%0 Generic
%A Maldonado de León, Héctor
%A Straathof, Adrie
%A Haringa, Cees
%D 2024
%T Data underlying the publication:  Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations
%U 
%R 10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6.v1
%K Computational fluid dynamics
%K bioprocess
%K industrial fermentation
%K Compartment modelling
%K CFD
%K fermentation
%X <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>
%I 4TU.ResearchData