Machine Learning Innovations for Improving Mineral Recovery and Processing: A Comprehensive Review*

Abstract

To overcome the limitations of traditional mineral processing and recovery methods, cutting-edge
technologies, including Machine learning (ML), emerge as a paradigm shift in this sector, offering predictive
insights, data analysis, and real-time monitoring capabilities. The emergence of ML algorithms, such as
Artificial Neural Networks (ANN), Support Vector Machines, and others, trigger this paradigm. This review
explores real-world examples and case studies to unveil the transformative potential of ML in mineral
processing and recovery (exploration, mining, production). This attempt unveils that ML algorithms are
extensively utilized in enhanced ore sorting and classification, predictive modeling, real-time process control
and fault diagnosis, and automated mineral identification. Among these applications, predictive modeling
for process optimization and enhanced ore sorting and classification stand out, with ANN being the most
frequently employed algorithm. While challenges persist, such as limited data availability, non-normally
distributed and non-linear data, and varying data dimensions and rates, the advantages of employing ML
algorithms are undeniable. These advantages include enhanced operational efficiency, waste reduction,
increased recovery rates, real-time monitoring, cost-effectiveness, time efficiency, and reduced energy
consumption. This article aims to catalyze further research and promote the widespread adoption of ML for
more efficient and sustainable mineral processing and recovery practices.

Keywords: Mineral recovery, conventional mineral processing, machine learning, artificial intelligence.