Semi-supervised soft sensor method for fermentation processes based on physical monotonicity and variational autoencoders-Reference-Cited by-同舟云学术

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Semi-supervised soft sensor method for fermentation processes based on physical monotonicity and variational autoencoders

Published:2024-11 Issue: Volume:137 Page:109065
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Cheng Xinyue,Yu Zhenhua,Wang Guan,Jiang Qingchao^ORCID,Cao Zhixing

Publisher

Elsevier BV

Reference46 articles.

1. Opening the black box: interpretable machine learning for geneticists;Azodi;Trends Genet.,2020

2. 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

3. Deep autoencoding models for unsupervised anomaly segmentation in brain MR images;Baur,2019

4. Physics-informed deep learning for one-dimensional consolidation;Bekele;J. Rock Mech. Geotech. Eng.,2021

5. A modular simulation package for fed-batch fermentation: penicillin production;Birol;Comput. Chem. Eng.,2002

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