Application of Machine Learning in a Mineral Leaching Process─Taking Pyrolusite Leaching as an Example

Abstract

In this study, several machine learning models were used to analyze the process variables of electric-field-enhanced pyrolusite leaching and predict the leaching rate of manganese, and the applicability of those models in the leaching process of hydrometallurgy was compared. It showed that there was no correlation between the six leaching conditions; in addition to the leaching time, the concentrations of sulfuric acid and ferrous sulfate had great influences on the leaching of pyrolusite. The results of the prediction models showed that the support vector regression model has the best prediction performance, with regression index (R 2) = 0.92 and mean square error = 25.04, followed by the gradient boosting regression model (R 2 > 0.85). In this research, machine learning models were applied to the optimization of the manganese leaching process, and the research process and methods were also applicable to other hydrometallurgical processes for majorization and result prediction.

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

“…The application of ML algorithms for leaching other than nitrate is also compared. Zhang et al (2022) used ML algorithms in hydrometallurgy and found the best-fit model (SVM) has an RMSE value of 5.004, respectively 136 . The ridgeline plot comparing the result of this study with the past studies has been presented in Fig.…”