Artificial Bee Colony-Artificial Neural Network (ABC-ANN) Hybrid Algorithm’s Performance on the Modeling of Thermodynamic Properties of a Refrigerant Gas (R404a)

Abstract

ABSTRACT In this study, it was aimed to determine the thermodynamic properties of an environmentally friendly refrigerant (R404a) for both the saturated liquid–vapor region (wet vapor) and superheated vapor region, in the temperature range of 173–498°K, and the pressure range of 10–3600 kPa by using an adaptive artificial neural network algorithm. Performing the analysis of these gases with differential equations by using a computer is very time consuming and requires high computational power for calculation. Using numerical equations for modeling the thermodynamic properties of gasses to eliminate these drawbacks is a more accurate approach and this modeling can be accomplished with artificial intelligence algorithms such as the Artificial Neural Network (ANN). The proper selection of the activation function, which is one of the most important parameters for ANN, directly affects the validity of the model according to the problem and its application. In this study, an adaptive ANN was developed in which the optimal activation function combination was found by using the ABC algorithm and thus the error of the network were reduced when compared to the classical ANN. The improvements of in percentage errors were observed to increase from 7.55% to 76.68%. Finally, the accuracy of the numerical equations that describe the thermodynamic properties of R404a gas was increased. Using this technique helps to figure out the performance of the gas under related working conditions.