Modeling natural gas compressibility factor using a hybrid group method of data handling

Abdolhossein Hemmati-Sarapardeh, Sassan Hajirezaie, Mohamad Reza Soltanian, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband, Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

The 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.

Original languageEnglish
Pages (from-to)27-37
Number of pages11
JournalEngineering Applications of Computational Fluid Mechanics
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • artificial intelligence (AI)
  • big data
  • correlation
  • data-driven model
  • equations of state (EOSs)
  • Group method of data handling (GMDH)
  • natural gas compressibility factor

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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