Using a Feed Forward Neural Network to Predict External Corrosion in Oil Pipelines

Authors

  • Ahmad Salamah Tishreen University
  • Hassan Hamed Al-Baath University
  • Rabie Mahmoud Researcher; General Organization of Remote Sensing; Coastal Branch
  • Tarek Aiashee Tishreen University

Keywords:

الأنابيب

Abstract

This study aims to predict external corrosion in oil pipelines using feed forward neural networks, based on the Smart Pig data, which oil companies resort to technical inspections to have a detailed data on the current corrosion reality in oil pipelines. Most important external corrosive factors applied to the oil pipeline linking the Banias and Tartous estuaries where data was divided into two groups: 80 points to train the network, and 25 points to test the results.

The study showed that using feed forward neural networks in the prediction process gives high-accuracy results, and matching between the prediction data and the technical inspection data with high rate, which allows pipeline operators to know the weakest sites in these pipes and the most vulnerable to external corrosion. Thus, the appropriate cathodic protection and isolation prevent the occurrence of corrosion which reduces wastage in time and costs.

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Published

2022-08-02

How to Cite

1.
أحمد سلامة, حسان حامد, ربيع محمود, طارق عياشي. Using a Feed Forward Neural Network to Predict External Corrosion in Oil Pipelines. Tuj-eng [Internet]. 2022Aug.2 [cited 2024Dec.4];44(3):189-201. Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/13202

Issue

Vol. 44 No. 3 (2022): Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series

Section

Articles

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Copyright (c) 2022 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

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