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

• Published in: Journal of the Air & Waste Management Association (1995) Vol. 69; no. 5; pp. 565 - 575
• Main Authors: Guo, Yishan, Xu, Zhewei, Zheng, Chenghang, Shu, Jian, Dong, Hong, Zhang, Yongxin, Weng, Weiguo, Gao, Xiang
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 04.05.2019; Taylor & Francis Ltd
• Subjects: Air Pollutants - analysis; Air pollution; Air pollution control; Artificial neural networks; China; Coal; Coal-fired power plants; Construction costs; Desulfurization; Desulfurizing; Electric power generation; Emissions; Flue gas; Flue gas desulfurization; Hybrid systems; Industrial plant emissions; Industrial plants; Mathematical models; Models, Theoretical; Neural networks; Operating costs; Parameters; Particle swarm optimization; Penalty function; Pollutants; Pollution control equipment; Power Plants; Real time operation; Root-mean-square errors; Sulfur; Sulfur dioxide; Sulfur Dioxide - analysis; Training; Waste Management - methods; China
• ISSN: 1096-2247; 2162-2906; 2162-2906
• DOI: 10.1080/10962247.2018.1551252

Sulfur dioxide (SO 2 ) 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 SO 2 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 SO 2 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 SO 2 emissions within the standard, whereas the SO 2 emissions was decreased by 30.79% with less than 2% increase of total operating cost. Implications: Sulfur dioxide (SO 2 ) is one of the main pollutants generated during coal combustion in power plants, and wet flue gas desulfurization (WFGD) is the main facility for SO 2 removal. A hybrid model combining SO 2 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.