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Kibi N, Sasseville J.L., Martel J.M. and J.F. Blais (2000). Multicriteria Choice of Municipal Wastewater Treatment Processes. Rev. Sci. Eau 13 (1) : 21-38.

Original title: Choix multicritère de procédés d'épuration des eaux usées municipales.

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Quebec municipal wastewater treatment facilities, like those elsewhere in Canada and the United States, generally are low efficiency energy consumers (ELECTRIC POWER RESEARCH INSTITUTE (EPRI), 1993; OWEN, 1982; ONTARIO-HYDRO, 1993; METCALF and EDDY, INC., 1992, SASSEVILLE et al., 1995). The work of METCALF and EDDY, INC. (1992) and of EPRI (1993) concluded that it would be possible to substantially reduce electricity demand and to improve the utilization of electrical energy in the municipal wastewater treatment processes by introducing Electricity Saving Measures (ESMs) in the processes and their management.

In the province of Quebec, given the potential savings linked to the reduction of electricity consumption in municipal wastewater treatment facilities, and the progressive expansion of the province's wastewater treatment facilities, the adoption of energetically efficient wastewater treatment technologies is particularly timely. SASSEVILLE et al. (1995) estimated that it would be possible to save 5 M $ at the present level of wastewater treatment, based on a cost of about 24 M $ for the 400 GWh of electricity annually consumed in the municipal wastewater treatment facilities. This saving would come solely from the implementation of appropriate energy-saving measures.

In hypothesizing on one hand that adding energy-saving measures can improve the energy consumption of a wastewater treatment chain, and on the other hand that the introduction of segments of processes can contribute in improving overall performance, we have elaborated from existing wastewater treatment facilities six hypotheses of liquid treatment chains that respect operational and regulation requirements, on the basis of the experience developed in the operation of municipal wastewater treatment facilities. The six hypothetical treatment chains were elaborated from facilities of the following types: biofiltration (chain 1), physico-chemical (chain 2), sequencing biological (batch) reactors system A (chain 3), sequencing biological (batch) reactors system B (chain 4), activated sludge (chain 5) and aerated lagoons (chain 6). The energy-saving measures utilized in the elaboration of these hypothetical treatment chains were chosen on the basis of a conjunctive analysis (KIBI et al., 1997). However, the problem of choosing among these treatment chains the one corresponding to the most adequate process for a particular situation is still present. The present article analyzes this choice for wastewater treatment facilities of a capacity between 5 000 m3/d ({short description of image} 5 000 persons) and 100 000 m3/d ({short description of image}100 000 persons).

How then to choose the most efficient of these hypothetical municipal wastewater treatment chains?

Generally, the choice of the treatment technologies is done on the basis of single-criterion mathematical models: for example, the reduction of construction costs, or of exploitation and maintenance costs (ECKENFELDER, 1982; PINEAU et al., 1985; WANG and WANG, 1979; TYTECA et al., 1977). There are other approaches based on dynamic simulation models or on technological and econometric analyses (HYDRO-QUÉBEC, 1993; MACRAE 1989; LESSARD, 1989; BROCKTON, 1987; HOLDREN, 1987; FOSBERG and MUKHOPADHYAY, 1981; REID, CROWTHER and PARTNERS, 1978; KLEMETSON and GRENNEY, 1976).

These different approaches are often insufficient to distinguish the real value of the different technological options. Furthermore, they do not take into account many important factors (technological, economic, financial and environmental, ergonomic and socio-political) that affect their implementation and should be considered in identifying an acceptable and viable solution.

The multicriteria approach of decision-making advocated in this article can mitigate this difficulty. It will take into account key factors in the conception and operation of treatment technologies, especially energy and environmental factors, likely to give rise to efficient treatment facilities from both an energy and a treatment point of view. However the analysis of decisional factors to consider in this multicriteria analysis gives rise to a particular problem. They affect the decision that can be appreciated by deterministic relationships, offering a high level of certainty as to its evaluation, while others have a non-deterministic nature (uncertainty and imprecision). Research using the multicriteria analysis approach has been performed in similar situations over the last twenty years, in some cases applied to the environmental and energy sectors (e.g. KEENEY and NAIR 1977; ROY and VINCKE 1981; TEGHEM and KUNSCH 1985; SIMOS 1990; HANSON 1991; ROUSSEAU and MARTEL 1994). Here again, these approaches have limits since the cases were treated either in a situation of certainty or a situation of uncertainty and imprecision. The proposed model deals with the case of certainty and uncertainty at the same time, therefore improving the applicability of the multicriteria approach in the situation under study.

The solution retained consists of applying this type of modelling in order to classify from the best to the worst, the six hypothetical treatment chains. This approach utilizes in the modelling process, fourteen evaluation criteria, various criteria weights, quantitative and qualitative evaluations, as well as the indifference, preference and veto thresholds. The main steps of the model are the construction of evaluated outclass relationships and the exploitation of these outclass relationships. The multicriteria aggregation procedure utilizes an elaborated mathematical model based upon the methods of ELECTRE III (Roy, 1978) and PROMETHEE II (BRANS et al., 1984), as well as the works of DÉROT et al. (1994).

The ranking of these treatment chain hypotheses, elaborated on an empirical level in consultation with the operators and others involved in wastewater treatment and obtained on the basis of this procedure, can discriminate among their overall performance characteristics rather well. It also emphasizes their energy efficiency, since the energy criteria have on average in the analysis a weight that is 28% higher than the other evaluation criteria. The results obtained show that the hypothetical chain 3 is ranked first, chain 1 occupies the second rank, whereas chain 4 is in the third rank. The last three ranks are occupied respectively by chains 6, 5 and 2.

In a decisional and strategic approach, the first three treatment chain hypotheses can be considered overall as being the highest achievers. This result signifies that in the scope of investments related to the expansion of treatment facilities and the construction of facilities with a flow rate contained within the considered range, these three treatment chains (when considering different modification hypotheses), should be preferred over the other chains when the emphasis is on their overall performance including energy efficiency. However, other analyses would be necessary in the case of the construction of a new wastewater treatment facility with a flow rate above the level considered in this study.

Generally, the results of this analysis can assist in discriminating among the behaviors of the technologies considered, and in judging their relative performance in the investments of the construction of new wastewater treatment facilities, in addition to a technico-economic analysis. Overall, the multicriteria model described in this study identified a compromise solution between evaluations of a different and conflicting nature. This result demonstrates that this type of analysis is appropriate for tackling multidimensional problems.


Wastewater treatment, multicriteria approach, decision making, electricity saving measures, global performance.

Corresponding author

Nlombi Kibi, Industrielle de l'Environnement, INRS-Eau, Terre & Environnement, Université du Québec, 2800, rue Einstein, suite 105, CP 7500, Sainte-Foy (Québec) G1X 4N8, CANADA

Email : indusenv@ete.inrs.ca

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Update: 2006-12-19
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