文献类型：期刊文献

英文题名：A soft sensing method for biomanufacturing processes based on physics-informed variational learning with applications

作者：Yu, Zhenhua[1];Cheng, Xinyue[1];Wang, Guan[2];Sun, Lihua[1];Jiang, Qingchao[1];Zhong, Weimin[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2026

卷号：92

起止页码：205

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20261420422684);WOS:【SCI-EXPANDED(收录号:WOS:001737342700001)】；

基金：The authors gratefully acknowledge the support from the following foundations: the National Natural Science Foundation of China (62322309) , Shanghai Science and Technology Innovation Action Plan (23S41900500) , Shanghai Pilot Program for Basic Research (22TQ1400100-16) , and Shanghai Explore Program under Grant 24TS141170 0.

语种：英文

外文关键词：Bioprocesses; Soft sensing; Neural networks; Physics-informed neural network; Fermentation

摘要：Although neural network-based soft sensing in biomanufacturing processes shows substantial promise, many existing approaches either fail to encode physical prior knowledge or do not make effective use of unlabeled data. These limitations hinder both the generalization of the model and its adherence to physical consistency. To address these issues, this study proposes the Physics-Informed Variational Autoencoder Regression (PIVAER)-based soft sensing, which improves the VAE by incorporating: (1) monotonicity constraints on specified input-output relations to ensure that predictions respect known monotonic trends, and (2) embedded differential constraints derived from the kinetic equation. Soft penalties in the form of physics residual and monotonicity terms are embedded within a unified objective function, thereby enabling semi-supervised training that integrates both labeled and unlabeled data. Through this design, the PIVAER achieves improved physical consistency and generalization capability. Its effectiveness has been demonstrated by experiments conducted on both simulated and real penicillin fermentation datasets. Comparative experiments using state-of-the-art methods demonstrate superior predictive accuracy and enhanced generalization capability. (c) 2025 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.