Soft sensors based on interpretable learners for industrial-scale fed-batch fermentation: Learning from simulations-Reference-Cited by-同舟云学术

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Soft sensors based on interpretable learners for industrial-scale fed-batch fermentation: Learning from simulations

Published:2024-08 Issue: Volume:187 Page:108736
ISSN:0098-1354
Container-title:Computers & Chemical Engineering
language:en
Short-container-title:Computers & Chemical Engineering

Author:

Acosta-Pavas Juan Camilo^ORCID,Robles-Rodriguez Carlos Eduardo^ORCID,Griol David^ORCID,Daboussi Fayza,Aceves-Lara Cesar Arturo,Corrales David Camilo^ORCID

Publisher

Elsevier BV

Reference43 articles.

1. A novel unsupervised approach for batch process monitoring using deep learning;Agarwal;Comput. Chem. Eng.,2022

2. Unsupervised fault detection of pharmaceutical processes using long short-term memory autoencoders;Aghaee;Ind. Eng. Chem. Res.,2023

3. Alexandre, L., Costa, R.S., & Henriques, R. (2023). TriSig : Assessing the statistical significance of triclusters. https://doi.org/10.48550/ARXIV.2306.00643.

4. Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae;Bangi;Chem. Eng. Res. Des.,2022

5. Random Forests;Breiman,2001

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