Hydrocracker Reactor
Conversion Optimization
Maximize Reactor Performance with Real-Time
AI Optimization
AI Optimization
Physics Meets AI: Hybrid Models for Optimization.
Introducing the Hydrocracker Reactor Conversion Optimization Solution—an AI/ML-powered platform that maximizes conversion by tapping into your DCS and Feed Quality data and predicting Product Quality in real time. Tailored for process engineers and plant operators, it helps you run your unit at peak performance, maintain premium product quality, and unlock significant cost savings across your operations.
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HCU Reactor Performance Optimizer
Hybrid Model
Our model initiates by accurately predicting the maximum possible conversion, leveraging the Stage 1 Feed Quality properties of Distillation, Sulphur Content, Nitrogen, asphaltenes and Stage 2 feed properties along the similar parameters. This conversion target is then used by the optimizer to generate precise DCS operator set points—such as Heater COT, Reactor CAT, and H2 flow—guiding operators to achieve the optimal conversion efficiently and reliably.
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Transform your Challenges into Opportunities
The Hydrocracker Reactor Conversion Optimization Model aims to achieve maximum conversion by utilizing DCS and Feed Quality data in real time. The AI/ML platform delivers essential DCS set points and recommendations for plant operators and process engineers, facilitating the achievement of desired conversion rates. Experience increased conversion, real-time predictions of downstream Feed and product quality parameter values, and optimized hydrogen consumption, transforming your operations for enhanced efficiency and profitability!
Experience increased conversion, real-time predictions of downstream Feed and product quality parameter values, and optimized hydrogen consumption, transforming your operations for enhanced efficiency and profitability!
Pain Point
Unclear Potential Conversion
Benefit
Dynamic Target Conversion: Instantly adjust to feed quality changes.
Pain Point
Can't Boost Reactor Performance Without Vacuum Tower Fouling
Benefit
Optimized Reactor Efficiency: Utilize our Conversion and SHFT model to boost conversion while minimizing vacuum tower fouling.
Pain Point
Insufficient Stage 2 Feed and Product Quality values
Benefit
Hourly Insights on Product Quality: Leverage our Quality models for Sulphur in stage 2 feed, Nitrogen in Stage 2 Feed etc. for real-time predictions
Pain Point
High Hydrogen Consumption
Benefit
Reduced Excess H2: Lower H₂ consumption during operations.
Performance Deviation Prediction
Takes the Stage 1 Feed and Stage 2 Feed LIMS data and performs the predictions for the quality parameters of both Stage 1 and Stage 2 products.
Key Parameter Monitoring
The operator selects the mode of optimization as one of the following options:
- Fixed Conversion
- Maximum Conversion
Process and Efficiency Trends
Using the stage 1 Feed Quality and Stage 2 Feed Quality data for the model Calculates the Potential Conversion which can be achieved for the given Feed Quality
Operational Benchmarking
Model runs and publishes the recommended values of the Operator Controlled Set points needed to achieve the Maximum Possible Conversion.
Actionable Insights for Improvement
Comparison plot between “Current Operation” vs. “Recommended Operation“ is shown on the Dashboard
