Imagine: at a polypropylene production site, laboratory analysis of the melt flow index (MFI) takes 4 hours. During that time, the reactor produces 8 tons of product that may not meet specifications. The solution — a soft sensor based on LSTM, predicting MFI in seconds. We implemented such a solution at one plant, reducing lab analyses by 70% and saving 22% on operational costs. Below, we break down how we do it.
Why Traditional DCS Can't Handle the Nonlinearity of Chemical Processes?
Chemical productions are multidimensional objects with nonlinear dependencies. DCS controls based on rigid setpoints but does not account for feedstock drift, catalyst contamination, or equipment wear. ML models complement DCS by predicting optimal setpoints in real time. Compare: LightGBM trains 10x faster than LSTM on datasets up to 5000 points and provides interpretable SHAP reports—convenient for technologists.
Key Applications of ML in Chemistry
- Soft sensor — virtual product quality analyzer. Predicts viscosity, purity, molar mass from T/P/flow in seconds instead of 2–8 hours in the lab.
- Predictive maintenance — predicts compressor or heat exchanger failure 2 weeks before breakdown. Unplanned shutdowns cause significant financial losses.
- Quality control — online monitoring of deviations via KKS spectra or analytical signals.
- Materials development — screening polymer and catalyst compositions using surrogate models.
How AI Helps Reduce Costs in Chemical Production?
AI solutions directly impact operational expenses. Soft sensor eliminates some lab analyses, predictive maintenance prevents emergency shutdowns, and setpoint optimization increases target product yield. Together, this yields up to 30% savings in operational costs. We confirm this across 30+ projects.
How We Build a Soft Sensor for Your Reactor
Pipeline includes:
- Data audit: inventory of historian tags (up to 5000 tags in 2 weeks), outlier detection, dead time identification.
- Feature engineering: lag features, moving averages, derivatives, FFT features.
- Baseline model: LightGBM with SHAP interpretation—transparent and fast.
- Validation: time-series cross-validation instead of random split.
- Deployment via OPC-UA into the MPC loop.
- Monitoring: PSI on input, prediction drift vs. lab analyses.
Comparison of Soft Sensor Approaches
| Approach | Applicability | Data Requirements |
|---|---|---|
| LightGBM/XGBoost | 100–5000 points, interpretability needed | Hourly timestamps, 10–50 tags |
| LSTM / Temporal Fusion Transformer | >5000 points, complex temporal dynamics | Minute-level, 20+ tags, 6+ months history |
| Gaussian Process Regression | <1000 points, uncertainty estimation needed | Noisy labels, 5–15 tags |
| Physics-Informed NN | Material balance equations available | Process equations + 1000+ points |
On the polypropylene project, LSTM with lag features achieved RMSE of 0.31 g/10 min in 30 minutes at average MFI 12 — error ~2.6%, replacing the 4-hour lab analysis.
When Should You Apply a Soft Sensor?
Soft sensor is effective if lab analysis takes more than 2 hours and sampling frequency is low. It is especially useful for continuous processes: polymerization, cracking, ammonia synthesis. It also suits aggressive environments where physical sensors degrade quickly.
Setpoint Optimization via Bayesian Optimization
After training the soft sensor, we use Bayesian Optimization (BoTorch) to find optimal temperature and pressure. Safety constraints from HAZOP are applied as box constraints. Result — target product yield increased by 8%.
What Predictive Maintenance Delivers?
- Vibration-based fault detection — CNN (1D) on spectra with 99% accuracy on CWRU, 88–93% on real data with variable load.
- Multivariate anomaly detection — Isolation Forest or Autoencoder on 20+ sensors. Reconstruction error as anomaly score.
- Degradation tracking — predicting heat exchanger fouling via U-value. Triggers scheduled cleaning 2 weeks before failure.
Comparison of Predictive Maintenance Methods
| Method | Accuracy | Data Requirements |
|---|---|---|
| CNN (1D) | 88-99% | Vibration spectra, up to 1000 examples |
| Isolation Forest | 85-92% | 10+ sensors, 500+ records |
| Autoencoder | 90-95% | 20+ sensors, 2000+ records |
Integration with Production Systems
- OPC-UA — standard protocol for real-time DCS/SCADA data.
- AspenTech APC — embedding ML model as external soft sensor.
- OSIsoft PI / AVEVA — historian source, Event Frames for anomaly labeling.
- SAP PM — integration of predictive maintenance work orders.
Our Work Process
- Data audit — historian survey, technologist interviews, dead time analysis.
- Design — model architecture selection, KPI definition, safety constraints.
- Development — baseline implementation, iterative improvement, testing on historical data.
- Validation — A/B test on shop floor, comparison with lab.
- Deployment — OPC-UA integration, SCADA visualization, operator training.
- Monitoring — drift detection, monthly retraining, reporting.
Estimated Timelines
- Soft sensor for one KPI: 6–10 weeks.
- Comprehensive AI platform (optimization + predictive maintenance + quality): 6–18 months.
- Cost is calculated individually after audit.
Example: LSTM Soft Sensor for Polypropylene
Model configuration:
- Architecture: LSTM with 2 layers of 64 neurons, dropout 0.2
- Input features: temperature (T), pressure (P), flow (flow) over last 30 minutes
- Lag: 30 time steps
- Optimizer: Adam, learning rate 0.001
- Metric: RMSE = 0.31 g/10 min
- Deployment: OPC-UA server, update every 5 seconds
What Is Included in the Work
- Data and tag audit of historian (up to 5000 tags in 2 weeks).
- Baseline LightGBM/XGBoost model with SHAP report.
- Prototype on historical data with metrics.
- Documentation: model card, API, operator manual.
- Client team training (2 days).
- Integration via OPC-UA into existing DCS.
- 3 months of drift monitoring and support.
Our Experience and Guarantees
Our team has 5+ years in industrial AI, 30+ projects at chemical and petrochemical plants. Certifications in PyTorch, AWS, and OPC-UA integration. We guarantee model quality on historical data and stable operation for 6 months after deployment. Each project is supported by a chemical process expert.
Contact us to evaluate your project. Get a consultation on implementing AI at your production site.