Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction

Zhuang Yu; Zhang Zhongyi; Tao Jin; Li Yi; Li Fan; Wang Yu; Zhang Lei; Du Jian

doi:10.1016/j.cjche.2025.09.016

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Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction

Updated：2026-03-13

- Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction
- Chinese Journal of Chemical Engineering Vol. 89, Issue 1, Pages: 132-144(2026)
- Affiliations：
  
  Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
  COFCO Biotechnology Co., Ltd., Beijing 100005, China
- Author bio：
  
  Corresponding author. E-mail address: dujian@dlut.edu.cn(J. Du).
- Funds：
- DOI：10.1016/j.cjche.2025.09.016
  CLC：
- Received：13 June 2025，
  
  Revised：2025-08-27，
  
  Accepted：01 September 2025，
  
  Online First：27 October 2025，
  
  Published：2026-01
- Accepted：
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Zhuang Yu, Zhang Zhongyi, Tao Jin, et al. Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction[J]. Chinese Journal of Chemical Engineering, 2026, 89(1): 132-144.
DOI：

Zhuang Yu, Zhang Zhongyi, Tao Jin, et al. Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction[J]. Chinese Journal of Chemical Engineering, 2026, 89(1): 132-144. DOI： 10.1016/j.cjche.2025.09.016.

摘要

Abstract

Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes. However

the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities. This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory (LSTM) networks to capture global temporal patterns

thereby taking full advantage of available data. In addition

variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance. Furthermore

by analyzing the attention weights

the model quickly identifies the key variables that significantly affect the predictions. Finally

the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an

value of 0.9392

which represents a 4% to 9% improvement over traditional models and demonstrates the superiority of the proposed approach.

关键词

Keywords

references

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