Combining multiscale decomposition with time-series modeling: An approach for petrochemical processes

Luo Mengjie; Zhang Mengxuan; Shi Xiaogang; Lan Xingying; Liu Yongdong; Li Wei

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Combining multiscale decomposition with time-series modeling: An approach for petrochemical processes

Updated：2026-09-17

- Combining multiscale decomposition with time-series modeling: An approach for petrochemical processes
- Chinese Journal of Chemical Engineering Vol. 95, Issue 7, Pages: 313-327(2026)
- Affiliations：
  
  State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing, Beijing 102249, China
  China Industrial Control Systems Cyber Emergency Response Team, Beijing 100040, China
- Author bio：
  
  *Corresponding authors. E-mail addresses:lanxy@cup.edu.cn(X. Lan)
  *Corresponding authors. E-mail addresses:liuyongdong@cics-cert.org.cn(Y. Liu).
- Funds：
- DOI：
  CLC：
- Received：15 April 2025，
  
  Revised：2026-03-01，
  
  Accepted：03 March 2026，
  
  Online First：01 April 2026，
  
  Published：2026-07
- Accepted：
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Luo Mengjie, Zhang Mengxuan, Shi Xiaogang, et al. Combining multiscale decomposition with time-series modeling: An approach for petrochemical processes[J]. Chinese Journal of Chemical Engineering, 2026, 95(7): 313-327.
DOI：

Luo Mengjie, Zhang Mengxuan, Shi Xiaogang, et al. Combining multiscale decomposition with time-series modeling: An approach for petrochemical processes[J]. Chinese Journal of Chemical Engineering, 2026, 95(7): 313-327. DOI：

摘要

Abstract

Accurately predicting key pr

oduct features is essential for process optimization and safety in petrochemical processes. However

conventional data-driven AI models often struggle to adequately capture the strong nonlinearity and multi-scale characteristics of these systems. To address this challenge

this paper proposes a multi-scale prediction framework that integrates variational mode decomposition (VMD)

attention mechanisms

and temporal convolutional networks (TCN)

referred to as DATCN. This framework facilitates the extraction of multi-scale features across temporal

spatial

and frequency domains

enabling cross-scale interaction modeling in complex processes. Comparative experiments on the Tennessee Eastman (TE) benchmark and industrial fluid catalytic cracking (FCC) long-term time-series datasets demonstrate that the proposed model achieves superior prediction accuracy (RMSE reduced by 24.75% to 37.26%

versus

other deep-learning models). The results demonstrate the framework's capability to address both nonlinear dynamics and spatiotemporal heterogeneity

offering a promising solution for intelligent optimization in petrochemical production.

关键词

Keywords

references

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