Divide and conquer: Benefits from modular surrogate modelling of flowsheets
The availability of sufficient plant data, both in quality and quantity, is a barrier to widespread usage of Machine Learning (ML) models in the process industry. Flowsheet simulators can solve this problem effectively by providing reliable data that considers the underlying physical models. They can also act as evaluation and validation tools for ML models. The resulting hybrid models unleash a true potential for ML applications by combining data-driven predictions with mass and energy balances. The ML model then becomes an attractive alternative to rigorous simulation, which can solve more complex process engineering problems in lower calculation times.
In this presentation, we introduce our 5-step workflow for building and evaluating ML models based on rigorous simulation models. We build an ML model for a pre-reformer reactor and evaluate it by implementing the model as a unit operation.