Sustainable Operation Strategy for Wet Flue Gas Desulfurization at a Coal-Fired Power Plant via an Improved Many-Objective Optimization

Jianfeng Huang, Zhuopeng Zeng, Fenglian Hong, Qianhua Yang, Feng Wu and Shitong Peng ()
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Jianfeng Huang: Department of Mechanical Engineering, Shantou University, Shantou 515063, China
Zhuopeng Zeng: Department of Mechanical Engineering, Shantou University, Shantou 515063, China
Fenglian Hong: Department of Mechanical Engineering, Shantou University, Shantou 515063, China
Qianhua Yang: Department of Mechanical Engineering, Shantou University, Shantou 515063, China
Feng Wu: Datang Chaozhou Power Co., Ltd., Chaozhou 515154, China
Shitong Peng: Department of Mechanical Engineering, Shantou University, Shantou 515063, China

Sustainability, 2024, vol. 16, issue 19, 1-18

Abstract: Coal-fired power plants account for a large share of the power generation market in China. The mainstream method of desulfurization employed in the coal-fired power generation sector now is wet flue gas desulfurization. This process is known to have a high cost and be energy-/materially intensive. Due to the complicated desulfurization mechanism, it is challenging to improve the overall sustainability profile involving energy-, cost-, and resource-relevant objectives via traditional mechanistic models. As such, the present study formulated a data-driven many-objective model for the sustainability of the desulfurization process. We preprocessed the actual operation data collected from the desulfurization tower in a domestic ultra-supercritical coal-fired power plant with a 600 MW unit. The extreme random forest algorithm was adopted to approximate the objective functions as prediction models for four objectives, namely, desulfurization efficiency, unit power consumption, limestone supply, and unit operation cost. Three metrics were utilized to evaluate the performance of prediction. Then, we incorporated differential evolution and non-dominated sorting genetic algorithm-III to optimize the multiple parameters and obtain the Pareto front. The results indicated that the correlation coefficient (R2) values of the prediction models were greater than 0.97. Compared with the original operation condition, the operation under optimized parameters could improve the desulfurization efficiency by 0.25% on average and reduce energy, cost, and slurry consumption significantly. This study would help develop operation strategies to improve the sustainability of coal-fired power plants.

Keywords: many-objective optimization; differential evolution; NSGA-III; parameters prediction; extreme random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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