Error Correction of Support Vector Regression Model for Copper-Matte Converting Process

To improve the performance of copper-matte Peirce-Smith Converting (PSC), the influence of local process data to epsilon-support vector regression (SVR) model for converting process is studied. This paper proposes an Error Correction method for epsilon-Support Vector Regression (EC_SVR), in which the influence of local support vector to prediction results is considered. Two EC_SVR models for slag weight and blowing time of S1 period (that is, the first slag producing period of PSC) are developed by the real production data. Simulation results show that EC_SVR model can significantly improve prediction accuracy and generalization of the converting decision variable in S1 period.