🎓 Lesson 7
D5
Advanced Techniques and Optimization
Freight cost optimization is about finding the most efficient way to move mined materials from the pit to processing or shipping points—spending the least money while meeting production and safety goals.
🎯 Learning Objectives
- ✓ Calculate total freight cost per tonne-kilometer using integrated blast-haulage parameters
- ✓ Design an optimized truck-shovel matching strategy based on fragmentation distribution and payload capacity
- ✓ Analyze how blast-induced muck pile geometry affects haul truck cycle time and fuel efficiency
- ✓ Explain the impact of powder factor and burden-spacing ratios on downstream freight economics
- ✓ Apply linear programming techniques to allocate haulage resources across multiple destinations under tonnage and time constraints
📖 Why This Matters
In open-pit mines, haulage accounts for 40–60% of total mining OPEX. A poorly fragmented blast creates oversized boulders that force trucks to operate at suboptimal payloads, increase tire wear by up to 35%, and raise fuel consumption by 12–18%. Conversely, over-fragmentation increases loading time and dust control costs. Freight cost optimization bridges blasting and logistics—it ensures every kilogram blasted translates into predictable, low-cost movement. Ignoring this link leads to $1M–$5M/year avoidable losses in mid-sized operations.
📘 Core Principles
Freight cost optimization rests on three interdependent pillars: (1) Blast-driven muck characteristics—fragmentation distribution (P80), muck pile shape (swell factor, throw distance), and floor condition directly govern shovel productivity and truck fill consistency; (2) Haulage system dynamics—truck cycle time depends on payload utilization, road gradient, rolling resistance, and queueing at loading points; (3) Cost structure decomposition—freight cost = (truck OPEX + fuel + maintenance + labor + tire amortization) / (tonnes × km). Critical insight: blast design is not just about rock breakage—it’s the first step in supply chain cost engineering. Optimization occurs at three levels: tactical (shift-level fleet assignment), operational (blast-to-haul scheduling), and strategic (fleet procurement aligned with expected P80 trends).
📐 Integrated Freight Cost per Tonne-Kilometer
This formula synthesizes blast output and haulage performance to quantify true transport efficiency. It enables comparison across blasts and identifies whether cost overruns stem from poor fragmentation or inefficient routing.
Total Freight Cost per Tonne-Kilometer (Cₜₖ)
Cₜₖ = [ (OPEXₜᵣᵤcₖ / Pₜ) + (Fuelₜ + Tireₜ) ] / DₜₒₜₐₗCalculates unit transport cost integrating equipment, energy, and consumables across the full haul cycle.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| OPEXₜᵣᵤcₖ | Truck hourly operating cost | $/hr | Includes labor, maintenance, insurance, and depreciation (excluding fuel and tires) |
| Pₜ | Truck productivity | t/hr | Actual average tonnes hauled per hour (accounts for fragmentation-limited payload) |
| Fuelₜ | Fuel cost per tonne | $/t | Loaded + empty leg fuel cost, derived from consumption rates and distance |
| Tireₜ | Tire amortization cost per tonne | $/t | Tire cost per tonne-kilometer multiplied by round-trip distance |
| Dₜₒₜₐₗ | Round-trip haul distance | km | Loaded distance + empty distance |
Typical Ranges:
Large-scale iron ore (dry, flat terrain): $0.45 – $0.65/t-km
Copper porphyry (moderate grade, 3–5 km haul): $0.75 – $1.20/t-km
Coal (long-distance, rail-integrated): $0.30 – $0.55/t-km
💡 Worked Example
Problem: A copper mine conducts a 12-m bench blast. Post-blast survey shows P80 = 420 mm, swell factor = 1.32, and average truck payload = 215 t (92% of rated 234 t capacity). Fleet consists of 220 t rigid-frame trucks (OPEX = $185/hr, avg. speed = 32 km/h loaded, 41 km/h empty). Round-trip haul distance = 4.8 km (2.4 km loaded, 2.4 km empty); cycle time = 14.7 min. Fuel cost = $1.32/L; consumption = 68 L/100 km loaded, 42 L/100 km empty. Tire amortization = $0.14/t-km. Calculate Cₜₖ.
1.
Step 1: Compute hourly truck productivity = (60 min/hr ÷ 14.7 min/cycle) × 215 t/cycle = 878 t/hr
2.
Step 2: Compute loaded km/t = 2.4 km ÷ 215 t = 0.01116 km/t; empty km/t = 2.4 km ÷ 215 t = 0.01116 km/t (same distance, but fuel/t differs)
3.
Step 3: Fuel cost/t = [(2.4 km × 0.68 L/km) + (2.4 km × 0.42 L/km)] × $1.32/L = (1.632 + 1.008) × $1.32 = $3.51/t
4.
Step 4: Truck OPEX/t = $185/hr ÷ 878 t/hr = $0.211/t; tire cost/t = $0.14/t-km × 4.8 km = $0.672/t
5.
Step 5: Sum cost components per tonne: $0.211 (OPEX) + $3.51 (fuel) + $0.672 (tires) = $4.393/t; then divide by total km moved per tonne (4.8 km): Cₜₖ = $4.393/t ÷ 4.8 km = $0.915/t-km
Answer:
The result is $0.915/t-km, which falls within the safe range of $0.75–$1.20/t-km for large-scale copper porphyry operations (per SME 2022 Benchmarking Report).
🏗️ Real-World Application
At BHP’s Escondida Mine (Chile), integration of blast fragmentation modeling (using DFN-based Swebrec) with haul truck telematics reduced average freight cost by 9.3% over 18 months. Engineers correlated P80 > 450 mm with 11% payload loss and 23% higher tire replacement frequency. By adjusting burden from 5.2 m to 4.7 m and reducing powder factor from 0.52 to 0.47 kg/t, they achieved P80 = 385 mm—increasing average payload utilization from 89% to 95.4%. This translated to a $2.1M annual saving in fuel and tires alone, validated via Fleet Management System (FMS) cycle-time histograms and Weibull-distributed fragmentation validation.
🔧 Interactive Calculator
🔧 Open Freight Cost Optimization Calculator📋 Case Connection
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📋 Small-Scale Freight Cost Optimization Implementation
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