Insights

Energy is one of the largest operating expenses in mining – from running haul trucks and drills to crushing and grinding rock, the energy consumption per ton of output is substantial. Reducing the energy cost per ton not only saves money but also lowers the carbon footprint. Mines deploying AI for energy management have achieved 10–15% reductions in energy costs per ton by adjusting operations to be more energy-efficient (for example, running certain processes during off-peak power rate hours or tweaking equipment settings for optimal energy usage).

An AI-first solution to cut energy costs per ton focuses on intelligent energy management systems. Smart power scheduling can coordinate the operation of high-power equipment (like mills, hoists, ventilation fans) such that they run during times of lower electricity rates or when renewable energy is available. Machine learning models analyze production schedules, equipment performance, and electricity price trends to suggest an optimal plan that meets production targets at minimum energy cost. AI can also continuously tune equipment settings – for example, regulating conveyor speeds or pump pressures to avoid energy waste when full power isn’t necessary. These strategies can be implemented with the help of Microsoft’s cloud and AI technologies. Azure Machine Learning can forecast energy demand for different production levels and identify when and where energy usage can be lowered without affecting output. Azure IoT Central can then adjust equipment operation parameters in response to these insights, and Power BI can track energy consumption per ton in real time. By making energy efficiency decisions autonomously and proactively, an AI-driven approach enables significant reductions in Energy Cost per Ton

while maintaining high production levels.

Contact us at info@acclero.ai for demos and discussions.