Prediction of corrosion rate of submarine oil and gas pipelines based on ASO-BP neural network

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Abstract With the development and construction of offshore oil and gas pipeline network, the amount of pipeline data collection has increased accordingly. Excellent prediction models can cope with a large amount of data and accurately predict the corrosion rate of pipelines, which is of great significance to ensure the safe and healthy operation of pipelines. The idea of atomic search optimization algorithm (ASO) is introduced into BP (Back propagation) neural network, and ASO-BP neural network is constructed to predict the corrosion rate of submarine oil and gas pipelines. Taking 50 sets of field data as an example, Matlab was used for simulation calculations, and representative BP, GA-BP, and ACO-BP models were constructed for comparison. The corrosion rate data of submarine oil and gas pipelines were trained and predicted. The results show that the prediction accuracy of the ASO-BP model is high, the mean absolute percentage error (MAPE) of the model is 3.16%, and the prediction results are better than those of BP, GA-BP and ACO-BP, its reliability and nice prediction performance are verified, which provides a new method and idea for the prediction of submarine pipeline corrosion rate.

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	XIAO Rongge
	WANG Dong
	WANG Qinxue

Keywords： submarine oil and gas pipeline corrosion rate atomic search optimization algorithm BP neural network prediction accuracy

Received 2021-08-30;

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XIAO Rongge, WANG Dong, WANG Qinxue.Prediction of corrosion rate of submarine oil and gas pipelines based on ASO-BP neural network[J] Chemcial Industry and Engineering, 2022,V39(6): 109-116

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