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Beyond productivity – Mining Haulage

Beyond Productivity
The digital twin that co-optimizes energy and production in high-altitude CAEX haulage — and why the system dispatching your fleet is optimizing the wrong thing

In Chilean open-pit copper mining, loading and haulage consume close to 75% of the energy of the extraction process — and the decision that moves that consumption most, each truck’s speed profile on every cycle, is made by a system that does not look at energy. FMS and AHS optimize cycle time and throughput; energy lives in another lane. That silo produces a local optimum with a hidden cost, and it gets paid where physics is least forgiving: on the ramps, where energy is non-linear in payload, grade and environment. This deep dive presents a closed-loop operational digital twin that puts energy inside the decision in real time: in calibrated physics simulation, it delivered 6.4% less energy per tonne while conceding just 1.8% of cycle time — and cut cycle variability and drivetrain thermal-stress events along the way. With its limits in plain sight: it is not yet field-validated, and the paper says so without spin

What you’ll learn
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Why conventional dispatch over-prioritizes throughput exactly where the energy penalty spikes — and the difference in kind between the digital twin that displays and the one that decides.

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The simulated evidence, metric by metric (energy, cycle time, variability, thermal stress), and where the gain yields on purpose to protect the asset

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The context that makes it urgent: trolley assist operating at Collahuasi, Chile’s Law 21.305 and CMF Rule 461, Antofagasta Minerals’ 2035 target — and what to demand before signing a “fleet optimization” project, including the friction that almost never appears in the proposal: the vendor’s dispatch API

Who it is for

Operations, energy and maintenance managers, mine planners and owners of FMS/AHS systems at high-altitude copper operations in LATAM and beyond

Download the full analysis and walk into the next “fleet optimization” meeting with the question that separates a serious optimizer from an expensive dashboard: what does your system do when energy, production and asset health come into conflict?










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