AI-Driven Model to Reduce Energy Use in SAG and Ball Mills
Role
Consultant
Budget
US$150,000
Annual Value / Savings
US$1,800,000
Annual Value Multiple
12.0x
Project context:
SAG and ball mill circuits consume significant electrical energy and grinding media. This project focused on optimizing grinding media injection using plant data and AI-driven decision support.
Expanded objectives:
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Develop an AI-based model connecting mill power draw, feed rate, ore hardness, mill load, media charge, particle size and circuit stability.
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Identify the optimal grinding-media injection strategy for different ore and operating conditions.
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Reduce electricity consumption by improving energy transfer efficiency within SAG and ball mill circuits.
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Support control-room decision-making through interpretable recommendations rather than black-box optimization only.
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Quantify savings from lower kWh/t, improved mill stability and reduced media-related operating cost.
Value delivered:
Provided a practical AI framework for comminution energy reduction and grinding-media optimization.