AI USE CASE
Distillation Column ML Optimization
Optimize distillation parameters in real time to cut energy costs while preserving product purity.
What it is
Machine learning models analyze sensor data from distillation columns to continuously tune operating parameters such as reflux ratio, feed temperature, and pressure. This reduces energy consumption by 10–25% while maintaining or improving product purity specifications. Plants typically see payback within 6–18 months through lower utility costs and reduced off-spec product. The approach also improves column stability, reducing unplanned shutdowns and operator intervention.
Data you need
Historical and real-time process sensor data from the distillation column including temperature, pressure, flow rates, feed composition, and energy consumption over at least 12 months of operation.
Required systems
- data warehouse
- erp
Why it works
- Engage process engineers and operators early to build trust and incorporate domain knowledge into the model.
- Ensure robust, calibrated sensor infrastructure and data historian before model development begins.
- Deploy in advisory mode first, then closed-loop once operator confidence is established.
- Set up continuous model monitoring and retraining pipelines to handle process drift over time.
How this goes wrong
- Insufficient or low-quality sensor data leads to unreliable model predictions and operator distrust.
- Model trained on historical steady-state data fails to generalize to feed composition changes or seasonal variation.
- Operators override the system frequently due to lack of trust, negating optimization gains.
- Integration with legacy DCS or SCADA systems proves technically complex, delaying deployment.
When NOT to do this
Do not pursue this if the plant lacks a functioning data historian with at least one year of clean sensor data, as the model will be unreliable and the project will stall in data remediation.
Vendors to consider
Sources
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