Development and application of a multi-objective-optimization and multi-criteria-based decision support tool for selecting optimal water treatment technologies in India

“Despite considerable efforts to improve water management, India is becoming increasingly water stressed due to multiple factors, including climate change, increasing population, and urbanization. We address one of the most challenging problems in the design of water treatment plants: how to select a suitable technology for a specific scenario or context. The process of decision making first requires the identification of feasible treatment configurations based on various objectives and criteria. In addition, the multiplicity of water quality parameters and design variables adds further complexity to the process. In this study, we propose a novel Decision Support Tool (DST), designed to address and support the above challenges. In this user-friendly tool, both Multi-Criteria Decision Analysis (MCDA) and Multi-Objective Optimization (MOO) methods are employed. The integration of MCDA with MOO facilitates the generation of feasible drinking water treatment solutions, identifies optimal options, and ultimately, improves the process of decision making. This implemented approach has been tested for different contexts, including for different types of raw water sources and system implementation scales. The results show that this tool can enhance the process of decision making, supporting the user (e.g., stakeholders and decision makers) to implement the most suitable water treatment systems, keeping in view the trade-offs.”

(Citation: Sadr, S.M.K., Johns, M.B., Memon, F.A., et al. – Development and application of a multi-objective-optimization and multi-criteria-based decision support tool for selecting optimal water treatment technologies in India – Water 12(2020)10, art. no. 02836 – DOI: 10.3390/w12102836 (Open Access))

(This article belongs to the Section Wastewater Treatment and Reuse)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited