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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.
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.
Convolution neural network algorithm-based fouling organisms classification model of seawater circulation cooling system
ZHANG Yi (author)
2024
Article (Journal)
Electronic Resource
Unknown
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