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 & thermodynamics models
- However, doesn’t capture all phenomenon
- Is 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
- Improve predictive and prescriptive insights
- Self-sustaining Fit-for-Purpose models
Different types of Hybrids and what suits
your plant/process!
AI Dominant Hybrid
AI Dominant Hybrid
- Applied in complex reaction unit operations, new material processes and new technology processes such as Hydrocracker reactor model
- AI/ML model developed based on plant/experimental data, augmented with first principles, constraints and domain knowledge
- Generates completely new predictive, more accurate models, fully scaling the use of AI in the plant.
