LC-Fining Reactor
Conversion Optimization
Maximize yields, extend catalyst life, enhance refinery performance !
Physics Meets AI: Hybrid Models for Optimization.
The hybrid digital solution for LC-FINING reactor conversion combines physics-based models with advanced data analytics to optimize reactor performance. By adjusting key operational parameters such as temperature and hydrogen flow and leveraging real-time plant data alongside insights into thermodynamics and reaction kinetics, it maximizes product yields, reduces energy consumption, and extends catalyst life. Operators can also simulate various feedstock scenarios for smarter decision-making.
The result: improved reactor efficiency, minimized downtime, and enhanced refinery profitability through increased product output and lower operational costs.
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LC-FINING Reactor Performance Optimizer
Hybrid Model
Our model optimizes conversion by accurately predicting the maximum possible yield, leveraging the Feed Operability Index and the current predicted SHFT value in Vacuum Residue. This conversion target is then used by the optimizer to generate precise DCS operator set points—such as Heater COT, Reactor CAT, and Feed H2 flow – guiding operators to achieve the optimal conversion efficiently and reliably.
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Transform your Challenges into Opportunities
The LC-Fining 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 SHFT 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 fouling the vacuum tower.
Benefit
Optimized Reactor Efficiency: Utilize our Conversion and SHFT model to boost conversion while minimizing vacuum tower fouling.
Pain Point
Insufficient vacuum residue SHFT values
Benefit
Hourly Insights on Vacuum Residue: Leverage our SHFT model for real-time predictions of vacuum residue values.
Pain Point
High Hydrogen Consumption
Benefit
Reduced Excess H2: Lower H₂ consumption during operations.
Pain Point
Reduced conversion efficiency lowers valuable products' yields
Benefit
Optimizing temperature and hydrogen flow boosts reactor performance, increasing diesel and naphtha yields while enhancing efficiency and minimizing waste.
Pain Point
Increased residues strain resources
Benefit
Advanced analytics and thermodynamic insights reduce residue, lowering energy and hydrogen consumption while minimizing equipment wear and extending its lifespan.
Pain Point
Catalyst deactivation leads to higher maintenance costs and reduced refinery flexibility.
Benefit
Advanced analytics and thermodynamic insights reduce residue, lowering energy and hydrogen consumption while minimizing equipment wear and extending its lifespan.
Performance Deviation Prediction
Takes the Feed Oil LIMS Data and calculates the FOI and predicts the Vacuum Residue SHFT at current Operation
Key Parameter Monitoring
The Operator Selects the mode of Optimization as
- Fixed Conversion
- Maximum Conversion
Process and Efficiency Trends
Using the FOI and predicted SHFT Value 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
