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周佳怡2023自动化大会会议报告

发布时间:2024年01月16日 作者: 浏览次数:

报告时间:2024119日晚上1900-1930

报告地点:民主楼313

报告人:周佳怡

报告标题:

A Soft Sensor Modeling Framework Embedded with Domain Knowledge based on Spatio-Temporal Deep LSTM for Process Industry

报告摘要:

In the process industries, the complex mechanisms, the many variables with complex interactions, 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-delay between the key variable and the other process variables is analyzed to align the process variables temporally and to 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 variables in industrial processes. The effectiveness of the model framework is verified by prediction of the key indices in two mineral processing processes.


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