Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling system
In the water intake head and pipeline of the seawater circulation cooling system, fouling organisms will block the pipeline, accelerate corrosion, and seriously affect the normal operation of the equipment. The biocide dosing scheme is closely related to the type of fouling organisms. Due to the dif...
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| Format: | Article |
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Editorial Office of Industrial Water Treatment
2024-12-01
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| Series: | Gongye shui chuli |
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| Online Access: | https://www.iwt.cn/CN/10.19965/j.cnki.iwt.2023-1122 |
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| author | ZHANG Yi |
| author_facet | ZHANG Yi |
| author_sort | ZHANG Yi |
| collection | DOAJ |
| description | In the water intake head and pipeline of the seawater circulation cooling system, fouling organisms will block the pipeline, accelerate corrosion, and seriously affect the normal operation of the equipment. The biocide dosing scheme is closely related to the type of fouling organisms. Due to the difficulty of monitoring, the fixed biocide dosing scheme is usually adopted. The attachment of fouling organisms on the pipe wall structures is mainly in the form of aggregation of dominant species. Therefore, cameras can be installed at the head of the seawater pipe and in the pipe to realize the monitoring of fouling organisms, so as to adjust the dosing scheme in time. In this paper, the convolution neural network algorithm was used to establish the classification and recognition model of fouling organisms, and to realize the automatic classification and recognition of common fouling organisms. The cross entropy loss function and accuracy rate were used as model evaluation indicators to train the model. The model could be used for automatic identification of fouling organisms in automatic dosing equipment. On this basis, with automatic dosing equipment, the automatic real-time adjustment of dosing scheme could be realized to improve the refined management level of seawater circulation cooling system. |
| format | Article |
| id | doaj-art-1d1204ce0ac44afaa6e8ef9bbaeec3c0 |
| institution | Kabale University |
| issn | 1005-829X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Industrial Water Treatment |
| record_format | Article |
| series | Gongye shui chuli |
| spelling | doaj-art-1d1204ce0ac44afaa6e8ef9bbaeec3c02025-01-14T02:07:08ZzhoEditorial Office of Industrial Water TreatmentGongye shui chuli1005-829X2024-12-01441216016510.19965/j.cnki.iwt.2023-11221005-829X（2024）12-0160-06Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling systemZHANG Yi0National Energy Zhejiang Ninghai Power Generation Co., Ltd., Ningbo315000, ChinaIn the water intake head and pipeline of the seawater circulation cooling system, fouling organisms will block the pipeline, accelerate corrosion, and seriously affect the normal operation of the equipment. The biocide dosing scheme is closely related to the type of fouling organisms. Due to the difficulty of monitoring, the fixed biocide dosing scheme is usually adopted. The attachment of fouling organisms on the pipe wall structures is mainly in the form of aggregation of dominant species. Therefore, cameras can be installed at the head of the seawater pipe and in the pipe to realize the monitoring of fouling organisms, so as to adjust the dosing scheme in time. In this paper, the convolution neural network algorithm was used to establish the classification and recognition model of fouling organisms, and to realize the automatic classification and recognition of common fouling organisms. The cross entropy loss function and accuracy rate were used as model evaluation indicators to train the model. The model could be used for automatic identification of fouling organisms in automatic dosing equipment. On this basis, with automatic dosing equipment, the automatic real-time adjustment of dosing scheme could be realized to improve the refined management level of seawater circulation cooling system.https://www.iwt.cn/CN/10.19965/j.cnki.iwt.2023-1122seawater coolingconvolutional neural networkscomputer visionclassification of fouling organisms |
| spellingShingle | ZHANG Yi Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling system Gongye shui chuli seawater cooling convolutional neural networks computer vision classification of fouling organisms |
| title | Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling system |
| title_full | Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling system |
| title_fullStr | Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling system |
| title_full_unstemmed | Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling system |
| title_short | Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling system |
| title_sort | convolution neural network algorithm based fouling organisms classification model of seawater circulation cooling system |
| topic | seawater cooling convolutional neural networks computer vision classification of fouling organisms |
| url | https://www.iwt.cn/CN/10.19965/j.cnki.iwt.2023-1122 |
| work_keys_str_mv | AT zhangyi convolutionneuralnetworkalgorithmbasedfoulingorganismsclassificationmodelofseawatercirculationcoolingsystem |