## 1 Introduction
[PERSON] et al. [14] studied the column-leaching process of copper oxide ore. A BP neural network model with the optimal network structure of 4-15-10-1 was designed to predict the copper recovery rate. GA was utilized to optimize the network connection weights and thresholds. The results show that the mean square error decreased from 66.67 to 0.02. [PERSON], [PERSON] et al. [15] proposed a power supply optimization strategy based on the improved BP neural network and compared PSO-BP (Particle Swarm Optimization-Backpropagation) neural network with GA-BP neural network. The results indicated the prediction error of the power supply strategy of GA-optimizing BP (GA-BP) neural network is the minimum. [PERSON] et al. [16,17] established a BOF end-point temperature and carbon content prediction model and a BOF end-point P and O content prediction model based on principal component analysis (PCA)--genetic algorithm (GA)--backpropagation (BP) neural network. The root mean square temperature error between predicted and actual values is 7.89, carbon content is 0.0030, P content is 0.0015%, and O content is 0.0049%. [PERSON] et al. [18] used a real-coded genetic algorithm to optimize the BP neural network to predict the phase transition temperature during continuous steel cooling. The network's input layer included seven influencing factors, e.g., the chemical composition of steel, cooling rate, and austenitizing temperature; the output layer outputs six temperature values, e.g., ferrite start transition temperature and end temperature, pearlite start transition temperature and end temperature. The experimental results show that the optimized prediction results are closer to the actual values. [PERSON] et al. [19,20] and [PERSON] [21] established a copper flash smelting neural network model to predict matte grade, optimized the weight of the matte-grade neural network model by GA, and then simulated and optimized the input layer process parameters by GA. The optimization results are better than the single BP neural network model, but this model only considers the energy costs of the flash smelting process. [PERSON] et al. [22] proposed a neural network algorithm based on GA-Elman-Regularization. Regularization is introduced to improve the generalization ability of the network and to enhance the network accuracy. In comparison of experimental results of motion trajectory prediction by the Elman neural network, GA-Elman neural network, and GA-Elman-Regularization neural network on the semiphysical dataset, the average error of prediction is 1.37%, 0.82%, and 0.556%. The experiments show that the optimized algorithm improves the ability of network generalization and the accuracy of prediction. In this paper, a matte-grade optimization control model is established to solve the optimal matte grade, with the objective of minimizing the cost in the whole process of copper smelting. Meanwhile, based on the global optimization ability of GA and the strong generalization ability of the BP neural network, a GA-BP neural network matte-grade prediction model is established to predict the matte grade of ISA smelting process parameters in real time. Then, the prediction result is compared with the optimal matte grade to regulate and control each furnace to make the production operate economically. In this model, the BP neural network prediction model is trained to obtain weights and thresholds, and GA is used to find the optimal weights and thresholds. The prediction results of a single BP neural network and GA-BP neural network are compared with the expected results to verify the effectiveness of the prediction model.