BP neural network predictive model for stray current density of a buried metallic pipeline

A. Lin Cao; Qing Jun Zhu; Sheng Tao Zhang; Bao Rong Hou

doi:10.1108/00035591011075869

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Anti-Corrosion Methods and Materials

ISSN: 0003-5599

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BP neural network predictive model for stray current density of a buried metallic pipeline

Document Information:
Title:	BP neural network predictive model for stray current density of a buried metallic pipeline
Author(s):	A. Lin Cao, (College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China and Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China), Qing Jun Zhu, (Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China), Sheng Tao Zhang, (College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China), Bao Rong Hou, (Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China)
Citation:	A. Lin Cao, Qing Jun Zhu, Sheng Tao Zhang, Bao Rong Hou, (2010) "BP neural network predictive model for stray current density of a buried metallic pipeline", Anti-Corrosion Methods and Materials, Vol. 57 Iss: 5, pp.234 - 237
Keywords:	Corrosion, Electric current, Pipelines, Predictive process
Article type:	Research paper
DOI:	10.1108/00035591011075869 (Permanent URL)
Publisher:	Emerald Group Publishing Limited
Acknowledgements:	This paper was funded by China National Offshore Oil Corporation (JDBF-XXJS-08-ZC-059).
Abstract:	Purpose – The purpose of this paper is to analyze and estimate the stray current corrosion hazard of a buried metallic pipeline using a predictive model for stray current density. Design/methodology/approach – A predictive model for stray current density of the buried metallic pipeline was built, using a back propagation (BP) neural network method and experimental data. The accuracy of the model was tested using test samples. The single sensitivity analysis predictive method was used to establish the relationship between stray current density with the soil resistivity. The effects of buried depth and the pipe-to-ground voltage offset were researched using this network model. Findings – The feasibility of the BP neural network to forecast stray current effects from the buried metallic pipeline was confirmed. Originality/value – The paper provides a new method to analyze and estimate the stray current corrosion hazard of buried metallic pipelines.