Modeling and Optimization of Wet Flue Gas Desulfurization System based on a Hybrid Modeling Method.

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 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 was developed to forecast SO2 emissions. Operation data from a 1000MW 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-inut 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, while the SO2 emissions was decreased by 30.79% with less than 2% increase of total operating cost.