Optimal selection of desalination systems using fuzzy AHP and grey relational analysis

Water scarcity is an alarming global problem for a growing population with depleting sources of fresh water. Desalination is thus becoming an important solution for water management to address such looming shortage of the municipal water supply. At present, several technologies dominate the desalination industry which can be categorized either as a thermal process such as multi-stage flash distillation or a membrane process such as that of reverse osmosis. New desalination systems are also being developed to make the process more cost-effective and energy efficient. Hence, this work proposes a systematic approach for optimal selection of desalination systems using fuzzy analytic hierarchy process (FAHP) and grey relational analysis (GRA). Fuzzy AHP addresses the vagueness involve in the trade-off of the criteria or attributes used in evaluating the alternatives. On the other hand, the GRA solves the multiple criteria decision problem by aggregating the entire range of performance attribute values for every alternative into a single score in spite of incomplete information. An illustrative case study was presented wherein five desalination systems namely reverse osmosis (RO), combined reverse osmosis and forward osmosis (RO-FO), electrodialysis (ED), multi-stage flash distillation (MSF), and combined forward osmosis and membrane distillation (FO-MD) were evaluated. These desalination systems were compared to each other with respect to energy requirement, land footprint, system efficiency, economic viability, and maturity of technology. Sensitivity analysis was also done to determine the robustness of the modeling results from the variation of weights of the criteria. © 2016, AIDIC Servizi S.r.l

A stochastic fuzzy multi-criteria decision-making model for optimal selection of clean technologies

Selection of clean technology options requires systematic evaluation based on multiple criteria which are often conflicting. The optimal choice should consider not just technical performance but also the economic, environmental and social aspects of technologies. Furthermore, the interdependencies of these aspects should also be considered. The decision-maker often needs to make explicit trade-offs while ranking the alternatives. In addition, data gaps and imprecise information that are typical when dealing with emerging technologies make conventional methods ineffective. This work thus proposes a Stochastic Fuzzy Analytic Hierarchical Network Process decision model to address the complexity and uncertainty involved in the clean technology selection process. This method first decomposes the problem into a hierarchical network structure, and then derives the probability distribution of the priority weights needed for ranking. The capabilities of the methodology are demonstrated with three case studies, involving the comparison of different carbon nanotube synthesis methods, nutrient removal treatment technology options for municipal wastewater, and low-carbon electricity sources in the Philippines. © 201