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

• Published in: Neural computing & applications Vol. 31; no. 1; pp. 55 - 64
• Main Authors: Azizi, Navid, Rezakazemi, Mashallah, Zarei, Mohammad Mehdi
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Compressibility; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Empirical equations; Equations of state; Image Processing and Computer Vision; Mathematical models; Neural networks; Original Article; Predictions; Probability and Statistics in Computer Science; Critical pressure and temperature; Artificial neural network; Compressibility factor; Natural gas
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-017-2979-7

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.