Data underlying the publication: Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations
doi: 10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6
CC BY-NC-SA 4.0
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.
- 2024-12-11 first online, published, posted
DATA
- 4,718 bytesMD5:
a53a6cea7f41abc2cd5819d9881c437dREADME.md - 1,607 bytesMD5:
3482a15bbdeb58fd0aa530b7ec623d56CITATION.cff - 25,990,842 bytesMD5:
3f69837332c07ac41f8bccf6cd9c0ae8CM_files.zip - 7,695,062,611 bytesMD5:
d3a0659fa068cd211c3d9914b406bbb7FLUENT_files.zip - 7,632 bytesMD5:
399c1783b65a01648a230d9578d3ac03LICENSE.md - 6,599 bytesMD5:
0762781409a136a83b2bfd82aeccc3bfrequirements.txt -
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