Processing plants represent the highest-value concentration of assets at most mining operations. Unplanned downtime costs can exceed hundreds of thousands of dollars per hour at large operations. Predictive analytics is transforming how these critical assets are managed.

The Processing Plant Challenge

Processing plants are complex systems with hundreds of interacting components. Crushers, mills, flotation cells, thickeners, and supporting infrastructure must operate in coordination to maintain throughput and recovery.

Traditional maintenance approaches relied on scheduled maintenance intervals and reactive response to failures. This approach has inherent inefficiencies: scheduled maintenance happens whether needed or not, and reactive maintenance responds to failures after they occur.

The cost of these inefficiencies is substantial. Over-maintenance wastes resources and creates unnecessary downtime. Under-maintenance leads to failures that could have been prevented. Reactive maintenance happens at the worst possible times – when equipment has already failed.

Predictive analytics offers a better approach: maintaining equipment when it actually needs maintenance, before failures occur.

Data Sources for Prediction

Processing plants generate vast amounts of data that can inform predictive models.

Vibration monitoring detects changes in rotating equipment condition. Subtle vibration pattern changes can indicate bearing wear, imbalance, or other developing issues months before failure occurs.

Process parameters including flows, pressures, temperatures, and compositions reveal equipment and process health. Deviations from normal patterns often precede equipment issues.

Power consumption patterns correlate with equipment condition. Motors drawing unusual current may indicate mechanical problems developing.

Oil analysis results provide direct insight into equipment wear. Metal particles in lubricating oil indicate which components are degrading.

Acoustic monitoring captures sounds that indicate developing problems. Trained algorithms can identify the auditory signatures of various failure modes.

The challenge is not data availability but data integration and analysis. Most processing plants have ample sensors – making use of the data they generate is where value is created.

Analytics Approaches Delivering Value

Several analytics approaches are proving effective in processing plant applications.

Machine learning models trained on historical data can identify patterns that precede failures. These models learn from actual plant experience rather than relying on generic failure signatures.

Physics-based models incorporate engineering understanding of failure modes. These models are particularly valuable for equipment where limited historical failure data exists.

Hybrid approaches combine machine learning pattern recognition with physics-based constraints. This combination often outperforms either approach alone.

Anomaly detection algorithms identify deviations from normal operation even when specific failure modes aren’t known. Unusual patterns trigger investigation before problems develop.

Ensemble methods combine multiple models to improve prediction reliability. When several independent models agree on an impending issue, confidence in the prediction increases.

Implementation Realities

Successful predictive analytics implementations share common characteristics.

Data quality investment precedes analytics deployment. Sensors must be calibrated, data transmission must be reliable, and historical data must be cleaned and organised.

Domain expertise integration ensures predictions reflect physical reality. Process engineers must be involved in model development and validation.

Workflow integration connects predictions to action. Predictions that don’t trigger maintenance planning or operational response deliver no value.

Continuous improvement refines models as more data accumulates. Predictions are validated against actual outcomes, and models are updated to improve accuracy.

Change management ensures maintenance teams trust and act on predictions. Shifting from time-based to condition-based maintenance requires cultural change.

Results Being Achieved

Operations with mature predictive analytics programmes report meaningful improvements.

Unplanned downtime reductions of 20-40% are commonly reported. Equipment failures that would have halted production are prevented through advance maintenance.

Maintenance cost reductions result from doing the right maintenance at the right time. Unnecessary scheduled maintenance is eliminated while failure-induced emergency maintenance decreases.

Throughput improvements follow from more stable operations. Reducing process upsets increases average throughput and recovery.

Safety improvements occur when equipment operates within design parameters rather than failing unexpectedly. Emergency responses to equipment failures carry inherent safety risks.

The Expanding Frontier

Predictive analytics in processing is expanding beyond maintenance to encompass process optimisation.

Real-time process adjustments based on predictive models optimise recovery and throughput continuously. Rather than waiting for laboratory results, models predict optimal settings based on current conditions.

Feed characterisation prediction anticipates how incoming material will behave in the plant. Advance knowledge of feed properties enables proactive adjustment.

Energy optimisation models predict power consumption under different operating scenarios. Operations can be adjusted to minimise energy costs while maintaining production.

The processing plant of the future will operate with predictive intelligence integrated throughout – anticipating issues, optimising performance, and minimising both cost and risk.