Hybrid Physics-Data Models for Energy Efficiency Optimization in Heavy Process Industries
Hybrid Physics-Data Models for Energy Efficiency Optimization in Heavy Process Industries
Description
Details
Context and Problem Statement
Heavy process industries are energy intensive and often rely on expert-derived operating setpoints. Purely data-driven models may extrapolate poorly and violate physical consistency, while first-principles models may not capture fouling, degradation, or feedstock variability.
Research Question
Does a hybrid model constrained by mass and energy balances generalize better outside observed operating conditions than a purely data-driven model, and can it support safe setpoint recommendations?
Proposed Approach
Enforce conservation equations in a hybrid process model, learn residual dynamics from data, optimize setpoints under quality and safety constraints, and use sensitivity analysis to identify robust energy-saving levers.
Expected Contribution
A quantitative demonstration of the extrapolation benefit of physical constraints and an operational setpoint-optimization framework.
Expected Prototype
An operator advisory tool displaying expected energy savings, active constraints, and predictive uncertainty.
Datasets
Historical plant supervisory data, Tennessee Eastman, and laboratory quality analyses.
Challenges
Data synchronization, sensor drift, limited ability to experiment on live plants, and energy-quality trade-offs.
Research Question
Innovation
Expected Deliverable
Technologies
Required Skills
- Process Engineering and Thermodynamics
- Machine Learning and Optimization
- Industrial Data Processing
- Collaboration with Plant Operations Teams
Datasets
- Historical process data from an industrial partner
- Tennessee Eastman Process benchmark
- Laboratory product-quality analyses