Abstract:Copper matte grade is a key process parameter in the oxygen rich bottom blown copper smelting process. Aiming at the difficulties of real-time detection of copper matte grade, the long lag time of detection results, and the delay in guiding the optimization of production process parameters, a copper matte grade prediction model based on FA-PSO-RBF neural network was proposed based on in-depth mining and processing of production data. First, in order to reduce the prediction error of the model, the FA analysis method is used to reduce the dimension of the original production data, determine the number of main factors as 6, and calculate the factor score. Then, in view of the deficiency of the RBF neural network model that relies heavily on key parameters, the improved PSO algorithm is used to optimize the key parameters in the network structure. Finally, the factor score is used as the input, and the copper matte grade value is used as the output, The accuracy of the model is verified by actual production data, and compared with RBF and standard PSO-RBF prediction models. The results show that the prediction accuracy of the copper matte grade prediction model constructed in this paper is higher, and the values of RMSE and MAE are reduced by 17.2% and 21.2% respectively compared with the standard PSO-RBF prediction model. This prediction model provides a method reference for the optimal control of parameters in the oxygen rich bottom blown copper smelting production process.