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Technical Paper

Modeling and optimization of wet flue gas desulfurization system based on a hybrid modeling method

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Pages 565-575 | Received 19 Jul 2018, Accepted 12 Nov 2018, Published online: 13 Mar 2019
 

ABSTRACT

Sulfur dioxide (SO2) is one of the main air pollutants from many industries. Most coal-fired power plants in China use wet flue gas desulfurization (WFGD) as the main method for SO2 removal. Presently, the operating of WFGD lacks accurate modeling method to predict outlet concentration, let alone optimization method. As a result, operating parameters and running status of WFGD are adjusted based on the experience of the experts, which brings about the possibility of material waste and excessive emissions. In this paper, a novel WFGD model combining a mathematical model and an artificial neural network (ANN) was developed to forecast SO2 emissions. Operation data from a 1000-MW coal-fired unit was collected and divided into two separated sets for model training and validation. The hybrid model consisting a mechanism model and a 9-input ANN had the best performance on both training and validation sets in terms of RMSE (root mean square error) and MRE (mean relative error) and was chosen as the model used in optimization. A comprehensive cost model of WFGD was also constructed to estimate real-time operation cost. Based on the hybrid WFGD model and cost model, a particle swarm optimization (PSO)-based solver was designed to derive the cost-effective set points under different operation conditions. The optimization results demonstrated that the optimized operating parameters could effectively keep the SO2 emissions within the standard, whereas the SO2 emissions was decreased by 30.79% with less than 2% increase of total operating cost.

Implications: Sulfur dioxide (SO2) is one of the main pollutants generated during coal combustion in power plants, and wet flue gas desulfurization (WFGD) is the main facility for SO2 removal. A hybrid model combining SO2 removal mathematical model with data-driven model achieves more accurate prediction of outlet concentration. Particle swarm optimization with a penalty function efficiently solves the optimization problem of WFGD subject to operation cost under multiple operation conditions. The proposed model and optimization method is able to direct the optimized operation of WFGD with enhanced emission and economic performance.

Additional information

Funding

This work was supported by National Key Research and Development Program of China (no. 2016YFC0209102) and the National Natural Science Foundation of China (U1609212, 51621005)

Notes on contributors

Yishan Guo

Yishan Guo is a Ph.D. candidate at State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, People’s Republic of China.

Zhewei Xu

Zhewei Xu is a graduate student at State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, China.

Chenghang Zheng

Chenghang Zheng is an associate professor at State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, People’s Republic of China.

Jian Shu

Jian Shu is a graduate student at State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, China.

Hong Dong

Hong Dong is an engineer at Zhejiang University Energy Engineering Design and Research Institute Co., Ltd., Hangzhou, People’s Republic of China.

Yongxin Zhang

Yongxin Zhang and Weiguo Weng are senior engineers at State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, China.

Weiguo Weng

Yongxin Zhang and Weiguo Weng are senior engineers at State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, China.

Xiang Gao

Xiang Gao is a professor at State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, People’s Republic of China.

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