Copper Matte Grade Prediction Based on APSO-Stacking Integrated Model
LI Linbo1,*, LIU Ziyang1, ZHANG Yexin2,*, YANG Jianjun1, DUAN Zhongxing3, WANG Zhaofeng1
1 School of Metallurgical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China 2 SDIC Jincheng Metallurgical Co., Ltd., Lingbao 472500, Henan, China 3 School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Abstract: Copper matte is the main product and key raw material of matte smelting and crude copper production. Accurate prediction of copper matte grade is crucial to the optimization of smelting process. Aiming at the problem of poor prediction effect of traditional models, an optimized integrated prediction method is proposed. The original process datais preprocessed by box plot and Lagrange interpolation method, and the data set is constructed by combining grey correlation analysis (GRA) and Pearson correlation coefficient (Pearson) to screen high correlation features. In this work, RF and SVM are used as base learners, and LR is used as meta-learner. Combined with adaptive particle swarm optimization (APSO) and 5-fold cross-validation, a prediction model with high accuracy and anti-overfitting ability is constructed, and the prediction trend is verified by FactSage7.1. The results show that the prediction trend of the model is highly consistent with the theoretical calculation. Compared with the single models, it shows significant advantages in the prediction of copper matte grade. The MSE, MAE, and MAPE are 0.737 1, 0.631 2, and 0.913 6, respectively, which shows higher prediction accuracy and provides reference for copper matte grade control and smelting process optimization.
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2026, Vol. 40 
