Complex
thermodynamic models such as the perturbed chain statistical
associating fluid theory (PC-SAFT) model describe the phase equilibria
in a chemical process in a very precise way; however, because of their
implicit and complex nature, the application of such models in process
simulation and optimization can lead to a high computational effort,
which may prevent the direct application of such models in process
simulation and optimization. In this contribution, we replace the
iterative calculation of the fugacity coefficient using PC-SAFT with
explicit surrogate models that are trained using a novel adaptive
sampling method.
Fermentation processes are difficult to describe using purely mechanistic relations as the underlying biochemical phenomena are complex and often not fully understood. In order to cope with this challenge, we developed an approach to augment standard dynamic model equations by data‐based components that are fitted to data using machine learning techniques, which results in dynamic gray‐box models. This methodology is applied here to the batch fermentation process of the sporulating bacterium Bacillus subtilis, using experimental data from a lab‐scale fermenter. The key step in developing the model is the estimation of a training set for the machine learning submodels. The quality of the resulting model is analyzed, and the predictions are compared with real data.
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