Version 1
: Received: 8 May 2019 / Approved: 22 May 2019 / Online: 22 May 2019 (08:29:32 CEST)
How to cite:
Hemmati-Sarapardeh, A.; Hajirezaie, S.; Soltanian, M.R.; Mosavi, A.; Shamshirband, S. Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling. Preprints2019, 2019050263. https://doi.org/10.20944/preprints201905.0263.v1
Hemmati-Sarapardeh, A.; Hajirezaie, S.; Soltanian, M.R.; Mosavi, A.; Shamshirband, S. Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling. Preprints 2019, 2019050263. https://doi.org/10.20944/preprints201905.0263.v1
Hemmati-Sarapardeh, A.; Hajirezaie, S.; Soltanian, M.R.; Mosavi, A.; Shamshirband, S. Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling. Preprints2019, 2019050263. https://doi.org/10.20944/preprints201905.0263.v1
APA Style
Hemmati-Sarapardeh, A., Hajirezaie, S., Soltanian, M.R., Mosavi, A., & Shamshirband, S. (2019). Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling. Preprints. https://doi.org/10.20944/preprints201905.0263.v1
Chicago/Turabian Style
Hemmati-Sarapardeh, A., Amir Mosavi and Shahab Shamshirband. 2019 "Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling" Preprints. https://doi.org/10.20944/preprints201905.0263.v1
Abstract
A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.
Keywords
natural gas; gas compressibility factor; group method of data handling (GMDH); big data; equation of state; correlation
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
