VDUX360
VDUX360 is an AI-powered decision-support solution designed for Vacuum Distillation Units (VDUs). It combines domain expertise and machine learning to benchmark column, heater, and ejector performance, transforming historian and DCS data into ranked and quantified recommendations across yield, energy efficiency, throughput, and asset performance.
Configured to each unit’s feedstock and operating envelope, VDUX360 analyzes plant conditions to identify performance gaps and their underlying causes, helping process and operations teams make informed decisions to improve VDU performance.
Salient Features
Client Benefits
Increase VGO Recovery by 0.5–2 wt%
Improve VGO recovery through cut-point optimization and better control of operating conditions.
Reduce Vacuum Heater Energy Consumption by 2–5%
Reduce excess O₂ and stack losses to improve vacuum heater energy performance and lower fuel consumption.
Achieve 2–5% Vacuum-System Steam Savings
Optimize ejector performance and vacuum-system operation to reduce steam consumption while maintaining required vacuum conditions.
Increase Column Throughput by 1–3%
Improve throughput through pumparound and column optimization while maintaining operating requirements.
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.
