Arena Improves Production and Reduces Waste

Surface coal mining operations face significant energy inefficiencies in truck-and-shovel overburden removal — one of the most fuel-intensive processes in open-pit mining. Without a systematic method to evaluate operational variables, mine managers had no reliable way to identify which interventions would yield the greatest energy savings. At this anonymous coal mine, shovel engine load factors averaging 66.78% and truck fuel consumption of 3.68 gallons per cycle pointed to measurable inefficiency, but the complexity of stochastic processes made it impossible to assess improvement options through observation alone. The lack of a ranked, evidence-based improvement framework meant that energy waste persisted at scale across multiple operational variables.

Rockwell Automation's Arena simulation software was used to build a stochastic digital twin of the truck-and-shovel overburden removal operation. The research team first conducted detailed energy audits of both truck-and-shovel and highwall miner operations, then used chi-squared goodness-of-fit testing to fit theoretical distributions to real-world cycle time and payload data. These distributions were embedded into the Arena model to accurately represent process variability. The validated simulation — benchmarked against actual truck fuel consumption data — was then used to run structured simulation experiments across a range of operational strategies. This approach allowed engineers to systematically evaluate and rank energy-saving interventions before any physical changes were made, eliminating trial-and-error on the mine floor.

The simulation-driven approach produced a ranked list of high-impact energy reduction strategies, enabling the operation to prioritize interventions with the greatest return. The headline outcome was a 49% reduction in energy consumption, representing nearly $3.7 billion in potential savings across the scope of the study. Baseline measurements established clear benchmarks: shovel fuel consumption at 35.36 gallons/hour, overall truck-and-shovel fuel efficiency at 19.09 tons/gallon of diesel. Key outcomes included:

• 49% decrease in energy consumption identified through simulation-based optimization
• ~$3.7 billion in energy cost reduction potential quantified
• Fuel efficiency metrics established for both shovel and truck fleet as ongoing performance baselines
• Operational strategies ranked by impact, giving management a clear implementation roadmap

• Model before you modify: Stochastic simulation allows mines to test dozens of operational scenarios without disrupting production — a critical advantage in high-capital environments.
• Data quality drives model accuracy: Rigorous field audits and statistical distribution fitting (chi-squared testing) were essential to producing a validated, trustworthy digital twin.
• Rank interventions by impact: Not all energy-saving measures are equal; a simulation-based ranking prevents resources from being spent on low-yield changes.
• Establish baselines first: Quantifying current fuel consumption per cycle and per ton is a prerequisite for measuring any future improvement.
• Digital twins are decision tools, not just monitoring tools: The value here was in prospective planning, not real-time control.