Keep Crushers Running, Furnaces
Firing, and Conveyors Moving — Without Surprise Shutdowns

PlantOS™ provides 24×7 prescriptive monitoring across the most failure-prone rotating and auxiliary equipment in mining — including SAG mills, ball mills, jaw crushers, cone crushers, vibrating screens, grizzly feeders, slurry pumps, agitators, cyclone separators, conveyor belts and rollers, stackers, reclaimers, and draglines. On the metals processing side, coverage extends to cast house cranes, potline circulation blowers, baking furnace fans, rolling mill drives, and cooling tower systems. Each asset is mapped to vertical AI models trained on mining-specific failure modes — bearing degradation under abrasive slurry conditions, crusher liner wear patterns, mill pinion gear faults — so prescriptions are precise, not generic.

Yes. PlantOS™ is designed to adapt across open-pit mining, underground mining, and mineral processing plants regardless of the commodity being extracted — whether it’s zinc, copper, aluminum, iron ore, gold, or coal. The platform’s vertical AI models are trained on equipment behaviour patterns common across these environments, from haul-road conveyors in open-pit operations to ventilation fans and hoisting systems underground. Whether you run a large-scale surface mine with draglines and bucket-wheel excavators or an underground operation with shaft hoisting and ore passes, PlantOS™ unifies sensor data from every critical asset into a single prescriptive intelligence layer.

PlantOS™ has delivered measurable, bottom-line impact across Vedanta Group — one of the world’s largest producers of zinc, aluminum, copper, and iron ore. Across 77 plant sites, PlantOS™ monitors 9,055 critical equipment units through 26,300+ sensors spanning 489 areas. The platform maintains an MTBF of 5,206 hours and has driven 1,554 prescriptions to action — with an 83.64%+ user-validation rate, reflecting the trust operators place in its recommendations.

At Hindustan Zinc Ltd. (HZL) alone, the results speak directly to the bottom line: 5,039 hours of unplanned downtime eliminated and over $700K USD in production savings across its mines and smelters.

Predictive maintenance tells you that a SAG mill bearing might fail in 30 days. Prescriptive AI — the way PlantOS™ delivers it — tells you exactly what’s wrong (inner race defect at 2.4 mm/s velocity), what action to take (replace bearing, re-align coupling), when to schedule it (sync with the next planned shutdown), and what the production impact will be if you don’t. In mining, where a single crusher or mill failure can halt the entire comminution circuit and back up ore flow for hours, the difference between knowing something might fail and knowing what to do about it is the difference between a controlled intervention and a catastrophic stoppage. PlantOS™ delivers 99.97% prediction accuracy with prescriptions digitally verified by domain experts before they reach your maintenance team for validation.

Mining is one of the most energy-intensive industries — comminution alone (crushing and grinding) can account for up to 50% of a mine’s total energy consumption. PlantOS™ correlates real-time equipment health data with process parameters from PLCs and DCS to detect energy inefficiencies at the asset level — things like conveyor belt misalignment causing motor overload, crusher feed imbalance driving excess power draw, or fan systems running at suboptimal speeds. By prescribing corrective actions that address both reliability and energy efficiency simultaneously, PlantOS™ helps mining plants reduce energy cost per ton of ore processed while maintaining throughput stability.

Most mining and metals plants start seeing measurable improvements in downtime reduction, equipment availability, and energy efficiency within 6–12 months, with PlantOS™’s Production Outcomes-as-a-Service (POaaS) model ensuring value delivery without significant upfront capital investment. The platform integrates with existing historian data, PLCs, and DCS systems, minimizing disruption to ongoing operations.

This is where the 99% Trust Loop™ comes in. Unlike black-box AI systems that generate alerts nobody acts on, PlantOS™ combines vertical AI models — trained specifically on mining equipment failure modes — with 24×7 domain expert review. Every prescription is validated by reliability engineers before it reaches the plant floor, ensuring it’s contextually accurate for that specific asset, operating condition, and process stage. At Vedanta Group, this approach achieved an 83.64%+ user-validation rate across 1,554 prescriptions, with maintenance teams actively choosing to act on PlantOS™ recommendations. The result is not just higher prediction accuracy but higher prescription adoption — which is ultimately what eliminates downtime.