Data-driven multi-objective optimization of coal-fired power plant desulfurization systems
In this paper,data from the wet flue gas desulfurization system in a coal-fired power plant is used to compare the results show that this prediction model is more precise than static modeling and models that do not use first order differential prediction method.The predictive performances of five models,including random forest,extreme gradient boosting,support vector regression,deep neural network,and long short-term memory neural network.The results show that the long short-term memory neural network performs better than the other four models.Based on this model and multi-objective particle swarm optimization algorithm,multi-objective optimization of desulfurization system parameters is carried out,and the optimization results verify the effectiveness of the proposed data-driven desulfurization system multi-objective optimization model.
万方数据
维普
