Vertical AI for Chemical and Fertilizer Plants

Because most of them are not purely mechanical. A process pump does not fail from seal wear alone, it fails because a change in feed concentration or temperature drives corrosion and cavitation that accelerate that wear. Conventional condition monitoring reads the vibration signature and misses the process condition driving it, so the alert arrives late and without a cause.

Generic industrial AI applies one anomaly model across every asset in every industry. Vertical AI is built for the equipment and process conditions of a single sector. Infinite Uptime's PlantOS™ applies chemicals-specific failure logic through Dynamic FMEA, so a fault on a synthesis gas compressor is evaluated against how those compressors actually fail under process gas variation, not against a generic deviation threshold.

PlantOS™ analyses vibration, temperature, pressure, and process data from the same asset simultaneously, then correlates the two signature types. A rising vibration reading on a reactor circulation pump is interpreted alongside feed concentration and temperature, which separates a genuine developing fault from a normal response to a process swing. The prescription names the mechanical fault and the process condition sustaining it.

Predictive maintenance forecasts that an asset is likely to fail. Prescriptive maintenance states which asset, which fault, which corrective action, and by when. In practice the difference is workload: a predictive alert still needs a reliability engineer to diagnose and decide, whereas a PlantOS™ prescription arrives already diagnosed and ready to convert into a work order. Prediction accuracy is 99.97 percent.

Coverage spans the assets where mechanical and process faults compound: centrifugal and process pumps, reciprocating and centrifugal compressors, synthesis gas and CO₂ compressors, reactor agitators, vacuum pumps, boiler feed pumps, cooling water pumps, and ID and FD fans. These operate under corrosion, thermal load, and pressure conditions where generic models perform worst.

Three, delivered together: reduced unplanned downtime, higher throughput on the trains carrying output, and lower cost per tonne through fewer emergency interventions, reduced spares consumption, and less energy waste on rotating equipment. Across deployments, PlantOS™ has digitalised 55 plants, prevented 2,462 equipment breakdowns, and eliminated 6,128 hours of unplanned downtime as of June 2026, per the PlantOS™ Digital Reporting System.

Each prescription carries the asset, the fault, the recommended action, and the timeframe, and is delivered to the maintenance team on mobile for work order creation and digital sign-off. Execution and outcome are logged, and validated outcomes feed back into the models through the 99% Trust Loop.

No. PlantOS™ integrates with the infrastructure already in the plant, unifying data from PLCs, DCS historians, and installed sensors alongside its own hardware where additional coverage is needed. Prescriptions are delivered into the existing maintenance workflow, so the plant gains process context on its critical assets without a rip and replace programme.

By reducing unplanned equipment trips, PlantOS™ reduces the process upsets and emergency shutdowns that carry the highest safety and regulatory exposure. Prescriptions are validated by domain experts before reaching the plant floor, and every action is logged with a digital audit trail, which supports compliance reporting. The hardware is ATEX and IP68 certified for hazardous and corrosive areas.

Coromandel International and Indorama Ventures are among the producers using PlantOS™ for prescriptive maintenance and reliability. Coromandel uses PlantOS™ real-time alerts across its plants to plan maintenance and avoid surprise breakdowns.