Prescriptive AI for
Pulp & Paper Plants

Because most of them are not purely mechanical. A press roll does not fail from bearing wear alone, it fails because web tension and moisture variation load the nip unevenly and 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 paper-specific failure logic through Dynamic FMEA, so a fault on a dryer cylinder is evaluated against how dryer cylinders actually fail under thermal and tension load, not against a generic deviation threshold.

PlantOS™ analyses vibration, temperature, load, and process data from the same asset simultaneously, then correlates the two signature types. A rising vibration reading on a press roll is interpreted alongside web tension and moisture, which separates a genuine developing fault from a normal response to a process change. 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.

PlantOS™ covers the full mill, matched to how critical each asset is. Your production-critical assets, the refiners, press rolls, and dryer cylinders where a failure breaks the sheet and stops the machine, are covered by AI Shields, the deep-domain models built for exactly those machines. Standard and critical rotating equipment across pulping, forming, drying, and calendering is covered through wired and wireless sensing, piezoelectric or MEMS, so you match coverage to the asset and the budget. And balance-of-plant equipment, the boiler feed pumps, fans, and cooling systems, is covered by the self-powered, wireless InfiSense 3XT with no gateways or cabling.

Three, delivered together: reduced unplanned downtime, higher throughput on the machine, and fewer paper breaks through earlier detection of the faults that cause them. Across deployments, PlantOS™ has digitalised 29 plants, prevented 1,833 equipment breakdowns, and eliminated 3,811 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 business outcomes, and is delivered to the maintenance team on dashboard, through email, and 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 mill, 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 mill gains process context on its critical assets without a rip and replace programme.

Many paper breaks trace back to a developing equipment or process condition that condition monitoring alone does not connect: a press roll bearing degrading, a dryer running with a thermal imbalance, web tension drifting. PlantOS™ reads the mechanical and process signatures together and prescribes the correction before the condition reaches the sheet, which reduces breaks and the costly restarts they cause.

Deployments typically reach validated ROI within 6 to 12 months. Reports put the cost of a single unplanned paper mill stoppage at $225,000 to $400,000, so avoiding even a few generally covers the cost of the platform. Delivered as Production Outcomes as a Service, PlantOS™ scales across the mill without capital expenditure.