Applied Machine Learning for Geometallurgical Throughput Prediction—A Case Study Using Production Data at the Tropicana Gold Mining Complex

Roussos Dimitrakopoulos,Christian Both

doi:10.3390/min11111257

Abstract

With the increased use of digital technologies in the mining industry, the amount of centrally stored production data is continuously growing. However, datasets in mines and processing plants are not fully utilized to build links between extracted materials and metallurgical plant performances. This article shows a case study at the Tropicana Gold mining complex that utilizes penetration rates from blasthole drilling and measurements of the comminution circuit to construct a data-driven, geometallurgical throughput prediction model of the ball mill. Several improvements over a previous publication are shown. First, the recorded power draw, feed particle and product particle size are newly considered. Second, a machine learning model in the form of a neural network is used and compared to a linear model. The article also shows that hardness proportions perform 6.3% better than averages of penetration rates for throughput prediction, underlining the importance of compositional approaches for non-additive geometallurgical variables. When adding ball mill power and product particle size, the prediction error (RMSE) decreases by another 10.6%. This result can only be achieved with the neural network, whereas the linear regression shows improvements of 4.2%. Finally, it is discussed how the throughput prediction model can be integrated into production scheduling.

Highlights

In recent years, the amount of collected and centrally stored production data in the mining industry has increased massively with the implementation of digital technologies
Value is only added to the operation when the gained geometallurgical knowledge is integrated into decision-making processes, whereas appropriate methods are still mostly lacking for the tactical or short-term production planning horizon [7]
The present article shows a case study at the Tropicana Gold mining complex that demonstrates how production data combined with machine learning can be used to construct a data-driven geometallurgical throughput prediction model and how such a model can subsequently be utilized for short-term mine production scheduling

Summary

Introduction

The amount of collected and centrally stored production data in the mining industry has increased massively with the implementation of digital technologies. It is clearly visible how ROP reflects the heterogeneity of the rock and decreases with depth. Material tracking includes all dumping and rehandling activities at run-of-mine (ROM) stockpiles, since rehandled material accounts for 80–90% of processed ore in the Tropicana Gold mining complex In this way, ROP entries recorded in the pits can be successfully linked to observed measurements in the processing plant, including the observed throughput of the ball mill.

Application of Supervised Machine Learning for Throughput Prediction

Neural Networks

Dataset and Statistical Analysis

Network Architecture and Hyperparameter Search

Results and Analysis

Discussion