文献类型：期刊文献

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

作者：Cheng, Xinyue[1];Yu, Zhenhua[1];Wang, Guan[2];Jiang, Qingchao[1];Cao, Zhixing[1,2]

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：2024

卷号：137

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20243216845141);WOS:【SCI-EXPANDED(收录号:WOS:001292573600001)】；

基金：The authors gratefully acknowledge the support from the following foundations: National Key R & D Program of China under Grant No. 2021YFC2101100, National Natural Science Foundation of China under Grant No. 62322309, Shanghai Science and Technology Innovation Action Plan under Grant No. 23S41900500, and Shanghai Rising-Star Program under Grant No. 21QA1402400.

语种：英文

外文关键词：Soft sensor; Variational autoencoder; Fermentation process; Semi-supervised learning

摘要：Data-driven models have shown broad application prospects in soft sensor modeling. However, numerous challenges persist. On the one hand, data-driven soft sensor methods have high requirements on data quality. On the other hand, models relying on limited experimental data often lack physical interpretability. To tackle these challenges, a semi-supervised soft sensor method (PMVAER) for fermentation processes based on physical monotonicity and variational autoencoders (VAEs) is introduced. First, physical monotonicity constraint is incorporated into the loss function of VAEs for regression to ensure that the model's predictions adhere to physical feasibility. Next, considering the disparate sampling frequencies for process and quality variables, this approach is extended to learn from unlabeled data, creating a semi-supervised soft sensor model. The proposed model is validated on simulation and real cases of penicillin fermentation. Comparisons with five other methods verify that the proposed method exhibits exceptional predictive accuracy along with enhanced generalization ability.