Hybrid Process Modelling
Integrated AI and Data Intelligence for Enhanced Plant Operations.
Why Hybrid Process Modelling?
First-Principles Models
Based on engineering principles, process simulations, and thermodynamics models
- However, doesn’t capture all phenomenon
- Susceptible to errors and doesn’t reflect operating plant
AI/ ML
Models
Based on core data science principles and AI-ML modelling
- Big Data requirements
- Extrapolation issues on violating physical constraints
- Difficult to interpret if inferior data quality
Hybrid Process Modelling
First-principles foundations complement data-based AI predictions for precision!
- Accurately simulates complex processes in plants
- Improves predictive and prescriptive insights
- Self-sustaining Fit-for-Purpose models
Different Types of Hybrids & What Suits
Your Plant/Process
AI Dominant Hybrid
AI Dominant Hybrid
- Applied to complex reaction unit operations, new material processes, and new technology processes such as the Hydrocracker reactor model
- AI/ML models are developed based on plant/experimental data, augmented with first principles, process constraints, and domain knowledge
- Generates entirely new, more accurate predictive models, enabling full-scale AI deployment across plant operations
