Prediction of Gas Hydrate Formation Using Radial Basis Function Network and Support Vector Machines
The oil and gas industry struggles to prevent formation of hydrates in pipeline by spending substantial amount of dollars. Hydrates are ice-like crystalline compounds that are composed of water and gas in which the gas molecules are trapped in water cavities. The hydrate formation is favorable at...
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2016
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my-inti-eprints.3532016-09-05T07:37:58Z http://eprints.intimal.edu.my/353/ Prediction of Gas Hydrate Formation Using Radial Basis Function Network and Support Vector Machines Ibrahim, Abdulwehab A. Lemma, Tamiru Alemu Moey, Lip Kean Mesfin, Gizaw Zewge TA Engineering (General). Civil engineering (General) The oil and gas industry struggles to prevent formation of hydrates in pipeline by spending substantial amount of dollars. Hydrates are ice-like crystalline compounds that are composed of water and gas in which the gas molecules are trapped in water cavities. The hydrate formation is favorable at elevated pressure and reduced temperature and can be determined through experiment. However, the cost involved to determine early hydrate formation using experiment is driving researchers to seek for robust prediction methods using statistical and analytical methods. Main aim of the present study is to investigate applicability of radial basis function networks and support vector machines to hydrate formation conditions prediction. The data needed for modeling was taken from well-established literature. Performance of the models was assessed based on MSE, MAE, MAPE, MSPE, and Modified Pearson’s Correlation Coefficient. Data-based models enable the oil industry to predict the conditions leading to hydrate formation hence preventing clogging of the pipeline and high pressure buildup that could lead to sudden burst at the connections. Trans Tech Publications Ltd 2016 Article PeerReviewed Ibrahim, Abdulwehab A. and Lemma, Tamiru Alemu and Moey, Lip Kean and Mesfin, Gizaw Zewge (2016) Prediction of Gas Hydrate Formation Using Radial Basis Function Network and Support Vector Machines. Applied Mechanics and Materials, 819. pp. 569-574. ISSN 1662-7482 http://www.scientific.net/AMM.819.569 10.4028/www.scientific.net/AMM.819.569 |
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TA Engineering (General). Civil engineering (General) Ibrahim, Abdulwehab A. Lemma, Tamiru Alemu Moey, Lip Kean Mesfin, Gizaw Zewge Prediction of Gas Hydrate Formation Using Radial Basis Function Network and Support Vector Machines |
| description |
The oil and gas industry struggles to prevent formation of hydrates in pipeline by
spending substantial amount of dollars. Hydrates are ice-like crystalline compounds that are
composed of water and gas in which the gas molecules are trapped in water cavities. The hydrate
formation is favorable at elevated pressure and reduced temperature and can be determined through
experiment. However, the cost involved to determine early hydrate formation using experiment is
driving researchers to seek for robust prediction methods using statistical and analytical methods.
Main aim of the present study is to investigate applicability of radial basis function networks and
support vector machines to hydrate formation conditions prediction. The data needed for modeling
was taken from well-established literature. Performance of the models was assessed based on MSE,
MAE, MAPE, MSPE, and Modified Pearson’s Correlation Coefficient. Data-based models enable
the oil industry to predict the conditions leading to hydrate formation hence preventing clogging of
the pipeline and high pressure buildup that could lead to sudden burst at the connections. |
| format |
Article |
| author |
Ibrahim, Abdulwehab A. Lemma, Tamiru Alemu Moey, Lip Kean Mesfin, Gizaw Zewge |
| author_facet |
Ibrahim, Abdulwehab A. Lemma, Tamiru Alemu Moey, Lip Kean Mesfin, Gizaw Zewge |
| author_sort |
Ibrahim, Abdulwehab A. |
| title |
Prediction of Gas Hydrate Formation Using Radial Basis Function
Network and Support Vector Machines |
| title_short |
Prediction of Gas Hydrate Formation Using Radial Basis Function
Network and Support Vector Machines |
| title_full |
Prediction of Gas Hydrate Formation Using Radial Basis Function
Network and Support Vector Machines |
| title_fullStr |
Prediction of Gas Hydrate Formation Using Radial Basis Function
Network and Support Vector Machines |
| title_full_unstemmed |
Prediction of Gas Hydrate Formation Using Radial Basis Function
Network and Support Vector Machines |
| title_sort |
prediction of gas hydrate formation using radial basis function
network and support vector machines |
| publisher |
Trans Tech Publications Ltd |
| publishDate |
2016 |
| url |
http://eprints.intimal.edu.my/353/ http://www.scientific.net/AMM.819.569 |
| _version_ |
1644541186006843392 |
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13.211869 |