📋 Case Study

Peruvian Silver Mine: Ventilation-Integrated Haul Route Planning

Traditional haul route planning prioritized shortest distance and gradient, neglecting ventilation airflow distribution—resulting in recirculation zones, elevated CO concentrations (>125 ppm) in lower-level loading zones, and frequent ventilation-on-demand (VOD) system overrides that disrupted truck cycle times by up to 18%.

🏗️ Project Overview

A major underground silver mine in the Andes Mountains of southern Peru, operating at elevations between 4,200–4,600 m above sea level. The mine produces ~3.2 million tonnes of ore annually across three primary extraction levels (1,850 m, 1,800 m, and 1,750 m RL), with a network of 42 km of development and production haulage drifts. Ventilation demand exceeds 320 m³/s due to diesel emissions, heat load, and dust control requirements.

🎯 Challenge

🔧 Design Approach

Integrated multi-objective optimization combining ventilation network analysis (using Ventsim™ Digital) with discrete-event haulage simulation (using Deswik HAUL). Routes were evaluated using a weighted scoring matrix incorporating: (1) normalized travel time, (2) ventilation efficiency factor (ratio of local airflow velocity to design minimum), (3) diesel particulate matter (DPM) accumulation index, and (4) maintenance access feasibility. Pareto-optimal routes were selected via genetic algorithm iteration over 120 scenarios.

📐 Design Diagram

AI-generated project design illustration

📐 Key Calculations

Ventilation-Weighted Haul Distance Index (VHDI)

(L × G × 1000) / (Q_local / Q_design)

Result: 1,942 m·% (unitless index)

Quantifies penalty for routing through under-ventilated zones; values >1,500 indicate unacceptable recirculation risk—used to filter non-compliant routes.

DPM Accumulation Time Constant

V / (Q_local − Q_source)

Result: 124 s

Time required for DPM concentration to reach 63% of steady-state in a 320 m³ loading pocket; validated against field NIOSH 5040 sampling—critical for defining safe dwell time limits.

Effective Cycle Time Adjustment Factor

1 + (t_delay / t_cycle_base)

Result: 1.142

14.2% increase in nominal cycle time due to ventilation-induced waiting; served as baseline for ROI calculation of optimized routing.

📊 Results

Metrics: Cycle time reduced by 11.3% (from 28.4 to 25.2 min), CO exposure reduced from 98 ppm-avg to 22 ppm-avg, Ventilation energy use decreased by 7.6%, Truck availability improved from 82.1% to 91.4%

Integrated ventilation-aware haul route planning eliminated recirculation hotspots, increased fleet productivity by 11.3%, and reduced ventilation-related downtime—achieving payback in 14 months despite $2.1M implementation cost.

💡 Lessons Learned

•Ventilation constraints must be embedded as hard constraints—not post-hoc filters—in haulage optimization algorithms
•High-altitude effects on diesel combustion efficiency necessitate site-specific DPM emission factors, not generic OEM values

✅ Key Takeaways

1Haul route optimization cannot be decoupled from ventilation system performance in deep, high-diesel underground mines.