基于PSO-SVM的脱硫系统浆液循环泵优化系统应用研究OA

The energy consumption of the slurry circulation pump in the limestone-gypsum wet flue gas desulfurization system of coal-fired power plants in China is relatively high.Taking a certain 660 MW unit as the research object,based on historical operation data,the key factors affecting the power consumption of the slurry circulation pump are determined through grey relational degree analysis,and the target operating condition library is constructed by using K-means clustering analysis.And the particle swarm optimization-support vector machine(PSO-SVM)classification prediction model is adopted for operation optimization.The results show that after optimization,the unit power consumption of the slurry circulation pump is reduced by more than 15%,saving 491 000 yuan in electricity fees annually and 766 600 yuan in limestone costs annually.This effectively reduces the plant power consumption rate of the desulfurization system,improves the economic and safety operation,and provides a reference for the optimization of desulfurization systems in units of the same level.