Empirical modeling of hydrate formation prediction in deepwater pipelines

Gas hydrate is a challenging problem in deep-water natural gas transmission lines. Temperature, pressure, and composition of gas mixtures in deep-water pipeline promote rapid formation of gas hydrates. The petroleum industry spends millions of dollars yearly to minimize the effects of hydrate format...

Full description

Main Authors: Hashim, F.M., Abbasi, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.25455 /
Published: Asian Research Publishing Network 2016
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994246712&partnerID=40&md5=bc8ea7c2d71509769160b100484ce662
http://eprints.utp.edu.my/25455/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.25455
recordtype eprints
spelling utp-eprints.254552021-08-27T13:01:16Z Empirical modeling of hydrate formation prediction in deepwater pipelines Hashim, F.M. Abbasi, A. Gas hydrate is a challenging problem in deep-water natural gas transmission lines. Temperature, pressure, and composition of gas mixtures in deep-water pipeline promote rapid formation of gas hydrates. The petroleum industry spends millions of dollars yearly to minimize the effects of hydrate formation on flow assurance. In this scenario, on the basis of experimental data from Sloan and Avlonits work, an artificial intelligence (AI) for methane gas hydrate of deepwater gas pipelines has been developed. This model is based on temperature and pressure conditions. The correlations between temperature and pressure are developed by using MATLAB software and then optimize with optimization techniques, such as genetic algorithm and particle swarm optimization. All correlations are computed with the existing experimental work and it satisfies that the new correlation has the minimum error with high accuracy. ©2006-2016 Asian Research Publishing Network (ARPN). Asian Research Publishing Network 2016 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994246712&partnerID=40&md5=bc8ea7c2d71509769160b100484ce662 Hashim, F.M. and Abbasi, A. (2016) Empirical modeling of hydrate formation prediction in deepwater pipelines. ARPN Journal of Engineering and Applied Sciences, 11 (20). pp. 12212-12216. http://eprints.utp.edu.my/25455/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Gas hydrate is a challenging problem in deep-water natural gas transmission lines. Temperature, pressure, and composition of gas mixtures in deep-water pipeline promote rapid formation of gas hydrates. The petroleum industry spends millions of dollars yearly to minimize the effects of hydrate formation on flow assurance. In this scenario, on the basis of experimental data from Sloan and Avlonits work, an artificial intelligence (AI) for methane gas hydrate of deepwater gas pipelines has been developed. This model is based on temperature and pressure conditions. The correlations between temperature and pressure are developed by using MATLAB software and then optimize with optimization techniques, such as genetic algorithm and particle swarm optimization. All correlations are computed with the existing experimental work and it satisfies that the new correlation has the minimum error with high accuracy. ©2006-2016 Asian Research Publishing Network (ARPN).
format Article
author Hashim, F.M.
Abbasi, A.
spellingShingle Hashim, F.M.
Abbasi, A.
Empirical modeling of hydrate formation prediction in deepwater pipelines
author_sort Hashim, F.M.
title Empirical modeling of hydrate formation prediction in deepwater pipelines
title_short Empirical modeling of hydrate formation prediction in deepwater pipelines
title_full Empirical modeling of hydrate formation prediction in deepwater pipelines
title_fullStr Empirical modeling of hydrate formation prediction in deepwater pipelines
title_full_unstemmed Empirical modeling of hydrate formation prediction in deepwater pipelines
title_sort empirical modeling of hydrate formation prediction in deepwater pipelines
publisher Asian Research Publishing Network
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994246712&partnerID=40&md5=bc8ea7c2d71509769160b100484ce662
http://eprints.utp.edu.my/25455/
_version_ 1741196977710825472
score 11.62408

Similar Items