AI USE CASE
Catalyst Performance Prediction and Replacement
Predict catalyst degradation and optimize replacement schedules for chemical continuous processes.
What it is
Machine learning models trained on process sensor data forecast catalyst activity decline, enabling maintenance teams to schedule replacements before costly yield losses occur. The approach integrates with existing DCS/SCADA infrastructure and delivers actionable dashboards for process engineers.
Data you need
Historical process sensor data (temperature, pressure, flow rates, conversion rates) and catalyst replacement event logs spanning multiple run cycles.
Required systems
- erp
- data warehouse
Why it works
- Secure buy-in from process engineers early by involving them in feature selection and model validation.
- Ensure clean, timestamped sensor data is accessible from the plant historian before model development begins.
- Build a model retraining pipeline triggered by process regime changes or new catalyst batches.
- Define clear KPIs (yield, run-length, unplanned downtime) before go-live to demonstrate business value.
How this goes wrong
- Insufficient historical data on catalyst degradation cycles makes it impossible to train a reliable predictive model.
- Process conditions change (new feedstocks, different operating regimes) and the model drifts without continuous retraining.
- Lack of integration between plant historian/DCS and the ML pipeline creates data latency that undermines real-time alerting.
- Process engineers distrust model outputs and revert to experience-based replacement schedules, negating adoption.
When NOT to do this
Avoid this approach when catalyst run-lengths are very short (days), process conditions are highly variable, or fewer than 10 full replacement cycles of historical data exist, the model will lack the signal needed to be reliable.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.