Nakhaei, Mahdi, Ahmadi, Amirhossein, Gheibi, Mohammad, Chahkandi, Benyamin, Hajiaghaei-Keshteli, Mostafa and Behzadian, Kourosh ORCID: https://orcid.org/0000-0002-1459-8408 (2023) A Smart Sustainable Decision Support System For Water Management Of Power Plants In Water Stress Regions. Expert Systems with Applications, 230. p. 120752.

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Abstract

Power Plants (PPs) is considered as critical facilities in each region because of essential role through energy generation processes. These facilities are also depended to water availability especially in water stress areas. Due to the critical water shortage in many areas around the world, it is necessary to make an optimal condition among water consumption and the increasing demand for electricity to prevent any further conflict of interests between industry, householders and the environmental goals. There are different techniques for controlling Water Consumption (WC) in these industries. This paper develops a smart Decision Support System (DSS) for monitoring, prediction and control sections based on Artificial Intelligent (AI) and integration of the PESTEL matrix and Multi Criteria Decision Making (MCDM) methods. Monitoring section comprises Fuel Consumption (FC), Atmospheric Temperature (AT), Power Plant Temperature (PPT) and Power Plant Efficiency (PPE), in which FC has the most influence on WC based on ANOVA evaluations in both cold and warm seasons. The prediction results have illustrated that Adaptive Neuro Fuzzy Inference System model is more efficient for the WC estimation with a correlation coefficient over 0.99. Ordered Weighted Averaging (OWA) also demonstrated that in the optimistic and pessimistic states, the most priority is linked to E3 (Establishment of evaporation control systems by contractor companies and concluding a guaranteed purchase contract with a power plant worth one and a half times the current amount of water price). In the last step of technical approaches, the smart controlling system is added for execution of water-energy nexus in the PP based on proportional–integral–derivative controller system. Finally, the performance of the DSS is approved with more than 80% agreement of experts and more than 90% precision in prediction procedure through this investigation. Application of this DSS can also be helpful for developing countries to achieve the UN Sustainable Development Goals.

Item Type:	Article
Identifier:	10.1016/j.eswa.2023.120752
Depositing User:	Kourosh Behzadian
Date Deposited:	05 Jun 2023 19:59
Last Modified:	28 Sep 2024 08:59
URI:	https://repository.uwl.ac.uk/id/eprint/10035

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