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Neural Computing and Applications

, Volume 31, Issue 1, pp 55–64 | Cite as

An intelligent approach to predict gas compressibility factor using neural network model

  • Navid Azizi
  • Mashallah Rezakazemi
  • Mohammad Mehdi ZareiEmail author
Original Article

Abstract

This research illustrates the utilization of a new model based on artificial neural networks (ANNs) in prediction of compressibility factor (z-factor) of natural gases using experimental data based on Standing and Katz z-factor diagram. Although equations of state and empirical correlations have been applied for predicting compressibility factor, the demands for the modern, more reliable and easy-to-use models encouraged the researchers to recommend modern facilities such as intelligent systems. This investigation describes a new technique for computing z-factor of natural gases. The base of the approach is ANN in which a 2:5:5:1 structure is used as an optimum network to predict the z-factor. The statistical results show that the developed ANN is an excellent tool for estimating z-factor values; therefore, it can be confidently used for natural gases with various compositions at a specific temperature and pressure.

Keywords

Artificial neural network Compressibility factor Natural gas Critical pressure and temperature 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Chemical Engineering, Shiraz BranchIslamic Azad UniversityShirazIran
  2. 2.Department of Chemical EngineeringShahrood University of TechnologyShahroodIran
  3. 3.Department of Petroleum EngineeringKhazar UniversityBakuAzerbaijan

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