scores of the two rejected options are so close together that in practice they can be considered equivalent. MLE was the least favored activated sludge configuration due to the lowest score in criteria C1 (minimize investment costs), C3 (minimize effect on water), C9 (avoid separation problems), and C14 (meet the limit for TN). The high construction cost of the MLE option (see the breakdown of investment costs in Table 4) is mainly due to the internal recirculation pumping station and associated equipment (see Figure 6), the sensitivity to filamentous growth is medium because it is a predenitrification system (with potential problems with the Nocardia microorganisms according to Metcalf & Eddy7),

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Figure 4. Schematic representations of the studied configurations. Table 6. Normalized Criteria, Gray Scale Evaluation, and Weighted Sums

Figure 5. Simulation of TN for the three options evaluated.

and the high value of nitrogen in the effluent (see the profile in Figure 5) implies a low percentage of nitrogen removal (index X9 in Table 4) and a high TIV for nitrogen (X20 in Table 4). On the other hand, MLE is the configuration with the best score for C4 (minimize land occupation). This fact is poorly reflected in the final score because of the low weight assigned to this criterion. In the second position is the DS configuration. Despite the fact that this option has the best score for the technical criteria (C7-C9), due to its good adaptation to short- and long-term variations and the capacity to select floc forming organisms in the presence of filamentous bacteria, it has the lowest score for C2 (minimize operation and maintenance costs) and C3 (minimize effect on water). This configuration has the highest operation and maintenance costs due to: the periodic purchase of methanol for post-anoxic denitrification (see index X5 in Table 4); the highest aeration costs because

Figure 6. Breakdown of construction costs (X1) for the modified Ludzack-Ettinger option.

the entire BOD is removed in the aerobic zone (index X2 in Table 4); the highest sludge production due to the addition of methanol, and thus the highest costs related to its disposal (index X4 in Table 4); and the highest personnel costs for operation and maintenance, due to the duplication of the reaction and settling section (see X7 and X8 in Table 4). Moreover, DS has a high impact on water because the total nitrogen load in the effluent

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Figure 8. Sensitivity analysis between w4 and w5. Figure 7. Breakdown of construction costs (X1) for the oxidation ditch option.

is high. Although, as can be observed in Figure 5, time in violation for nitrogen is better than for the MLE option (X20 in Table 4). Finally, the OxD configuration has the highest score because it scored highest in criteria C1 (minimize investment cost), C2 (minimize operation and maintenance cost), C3 (minimize effect on water), and C14 (meet the limit for TN). The OxD configuration has the lowest construction cost (see Table 4, index X1) because it does not include an internal recirculation (compared to the MLE option) and does not have two stages (as compared to the DS option). Figure 7 details the breakdown of the construction cost for this option. Moreover, this option has the lowest operation and maintenance costs because all the related indexes, except the mixing energy, have the minimum value. Also, OxD has the best process performance in terms of impact on water (X9) and time in violation (X20). This option is the one recommended by the system, despite the fact that it has the highest value for land occupation (C4) and that its technical reliability has not been maximized (C7-C9). Sensitivity Analysis In conceptual design, the context in which the decisions are taken has a great influence on the selection of the best option. In the proposed methodology, the context is defined by the design team according to the weighting factor assigned to each objective. Giving more or less weight to a determined objective clearly restricts some of the options generated during the decision procedure. To determine the relevance of the context in our case study, a simplified weight sensitivity analysis is described. The results show the influence of the different design objectives (assuming a change in the design context) in the final result. The first example consists of a sensitivity analysis between objectives 4 and 5 (maximizing technical reliability and meeting the European Directive). The weights for objectives 1-3 remain constant (w1 ) 0.25, w2 ) 0.075, and w3 ) 0.025, respectively), while the remaining 0.65 (as mentioned above, the sum of all the weights has to be 1) is distributed between the weights of objectives 4 (w4) and 5 (w5). The weighted sum for the three competing options is recalculated to obtain and rank the final scores.

Figure 9. Sensitivity analysis between w1 and w3.

From the results reported in Figure 8, we notice that high values of w5 (thus prioritizing the legal aspects to meet the European Directive) clearly favor the OxD option in front of DS and MLE (e.g., w4 ) 0 and w5 ) 0.65 results on scores 0.92, 074, and 0.71 for OxD, MLE, and DS, respectively). However, as w4 increases in value, the three options become roughly equivalent with DS being marginally the best (e.g., w4 ) 0.65 and w5 ) 0 results on scores 0.64, 0.61, and 0.59 for DS, OxD, and MLE respectively). At the same time, the sensitivity analysis shows how sensitive an option is to a change of weight. For instance, in this example, there is a strong variation in the score of OxD as compared to MLE and DS. This means that OxD can significantly improve its fulfillment of the European Directives (objective 5) at the expense of sacrificing (to an extent) its technical reliability (objective 4: robustness, flexibility, and sensitivity to filamentous problems). On the other hand, if the sensitivity analysis is made between objectives 1 (minimize economic cost) and 3 (minimize social impact, land occupation in this case), the solution switches between MLE and OxD (as can be seen in Figure 9). If economic costs are prioritized (w1 ) 0.275) at the expense of social impact (w3 ) 0), the most favored option is OxD (with a score in the weighted sum of 0.80), and DS and MLE are rejected (with scores of 0.67 and 0.66, respectively). Nevertheless, if social impact is prioritized (w3 ) 0.275 and w1 ) 0), the selected option is MLE (with a score of 0.80), closely followed by DS (with a score of 0.78), and with OxD clearly rejected (with a score of 0.58). In this case, OxD accomplishes objective 1 (it has the best values in C1 and C2 in Table 6) by decreasing the accomplishment of objective 3 (it has the highest land occupation).

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Conclusions

Figure 10. Sensitivity analysis between w1, w4, and w5.

A more in-depth analysis can be made combining three objectives (e.g., using objectives 1 (minimize economic costs), 4 (maximize technical reliability), and 5 (meet the European Directive)). Note that for this example the sum of the corresponding weights of the three objectives is 0.9 (i.e., they dominate the evaluation). As shown in Figure 10, the base of the 3-D graph is formed by w4 and w5, and the vertical axis represents the score for each option (note that the parameter w1 is dependent on w4 and w5 due to the restriction w1 + w4 + w5 ) 0.9). Thus, we obtain three 3-D surfaces that show the correlation between the objectives and the selected options. From the results generated in Figure 10, we notice that when objective 1 is favored (w1 ) 0.9, w4 ) 0, and w5 ) 0), the selected option is OxD (with a score of 0.79 for OxD and 0.51 and 0.35 for MLE and DS). If objective 4 is the most favored (w1 ) 0, w4 ) 0.9, and w5 ) 0), the selected option is DS (with a score of 0.75 for DS and 0.62 and 0.54 for MLE and OxD). Finally, if we prioritize objective 5 (w1 ) 0, w4 ) 0, and w5 ) 0.9), the most favored option is OxD (with a score for OxD of 0.96 and scores of 0.82 and 0.84 for MLE and DS, respectively). Thus, for this analysis, we can conclude that objectives 1 and 5 are best met by selecting the OxD option and that DS should be the selected option to favor objective 4. It is important to point out that this conclusion was already observed in the previous gray-scale evaluation (see Table 6) where OxD had the lightest shades for the criteria used to measure the accomplishment of objectives 1 and 5 and DS for objective 4. Note that for the MLE option all the proposed criteria for objectives 1, 4, and 5 have gray shades, which means that it will never be the selected option for any combination of weights. However, MLE has the lightest shades for the criterion related to objective 3. Thus, by prioritizing this objective in the sensitivity analysis (e.g., w3 ) 0.675; w4 ) 0, and w5 ) 0) MLE would be the selected option (with a score of 0.86 in this case) in front of DS and OxD (with scores of 0.60 and 0.27, respectively). The methodology presented in this paper allows sensitivity analyses with n - 1 parameters (where n is the number of design objectives) to be generated. However, it is not possible to visualize the n-dimensional results.

This paper has presented an extended conceptual design methodology that combines a hierarchical decision process with multicriteria analysis. The usefulness of this methodology has been tested for the first issue in the hierarchy of decisions that constitutes the complete conceptual design of an activated sludge plant. The results show that the methodology is extremely useful to enable the understanding of the whole design process while at the same time the designer knows the reasons that result in either refusal or acceptance for each issue. Moreover, the designer is conscious of both the weak and the strong points of the chosen option. To extent the existing methodology, the criteria used are refined, and different software packages (GPS-X and CAPDETworks) are integrated to automate the process design. At the same time, the inclusion of qualitative criteria allows expert knowledge to be included in the process design. Different value functions have been proposed as normalization methods. The definition of the best and the worst situation as extreme points of the evaluation range facilitates the formulation of a mathematical equation to evaluate the intermediate effects. The gray-scale representation gives a clear overview of the performance of the competing options. This evaluation supports the designer in interpreting the different effects of the alternatives considered and, as there is no longer any need to examine the absolute magnitude of the data, only relative values are considered. At the same time, this methodology shows the main handicaps of any option in easily interpretable way. As a side result of the study, performing a sensitivity analysis is recommended to highlight the influence of the different design objectives in the final decision or simply when two or more options have a similar score in the weighted sum. To sum up, this paper has contributed to extending of the methodology by (i) refining the criterion selection and classification; (ii) improving criterion quantification; (iii) maximizing the automation of the design process; (iv) improving criterion normalization by the inclusion of value functions; (v) facilitating the interpretation of the design data through gray-scale evaluation without the need to examine the magnitude of specific indicative variables; and (vi) including methods such as sensitivity analysis. Acknowledgment This work has been supported by the Spanish Ministry of Science and Technology project DPI2003-09392C02-01. Nomenclature βk ) effluent weighting factors ∆M(TSSSYSTEM) ) change in total sludge mass in system during the studied period (kg of TSS‚d-1) AE ) aeration energy (kW‚h‚d-1) AIRFLOW ) airflow into the tank (m3‚d-1) ASM ) activated sludge model blower_eff ) blower efficiency (unitless) BOD ) biochemical oxygen demand (g of BOD‚m-3) CAPDET ) Computer Assisted Procedure for Design and Evaluation of wastewater Treatment systems chemdosage_rate ) chemical dosage flow (m3‚d-1)

Ind. Eng. Chem. Res., Vol. 44, No. 10, 2005 3565 chemicals ) amount of chemicals (m3‚d-1) Ci ) criterion Ci* ) best situation considered for a determined criterion Ci* ) worst situation considered for a determined criterion COD ) chemical oxygen demand (g of COD‚m-3) COST ) Simulation benchmark for WWTP control strategies CSTR ) continuous stirred tanks reactors DS ) double stage EQ ) effluent quality index (kg of pollution‚d-1) EQ25% ) effluent quality index with a step increase of 25% (kg of pollution‚d-1) EQrain ) effluent quality index under rain conditions (kg of pollution‚d-1) EQstorm ) effluent quality index under storm conditions (kg of pollution‚d-1) ER ) energy ratio (kW‚h‚m-3) GPS-X ) general purpose simulator H2O.dens ) density of water (N‚m-3) head ) hydraulic head (m) IAWQ ) International Association of Water Quality IQ ) influent quality index (kg of pollution‚d-1) Lk ) pollutant concentration (g‚m-3) m ) toggle indicator of mixing at time t (1 ) on; 0 ) off) ME ) mixing energy (kW‚h‚d-1) MLE ) Modified Ludzack-Ettinger N0 ) time when plant is in violation of legal limits (d) NOx ) nitrite and nitrate (g of N‚m-3) NT ) total simulation time (d) OBJn ) design objective OxD ) oxidation ditch PE ) pumping energy (kW‚h‚d-1) PI ) proportional integral PSLUDGE ) sludge for disposal (kg of TSS‚d-1) PUK ) polluting loads (kg‚d-1) QE ) volumetric flow rate of cleaned wastewater stream (m3‚d-1) Qw ) waste flow (m3‚d-1) SRT ) solids retention time (d) tF - t0 ) time of the evaluation period (7 d in our case study) TIV ) time in violation (%) TN ) total nitrogen (g of N‚m-3) TP ) total phosphorus (g of P‚m-3) TSS ) total suspended solids (g of TSS‚m-3) TSSw ) solid concentration in waste flow (kg of TSS‚d-1) M(TSS)w ) total wasted suspended solids mass flow (kg of TSS‚d-1) VANOX ) mixing volume of anoxic tank (m3) WATERFLOW ) activated sludge plant pumped flow (internal, external recirculation, and waste flow) (m3‚d-1) Xj ) index Y(Ci) ) value function

in eqs A3-A6.12

PSLUDGE )

∆M(TSSSYSTEM) + M(TSSW) tF - t0

(A3)

∆M(TSSSYSTEM) ) M(TSSSYSTEM)tF - M(TSSSYSTEM)t0 (A4) M(TSSSYSTEM) ) M(TSSREACTOR) + M(TSSSETTLER) (A5) M(TSSW) )

∫t0tFTSSWQW(t) dt

(A6)

The amount of chemicals is modeled as presented in eq A7.14

chemicals )

1 (tF - t0)

∫t0tFchemdosage_rate dt

(A7)

where chemdosage_rate is the chemical dosage flow (m3‚d-1). Mixing energy (ME) is modeled as expressed in eq A8.21

ME )

24 (tF - t0)

∫t0tF [0.010VANOXm(t)] dt

(A8)

Effluent and influent quality index (EQ and IQ). EQ is calculated as shown in eq A9.12

EQ )

1 (tF - t0) × 1000

∫t0tF PU(t)QE(t) dt

(A9)

PU is the result of applying eq A10.

PU(t) ) PUTSS(t) + PUCOD(t) + PUBOD(t) + PUTKN(t) + PUNOx(t) (A10) The polluting loads PUK (kg‚d-1) corresponding to the component k (TSS, COD, BOD, TKN, and NOx) are calculated through eq A11:

PUK ) βKCK

(A11)

where βTSS ) 2, βCOD ) 1, βBOD ) 2, βTKN ) 20, and βNOX ) 20. IQ can also be calculated in a similar way to the EQ index, by simply replacing the effluent data with influent data.

Appendix

Literature Cited

Aeration energy (AE) is modeled as presented in eq A1.14

(1) McGuire, M. L.; Jones, K. Maximizing the potential of process engineering databases. Chem. Eng. Prog. 1989, 85 (11), 78-83. (2) Douglas, J. Conceptual Design of Chemical Processes; McGraw-Hill: New York, 1988. (3) Smith, R. Chemical Process Design; McGraw-Hill: New York, 1995. (4) Nagl, M.; Westfechtel, B.; Schneider, R. Tool support for the management of the design processes in chemical engineering. Comput. Chem. Eng. 2002, 27 (2), 175-197. (5) Ban˜ares-Alca´ntara, R.; King, J. M. P. Design support system for process engineering. III. Design rationale as a requirement for effective support. Comput. Chem. Eng. 1997, 21 (3), 263-276. (6) WEF Manual or Practice No. 8. ASCE Manual and Report on Engineering Practice No. 76. Design of Municipal Wastewater Treatment Plants; Water Environment Federation. Alexandria, VA, 1992.

∫t0

24 AE ) (tF - t0)

tF

AIRFLOW × head × H2O.dens 86.4 × 107 dt blower_eff (A1)

Pumping energy (PE) is modeled as presented in eq A2.12

PE )

ER (tF - t0)

∫t0tF WATERFLOW dt

(A2)

Sludge for disposal (PSLUDGE) is modeled as presented

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(7) Metcalf & Eddy. Wastewater Engineering: Treatment, Disposal and Reuse; McGraw-Hill: New York, 2003. (8) Roda, I. R.; Poch, M.; Ban˜ares-Alca´ntara, R. Application of a support system to the design of wastewater treatment plants. Artif. Intell. Eng. 2000, 14, 45-61. (9) Rodriguez-Roda I.; Poch M.; Ban˜ares-Alca´ntara, R. Conceptual design of wastewater treatment plants using a design support system. J. Chem. Technol. Biotechnol. 2000, 75, 73-81. (10) Vidal, N.; Ban˜ares-Alca´ntara, R.; Rodrı´guez-Roda, I.; Poch, M. Design of wastewater treatment plants using a conceptual design methodology. Ind. Eng. Chem. Res. 2002, 41, 4992-5005. (11) Copp, J. B. The IWA simulation benchmark: background and use; IWA Scientific and Technical Report Task Group: Respirometry in Control of the Activated Sludge Process-Interim Report 6; 1999. (12) Copp, J. B. The COST Simulation Benchmark: Description and Simulator Manual; Office for Official Publications of the European Community: Luxembourg, 2001. (13) Grady, C. P. L., Jr.; Daigger, G. T.; Lim, H. C. Biological Wastewater Treatment, 2nd ed.; Marcel Dekker: New York, 1999. (14) Hydromantis. GPS-X Technical Reference; Hydromantis, Inc.: Ontario, Canada, 2004. (15) Hydromantis. CAPDETworks; Hydromantis, Inc.: Ontario, Canada, 2004. (16) Henze, M.; Grady, C. P. L., Jr.; Gujer, W.; Marais, G.v.R.; Matsuo, T. Activated Sludge Model No. 1; IAWPRC Scientific and Technical Reports No 1; IAWQ: London, 1986.

(17) Taka´cs I.; Patry, G. G.; Nolasco, D. A dynamic model of the clarification-thickening process. Water Res. 1991, 25 (10), 1263-1271. (18) Grossmann, I. E.; Morari, M. Operability, Resiliency and Flexibility-Process Design Objectives fir a Changing World. Proceedings of Second International Conference on Foundations of Computer Aided Process Design, Denver, June 1983; pp 931-1010. (19) U. S. EPA, Office of Water Program Operations. Process Design and Cost Estimating Algorithms for the Computer Assisted Procedure for Design and Evaluation of Wastewater Treatment Systems (CAPDET); Harris, Roy W., Cullinane, M. John, Jr., Sun, Paul T., Eds.; Environmental Engineering Division, Environmental Laboratory, U.S. Army Engineer Waterways Experiment Station: Vicksburg, MS, 1982; EPA PB82-190455. (20) Eikelboom, D. H.; Andreadakis, A.; Andreansen, K. Survey of filamentous populations in nutrient removal plants in four European countries. Water Sci. Technol. 1998, 37, 4-5, 281-289. (21) CEMAGREF. Stations d’e´ puration: Dispositions Constructives pour Ame´ liorer leur Fonctionnement et Faciliter leur Exploitation; Ministe`re de l’Agriculture: France, 1992; Document Technique FNDAE no. 5bis. Cemagref-DICOVA.

Received for review November 12, 2004 Revised manuscript received March 2, 2005 Accepted March 9, 2005 IE040278Q