Affiliation:
1. TU Dortmund Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering Emil-Figge-Straße 70 44227 Dortmund Germany
2. Evonik Operations GmbH Rodenbacher Chaussee 4 63457 Hanau Germany
Abstract
AbstractFermentation 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.
Subject
Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry
Cited by
4 articles.
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