Application of Resilient Back-Propagation Neural Networks for Generating a Universal Pressure Drop Model in Pipelines
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 cumberso...
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| Format: | Article |
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UNIVERSITY of KHARTOUM
2011
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| Online Access: | http://eprints.utp.edu.my/10572/1/115-408-1-PB.pdf http://ejournals.uofk.edu http://eprints.utp.edu.my/10572/ |
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| Summary: | 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%. |
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