Abstract
Exploring Key Parameters for Electric Haulage Feasibility in Open-Pit Mining
Batur Tokac, Qian Zhang, Yuksel Asli Sari
The Robert M. Buchan Department of Mining, Smith Engineering, Queen’s University, Kingston, ON, K7L 3N6, Canada
The mining activities generate an estimated 4% to 7% of global greenhouse gas (GHG) emissions, positioning the industry under pressure to meet net-zero carbon objectives by the year 2050 or sooner. One of the key strategies for meeting these objectives is transitioning from diesel-powered to battery-driven haulage equipment across all stages of haulage operations. This transition can eliminate Scope 1 emissions and reduce operational costs. However, field tests for emerging electric prototypes remain time-intensive, costly, and limited in scope, leaving critical knowledge gaps regarding the design, performance, and feasibility of large-scale electrification in production operations. Consequently, there is a need for reliable, scalable approaches to guide mining companies and equipment manufacturers through the complex transition to electric haulage systems.
To address these challenges, a Discrete Event Simulation (DES) model was developed to replicate haulage operations in open-pit mining. The simulation considers three main types of parameters. First, it includes external factors like weather conditions impacting battery performance and energy use. Second, it considers mine design factors, gradient and distance, which can improve regenerative braking savings. Lastly, it focuses on equipment specifications, like battery size and charging time, which affect vehicle weight and cycle time. The model goes beyond basic energy consumption estimates by providing actionable insights into how electrification impacts mining operations across diverse conditions and scenarios. It enables mining companies to predict equipment performance, evaluate operational trade-offs, and optimize system designs. Combining the DES model with genetic algorithms enables the exploration of trade-offs among metrics like energy consumption and production levels, offering an optimized framework for electric haulage adoption.
The developed methodology focuses on understanding how variations in key parameters affect system performance and energy use. An integrated approach identifies high-impact factors and quantifies benefits, providing a faster, cost-effective alternative to large-scale field tests. Calibrated with field test data from smaller electric equipment, this framework offers practical solutions for mining companies to optimize electrification, reduce emissions, and transition to sustainable operations.