Corrosion rate prediction model of carbon steel in regional soil based on BP artificial neural network

Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing

Volumn 31, Issue 12, 2009, Pages 1569-1575

  Qu, Liang Shan ^a,b   Li, Xiao Gang ^a   Du, Cui Wei ^a   He, Shu Quan ^b   Liu, Zhi Yong ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^b DAQING OILFIELD CO LTD   (China)

Author keywords

BP artificial neural network; Carbon steel; Prediction model; Soil corrosion

Indexed keywords

AIR CONTENT; BP ARTIFICIAL NEURAL NETWORK; CORROSION MODELS; CORROSION TESTS; FORECASTING MODELS; GENERALIZATION ABILITY; KEY FACTORS; LOGICAL RELATIONSHIPS; MASS TRANSFER PROCESS; MATLAB PLATFORM; PHYSICAL AND CHEMICAL PROPERTIES; PREDICTION MODEL; SHORT TERM PREDICTION; SOIL CORROSION; SOIL ENVIRONMENT; TOTAL DISSOLVED SALTS;

ATMOSPHERIC CORROSION; CARBON STEEL; CHEMICAL PROPERTIES; CORROSION RATE; DISSOLUTION; GEOLOGIC MODELS; MARTENSITIC STAINLESS STEEL; MATHEMATICAL MODELS; MATLAB; SOIL TESTING; SOILS; WATER CONTENT;

NEURAL NETWORKS;

EID: 74049148056     PISSN: 1001053X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. Song G L, Cao C N, Shi Z M. Review on electrochemistry measurement method of soil corrosion. Oil Gasfield Surf Eng, 1993, 12(3): 6
2. Feng G Q, Jin M H, Men X L. Fitting of corrosion data of carbon in several typical soils in China. Oil Gasfield Surf Eng, 2000, 19(3):45
3. Kim J G, Kim Y W. Cathodic protection criteria of thermally insulated pipeline buried in soil. Corros Sci, 2001, 43: 2011
4. Levlin E. Aeration cell corrosion of carbon steel in soil: in situ monitoring cell current and potential. Corros Sci, 1996, 38(12): 2083
5. Ismail A I M, El-Shamy A M. Engineering behaviour of soil materials on the corrosion of mild steel. Appl Clay Sci, 2009, 42:356
6. Jin M H, Men X L, Huang H T, et al. Corrosion mechanism of carbon steel in four types of soil. J Huazhong Univ Sci Technol Nat Sci Ed, 2002, 30(7):6)
7. Li S Y, Jung S, Park K, et al. Kinetic study on corrosion of steel in soil environments using electrical resistance sensor technique. Mater Chem Phys, 2007, 103:9
8. Li Y B, Huang K, Zhang L, et al. Artificial neural network-based evaluation on soil corrosion situation along pipelines. Oil Gas Storage Transp, 2007, 26(8):47
9. Guo Z H, Jin M H, Deng J L, et al. Predicting corrosion rate of mild steel in soil based on artificial neural network. J Chin Soc Corros Prot, 1996, 16(4):307
10. National Soil Corrosion Test Station Website. Test Method for Soil Corrosion of Materials. Beijing: Science Press, 1990
11. Luo J M, Deng W, Zhang X P, et al. Physical-chemical properties of sodic alkaline soil in micro scale and its influence on water and salinity transportation. J Zhejiang Univ Agric Life Sci, 2008, 34(6): 669
12. Li S L, Yang Z Y. Application of factors analysis for survey in regional soil corrosivity. Corros Sci Prot Technol, 1995, 7(3):263
13. Hornik K, Stinchcombe M, White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks, 1990, 3: 551
14. Zheng X X. Description of the corrosion rate of 16Mn pipeline steel in soil by using neural network. J Xi'an Shiyou Univ Nat Sci Ed, 2004, 19(1):74
15. Zhou S M. Process neural networks and its application in pipeline soil corrosion rate prediction. J Qiqihar Univ Nat Sci Ed, 2008, 24(4):6
16. Jin Y, Dong C F, Fu D M, et al. Application of neural network technology in corrosion research and engineering. Petrochem Corros Prot, 2001, 18(1): 13
17. Wang H T, Han E H, Ke W. Application of artificial neural network to the field of corrosion. Corros Sci Technol Prot, 2004, 16(3): 148
18. Li Y B, Huang K, Zhang L, et al. Artificial neural network-based evaluation on soil corrosion situation along pipelines. Oil Gas Storage Transp, 2007, 26(8): 47