Industry
Pulp & Paper
Where energy-intensive continuous process + tight grade specs + capex-heavy assets meet structural margin pressure.
Pulp & paper mills are continuous-process, energy-dominant, and grade-change-sensitive. The biggest operational levers are reducing steam/electricity per ton, anticipating refiner + paper machine maintenance, and minimizing waste at grade transitions. KPT runs on all three. Solution 1 + Solution 2 + Solution 4 together address >70% of the operational margin lever set.
How KPT helps
Energy reduction + grade-change optimization + predictive maintenance.
Pulp & paper has three structural margin pressures: energy is 10–30% of cost-of-goods, every grade change burns broke + setup time, and refiner/motor failures cascade into unplanned downtime. KPT's Solutions hit all three simultaneously without requiring custom hardware.
The fastest-paying single move is usually an Energy-Yield Correlation study on a single paper machine: find the operating envelope that maximizes basis-weight quality per kWh of steam + electricity. Typical payback inside 6 months on energy alone, before any maintenance value lands.
Specific outcomes KPT delivers
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Energy-Yield Correlation Finder
Find operating envelopes that maximize output per kWh — up to 5% energy reduction per ton.
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Vibration-Anomaly Predictor
Applied to refiners, motors, pumps — predict failure 7–14 days out.
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Changeover-Sequence Minimizer
Order paper-grade transitions to minimize aggregate broke + setup loss.
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Energy Procurement Optimizer
Schedule energy purchases against forecast load — substantial value at mill-scale energy bills.
Where to start
An energy-yield study on a single paper machine. We retrofit the power-monitoring + basis-weight sensors if needed, run a 6-week shadow study, and report measured kWh-per-ton reductions in dollars. From there, the same pipeline extends to refiner maintenance + grade-transition optimization.
Pulp & paper PoC
Start with the energy bill.
One paper machine. 6 weeks. KPT models the kWh-per-ton operating envelope, identifies the improvement opportunity, runs a shadow-validated change, and reports measured savings in dollars. From there, refiner predictive maintenance + grade-change optimization layer on top.