Data underlying the publication: Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations

DOI:10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6.v2
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6

Datacite citation style

Maldonado de León, Héctor; Straathof, Adrie; Haringa, Cees (2025): Data underlying the publication: Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Version 2 - 2025-02-21 (latest)
Version 1 - 2024-12-11

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.

History

  • 2024-12-11 first online
  • 2025-02-21 published, posted

Publisher

4TU.ResearchData

Format

Flux matrices/.csv, dynamic compartment model/ .h5, Fluent Case Files/.cas

Organizations

TU Delft, Faculty of Applied Sciences, Department of Biotechnology

DATA

Files (6)