Ayoub, Mohammed A. Ayoub and Demiral, Birol M. Demiral (2011) Application of Resilient Back-Propagation Neural Networks for Generating a Universal Pressure Drop Model in Pipelines. UNIVERSITY of KHARTOUM ENGINEERING JOURNAL (UOKEJ), 1 (2). pp. 9-21. ISSN 1858-6333
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Abstract
This study aims at generating and validating a universal pressure drop model at pipelines under three phase flow conditions. There is a pressing need for estimating the pressure drop in pipeline systems using a simple procedure that would eliminate the tedious and yet the non accurate and cumbersome methods. In this study resilient back-propagation Artificial Neural Network technique will be utilized as a powerful modeling tool to establish the complex relationship between input parameters and the pressure drop in pipeline systems under wide range of angles of inclination. A total number of data points consists of 335 sets has been used for generating, validating, and testing the ANN model. A model performance has been evaluated against the best empirical correlations and mechanistic models (Xiao et al., Gomez et al., and Beggs and Brill). A series of statistical and graphical analysis were conducted to show the significance of the generated model. The new developed model outperforms all investigated models with correlation coefficient reaches 98.82%.
Item Type: | Article |
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Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
Academic Subject One: | Multiphase flow |
Academic Subject Two: | Pressure Drop |
Academic Subject Three: | petroleum engineering |
Departments / MOR / COE: | Departments > Geoscience & Petroleum Engineering |
ID Code: | 10572 |
Deposited By: | Dr Mohammed Abdalla Ayoub |
Deposited On: | 16 Dec 2013 23:48 |
Last Modified: | 20 Mar 2017 01:59 |
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