Prediction Model by Adaptive Neuro-Fuzzy Inference System of R600A Vapour Compression Refrigeration System Using Al2O3/TiO2 Composite Nanolubricants

Abstract

This study investigates the enhancement of a vapour compression refrigeration system using Al₂O₃/TiO₂ composite nanolubricants with R600a refrigerant. The main objective is the experiments were performed by varying the nanolubricants concentrations and to find out the optimal concentration. Al₂O₃/TiO₂ blended nano lubricants were used to produce a greater cooling effect of 200 W along with a 30% increase by employing the ANFIS approach, which is superior to results from experiments. The approach of ANFIS was used to obtain the minimum energy utilization of 95 W. The results indicates that, the improved COP of 3.2 with a 28% higher than standard refrigerant. In comparison to experimental results, the usage of Al₂O₃/TiO₂ composite Nano lubricants resulted in an increase of COP at an optimal level, cooling effect, and a 25% reduction in compressor work to decrease energy consumption when utilizing the ANFIS prediction technique. By dispersing 0.4 g/L in R600a leads in better results in comparison with the system without nano lubricants and other nano lubricants concentrations.