Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns
In chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2658 |
| _version_ | 1797614325266907136 |
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| author | Yi Liu Yuxin Jiang Zengliang Gao Kaixin Liu Yuan Yao |
| author_facet | Yi Liu Yuxin Jiang Zengliang Gao Kaixin Liu Yuan Yao |
| author_sort | Yi Liu |
| collection | DOAJ |
| description | In chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real time. Conventional flooding monitoring methods rely heavily on manual visual inspections or indirect information from process variables, which limit the real-time accuracy of results. To address this challenge, we proposed a convolutional neural network (CNN)-based machine vision approach for non-destructive detection of flooding in packed columns. Real-time images of the packed column were captured using a digital camera and analyzed with a CNN model, which was been trained on a dataset of recorded images to identify flooding. The proposed approach was compared with deep belief networks and an integrated approach of principal component analysis and support vector machines. The feasibility and advantages of the proposed method were demonstrated through experiments on a real packed column. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding, enabling process engineers to quickly respond to potential flooding events. |
| first_indexed | 2024-03-11T07:10:48Z |
| format | Article |
| id | doaj.art-c4dba7c7294b4521a467c6e3b8e4c5d9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:10:48Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c4dba7c7294b4521a467c6e3b8e4c5d92023-11-17T08:37:52ZengMDPI AGSensors1424-82202023-02-01235265810.3390/s23052658Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed ColumnsYi Liu0Yuxin Jiang1Zengliang Gao2Kaixin Liu3Yuan Yao4Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaShanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan 030051, ChinaDepartment of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, TaiwanIn chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real time. Conventional flooding monitoring methods rely heavily on manual visual inspections or indirect information from process variables, which limit the real-time accuracy of results. To address this challenge, we proposed a convolutional neural network (CNN)-based machine vision approach for non-destructive detection of flooding in packed columns. Real-time images of the packed column were captured using a digital camera and analyzed with a CNN model, which was been trained on a dataset of recorded images to identify flooding. The proposed approach was compared with deep belief networks and an integrated approach of principal component analysis and support vector machines. The feasibility and advantages of the proposed method were demonstrated through experiments on a real packed column. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding, enabling process engineers to quickly respond to potential flooding events.https://www.mdpi.com/1424-8220/23/5/2658flooding detectionnon-destructive evaluationdeep learningconvolutional neural networkimage processingclassification |
| spellingShingle | Yi Liu Yuxin Jiang Zengliang Gao Kaixin Liu Yuan Yao Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns Sensors flooding detection non-destructive evaluation deep learning convolutional neural network image processing classification |
| title | Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns |
| title_full | Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns |
| title_fullStr | Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns |
| title_full_unstemmed | Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns |
| title_short | Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns |
| title_sort | convolutional neural network based machine vision for non destructive detection of flooding in packed columns |
| topic | flooding detection non-destructive evaluation deep learning convolutional neural network image processing classification |
| url | https://www.mdpi.com/1424-8220/23/5/2658 |
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