Hybrid modeling for the prediction of leaching rate in leaching process based on negative correlation learning bagging ensemble algorithm

For predicting the leaching rate in hydrometallurgical process, it is very necessary to use an accurate mathematical model in leaching process. In this paper, a mechanism model is proposed for description and analysis of heat-stirring-acid leaching process. Due to some modeling errors existed between mechanism model and actual system, a hybrid model composed of mechanism model and error compensation model is established. A new support vector regression (SVR) bagging ensemble algorithm based on negative correlation learning (NCL) is investigated for solving the problem of error compensation. The sample of the next component learner is rebuilt continuously with this algorithm to improve the ensemble errors, and the optimum ensemble result also can be obtained. Simulation results indicate that the proposed hybrid model with the new algorithm has a better prediction performance in leaching process than other models.

Research highlights
► Mathematical mechanism model of the leaching process.
► Bagging ensemble of support vector regression based on negative correlation learning.
► Hybrid model based on negative SVR bagging.