TY - DATA T1 - Data underlying the publication: Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations PY - 2024/12/11 AU - Héctor Maldonado de León AU - Adrie Straathof AU - Cees Haringa UR - DO - 10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6.v1 KW - Computational fluid dynamics KW - bioprocess KW - industrial fermentation KW - Compartment modelling KW - CFD KW - fermentation N2 -

This data includes the files for developing a workflow to simulate fed-batch fermentations using a hybrid modeling approach based on flow-informed compartment models (CFD-CM) and a machine learning (ML) method. The proposed workflow circumvents the need for re-calibration of the compartment model upon changes in the working volume and stirring rate of the system. This is done using an inferring module based on a neural network. The methods to deploy the framework are described in the publication 'Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations'. The dataset includes the case and data files from FLUENT to generate the parameterization of the compartment models (i.e., intercompartmental fluxes - .csv files) used for training and testing of the neural network, which is also included. These files aim to ensure the reproducibility of the results presented in the corresponding publication.

ER -