This paper presents energy and exergy analysis of two vapor compression refrigeration cycles powered by organic Rankine cycle. Refrigeration cycle of combined system was designed with single and dual evaporators. R134a, R1234ze(E), R227ea, and R600a fluids were used as working fluids in combined systems. Influences of different parameters such as evaporator, condenser, boiler temperatures, and turbine and compressor isentropic efficiencies on COP_sys and η_ex,sys were analyzed. Second law efficiency, degree of thermodynamic perfection, exergy destruction rate, and exergy destruction ratio were detected for each component in systems. R600a was determined as the most efficient working fluid for proposed systems. Both COP_sys and η_ex,sys of combined ORC-single evaporator VCR cycle was detected to be higher than the system with dual evaporator.

The use of alternative refrigerants is among the popular topics of the refrigeration industry. In the first part of this study, thermodynamic performances of R290 and R600a gases were compared in a vapor compression refrigeration experiment setup. Although R600a caused an average of 33.44% less compressor power consumption compared to R290 refrigerant, R290 provided an average of 23.77% increase in COP (coefficient of performance), 82.55% in cooling capacity, and 20.99% increase in second law efficiency compared to R600a. In the second part of the study, the performance parameters of the refrigeration system were predicted with MLP (multi-layer perceptron), SVM (support vector machine), and DT (decision tree) machine learning algorithms. It was detected that the SVM method predicted all parameters with the least error. MAE (mean absolute error) values ​​detected in the COP prediction with test set were 0.0317, 0.0324, and 0.0989 for SVM, MLP, and DT, respectively. Results revealed that performance of the refrigeration system increased when utilizing R290, and SVM was superior in prediction of performance indicators compared to other machine learning methods.

The coefficient of performance (COP) is a crucial metric for evaluating the efficiency of heat pump systems. Real-time monitoring of heat pump system performance necessitates continuously collecting and processing data from various components utilizing multiple sensors and controllers. This process is inherently complex and presents significant challenges. In recent years, artificial intelligence (AI) models have increasingly been applied in refrigeration, heat pump, and air conditioning systems due to their capability to identify and analyze complex patterns and data relationships, demonstrating higher accuracy and reduced computation time. In this study, multilayer perceptron (MLP), support vector machines (SVM), and random forest (RF) are used to develop COP prediction models for solar-assisted heat pumps. By comparing the predictive accuracy and modeling time of the three models built, the results demonstrate that the random forest model achieves the best prediction performance, with a mean absolute error (MAE) of 2.42% and a root mean squared error (RMSE) of 4.01% on the train set. On the test set, the MAE was 2.35% and the RMSE was 3.84%. The modeling time for the RF model was 6.57 s.

The use of new generation low GWP refrigerants in cooling systems is becoming increasingly common due to thermal performance and less environmental impact. In this study, the thermal performance of the new generation R290 refrigerant and the old generation R404A refrigerant in the same cooling system were investigated experimentally and numerically. The thermal performance values of both gases were compared, provided that the temperature of the cold room cooled by the vapor compression cooling system drops to 0?C. ?5?C, 0?C, 5?C evaporator temperatures and 25?C, 30 ?C, 35 ?C condenser temperatures were used as operating parameters. The experimental results revealed that the highest COP values in the system for R290 and R404A were 4.68 and 3.94, respectively. An average of 9.38% increase in cooling capacity was detected with R290 compared to R404A refrigerant. In the numerical analysis part of the study, the evaporator surface temperature and air velocity distributions of both refrigerants in the cold room were shown numerically by using ANSYS FLUENT 19.1. As a result of the study, it was stated that R290 had higher thermal performance than R404A, according to both experimental and numerical analysis results.

In today’s world, most of the currently used refrigerants in refrigeration systems have an important influence on global warming. This study presents the experimental and numerical consumption of thermal and energy performances comparison of new generation low GWP R290 and R1234yf refrigerants for a cold room. Evaporator temperatures of -9 °C, -6 °C, -3 °C, and condenser temperatures of 25 °C, 30 °C, 35 °C were utilized as the operational parameters. Results revealed that the highest COP values in the system for R290 and R1234yf were 3.99 and 3.41, respectively. An average 11.1% increase in the cooling capacity was determined with R290 compared to the R1234yf refrigerant. In the second part of the study, evaporator surface temperature and air velocity distributions in the cold room for R290 and R1234yf refrigerants occurring until the cold room temperature reached from the ambient temperature to 0 °C were numerically shown using Ansys Fluent 18.1. Numerical analysis using experimental data indicated that the evaporator surface had a lower temperature with R290 refrigerant than R1234yf. Results of the study highlighted the usability of R290 refrigerant instead of R1234yf in refrigeration systems in terms of thermodynamic performance.