Insights into the prediction of the liquid density of refrigerant systems by artificial intelligent approaches

Abstract

This study presents a novel model for accurately estimating the densities of 48 refrigerant systems, categorized into five groups: Hydrofluoroethers (HFEs), Hydrochlorofluorocarbons (HCFCs), Perfluoroalkylalkanes (PFAAs), Hydrofluorocarbons (HFCs), and Perfluoroalkanes (PFAs). Input variables, including pressure, temperature, molecular weight, and structural groups, were systematically considered. The study explores the efficacy of both the multilayer perceptron artificial neural network (MLP-ANN) and adaptive neuro-fuzzy inference system (ANFIS) methodologies in constructing a precise model. Utilizing a comprehensive dataset of 3825 liquid density measurements and outlier analysis, the models achieved R2 and MSE values of 0.975 & 0.5575 and 0.967 & 0.7337 for MLP-ANN and ANFIS, respectively, highlighting their remarkable predictive performance. In conclusion, the ANFIS model is proposed as an effective tool for estimating refrigerant system densities, particularly advantageous in scenarios where experimental measurements are resource-intensive or sophisticated analysis is required.