It is difficult to manually process and analyze large amounts of data. Therefore, to solve a given problem, it is easier to reach the solution by studying the data obtained from the environment of the problem with computational intelligence methods. In this study, pool boiling heat flux was estimated in the isolated bubble regime using two optimization methods (genetic and artificial bee colony algorithm) and three machine learning algorithms (decision tree, artificial neural network, and support vector machine). Six boiling mechanisms containing eighteen different parameters in the genetic and the artificial bee colony (ABC) algorithms were used to calculate overall heat flux of the isolated bubble regime. Support vector machine regression (SVMReg), alternating model tree (ADTree), and multilayer perceptron (MLP) regression only used the heat transfer equation input parameters without heat transfer equations for prediction of pool boiling heat transfer over a horizontal tube. The performance of computational intelligence methods were determined according to the results of error analysis. Mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) error were used to calculate the validity of the predictive model in genetic algorithm, ABC algorithm, SVMReg, MLP regression, and alternating model tree. According to the MAPE error analysis, the accuracy values of MLP regression (0.23) and alternating model tree (0.22) methods were the same. The SVMReg method used for pool boiling heat flux estimation performed better than the other methods, with 0.17 validation error rate of MAPE.

Environmental pollution has been rising recently, and so clean water needing has also increased. People could have easy access to pure water in all environments and conditions. This study is the solar tracking parabolic dish collector with produced pure water independent of electricity (off-grid). This system is fully renewable and autonomous. This system possesses a receiver, a boiler, a condenser, six 1000 mm diameter parabolic dish collectors, and 1 kW photovoltaic (PV) collectors. Thermal oil uses as the working fluid in the experiments. The experiment measured solar radiation, air temperature, air velocity, oil inlet and outlet temperatures, oil flow rate, receiver side surface temperatures, and instantaneous amount of pure water. Numerical and thermal analysis of the receiver in the experimental setup carry out. ANSYS Fluent 18.1 uses for numerical analysis. Energy and exergy analysis calculates the receiver for pure water production rate. The time interval when the system operates at high performance determines. In this study investigates the behavior of some exergetic indicators on the performance of the experimental setup, the cost of the experimental setup, and the enviro-economic impact of the parabolic dish-type collector. For this purpose, some exergetic factors such as the exergy effect, environmental impact factor, waste exergy rate, improvement potential, and exergetic sustainability index examine. The receiver’s improvement potential is between 1700 and 3000 W. In addition, the sustainability index, waste exergy rate, and environmental impact factor range from 0.32 to 0.36, 0.90 to 0.94, and 2.6 to 3.0, respectively.

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.

This research offers valuable improvements in the efficiency and water yield of a parabolic dish concentrator (PDC) solar distillation system, contributing to more sustainable and effective renewable energy solutions. Three hybrid nanofluids were evaluated, and their performance was measured through experiments and simulations. The numerical model is within 5% agreement with the measurements. Daily distilled water production increases by 25.7% with hybrid nanofluids (from 4.50 L to 5.67 L). The average exergy efficiency is approximately 19%. Furthermore, an interpretable, rule-based AI controller optimized with the Coati algorithm was integrated; this controller suggested operating setpoints and revealed transparent decision thresholds. This work is the first systematic PDC study where three different hybrid nanofluids were examined and explainable artificial intelligence (XAI) was applied within a single framework. The results demonstrate that higher performance and more predictable operation are achievable for producing distilled water based on PDC.