PolymerX360
PolymerX360 is an AI-powered decision-support solution designed for PE and PP production units. It analyzes historian and DCS data to benchmark plant performance and identify optimization opportunities across reactor operations, hydrogen and co-monomer balance, condensation control, recovery systems, product quality, and extrusion efficiency.
Configured to each plant’s technology, grade slate, and operating envelope, PolymerX360 combines process intelligence and machine learning to generate predicted KPIs and ranked, quantified recommendations. This helps process engineers improve production performance, product quality, energy efficiency, catalyst utilization, and operational reliability.
Salient Features
Client Benefits
Reduce Hydrocarbon Losses by 1–2%
Optimize recovery-unit performance to improve condensable hydrocarbon recovery and reduce vent losses.
Reduce Energy Consumption by 2–3%
Optimize extruder operation and unit energy performance to reduce specific energy consumption.
Reduce Catalyst Consumption by 2–4%
Benchmark catalyst productivity against historical best performance for the same grade to identify opportunities for more efficient catalyst utilization.
Reduce Off-Grade Production by 2–5%
Support shorter and more repeatable grade transitions using plant-specific operating and product-quality insights.
Dashboard & Snapshots
![Solution configuration [Defining the Objective: Maximize Production, Minimize Utility]:](https://ingenerox.ai/wp-content/uploads/2025/05/Picture3-3.png)
![Solution configuration [Defining cases of interest – Multiple cases can also be added]:](https://ingenerox.ai/wp-content/uploads/2025/05/Picture4-3.png)
Case Studies
Development of Soft Sensor to predict the C5 contents in Debutaniser column Overhead:
Background
In modern petrochemical operations, maintaining product specifications within tight limits is critical for both quality assurance and process efficiency. One of the key challenges faced by a leading chemical plant was the need for real-time prediction and monitoring of C5 content in the overhead stream of the debutanizer column — a crucial step to ensure product specification compliance and optimize operations.
Challenge
Traditional laboratory testing methods for measuring C5 content introduced significant time delays, making real-time adjustments difficult. The plant required a fast, accurate, and reliable solution to continuously monitor C5 levels, allowing operators to take timely corrective actions and avoid off-spec products.
Solution with AnalyticX
Using AnalyticX, the engineering team was able to develop a soft sensor — a machine learning-based predictive model — specifically designed to estimate the C5 content in the debutanizer overhead stream in real time.
Key steps included:
- Seamless ingestion of historical operational data into AnalyticX without any coding requirements.
- Applying statistical analysis to identify key process variables influencing C5 concentrations.
- Rapid development and training of a machine learning model tailored to predict C5 content with high accuracy.
- Deployment of the predictive model into live operations for real-time product spec monitoring.
Results
- Enhanced Product Monitoring:
Operators now have a real-time view of the predicted C5 content, significantly reducing reliance on delayed lab measurements. - Improved Process Control:
By closely tracking C5 levels, the plant was able to make timely adjustments to operational parameters, ensuring consistent product quality. - Reduced Off-Spec Production:
Real-time insights helped minimize off-spec batches, leading to material savings and reduced reprocessing costs.
Key Takeaways
- AnalyticX enabled the rapid development of a soft sensor customized for critical KPI monitoring.
