State surveillance and fault diagnosis of distillation columns using residual network-based passive acoustic monitoring

doi:10.1016/j.cjche.2024.09.026

中国化学工程学报 ›› 2025, Vol. 77 ›› Issue (1): 248-258.DOI: 10.1016/j.cjche.2024.09.026

State surveillance and fault diagnosis of distillation columns using residual network-based passive acoustic monitoring

Haotian Zheng¹, Zhixi Zhang¹, Guangyan Wang², Yatao Wang³, Jun Liang¹, Weiyi Su¹, Yuqi Hu¹, Xiong Yu¹, Chunli Li¹, Honghai Wang¹

1. School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin 300130, China;
2. School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China;
3. Coal Chemical R&D Center of Kailuan Group, Tangshan 063007, China

收稿日期:2024-01-14 修回日期:2024-09-19 接受日期:2024-09-22 出版日期:2025-01-28 发布日期:2024-11-13
通讯作者: Guangyan Wang,E-mail:wanggy@tjcu.edu.cn;Chunli Li,E-mail:1989028@hebut.edu.cn
基金资助:
The authors would like to gratefully acknowledge the financial support granted by the National Natural Science Foundation of China (22308079), the Natural Science Foundation of Hebei Province, China (B2022202008, B2023202025), the Science and Technology Project of Hebei Education Department, China (BJK2022037).

State surveillance and fault diagnosis of distillation columns using residual network-based passive acoustic monitoring

Haotian Zheng¹, Zhixi Zhang¹, Guangyan Wang², Yatao Wang³, Jun Liang¹, Weiyi Su¹, Yuqi Hu¹, Xiong Yu¹, Chunli Li¹, Honghai Wang¹

1. School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin 300130, China;
2. School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China;
3. Coal Chemical R&D Center of Kailuan Group, Tangshan 063007, China

Received:2024-01-14 Revised:2024-09-19 Accepted:2024-09-22 Online:2025-01-28 Published:2024-11-13
Contact: Guangyan Wang,E-mail:wanggy@tjcu.edu.cn;Chunli Li,E-mail:1989028@hebut.edu.cn
Supported by:
The authors would like to gratefully acknowledge the financial support granted by the National Natural Science Foundation of China (22308079), the Natural Science Foundation of Hebei Province, China (B2022202008, B2023202025), the Science and Technology Project of Hebei Education Department, China (BJK2022037).

摘要/Abstract

摘要： The operational state of distillation columns significantly impacts product quality and production efficiency. However, due to the complex operation and diverse influencing factors, ensuring the safety and efficient operation of the distillation columns becomes paramount. This research combines passive acoustic monitoring with artificial intelligence techniques, proposed a technology based on residual network (ResNet), which involves the transformation of the acoustic signals emitted by three distillation columns under different operating states. The acoustic signals were initially in one-dimensional waveform format and then converted into two-dimensional Mel-Frequency Cepstral Coefficients spectrogram database using fast Fourier transform. Ultimately, this database was employed to train a ResNet for the purpose of identifying the operational states of the distillation columns. Through this approach, the operational states of distillation columns were monitored. Various faults, including flooding, entrainment, dry-tray, etc., were diagnosed with an accuracy of 98.91%. Moreover, an intermediate transitional state between normal operation and fault was identified and accurately recognized by the proposed method. Under the transitional state, the acoustic signals achieved an accuracy of 97.85% on the ResNet, which enables early warnings before faults occur, enhancing the safety of chemical production processes. The approach presents a powerful tool for the monitoring and diagnosis of chemical equipment, particularly distillation columns, ensuring the safety and efficiency.

关键词: Distillation, Column, Acoustic signal, Neural network

Abstract: The operational state of distillation columns significantly impacts product quality and production efficiency. However, due to the complex operation and diverse influencing factors, ensuring the safety and efficient operation of the distillation columns becomes paramount. This research combines passive acoustic monitoring with artificial intelligence techniques, proposed a technology based on residual network (ResNet), which involves the transformation of the acoustic signals emitted by three distillation columns under different operating states. The acoustic signals were initially in one-dimensional waveform format and then converted into two-dimensional Mel-Frequency Cepstral Coefficients spectrogram database using fast Fourier transform. Ultimately, this database was employed to train a ResNet for the purpose of identifying the operational states of the distillation columns. Through this approach, the operational states of distillation columns were monitored. Various faults, including flooding, entrainment, dry-tray, etc., were diagnosed with an accuracy of 98.91%. Moreover, an intermediate transitional state between normal operation and fault was identified and accurately recognized by the proposed method. Under the transitional state, the acoustic signals achieved an accuracy of 97.85% on the ResNet, which enables early warnings before faults occur, enhancing the safety of chemical production processes. The approach presents a powerful tool for the monitoring and diagnosis of chemical equipment, particularly distillation columns, ensuring the safety and efficiency.

Key words: Distillation, Column, Acoustic signal, Neural network

Haotian Zheng, Zhixi Zhang, Guangyan Wang, Yatao Wang, Jun Liang, Weiyi Su, Yuqi Hu, Xiong Yu, Chunli Li, Honghai Wang. State surveillance and fault diagnosis of distillation columns using residual network-based passive acoustic monitoring[J]. 中国化学工程学报, 2025, 77(1): 248-258.

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State surveillance and fault diagnosis of distillation columns using residual network-based passive acoustic monitoring

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