Model predictive control of coal‐fired power plant desulphurization system based on <scp>GJO</scp> ‐ <scp>TCN</scp> ‐ <scp>BiGRU</scp> ‐attention

Abstract The wet flue gas desulphurization (WFGD) system is characterized by nonlinearity, strong coupling, time‐varying, and input–output delays, which make it difficult for traditional control methods to accurately control the WFGD system. In this paper, a combination of neural network (NN) predictive model and model predictive control (MPC) is proposed to be used for the optimal control of the WFGD system. Firstly, the prediction model of the WFGD system is established by temporal convolutional network (TCN)‐bidirectional gated recurrent unit (BiGRU)‐attention. Secondly, the hyperparameters of the prediction model are optimized using the golden jackal optimization (GJO). Then, the WFGD system is controlled by MPC. Meanwhile, in this paper, the original dataset of the WFGD system is screened to remove outliers using a local outlier factor (LOF) detection algorithm. Finally, based on the Matlab R2023a platform, this method is compared with the PID algorithm, and the actual effect is improved by about 28%. The experimental results show that the GJO‐TCN‐BiGRU‐attention model can predict the desulphurization system well, and the control effect of the MPC method on the desulphurization system is better than PID algorithm.