CokerX360
CokerX360 is an AI-powered decision-support solution designed for Delayed Coking Units (DCUs). It combines domain expertise and machine learning with historian and DCS data to monitor and optimize heater performance, coke drum cycles, rotating equipment health, light ends fractionation, and unit energy performance.
Configured to each unit’s geometry, crude slate, and operating envelope, CokerX360 transforms plant data into predicted KPIs and ranked, quantified recommendations. Each action is connected to its underlying cause, helping process engineers identify performance gaps and take targeted actions to improve production, run length, reliability, and operating efficiency.
Salient Features
Client Benefits
Increase Production Potential by 3%
Improve production potential through optimized run length and more effective spalling operations.
Increase Furnace Run Length by 8%
Optimize feed and COT adjustments to extend furnace run length and support more effective operating and maintenance planning.
Improve Spalling Effectiveness by 5–8%
Optimize spalling timing based on bottom TMT recovery to improve spalling performance.
Improve Asset Reliability
Identify process anomalies and potential coke drum and compressor failures earlier, enabling teams to investigate developing reliability issues proactively.
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.
