A cost-benefit analysis of transiting to full autonomous surface mining machines in the mining industry

Abstract

The mining industry has continued to evolve and adopt necessary changes due to emerging digital technologies and automation. As a result, such an analysis has yet to be presented in the literature. This research project evaluates the anticipated costs and gains of adopting autonomous surface mining systems. It emphasises the extent of labour costs that may be saved, net production efficiency, and the payback period. This work employs case studies, statistical finance data, and cost curves to investigate the economic and functional parameters of the common, moving, cross-section autonomous mining complex. The results show that Autonomous Mining Systems (Dayo-Olupona et al., 2023) technology would allow efficiency increases and significant labour cost savings of over 60%, as there will be few employees on site, persons waiting for machines will not be idle, and machines will have less unproductive time. The Model-Based Maintenance (MBM) features of AMS significantly improve the working conditions of the machines, ensuring minimum idle time of the equipment while cutting costs by 15 to 20% of the maintenance costs, extending the lifetime of the machines. Integrating autonomous systems enhances production rates, decreasing unit costs to a low of US$20-US$30 at West Angelas and Hope Downs within twenty-four months after installation. This achieves a high level of production that reduces costs even further and makes returns on investment quicker. There are, however, drawbacks to achieving complete implementation of autonomous mining technology, such as timeline, operation capital expenditure, expansion/equipment modification, and sociological worker redundancy. There are difficulties that have been found in the research as constraints, which could be resolved using Integrated and Intelligent Remote Operation Centres (I2ROCs) to enhance interactions between Information Technology (IT) and Operational technology for better decision-making and monitoring of systems. The initiatives are concerned with operationalising distributed control of various sites with automation in the mining industry, explicitly improving work health and safety outcomes. The present research project highlights the important initiatives that mining companies should adopt in recommending the introduction of autonomous systems while trying to contain any negative impact this development would otherwise have on the industry, the economy, and society. Lastly, in the future, more levels of detail should be added to the models that allow coping with such processes as task distribution by Artificial Intelligence (AI), learning, and management of self-operating mining systems based on sensors and automatic control systems for cost management and performance enhancement.