B. A. Oladipo; O. O. Ajide; C. G. Monyei

Corrosion Assessment of some Buried Metal Pipes using Neural Network Algorithm

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Author(s)

B. A. Oladipo ^1,* O. O. Ajide ¹ C. G. Monyei ²

1. Department of Mechanical Engineering, University of Ibadan, Ibadan, Nigeria

2. Department of Electronic and Electrical Engineering, University of Leeds, Leeds, UK

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2017.06.03

Received: 25 Feb. 2017 / Revised: 8 Mar. 2017 / Accepted: 28 Mar. 2017 / Published: 8 Nov. 2017

Index Terms

Corrosion, Modified Artificial Neural Network, AISI 1015 Steel Pipe, Nickel Plated, weight loss

Abstract

The key aim of this assessment is to characterize the rate of corrosion of buried Nickel plated and non-plated AISI 1015 steel pipes using a Modified Artificial Neural Network on Matlab and taking the oil and gas area of Nigeria as a case study. Ten (10) metal specimens were used. Five (5) were nickel electroplated specimens buried differently in 5 plastic containers containing 5 different soil samples with the other 5 non-plated specimens also buried into the same 5 soil samples but different plastic containers. In carrying out the experiment, the data that was collected for 25 consecutive days were grouped into sets of input and output data. This was required so as to appropriately feed the modelling tool (Artificial Neural Network). The input data were; temperature of the soil sample, temperature of the immediate surroundings, and pH of the soil sample while the output data was weight loss. Conclusively, Modified Artificial Neural Network relationships between the varied selected input parameters that affects corrosion rate (soil sample temperature, immediate environment temperature and pH value) and the output parameter (Corrosion Penetration Rate) were derived. Also, soil sample temperature and the immediate surrounding temperature combined conditions had the strongest effect on corrosion penetration rate while the immediate surrounding temperature and the pH value combined conditions had the weakest effect on corrosion penetration rate.

Cite This Paper

B. A. Oladipo, O. O. Ajide, C. G. Monyei,"Corrosion Assessment of some Buried Metal Pipes using Neural Network Algorithm", International Journal of Engineering and Manufacturing(IJEM), Vol.7, No.6, pp.27-42, 2017. DOI: 10.5815/ijem.2017.06.03

Reference

[1]Oparaodu KO, Okpokwasili GC. Comparison of percentage weight loss and corrosion rate trends in different metal coupons from two soil environments. International Journal of Environmental Bioremediation & Biodegradation. 2014; 2(5):243-249.

[2]Emami MRS. Mathematical modelling of corrosion phenomenon in pipelines.The journal of Mathematics and Computer Science. 2011;3(2):202-211.

[3]Oyewole A. Characterization of external induced corrosion degradation of Ajaokuta-Abuja gas pipeline system, nigeria. International Journal of Engineering and Technology. 2011;3(11):8061-8068.

[4]Ekott EJ, Akpabio EJ, Etukudo UI. Cathodic protection of buried steel oil pipelines in Niger Delta. Environmental Research Journal. 2012;6(4):304-307.

[5]Sadiku-Agboola O, Sadiku ER, Biotidara OF. The properties and the effect of operating parameters on nickel plating (review). International Journal of the Physical Sciences. 2012;7(3):349-360.

[6]http://www.mathworks.com/products/neuralnet

[7]Lee J, Kim T. A messy genetic algorithm and its application to an approximate optimization of an occupant safety system. Journal of Automobile Engineering. 2009;757-758.

[8]Kennedy JL. Oil and gas pipeline fundamentals. 2nd ed. PennWell Books; 1993.

[9]Malik AU, Andijani I. Corrosion Behaviour of Materials in RO water containing 250-350 PPM Chloride, International Desalination Association (IDA) World Congress Conference, Singapore. 2005; 1-13.

[10]Malik AU, Ahmad S, Andijani I, Al-Muaili F, Prakash, TL, O’Hara J. Corrosion Protection Evaluation of some Organic Coatings in Water Transmission Lines (Report No.TR 3804/APP 95009). Kingdom of Saudi Arabia: Saline Water Conversion Corporation. 1999.

[11]Samimi A. Causes of Increased Corrosion in Oil and Gas Pipelines in the Middle East. International Journal of Basic and Applied Sciences. 2013; 572-577.

[12]Yahaya N, Noor NM, Othman SR, Sing LK, Din MM. New Technique for Studying Soil-Corrosion of Underground Pipeline. Journal of Applied Sciences. 2011; 11(9):1510-1518.

[13]Monyei CG, Aiyelari T, Oluwatunde S. Neural Network Modeling of Electronic Waste Deposits in Nigeria: Subtle Prod for quick Intervention’ in proceedings of the iSTEAMS Research. Nexus Multidisciplinary Conference. 2013; 1(4):181-188.

[14]Abd El-Lateef MH, Abbasov VM, Aliyeva LI, Ismayilov TA. Corrosion Protection of Steel Pipelines Against CO2 Corrosion-A Review. Chemistry Journal. 2012; 2(2):52-63.

[15]Shen W. Developing a Reliable and Efficient Corrosion Resistance Characterization Technique for Magnesium Alloys Used in Automotive and Other Manufacturing Industries. Faculty Research Fellowship.

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