Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neu...

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Bibliographic Details
Published in:Journal of marine science and engineering Vol. 8; no. 10; p. 766
Main Authors: Oh, Dohan, Race, Julia, Oterkus, Selda, Koo, Bonguk
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2020
Subjects:
Algorithms
Artificial intelligence
artificial neural network
Artificial neural networks
Boundary conditions
burst pressure
deep neural network
dent
Finite element method
Gas pipelines
Learning algorithms
Machine learning
Natural gas
Neural networks
ocean and shore technology (OST)
Petroleum pipelines
pipeline
Pipelines
Pressure
Submarine pipelines
Symmetry
ISSN:2077-1312, 2077-1312
Online Access:Get full text
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Summary:Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.
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ISSN:2077-1312
2077-1312
DOI:10.3390/jmse8100766