🎓 Lesson 19
D5
Ore Blending Optimization Using Haul Data
Ore blending optimization using haul data means mixing different types of mined material during transport to ensure the processing plant receives a consistent, target-grade feed.
🎯 Learning Objectives
- ✓ Calculate blend ratios required to achieve a target grade given assay values and tonnages of two or more ore sources
- ✓ Design a haul-based blending strategy that respects truck payload limits and cycle time constraints
- ✓ Analyze GPS and payload sensor data to identify deviations from planned blend targets
- ✓ Explain how dilution and ore loss impact blend quality and downstream metallurgical recovery
- ✓ Apply linear programming concepts to formulate a simple constrained blend optimization problem
📖 Why This Matters
In modern open-pit mines, inconsistent feed grade causes costly fluctuations in concentrator throughput, reagent consumption, and final concentrate quality. A 1% deviation in copper grade can reduce annual revenue by millions—yet many mines still rely on manual, reactive blending. Haul data (GPS location, payload weight, assigned shovel, real-time assay tags) provides the highest-resolution, lowest-latency input for proactive blending—turning haulage from a cost center into a precision control system.
📘 Core Principles
Blending relies on three foundational layers: (1) Geometallurgical modeling—linking spatial orebody domains to grade, hardness, and metallurgical response; (2) Operational fidelity—ensuring haul data (truck ID, start/end time, payload mass, GPS coordinates, assigned blast/face) is time-synchronized and validated against shovel dispatch and lab assay databases; and (3) Control logic—using weighted averaging, moving-window statistics, or model-predictive control to compute real-time blend deviations and trigger corrective actions (e.g., rerouting trucks, adjusting shovel assignment). Advanced implementations integrate digital twins that simulate blending outcomes under varying fleet availability and grade uncertainty.
📐 Weighted Average Blend Grade
The fundamental calculation for verifying whether a blended stream meets target grade. Used continuously in haul dispatch systems and stockpile management dashboards.
Weighted Average Grade
G_blend = Σ (m_i × G_i) / Σ m_iCalculates the grade of a mixture composed of multiple ore streams, weighted by their respective masses.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| G_blend | Blended grade | % | Target or resulting grade of the combined material stream |
| m_i | Mass of ore stream i | tonnes (t) | Net payload mass of material hauled from source i |
| G_i | Grade of ore stream i | % | Assay-determined grade (e.g., Cu%, Fe%, Au g/t) of source i |
Typical Ranges:
Copper porphyry: 0.3–1.2% Cu
Iron ore (hematite): 58–65% Fe
Gold oxide heap leach: 0.8–2.5 g/t Au
💡 Worked Example
Problem: Truck T-104 hauls 125 t from Zone A (assay: 0.82% Cu); Truck T-105 hauls 118 t from Zone B (assay: 1.45% Cu); Truck T-106 hauls 132 t from Zone C (assay: 0.61% Cu). Target blend grade = 0.90% Cu. Does this 3-truck batch meet target?
1.
Step 1: Compute total blended tonnage = 125 + 118 + 132 = 375 t
2.
Step 2: Compute weighted sum = (125 × 0.82) + (118 × 1.45) + (132 × 0.61) = 102.5 + 171.1 + 80.52 = 354.12 %·t
3.
Step 3: Compute weighted average grade = 354.12 / 375 = 0.9443% Cu
4.
Step 4: Compare to target: 0.9443% > 0.90% → batch is 4.9% above target; may require dilution with low-grade material in next cycle
Answer:
The result is 0.944% Cu, which exceeds the target of 0.90% Cu by 0.044 percentage points (4.9% relative error). This deviation falls outside typical allowable tolerance of ±0.03% Cu for primary crusher feed.
🏗️ Real-World Application
At Rio Tinto’s Yandi iron ore operation (Pilbara, WA), haul trucks equipped with RTK-GPS and integrated load cells transmit payload and origin-face ID every 15 seconds to the Mine Production System (MPS). Assay data from blast-hole samples is geo-referenced and interpolated to faces using conditional simulation. MPS calculates real-time blend grade per stockpile bin using 2-hour rolling windows and automatically redirects trucks exceeding grade tolerance (>±0.1% Fe) to alternate stockpiles—reducing crusher feed grade standard deviation by 37% and improving downstream sinter consistency.
📋 Case Connection
📋 Canadian Gold Mine: Steep Ramp Optimization in Narrow Vein Underground
Excessive truck cycle times and premature tire/brake wear due to suboptimal ramp gradient (15%) combined with tight hori...
📋 South African Platinum Mine: Waste Dump Reclaim Optimization
Inefficient haulage routing and underutilized fleet capacity during waste dump reclamation, resulting in excessive diese...