With the increased availability of process data, data-driven methods are becoming more appealing to use in process modelling. Data-driven methods are especially interesting in systems where the existing first-principles models are not adequate in describing the full system dynamics. This work compares a serial semi-parametric hybrid-modelling approach, integrating Machine Learning and first-principles modelling, to a stochastic greybox modelling approach proposed by (Kristensen et al. 2004). Through a CSTR case study both modelling approaches shows accurate predicting performance and robustness towards noisy data. The stochastic greybox modelling is found to be more robust towards measurement frequency as the true model structure is provided when fitting the parameters. Through this work, it is found that good modelling performance can be achieved without providing an explicit model structure by utilizing process data.
|33rd European Symposium on Computer Aided Process Engineering
|18/06/2023 → 21/06/2023
|Computer Aided Chemical Engineering
- Design methods
- Mechanistic Modelling
- Gradient SMB