AI USE CASE
ML-Driven Solvent Recovery Optimization
Optimize solvent recovery rates in distillation and extraction using machine learning to cut waste and costs.
What it is
By applying machine learning to real-time distillation and extraction process data, chemical manufacturers can identify optimal operating conditions that maximize solvent recovery rates. Models continuously learn from process variations, enabling adaptive control that outperforms static rule-based setpoints. Teams gain actionable process insights that also support sustainability reporting and regulatory compliance.
Data you need
Historical and real-time sensor data from distillation columns and extraction units, including temperature, pressure, flow rates, feed composition, and solvent recovery yields.
Required systems
- data warehouse
- erp
Why it works
- Strong collaboration between data scientists and process engineers to ensure domain knowledge is embedded in feature engineering.
- A robust data pipeline connecting sensors, historian databases, and the ML platform with low latency.
- A phased rollout starting with advisory mode (recommendations only) before enabling closed-loop control.
- Regular model retraining schedules tied to process audits and feedstock change events.
How this goes wrong
- Insufficient or poorly labelled historical process data prevents model training from producing reliable predictions.
- Integration with legacy DCS or SCADA systems proves too complex or costly, blocking real-time feedback loops.
- Process engineers distrust model recommendations and revert to manual setpoints, negating efficiency gains.
- Model drift due to feedstock composition changes goes undetected, leading to degraded recovery rates over time.
When NOT to do this
Do not pursue this if your plant lacks digitised sensor data collection or relies entirely on manual lab sampling, as the missing real-time data foundation will make any ML model unreliable.
Example vendors
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