Modeling an Adaptive Hybrid Soft Sensor with Co-training Learning toward Applications in Wastewater Treatment

• Published in: Industrial & engineering chemistry research 2023-10, Vol.62 (41), p.16841-16853
• Main Authors: Wu, Jing, Tang, Lili, Jin, Shaoqin, Li, Xuan, Liu, Huan, Li, Dong, Liu, Yiqi, Wang, Qilin
• Format: Article
• Language: English
• Subjects: Process Systems Engineering
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/acs.iecr.3c03067

In wastewater treatment processes, sampling delays, resulting from difficult-to-measure but important variables, usually shape wastewater treatment processes (WWTPs) as multirate systems. In addition, when soft sensor models are applied to the wastewater treatment process, the performance of the model prediction always degrades because of the time-varying characteristics, uncertainty in process variables, and inaccurate measurement instruments. In this paper, an adaptive hybrid soft sensor model with semisupervised learning is proposed to deal with multiple rate issues in the WWTPs. First, the method uses a semisupervised learning approach, called the co-training framework algorithm, to refine useful information hidden in partially sampling-rate-inconsistent data and then solves the multirate system problem with the optimized model. Second, two different types of regression models, Recursive Partial Least Squares and Recursive Extreme Learning Machine, are used to model and predict the hard-to-measure variables as well as to update the prediction model adequately and finally to solve the performance degradation of the soft sensor model. Additionally, we validate the proposed method with two cases, namely, a simulation data set and a full-scale WWTP data set. The experimental results show that the proposed method in this paper can achieve a shorter time consumption and better prediction accuracy than standard soft sensor models.