Aiming at solving the problem that it is difficult to control the SO_2 content at outlet of the desulfurization system in coal-fired power plant stably

a prediction model IPSO-LSTM based on long short-term memory(LSTM)neural network optimized by improved particle swarm optimization(IPSO) is proposed. Firstly

principal component analysis(PCA) is used to calculate the contribution rate of each variable to filter out the auxiliary variables of the model

so as to achieve the dimensionality reduction of the auxiliary variables. Secondly

the improved particle swarm algorithm is used to determine the number of neurons

the learning rate and the number of iterations of the LSTM neural network. Finally

the selected auxiliary variables are used as the input of the IPSOLSTM model to predict the SO_2 content at the outlet. The desulfurization data of a domestic 2×600 MW power plant is used for simulation and the result was compared with 11 related models. The simulation results show that

compared with other conventional models

when the LSTM neural network has two hidden layers

the IPSO-LSTM model has the highest prediction accuracy

and the improved PSO can improve the global optimization capability and convergence speed of the PSO.