Resumen
Environmental friendly refrigerants with zero ozone depletion potential (ODP) and zero global warming potential (GWP) are in great demand across the globe. One such popular refrigerant is isobutane (R600a) which, having zero ODP and negligible GWP, is considered in this study. This paper presents the two most popular artificial intelligence (AI) techniques, namely support vector regression (SVR) and artificial neural networks (ANN), to predict the heat transfer coefficient of refrigerant R600a. The independent input parameters of the models include mass flux, saturation temperature, heat flux, and vapor fraction. The heat transfer coefficient of R600a is the dependent output parameter. The prediction performance of these AI-based models is compared and validated against the experimental results, as well as with the existing correlations based on the statistical parameters. The SVR model based on the structural risk minimization (SRM) principle is observed to be superior compared with the other models and is more accurate, precise, and highly generalized; it has the lowest average absolute relative error (AARE) at 1.15% and the highest coefficient of determination (R2) at 0.9981. ANN gives an AARE of 5.14% and a R2 value of 0.9685. Furthermore, the simulated results accurately predict the effect of input parameters on the heat transfer coefficient.