Mineral processing plants are complex systems with numerous interacting variables. Traditional control approaches struggle to optimise these systems holistically. Artificial intelligence is enabling new approaches that deliver measurable improvements in throughput, recovery, and efficiency.

The Processing Optimisation Challenge

Processing plants present challenging optimisation problems:

Complexity: A typical concentrator has hundreds of control loops and thousands of sensors. Variables interact in non-linear ways.

Variability: Ore characteristics change continuously. Hardness, grade, mineralogy, and moisture vary with mine location and blending.

Conflicting objectives: Maximising throughput, maximising recovery, minimising energy consumption, and reducing reagent costs often conflict.

Time delays: Effects of changes may not appear for hours. This makes manual optimisation difficult.

Limited visibility: Not everything that matters can be measured. Key parameters like particle size distribution or liberation may only be available periodically.

Traditional control systems handle individual loops well but struggle with plant-wide optimisation.

AI Optimisation Approaches

Several AI techniques apply to processing optimisation:

Model predictive control (MPC): Uses process models to predict future behaviour and optimise control actions accordingly. AI enhances MPC by learning improved process models.

Reinforcement learning: Algorithms learn optimal control strategies through trial and error, within safe bounds. Systems improve continuously with operation.

Soft sensors: Machine learning models estimate parameters that can’t be directly measured, using available measurements as inputs.

Expert systems: Encode human expertise in rule-based systems that can operate continuously without fatigue.

Ensemble approaches: Combining multiple AI techniques often outperforms any single method.

Demonstrated Applications

AI optimisation has proven effective across processing operations:

Grinding circuits: AI systems optimise mill speed, water addition, classifier settings, and cyclone pressure to maximise throughput while maintaining target grind size. Improvements of 2-5% in throughput are commonly achieved.

Flotation: AI controls reagent dosing, air flow, and level setpoints across flotation banks to optimise recovery. Grade-recovery trade-offs can be managed dynamically.

Thickening: Flocculant dosing and bed level control optimised by AI reduces reagent consumption while maintaining underflow density targets.

Leaching: Acid addition, temperature control, and residence time optimisation improves extraction while managing costs.

Smelting and refining: AI optimises complex pyrometallurgical operations that were previously controlled by experienced operators.

Implementation Approach

Successful AI optimisation implementations share common elements:

Data foundation: AI systems need high-quality data. Sensor maintenance, data validation, and historian systems must be robust before AI deployment.

Process understanding: AI augments rather than replaces process expertise. Understanding what AI is optimising and why is essential.

Controlled deployment: Starting with limited scope and expanding after validation manages risk. Pilot implementations prove value before broader deployment.

Operator engagement: Operators must understand and trust AI systems. Engagement from the beginning improves adoption.

Continuous improvement: AI systems need ongoing attention. Models may need retraining as conditions change.

Specialists in this space working with processing operations focus on these implementation fundamentals alongside technical capability.

Economic Value

AI processing optimisation delivers substantial economic value:

Throughput improvement: Even 1% more tonnes through a concentrator can add millions in annual revenue.

Recovery improvement: Better recovery of valuable minerals directly increases revenue per tonne processed.

Energy reduction: Grinding circuits are energy-intensive. Optimising them reduces both costs and emissions.

Reagent savings: Optimised dosing reduces chemical costs while maintaining process performance.

Consistency: Reduced variability in plant performance enables better planning and reduces quality issues.

Knowledge preservation: AI systems capture expert knowledge that might otherwise be lost as experienced operators retire.

Challenges in Practice

AI processing optimisation faces challenges:

Data quality: Missing, erroneous, or biased data undermines AI performance. Data cleaning and validation are ongoing requirements.

Process changes: When ore sources, equipment, or operating modes change, AI models may need updating.

Extreme conditions: AI trained on normal operation may perform poorly during unusual events. Safety systems must handle exceptions.

Operator trust: If operators don’t trust AI recommendations, they won’t follow them. Building trust takes time and demonstrated performance.

Integration: AI systems must integrate with existing control systems. Interface challenges can complicate deployment.

Maintenance: AI systems require ongoing maintenance – model monitoring, retraining, and adaptation to changing conditions.

Vendor Landscape

Multiple vendors offer AI processing optimisation solutions:

Control system vendors: Honeywell, ABB, Siemens, and others incorporate AI capabilities in their control platforms.

Mining software specialists: Vendors like Petra Data Science and Imdex offer mining-specific AI solutions.

Independent AI companies: Specialist AI vendors serve processing operations with bespoke solutions.

Equipment OEMs: Major equipment manufacturers like Metso Outotec embed AI in their equipment offerings.

The team at Team400 may be appropriate when off-the-shelf solutions don’t address specific process characteristics or integration requirements.

Future Directions

AI processing optimisation will continue to advance:

Digital twins: Combining real-time optimisation with simulation capabilities will enable more sophisticated control strategies.

Autonomous plants: Progressive automation may eventually enable processing plants that largely run themselves, with humans handling exceptions.

Cross-plant learning: AI systems may learn from multiple plants, transferring knowledge across operations.

Extended optimisation: Optimisation scope will extend beyond individual plants to encompass entire value chains from pit to port.

Sustainability integration: AI optimisation will increasingly incorporate environmental parameters alongside economic objectives.

Processing plants that embrace AI optimisation will achieve performance levels that conventionally controlled plants cannot match. The technology is proven and the benefits are clear. Implementation requires investment and expertise, but the returns justify the effort.