An integrated Method for Evaluating the Energy-Saving and Emission Reduction of Thermal Power Plants with Interval-Valued Intuitionistic Fuzzy Numbers
Abstract
With the continuous development of the domestic economy, the demand for electricity is also increasing, and the number of power plants is also increasing. Thermal power plants have become the main source of power generation in my country. According to statistics, thermal power generation accounts for more than half of the country's power generation. In the process of thermal power generation, it is also accompanied by a large amount of exhaust gas and pollutant emissions. The emission of sulfur dioxide (sulfur dioxide) from thermal power plants accounts for about half of the total national emissions, and the emissions of greenhouse gases such as carbon dioxide and nitrogen oxides also account for more than 30% of the total national emissions. and clean emissions have become an important task. Before energy conservation and emission reduction of thermal power plants, it is necessary to find out the current situation of thermal power plants and to find out the current energy conservation and emission reduction effects of thermal power plants. row for evaluation. The problems of evaluating the energy-saving and emission reduction of thermal power plants are classical multiple attribute decision making (MADM) problems. In this paper, firstly the definition of interval-valued intuitionistic fuzzy sets (IVIFSs), Heronian mean (HM) operator, Dombi operations are introduced and the interval-valued intuitionistic fuzzy Dombi power Heronian mean (IVIFDPHM) operator is proposed. Some desirable properties of this operator are established, such as commutativity, idempotency and monotonicity. Then, the IVIFDPHM operator is used to deal with the interval-valued intuitionistic fuzzy (IVIF) multiple attribute decision making (IVIF-MADM) problems. Finally, an illustrative example for evaluating the energy-saving and emission reduction of thermal power plants is given to verify the built approach.
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DOI: https://doi.org/10.31449/inf.v46i9.4356
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