A smart Sustainable decision Support system for water management oF power plants in water stress regions

被引:7
|
作者
Nakhaei, Mahdi [1 ]
Ahmadi, Amirhossein [2 ]
Gheibi, Mohammad [3 ]
Chahkandi, Benyamin [4 ]
Hajiaghaei-Keshteli, Mostafa [2 ]
Behzadian, Kourosh [5 ]
机构
[1] Univ Tehran, Coll Engn, Dept Environm Engn, Tehran, Iran
[2] Tecnol Monterrey, Escuela Ingn & Ciencias, Puebla, Mexico
[3] Assoc Talent Liberty Technol TULTECH, Tallinn, Estonia
[4] Univ Tehran, Sch Civil Engn, Tehran, Iran
[5] Univ West London, Sch Comp & Engn, London, England
关键词
Sustainability; Power Plant; Artificial Intelligent; Decision Making; Water Consumption; GENERATION; OPTIMIZATION; NEXUS; FRAMEWORK; POLICY; UNITS;
D O I
10.1016/j.eswa.2023.120752
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页数:23
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