Automation in Mining

Autonomous, connected and intelligent mines. The digital mine: autonomous haulage and drilling, teleremote operation from control centres, IoT sensors, networks and digital twins, mine planning software and data workflows, and AI for ore sorting, prediction and optimisation — with working Python examples.

• Autonomous Haulage & Drilling — Driverless trucks and drills now move billions of tonnes per year. Learn how autonomous haulage systems (AHS) work — GNSS positioning, perception, path planning, fleet control and safety zones — their productivity and safety benefits, operating-hour gains, and implementation challenges.
• Teleremote & Remote Operations — Operators increasingly run machines from control rooms kilometres away. Learn teleremote vs semi-autonomous operation, remote operations centres (ROC), network latency and bandwidth, video requirements, safety systems and the human factors of remote work.
• IoT, Sensors & Digital Twins — Connected mines stream data from thousands of sensors. Learn mine IoT architecture (sensors, edge, networks, cloud), fleet management and dispatch data, condition monitoring and predictive maintenance with remaining-useful-life estimates, and digital twins that mirror the mine in real time — with a Python RUL example.
• Mine Planning Software — Mining runs on specialised software. Learn the software landscape (geological modelling, mine design, optimisation, scheduling, simulation), the data flow from drill holes to schedules, block model sizes and formats, scripting and automation — with a Python grade–tonnage analysis of a block model.
• AI & Ore Sorting — Artificial intelligence and sensors are changing how ore is found, mined and processed. Learn sensor-based ore sorting (XRT, XRF, NIR, laser), bulk sorting, pre-concentration mass balances, machine-learning applications (grade prediction, image-based fragmentation, predictive maintenance, prospectivity), classification metrics, and risks of AI — with a Python sorting simulation.