Prediction of Saturated Vapor Pressures Using Non-linear Equations and Artificial Neural Network Approach

Volume 8, Issue 4, pp 343 - 358 http://dx.doi.org/10.22436/jmcs.08.04.03

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Authors

Mehrdad Honarmand - Department of Mechanics, Tiran Branches, Islamic Azad University, Isfahan, Iran. Ehsan Sanjari - Department of Mechanics, Tiran Branches, Islamic Azad University, Isfahan, Iran. Hamidreza Badihi - Department of Mechanics, Tiran Branches, Islamic Azad University, Isfahan, Iran.

Abstract

A new method to estimate vapor pressures for pure compounds using an artificial neural network (ANN) is presented. A reliable database including more than 12000 data point of vapor pressure for testing, training and validation of ANN is used. The designed neural network can predict the vapor pressure using temperature, critical temperature, and acentric factor as input, and reduced pressure as output with 0.211% average absolute relative deviation. 8450 data points for training, 1810 data points for validation, and 1810 data points for testing have been used to the network design and then results compared to data source from NIST Chemistry Web Book. The study shows that the proposed method represents an excellent alternative for the estimation of pure substance vapor pressures and can be used with confidence for any substances.

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Keywords

• Vapor pressure
• ANN
• neural network
• correlation
• non-linear

MSC

•  92B20
•  49J15
•  91G60

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