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...
| Main Authors: | Ayoub, Mohammed A. Ayoub, Demiral, Birol M. Demiral |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.10572 / |
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UNIVERSITY of KHARTOUM
2011
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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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utp-eprints.105722017-03-20T01:59:36Z Application of Resilient Back-Propagation Neural Networks for Generating a Universal Pressure Drop Model in Pipelines Ayoub, Mohammed A. Ayoub Demiral, Birol M. Demiral TA Engineering (General). Civil engineering (General) 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%. UNIVERSITY of KHARTOUM 2011-10 Article PeerReviewed application/pdf http://eprints.utp.edu.my/10572/1/115-408-1-PB.pdf http://ejournals.uofk.edu 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 http://eprints.utp.edu.my/10572/ |
| institution |
Universiti Teknologi Petronas |
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UTP Institutional Repository |
| topic |
TA Engineering (General). Civil engineering (General) |
| spellingShingle |
TA Engineering (General). Civil engineering (General) Ayoub, Mohammed A. Ayoub Demiral, Birol M. Demiral Application of Resilient Back-Propagation Neural Networks for Generating a Universal Pressure Drop Model in Pipelines |
| description |
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%. |
| format |
Article |
| author |
Ayoub, Mohammed A. Ayoub Demiral, Birol M. Demiral |
| author_sort |
Ayoub, Mohammed A. Ayoub |
| title |
Application of Resilient Back-Propagation Neural Networks
for Generating a Universal Pressure Drop Model in Pipelines |
| title_short |
Application of Resilient Back-Propagation Neural Networks
for Generating a Universal Pressure Drop Model in Pipelines |
| title_full |
Application of Resilient Back-Propagation Neural Networks
for Generating a Universal Pressure Drop Model in Pipelines |
| title_fullStr |
Application of Resilient Back-Propagation Neural Networks
for Generating a Universal Pressure Drop Model in Pipelines |
| title_full_unstemmed |
Application of Resilient Back-Propagation Neural Networks
for Generating a Universal Pressure Drop Model in Pipelines |
| title_sort |
application of resilient back-propagation neural networks
for generating a universal pressure drop model in pipelines |
| publisher |
UNIVERSITY of KHARTOUM |
| publishDate |
2011 |
| url |
http://eprints.utp.edu.my/10572/1/115-408-1-PB.pdf http://ejournals.uofk.edu http://eprints.utp.edu.my/10572/ |
| _version_ |
1741196124684812288 |
| score |
11.62408 |