Review of the current state of machine learning technologies used in mining and mineral processing

The aim of this article is to review modern machine learning (ML) methods applied to the extraction and processing of solid minerals, including coal.The growing significance of ML technologies is emphasized, driven by their integration with computer vision and image analysis systems, which are widely used in coal mining in China and the EU.It is demonstrated that such systems exhibit high efficiency in solving tasks previously beyond the reach of traditional approaches in the mining industry.However, it should be noted that despite the active adoption of machine and deep learning technologies by mining companies, their development is primarily carried out by thirdparty organizations.Machine learning offers substantial opportunities for increasing productivity and optimizing mineral extraction processes.Yet, access to data on geological characteristics of deposits remains limited, hindering further advancement and implementation of these technologies.The authors also highlight the need for in-depth research into the potential of ML in automating production operations, improving occupational safety, and reducing environmental impact.Despite existing challenges, the prospects for machine learning technologies remain significant, especially given the growing volumes of data and advancements in algorithmic solutions for information processing.