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Neural Computing and Applications

Erschienen in:

18.04.2017 | Original Article

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

verfasst von: Navid Azizi, Mashallah Rezakazemi, Mohammad Mehdi Zarei

Erschienen in: Neural Computing and Applications | Ausgabe 1/2019

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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.

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Titel: An intelligent approach to predict gas compressibility factor using neural network model
verfasst von: Navid Azizi
Mashallah Rezakazemi
Mohammad Mehdi Zarei
Publikationsdatum: 18.04.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-2979-7

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