Ethylene Heater Optimizer
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 the two, 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 setpoint
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 Metrics
Performance Deviation Prediction
The PYPS+ Model takes the Plant Feed Composition, Feed rate, and the current DCS operating data as input
Key Parameter Monitoring
Process and Efficiency Trends
Based on the optimizer mode selection and the Scenario Analysis run (PYPS+), the RTO provides setpoints for the operator to achieve the desired results.
Operational Benchmarking
The model re-runs a scenario analysis after the optimizer run is complete to analyze actual results in PYPS+ once the changes are implemented.
Actionable Insights for Improvement
Margins are calculated for before RTO, with RTO, and after RTO to assess profit impact, and the results are published in a report.
