A soft sensor modeling framework embedded with domain knowledge based on spatio-temporal deep LSTM for process industry

• Published in: Engineering applications of artificial intelligence 2023-11, Vol.126, p.106847, Article 106847
• Main Authors: Zhou, Jia-yi, Yang, Chun-hua, Wang, Xiao-li, Cao, Si-yu
• Format: Article
• Language: English
• Subjects: Deep learning; Domain knowledge; Process industry; Soft sensor modeling; Spatio-temporal deep LSTM
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2023.106847

In the process industries, the complex mechanisms, the many variables with complex interactions, the high uncertainty and errors in instrumentation, etc. make it very difficult to build accurate soft sensor models. Domain knowledge plays a very important role in soft sensor modeling. However, current data-driven methods lack domain knowledge of the specific processes. Therefore, a spatio-temporal deep learning soft sensor modeling framework embedded with domain knowledge is proposed in this paper. First, the time-delays between the key variable and the other process variables are analyzed to align the process variables temporally and select secondary variables. A spatio-temporal structure model (STSM) of the process is then constructed by using domain knowledge to decouple the sub-processes. Finally, a deep Long Short-Term Memory (LSTM) model embedded with the STSM is proposed to learn the characteristics of the spatiotemporal nonlinear dynamic features between the sub-process variables and the key variable in industrial processes. The effectiveness of the framework is verified by prediction of the key indices in two mineral processing processes.