Oil flooded scroll compressors: Predicting the energy performance and evaluating the experimental data

The annual or seasonal performance evaluation of Oil Flooded Scroll Compressor (OFSC) is essential to maximize its performance and increase vapor compression cycle efficiency. An experimental data bank for detail evaluation of this component in the vapor compression systems has need of a highly equipped laboratory and proper instruments. The previous researchers have developed different theoretical, empirical, and semi-empirical models to solve this problem, which are not highly effective under different situations. We propose a Hybrid-Adaptive Neuro Fuzzy Inference System (Hybrid-ANFIS) for the fast and precise estimation of the discharge temperature, refrigerant mass flow rate, and electrical power of the OFSCs. The performance of the Hybrid-ANFIS is checked against the other well-known models, including the Particle Swarm Optimization-Artificial Neural Network, Coupled Simulated Annealing-Least Square Support Vector Machine, Genetic Algorithm-Least Square Support Vector Machine, and the existing correlations in the open literature. The results indicated that the proposed Hybrid-ANFIS model is more accurate and has the promising potential for estimating the desired parameters by introducing a coefficient of determination higher than 0.998. The application of the suggested model is further illustrated against the experimental data and new test conditions are evaluated. Finally, the Leverage algorithm, which is a novel statistical approach, is implemented to assess the quality of the experimental data and diagnose the probable doubtful data samples. The results of the data analysis and the outlier detection method clarified that the presented Hybrid-ANFIS model is statistically valid and four experimental data samples are doubtful reported for the OFSC.