文献类型：期刊文献

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

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

机构：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China;[2]State Key Laboratory of Bioreactor Engineering,East China University of Science and Technology,Shanghai 200237,China

年份：2026

卷号：92

期号：4

起止页码：205

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报(英文版)

基金：the National Natural Science Foundation of China(62322309);Shanghai Science and Technology Innovation Action Plan(23S41900500);Shanghai Pilot Program for Basic Research(22TQ1400100-16);Shanghai Explore Program under Grant 24TS1411700。

语种：英文

中文关键词：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.