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Title: Effects of Payload Variance on Haul Truck Energy Consumption, Greenhouse Gas Emissions, and Cost
Organization

Rio Tinto - The University of Queensland

Period

November 2013 - February 2014

Status

Completed

Est. Budget

USD 0.2M

Est. Business Benefit

USD 2.5M

Indicative Value-to-cost

10.0x

Keywords

Payload variance, diesel energy, CO2-e emissions, haul truck fuel, cost modelling, surface mining

Benefit basis

Estimated value from fuel and CO2-equivalent cost savings enabled by improved payload control and reduced loading variance.

Business Challenges 
Payload-management data from surface mining operations showed that payload variance was significant and affected fuel consumption, greenhouse gas emissions, and haulage cost. Mine operators needed a clear technical basis for understanding how reducing payload deviation could translate into cost and emissions savings.
Expanded Technical Solution
The project investigated the nonlinear relationship between payload variance, diesel energy consumption, greenhouse gas emissions, haul-road slope, rolling resistance, and cost. A physics-informed fuel-consumption model was used with emissions and cost calculations to quantify the effect of payload variation under different haul-road conditions. The analysis produced a correlation between payload-variance reduction and cost-saving potential.
Technical Work Packages/Methods
  • Modelled haul-truck energy consumption as a function of payload, gross vehicle weight, speed, rolling resistance, grade resistance, and haul-road conditions.
  • Calculated diesel-related greenhouse gas emissions using CO2-equivalent conversion logic and costed emissions and fuel use.
  • Performed scenario analysis for different payload standard deviations, road slopes, and friction/rolling-resistance conditions.
  • Developed a practical savings correlation independent of specific haul-road conditions for payload-variance reduction scenarios.
  • Applied the method to an Australian surface mine case study.
Key Deliverables
  • Technical model linking payload variance to fuel consumption and emissions.
  • Scenario-analysis results for different haul-road and loading conditions.
  • Cost-saving correlation for payload standard-deviation reduction.
  • Case-study analysis for an Australian surface mining operation.
Results and Value Created
The analysis showed that haul-truck fuel consumption, emissions, and associated cost increase nonlinearly as payload variance rises. The completed case study indicated that up to approximately 10% of the cost associated with fuel and CO2-equivalent emissions could be saved by reducing payload standard deviation from high variance toward zero variance. The estimated benefit reflects fuel savings, emissions cost reduction, and improved loading-control practices.
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