Unmanned Monitoring

Case I. Trend monitoring for unmanned operations

Case I demonstrates unmanned trend monitoring using continuous inline PSD measurement. Instead of relying on manual laboratory sampling at fixed intervals, the system automatically measures particle size in real time and continuously tracks process trends. Operators receive immediate alarms whenever the product approaches coarse or fine specification limits, allowing rapid corrective action before quality issues occur. This reduces labour requirements, improves process stability, and enables fully automatic monitoring of the grinding circuit without constant operator intervention.

Continuous in-process PSD monitoring creates a valuable historical database that supports Industry 4.0 and data-driven manufacturing. By collecting real-time particle size trends over long periods, plants gain deeper insight into grinding circuit behaviour, separator performance, process stability, and operating efficiency. This data helps engineers identify hidden process relationships, optimise control strategies, predict process deviations, and continuously improve production performance. Over time, the accumulated database becomes a powerful foundation for advanced analytics, automation, and smarter decision-making across the entire plant.

Typical ROI from implementing inline PSD monitoring is often achieved through labour and operational savings. First, automatic in-process measurement removes the need for frequent manual sampling, laboratory preparation, and PSD analysis, significantly reducing laboratory workload and associated labour costs. Second, operators no longer spend time collecting and analysing samples, allowing them to focus on higher-value operational tasks and overall plant optimisation. Together, these improvements reduce operating costs while increasing process efficiency and productivity.

Inline PSD monitoring forms the foundation of unmanned grinding circuit operation by providing the essential real-time feedback loop required for automatic process control.