Weir’s Kenneth Ulrich on AI and digital twins: reliability and throughput notes for plant engineers
Reviewed by Joe Ashwell

First reported on International Mining – News
30 Second Briefing
Weir’s Head of Data and AI, Kenneth Ulrich, outlines how the company’s NEXT Intelligent Solutions platform uses machine-learning models to optimise mineral processing plant performance in real time. By combining sensor-rich equipment data from Weir comminution circuits with digital twins of crushers, screens and pumps, the system can predict wear, adjust operating setpoints and flag impending failures before they hit throughput. Ulrich points to continuous model retraining on live plant data as critical for coping with ore variability and changing operating conditions.
Technical Brief
- Anomaly detection is used to distinguish normal process noise from patterns associated with incipient mechanical failure.
- Condition indicators for bearings, liners and seals are derived from vibration, pressure and power-draw signatures.
- Recommendations to operators are surfaced via advisory setpoints, leaving final control decisions with plant personnel.
- Ulrich notes that explainable-AI techniques are prioritised so engineers can audit why alarms or advisories are triggered.
- Data governance around sensor quality, calibration and timestamp synchronisation is described as a prerequisite for reliable alerts.
- For industry safety practice, Ulrich positions AI outputs as an additional protective layer within existing critical-control frameworks.
Our Take
Weir’s push into AI and digital twins sits alongside its recent ‘sustainability by design’ work in comminution and HPGR flowsheets, suggesting that any digital twin offering is likely to be tightly coupled to energy and water efficiency metrics rather than just uptime or throughput.
Our database shows several recent Weir items focused on pumps and slurry transport, including GEHO positive displacement pumps for an iron ore slurry pipeline in India, so AI-enabled twins could realistically extend from crushers into integrated mine-to-plant hydraulics and wear‑management models.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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