Wastewater Treatment and Multi-Criteria Decision-Making Methods: A Review

• Published in: IEEE access Vol. 11; pp. 143704 - 143720
• Main Authors: Sharma, Tina, Kumar, Anuj, Pant, Sangeeta, Kotecha, Ketan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activated carbon; Adsorbents; AHP; Artificial neural networks; Contaminants; Decision support systems; Literature reviews; MCDM; Multi-criteria decision-making; Multiple criteria decision making; Multiple criterion; Nanobioscience; Nanomaterials; Pollutants; Sanitation; Surface treatment; Sustainable development; TOPSIS; Wastewater; Wastewater treatment; Water consumption; Water pollution; Water treatment; Zinc oxide
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3343150

Incorporating numerous technical, economic, environmental, and social benchmarks makes multi-criteria decision-making (MCDM) a reliable decision-making tool in sustainable development. To achieve sustainability target six, related to clean water and sanitation, wastewater technology comes into play. They bring complicated scenarios with several indicators, criteria, and conflicting objectives. This article provides a thorough review of MCDM methods, including pure techniques such as AHP, TOPSIS, VIKOR, ELECTRE, and others, as well as hybrid MCDM approaches like the non-structural fuzzy decision support system (NSFDSS) in conjunction with Artificial Neural Network (ANN), applied to wastewater treatment technologies or processes to remove water contamination. This review draws upon articles sourced from the "Scopus" and "Web of Science (WoS)" databases, covering the period from 2015 to 2023. The primary aim of this endeavor is to offer a current and comprehensive review of the literature about MCDM to aid researchers and environmental engineers in their quest to identify the most appropriate techniques for tackling the complex challenges associated with wastewater treatment (WWT). The results demonstrate the versatile application of MCDM in various scenarios to remove water contaminants effectively. For example, these applications encompass assessing the efficacy of adsorbents for the removal of pollutants and effluents from wastewater, determining the optimal WWT technology from a range of alternatives, evaluating the performance of both metallic (such as ZnO and MnO) and non-metallic (like activated carbon) nanomaterial adsorbents, and scrutinizing strategies to minimize water consumption. Furthermore, within the context of specific WWT technologies, MCDM identifies and highlights critical criteria that are instrumental in enhancing system efficiency for removing pollutants. Ultimately, MCDM methods represent a contemporary decision support system. In future research, the potential utilization of MCDM can extend to tracking the environmental impacts of nanomaterial adsorbents released from treatment plants into the surrounding ecosystem.