Process Plant Optimization and Control

Replace manual reagent dosing and air flow adjustments with AI agents that continuously optimize for grade and recovery. The system analyzes real-time sensor data (pH, density, grade analyzers) and adjusts setpoints to maintain peak performance despite fluctuating feed. Key benefits include:

• 3-8% increase in metal recovery by minimizing losses to tailings.
• 10-20% reduction in reagent consumption, a major operating cost.
• Stabilized plant output, reducing the need for expert operator intervention.

Maximize throughput and minimize energy use in comminution circuits, which often consume over 50% of a site's power. AI models predict optimal crusher settings and mill load based on ore hardness and size distribution. This delivers:

• Throughput increases of 5-10% by preventing bottlenecks and overloads.
• Specific energy consumption reductions of 5-15% per ton processed.
• Extended liner life through controlled, consistent operation, reducing maintenance costs.

Prevent costly process upsets like overloads or poor underflow density. AI monitors rake torque, bed pressure, and feed density to predict stability issues hours in advance. The system enables:

• Avoidance of unplanned downtime and associated cleanup costs.
• Optimized polymer usage, a significant consumable expense.
• Consistent tailings density for safer, more efficient dam management.

Dynamically shift non-critical energy loads to leverage time-of-use tariffs and reduce peak demand charges. AI creates a digital twin of the plant's power circuit, simulating the impact of operational changes. Results include:

• 10-25% reduction in peak demand charges, a direct bottom-line saving.
• Integration of renewable energy sources and battery storage for greater grid independence.
• Real-time alerts for inefficient equipment, enabling proactive maintenance.

Transform blending from a planning exercise to a dynamic, AI-controlled process. Models integrate real-time grade data from the mine face and conveyors to prescribe optimal feed mixes to the plant. This ensures:

• Consistent head grade to the mill, maximizing recovery and protecting equipment.
• Extended mine life by enabling the processing of lower-grade or complex ores.
• Reduced processing costs by avoiding the treatment of unsuitable material.

Deploy AI to monitor thousands of sensor streams simultaneously, identifying subtle deviations that human operators miss. This acts as a 24/7 process guardian, detecting issues like pump cavitation, instrument drift, or unusual chemical reactions before they cause a shutdown. Benefits are:

• Transition from reactive to predictive operations, preventing 70%+ of minor upsets.
• Reduced product quality excursions and associated penalties.
• Enhanced safety by flagging abnormal pressure or temperature builds.

A major copper mine implemented an AI agent to autonomously manage its flotation circuit. The system continuously analyzes froth texture, grade sensors, and chemical assays to adjust airflow, reagent dosing, and cell levels in real-time.

• Result: Achieved a 1.5% increase in overall metal recovery and a 12% reduction in reagent consumption.
• ROI: The project paid for itself in under 8 months through increased yield and lower operational expenditure.

An iron ore processing plant faced bottlenecks in its SAG and ball mill circuits. An AI optimization platform was deployed to balance feed rate, mill load, and liner wear predictions.

• The system uses reinforcement learning to maximize throughput while staying within power and mechanical constraints.
• Outcome: Sustained a 7% increase in circuit throughput and extended liner life by 15%, deferring capital shutdowns. This is a core application within our Mining and Natural Resource Intelligence pillar.

Crushers are among the largest energy consumers on site. A gold mine deployed an AI model that predicts ore hardness from upstream geological data and autonomously adjusts crusher CSS (Closed Side Setting) and speed.

• Benefit: Maintains target product size while minimizing specific energy consumption (kWh/ton).
• Verified Savings: Reduced crushing circuit energy use by 18%, translating to over $2M in annual cost savings at scale.

Poor thickener control leads to water recovery losses and tailings density issues. An AI vision system now monitors bed level and rake torque, while predictive models adjust flocculant addition and underflow rates.

• Impact: Achieved a 20% improvement in underflow density consistency, enhancing water recycling and reducing tailings dam volume growth.
• Business Value: Directly supports ESG compliance and reduces long-term closure liabilities.

Beyond single-unit control, the highest ROI comes from plant-wide coordination. A digital twin powered by neuro-symbolic AI simulates the entire process chain—from ROM pad to concentrate loading.

• The system performs multi-variable optimization to balance recovery, throughput, and cost per ton.
• Case Study: A zinc-lead operation used this approach to identify a 5% latent capacity increase without capital investment, generating an additional $25M in annual EBITDA. This aligns with our focus on Digital Twins and Industrial Metaverse.

Fluctuating ore characteristics cause reagent demand to vary wildly. An AI system now forecasts weekly reagent needs (e.g., lime, xanthate) based on the mine plan and blending schedule, integrating with procurement systems.

• Outcome: Reduced reagent inventory holding costs by 30% and minimized emergency airfreight expenses.
• Justification: Transforms a cost center into a data-driven, just-in-time operation, a key principle of Supply Chain Resilience.