Improvement in Safety and Productivity in Underground Mining Operations: A Review of the Role and Efficiency of Autonomous Mining Technologies

Underground mining is a complex mining approach with less flexibility as compared to surface mining. It deals with challenges such as water control, ventilation, safety issues, cost of operations and others. Conventional underground mining exposes miners to hazards such as unstable rock structures, machinery-related accidents, and noxious gases. The advent of Artificial Intelligence (AI), machine learning, and process automation has the tendency to improve efficiency, reduce cost, ensure sustainability in mining operations. Over the years, mining companies have adopted various forms of technology in the form of robots, ventilation sensors, remote sensing, automated drills and haulage. There have been delays and setbacks in the adoption of such technological processes due to their cost implications and the fear of minimum return on investments, workers adapting to new trends and fear of job loss, the complexity of working underground, delays in operations that may arise during the period of integration which can affect productivity among others. This paper discusses the role of modern technology and processes, specifically autonomous mining technologies and their efficiency in improving safety and productivity in underground Mining operations. The paper focuses on reviewing current trends and case studies of adaptations of autonomous machinery and processes to evaluate the impact on mining operations in relation to safety and productivity. It also considers the limitations and challenges related to the process of automating underground mining and some of the social issues that may arise.