Predicting Scale Thickness in Oil Pipelines Using Frequency Characteristics and an Artificial Neural Network in a Stratified Flow Regime

Predicting Scale Thickness in Oil Pipelines Using Frequency Characteristics and an Artificial Neural Network in a Stratified Flow Regime

One of the main problems in oil fields is the deposition of scale inside oil pipelines, which causes problems such as the reduction of the internal diameter of oil pipes, the need for more energy to transport oil products, and the waste of energy. For this purpose, the use of an accurate and reliabl...

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Bibliographic Details
Main Authors: Tzu-Chia Chen, Abdullah M. Iliyasu, Robert Hanus, Ahmed S. Salama, Kaoru Hirota
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Energies
Subjects:
scale thickness
frequency characteristics
fast Fourier transform
multilayer perceptron neural network
Online Access:https://www.mdpi.com/1996-1073/15/20/7564

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