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Selecting the optimal wastewater treatment technology (WTT) is one of the biggest challenges in the sustainable management of industrial wastewaters. In Iran’s economy, the strategic role of the steel sector highlights its importance in sustainability assessments. Using Iran’s steel industry as a case study, this paper develops an integrated methodology for determining the most appropriate WTT. Since, the most common approach for technology evaluation, in both developed and developing countries, is expert judgment, this issue is studied by means of a multi-dimensional approach that considers the sector characteristics; the interaction of the technical, environmental and economic aspects; and the specific preferences in developing countries. The proposed modelling framework presents a rational decision-making process based on two multi-criteria decision-making (MCDM) techniques including the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in combination with fuzzy logic to make use of the combined benefits of several methods The methodology is carried out as a stepwise procedure and the fuzzy concept is introduced in both the weighting and ranking procedures. The most commonly used industrial WTTs in the steel industry are identified and ranked with respect to the six evaluation criteria and their thirty associated indicators. Finally, the results from different models, with crisp and fuzzy values, are compared to propose a straightforward and pragmatic operational decision tool. The study indicates that a hybrid model of AHP, TOPSIS and fuzzy logic offers better results and provides a higher degree of confidence for sophisticated judgments. According to the findings, the experts largely prefer the more advanced treatment systems, such as “Electrolytic splitting with Reverse osmosis and Evaporation in addition to the conventional technologies” because of their high system efficiency and compatibility with environmental impact criteria. This approach can be applied for other sectors at any industry level of decision-making—from the level of individual plants to national. [ABSTRACT FROM AUTHOR]