Applying Machine Learning Techniques to Decarbonize Mine Haulage and Accelerate the Transition to Zero Emission Mining

This chapter explores the application of machine learning to enhance the energy efficiency of diesel-powered mining haulage fleets, a critical step in meeting aggressive decarbonization targets. The framework presented leverages extensive haul cycle data and predictive models to identify inefficiencies in equipment, operators, road infrastructure, and technological advancements. By targeting these areas, significant fuel savings and emission reductions can be achieved, as demonstrated by real-world case studies. The integration of real-time decision intelligence further optimizes fuel efficiency, supporting both immediate and long-term sustainability goals. This approach not only reduces operational costs but also creates a more favorable environment for the eventual adoption of zero-emission technologies.