Empirical Correlations and an Artificial Neural Network Approach to Estimate Saturated Vapor Pressure of Refrigerants

Meghdadi Isfahani, Amir Homayoon; Reiszadeh, Mehdi; Yaghoubi Koupaye, Somaye; Honarmand, Mehrdad

doi:10.22036/pcr.2017.41111

Empirical Correlations and an Artificial Neural Network Approach to Estimate Saturated Vapor Pressure of Refrigerants

Document Type : Regular Article

Authors

Amir Homayoon Meghdadi Isfahani ¹

Mehdi Reiszadeh ²

Somaye Yaghoubi Koupaye ³

Mehrdad Honarmand ⁴

¹ Young Researchers and Elite Club, Najafabad Branch, Islamic Azad University, Najafabad, Iran

² Chemical Engineering Department, Shahreza Branch, Islamic Azad University, Shahreza, Iran

³ Department of mechanical engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

⁴ Department of Mechanics, Tiran Branch, Islamic Azad University, Isfahan, Iran

https://doi.org/10.22036/pcr.2017.41111

Abstract

The examination of available vapor pressure data in the case of the methane, ethane, propane and butane halogenated refrigerants, allowed recommendations of standard equations for this property. In this study, three new models include a general correlation; a substance-dependent correlation and an artificial neural network (ANN) approach have been developed to estimate the saturated vapor pressure of refrigerants. With the presented approaches, vapor pressures have been calculated and compared with source data bank for 5600 data points of 28 refrigerants. The accuracies of new correlations and ANN have been compared with most commonly used correlations and the comparison indicates that all new models provide more accurate results than other literature correlations considered in this work.

Graphical Abstract