Predicting gold accessibility from mineralogical characterization using machine learning algorithms

Abstract

Information about accessibility is of great relevance for gold recovery studies. Obtaining these variables from machine learning models can greatly assist in quickly determining accessibility. Few studies have been published relating the mineralogy of the gold ore process and the application of artificial intelligence, mainly algorithms in predicting variables related to gold recovery and extraction. Accessibility is an important variable for understanding the ability to recover gold from a cyanide solution, which can occur through fractures or some other means that provides access to the solution and consequent leaching of the gold grain. This study aims to present a model capable of predicting the accessibility variable using a data set with 168 characterization results from different ML methods, such as Linear Regression (LR), Random Forest (RF), Sequential Minimum Optimization for Support Vector Machine (SMOreg) and Gaussian Processes (GP). In this context, it was possible to establish that the random forest model performed best by presenting a coefficient of determination R2 (0.77), MAE (11.76), and RMSE (14.48). It was also reported from the SHAP analysis that the Au_grade, exposed_a, and As_grade showed the highest contribution level towards the perdition process of the model.