A robust soft sensor based on artificial neural network for monitoring microbial lipid fermentation processes using Yarrowia lipolytica
Author(s)
Type
Journal Article
Abstract
Microbial oils produced by Yarrowia lipolytica offer an environmentally friendly and sustainable alternative to petroleum as well as traditional lipids from animals and plants. The accurate measurement of fermentation parameters, including the substrate concentration, dry cell weight, and lipid accumulation, is the foundation of process control, which is indispensable for industrial lipid production. However, it remains a great challenge to measure the complex parameters online during the lipid fermentation process, which is nonlinear, multivariate, and characterized by strong coupling. As a type of AI technology, the artificial neural network model is a powerful tool for handling extremely complex problems, and it can be employed to develop a soft sensor to monitor the microbial lipid fermentation process of Y. lipolytica. In this study, we first analyzed and emphasized the volume of sodium hydroxide and dissolved oxygen concentration as central parameters of the fermentation process. Then, a soft sensor based on a four-input artificial neural network model was developed, in which the input variables were fermentation time, dissolved oxygen concentration, initial glucose concentration, and additional volume of sodium hydroxide. This provides the possibility of online monitoring of dry cell weight, glucose concentration, and lipid production with high accuracy, which can be extended to similar fermentation processes characterized by the addition of bases or acids, as well as changes of the dissolved oxygen concentration.
Date Issued
2023-04-01
Date Acceptance
2022-12-13
Citation
Biotechnology and Bioengineering, 2023, 120 (4), pp.1015-1025
ISSN
0006-3592
Publisher
Wiley
Start Page
1015
End Page
1025
Journal / Book Title
Biotechnology and Bioengineering
Volume
120
Issue
4
Copyright Statement
Copyright © 2022 Wiley Periodicals LLC. This is the peer reviewed version of the following article: Wang, K., Zhao, W., Lin, L., Wang, T., Wei, P., Ledesma-Amaro, R., Zhang, A.-H., & Ji, X.-J. (2023). A robust soft sensor based on artificial neural network for monitoring microbial lipid fermentation processes using Yarrowia lipolytica. Biotechnology and Bioengineering, 120, 1015–1025. https://doi.org/10.1002/bit.28310, which has been published in final form at https://doi.org/10.1002/bit.28310. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/36522163
Subjects
ACCUMULATION
ACID PRODUCTION
artificial neural network
BIODIESEL PRODUCTION
Biotechnology & Applied Microbiology
GROWTH
Life Sciences & Biomedicine
lipid
Science & Technology
soft sensor
Yarrowia lipolytica
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2022-12-15