cff-version: 1.2.0
abstract: "<p>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.</p>"
authors:
  - family-names: Maldonado de León
    given-names: Héctor
    orcid: "https://orcid.org/0009-0006-8266-2121"
  - family-names: Straathof
    given-names: Adrie
    orcid: "https://orcid.org/0000-0003-2877-4756"
  - family-names: Haringa
    given-names: Cees
title: "Data underlying the publication:  Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6.v1
license: CC BY-NC-SA 4.0
date-released: 2024-12-11