Fault diagnosis for distillation process based on CNN–DAE

Chuankun Li; Dongfeng Zhao; Shanjun Mu; Weihua Zhang; Ning Shi; Lening Li

doi:10.1016/j.cjche.2018.12.021

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Fault diagnosis for distillation process based on CNN–DAE

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Fault diagnosis for distillation process based on CNN–DAE
- Chinese Journal of Chemical Engineering Vol. 27, Issue 3, Pages: 598-604(2019)
- Affiliations：
  
  1. 2 State Key Laboratory of Safety and Control for Chemicals, SINOPEC Qingdao Research Institute of Safety Engineering,Qingdao,China,266071
  2. 3 College of Chemical Engineering, China University of Petroleum,Qingdao,China,266580
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2018.12.021
  CLC：
- Published：2019
- Accepted：
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Chuankun Li, Dongfeng Zhao, Shanjun Mu, Weihua Zhang, Ning Shi, Lening Li. Fault diagnosis for distillation process based on CNN–DAE[J]. Chinese Journal of Chemical Engineering, 2019, 27(3): 598-604.
DOI：

Chuankun Li, Dongfeng Zhao, Shanjun Mu, Weihua Zhang, Ning Shi, Lening Li. Fault diagnosis for distillation process based on CNN–DAE[J]. Chinese Journal of Chemical Engineering, 2019, 27(3): 598-604. DOI： 10.1016/j.cjche.2018.12.021.

摘要

Abstract

Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column

it is difficult to use deep auto-encoders (DAEs) alone to achieve good results in detecting and diagnosing faults

in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks (CNNs) and DAEs

by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models

in terms of the accuracy of fault detection in such a process. Also

with the increase of structural layers of the CNN-DAE model

the diagnostic accuracy will be improved

with an optimal accuracy of 92.2%.

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