Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81731
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorHemmati-Sarapardeh, Aen_US
dc.creatorHajirezaie, Sen_US
dc.creatorSoltanian, MRen_US
dc.creatorMosavi, Aen_US
dc.creatorNabipourg, Nen_US
dc.creatorShamshirband, Sen_US
dc.creatorChau, KWen_US
dc.date.accessioned2020-02-10T12:28:52Z-
dc.date.available2020-02-10T12:28:52Z-
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/81731-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Abdolhossein Hemmati-Sarapardeh, Sassan Hajirezaie, MohamadReza Soltanian, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband & Kwok-WingChau (2020) Modeling natural gas compressibility factor using a hybrid group method of datahandling, Engineering Applications of Computational Fluid Mechanics, 14:1, 27-37 is available at https://dx.doi.org/10.1080/19942060.2019.1679668en_US
dc.subjectGroup method of data handling (GMDH)en_US
dc.subjectNatural gas compressibility factoren_US
dc.subjectBig dataen_US
dc.subjectCorrelationen_US
dc.subjectEquations of state (EOSs)en_US
dc.subjectData-driven modelen_US
dc.subjectArtificial intelligence (AI)en_US
dc.titleModeling natural gas compressibility factor using a hybrid group method of data handlingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage27en_US
dc.identifier.epage37en_US
dc.identifier.volume14en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2019.1679668en_US
dcterms.abstractThe natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model?s superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 27-37en_US
dcterms.isPartOfEngineering applications of computational fluid mechanicsen_US
dcterms.issued2020-
dc.identifier.isiWOS:000494879000001-
dc.identifier.scopus2-s2.0-85074897544-
dc.identifier.eissn1997-003Xen_US
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Hemmati-Sarapardeh_Modeling_Natural_Gas.pdf2.4 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

102
Last Week
1
Last month
Citations as of Mar 24, 2024

Downloads

179
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

31
Citations as of Mar 28, 2024

WEB OF SCIENCETM
Citations

26
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.