Are you leaving value on the table in your mining operations?
Inconsistent decision making, dependency on a shrinking talent pool, declining ore grades, and difficulty optimizing economic outcomes, all contribute to avoidable value loss.
Traditional control approaches to advanced control weren’t designed to model complex processes or interactions between large flowsheet areas, which demands new technology for advanced decision automation.
What is AIPC?
AI-Assisted Process Control (AIPC) adds an intelligent, predictive layer on top of your existing control system, acting like an operational co-pilot that works directly with operators to keep the plant ahead of changing conditions.
By continuously learning from your data, it aligns its decisions to your site’s day-to-day operating realities and economic objectives and adapts as those priorities evolve. AIPC helps optimize performance throughout the value chain, not just within individual units.
Because it works with the infrastructure you already have, it’s a low-disruption way to unlock performance you already own. And when you partner with us on implementation and long-term sustainment, your sites see the benefits compounding over time.
At MMG’s Dugald River mine, Hatch deployed a closed-loop AIPC directly within the control layer.
By autonomously optimizing 16 control variables in real time, the solution delivered a 0.5% zinc recovery improvement on a circuit already operating above 97% recovery.
Why choose AIPC?
Adaptive process control for complex, multi‑variable systems
Achieve closed loop automation
Economically aligned, autonomous control that learns and responds to feed changes, while preserving operator oversight.
Build trust through glass-box transparency
Explainable decision logic that reinforces collaboration with operators.
Adapt to changing conditions
Models retrain automatically as ore and plant conditions shift.
Coordinate multi-unit decisions across the plant
Multi-unit optimization coordinates decisions across circuits and sites for broader value capture.
Preserve and apply operational expertise
Institutional knowledge and best practices are embedded directly into the model.
Blend deep process knowledge with extensive learning technology
Dynamic mass balancing, deep neural networks, and economic optimization work together for stable, adaptive control.
Meet the cross-functional experts behind AIPC
Warwick Smith
Global Practice Lead
Yale Zhang
Global Director, Analytics and Decision Solutions
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