A Control Method for Coal Slime Water Thickening Process in Coal Preparation Plants Based on BP- TSFNN

Aiming at the complex mechanism of coal slime water thickening process in coal preparation plant, which has the characteristics of multivariable and strong nonlinearity leading to the difficulty of realizing the intelligent optimization control, a model predictive control method based on back propagation neural network ( BPNN) and Takagi-Sugeno fuzzy neural network (TSFNN) cascade (BP-TSFNN-MPC) is proposed. Further, in order to realize the intelligent optimization control of the thickening process, the BP- TSFNN-MPC controller is constructed, and the gradient descent optimization algorithm is used to solve the optimization problem. In the experimental part, the effectiveness of the model and control method is verified by combining the production data of the slime water concentration process and the simulation model of the thickener. The results show that the BP- TSFNN model has high prediction accuracy for the concentration process and BP-TSFNN-MPC has good control performance.