A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

Yiming Bai; Shuaiyu Xiang; Feifan Cheng; Jinsong Zhao

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A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

Full Length Article | Updated：2026-01-06

- A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process
- A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process
- 中国化学工程学报（英文版） 2023年55卷第3期页码：266-276
- Affiliations：
  
  1. Department of Chemical Engineering, Tsinghua University,Beijing,China,100084
  2. Sinopec Engineering Incorporation,Beijing,China,100084
  3. Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University,Beijing,China,100084
- Author bio：
- Funds：
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  中图分类号：
- 纸质出版：2023
- Accepted：
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Yiming Bai, Shuaiyu Xiang, Feifan Cheng, 等. A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process[J]. 中国化学工程学报（英文版）, 2023,55(3):266-276.

Yiming Bai, Shuaiyu Xiang, Feifan Cheng, Jinsong Zhao. A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process[J]. Chinese Journal of Chemical Engineering, 2023, 55(3): 266-276.
Yiming Bai, Shuaiyu Xiang, Feifan Cheng, 等. A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process[J]. 中国化学工程学报（英文版）, 2023,55(3):266-276. DOI：

Yiming Bai, Shuaiyu Xiang, Feifan Cheng, Jinsong Zhao. A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process[J]. Chinese Journal of Chemical Engineering, 2023, 55(3): 266-276. DOI：

摘要

Abstract

With the increase in the complexity of industrial system

simply detecting and diagnosing a fault may be insufficient in some cases

and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data

this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis (DiPCA) and long short-term memory (LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance

which can provide the basis for fault prognosis and health management.

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