Research on corrosion rate prediction of buried pipeline based on PCA-BPNN model

Abstract

Abstract: In order to predict the corrosion rate of buried pipelines more accurately and reliably, the method integrating PCA analysis and BP artificial neural network simulation is studied. A buried oil pipeline of an oil and gas company in Shaanxi Province was selected to construct an 8-dimensional external corrosion index system, and the simulation training results were obtained in the PCA-multi-hidden layer BPNN model. The external corrosion index system was reduced to three dimensions by PCA pretreatment, so as to reduce the coupling influence brought by multi-element information. The BPNN model with the optimal hidden layer parameters was trained to predict the corrosion rate. The accuracy of the predicted value was obtained, and it was found that the number of accuracy greater than 95% of the improved method was 2.5 times that of the single BP method. In order to test the robustness of the PCA-multi-hidden layer BPNN method, an additional set of 20 groups of data are substituted for verification, reaffirming that the error of PCA-multi-hidden layer BPNN model is smaller and can better meet the needs of practical engineering.

Key words: buried pipes, corrosion rate, PCA-multiple hidden layer BPNN model

CLC Number:

TQ050.9

YU Yang, SUN Dong-liang. Research on corrosion rate prediction of buried pipeline based on PCA-BPNN model[J]. Journal of Lanzhou University of Technology, 2024, 50(4): 60-68.

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