📋 Case Study
South African Platinum Mine: Waste Dump Reclaim Optimization
Inefficient haulage routing and underutilized fleet capacity during waste dump reclamation, resulting in excessive diesel consumption (avg. 32 L/t-km), elevated maintenance costs, and inability to meet reclamation schedule due to bottlenecks at dump access ramps and queuing at loading/unloading points.
🏗️ Project Overview
A major platinum group metals (PGM) mine in the Bushveld Igneous Complex, North West Province, South Africa. The operation manages ~120 Mt/year of waste rock, with legacy dumps spanning >400 ha and up to 85 m high. Reclamation involves relocating waste from decommissioned dumps to active disposal areas while supporting concurrent mining expansion.
🎯 Challenge
Inefficient haulage routing and underutilized fleet capacity during waste dump reclamation, resulting in excessive diesel consumption (avg. 32 L/t-km), elevated maintenance costs, and inability to meet reclamation schedule due to bottlenecks at dump access ramps and queuing at loading/unloading points.
🔧 Design Approach
Integrated discrete-event simulation (DES) coupled with GPS-based fleet telemetry analysis and linear programming for dynamic route optimization. Field-validated truck cycle time models were calibrated using 6 weeks of real-time telematics data (n=42 rigid-frame 90-t haul trucks). Optimization prioritized minimizing total ton-kilometers while respecting geotechnical ramp gradients (<12%), traffic density limits (<8 trucks/km), and shift-based equipment availability constraints.
📐 Design Diagram
AI-generated project design illustration
📐 Key Calculations
Optimal Fleet Utilization Ratio
(Actual Loaded Haul Hours / Total Available Haul Hours) × 100
Result: 87.3%
Increased from baseline 64.1%; directly reduced idle time and improved asset ROI without adding trucks
Specific Energy Consumption Reduction
(Baseline kWh/t-km − Optimized kWh/t-km) / Baseline kWh/t-km × 100
Result: 22.6%
Quantified fuel and emissions savings; enabled compliance with South Africa’s Carbon Tax Act (2019) liability thresholds
Cycle Time Variance Reduction
σ²_baseline − σ²_optimized
Result: 142 s²
Lower variance improved predictability of reclamation progress and enabled tighter integration with mine production scheduling
📊 Results
Metrics: Fuel consumption reduced by 21.4% (from 32.1 to 25.2 L/t-km), Average daily reclaimed volume increased by 38% (from 18,200 to 25,100 t/day), Maintenance cost per t-hauled decreased by 17.8%, Reclamation schedule accelerated by 5.3 months
The optimized haulage system achieved full reclamation target 5.3 months ahead of schedule with 22.6% lower specific energy use, validating the scalability of simulation-driven transport optimization for complex legacy waste management in PGM operations.
💡 Lessons Learned
- •Telematics data quality (e.g., GNSS multipath in steep-sided dumps) must be validated pre-simulation—raw GPS timestamps required post-processing correction.
- •Operator buy-in is critical: co-developing dispatch rules with shift supervisors increased adherence to optimized routes from 61% to 94%.
✅ Key Takeaways
- 1Dynamic, constraint-aware haulage optimization delivers measurable sustainability and schedule benefits—even in mature, geometrically constrained waste reclamation—without capital fleet expansion.