A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm

Quande Dong, Cui Wang, Shitong Peng, Ziting Wang and Conghu Liu
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Quande Dong: School of Information Engineering, Suzhou University, Suzhou 234000, China
Cui Wang: Business School, Suzhou University, Suzhou 234000, China
Shitong Peng: College of Engineering, Shantou University, Shantou 515063, China
Ziting Wang: School of Fine Arts and Design, Suzhou University, Suzhou 234000, China
Conghu Liu: School of Information Engineering, Suzhou University, Suzhou 234000, China

Sustainability, 2021, vol. 13, issue 16, 1-17

Abstract: The flue gas desulfurization process in coal-fired power plants is energy and resource-intensive but the eco-efficiency of this process has scarcely been considered. Given the fluctuating unit load and complex desulfurization mechanism, optimizing the desulfurization system based on the traditional mechanistic model poses a great challenge. In this regard, the present study optimized the eco-efficiency from the perspective of operating data analysis. We formulated the issue of eco-efficiency improvement into a many-objective optimization problem. Considering the complexity between the system inputs and outputs and to further reduce the computational cost, we constructed a Kriging model and made a comparison between this model and the response surface methodology based on two accuracy metrics. This surrogate model was then incorporated into the NSGA-III algorithm to obtain the Pareto-optimal front. As this Pareto-optimal front provides multiple alternative operating options, we applied the TOPSIS to select the most appropriate alternative set of operating parameters. This approach was validated using the historical operation data from the desulfurization system at a coal-fired power plant in China with a 600 MW unit. The results indicated that the optimization would cause an improvement in the efficiency of desulfurization and energy efficiency but a slight increase in the consumption of limestone slurry. This study attempted to provide an effective operating strategy to enhance the eco-efficiency performance of desulfurization systems.

Keywords: data-driven modeling; many-objective optimization; NSGA-III; Kriging model; eco-efficiency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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