Naphtha Cracker Real Time Optimization
Peak Performance, Product Consistency, Enhanced Profitability
Physics Meets AI: Hybrid Models for Optimization.
Naphtha Cracker Real Time Optimization Solution—an integrated First Principles Simulation and AI/ML-powered platform that maximizes margin by tapping into your plant data system 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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Ethylene Heater Optimizer
Hybrid Model
A hybrid model integrates a first-principles model (based on physical laws) with a machine learning model (data-driven predictions) to enhance accuracy and flexibility. The first-principles model provides a structured, mechanistic understanding of the system, while machine learning fills in the gaps, manages uncertainties, and adapts to complex, real-world scenarios. By combining these, the fundamental engineering laws in the first-principles model work together with data-driven algorithms in the prediction model to optimize compressor performance.
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Transform your Challenges into Opportunities
By adopting a Real-Time Optimization (RTO) solution for ethylene heaters, you can turn challenges into strategic opportunities. With the right tools, your operations can achieve greater efficiency, reduced costs, and improved sustainability—all while maximizing profitability.
Pain Point
Struggling to boost throughput and profitability.
Benefit
Optimize throughput and profit with precise Heater COT and Steam-to-Oil Ratio adjustments..
Pain Point
Uncertainty in determining the ideal Heater COT set point.
Benefit
Get accurate Heater COT set points tailored to your feed using optimizer insights and PYPS+ analysis
Pain Point
Unclear achievable yield for specific feed compositions
Benefit
Unlock insights with the Scenario Analysis feature, which provides the potential yield based on Naphtha feed composition
Performance Deviation Prediction
The PYPS+ Model takes the Plant Feed Composition, Feed rate and the current DCS operating data as input
Key Parameter Monitoring
The operator selects the mode of optimization:
1.Margin Optimization (Profit maximization)
2.Yield Optimization (Increase in Propylene+Ethylene Yield)
Process and Efficiency Trends
Based on the Optimizer mode selection and the Scenario Analysis run (pyps+) the RTO provides the Set points for the operator to achieve the desired results
Operational Benchmarking
Model runs a Scenario Analysis again once the optimizer run is complete to analyze the actual results in PYPS+ once the changes are made
Actionable Insights for Improvement
The Margins are calculated for before RTO, With RTO and post RTO are calculated for profit and results are published in a report
