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Solar energy of its renewable nature is considered the best alternative for offering effective solutions to the energy and environmental challenges faced by the world. This study is carried out to optically model a parabolic trough collector (PTC) field and study the influence of Gassi-Touil climate on its optical and thermal efficiency. The study allowed the completion of the MATLAB code that allows the calculation of all design and optical parameters of the studied solar collector, taking into account the instantaneous change of incidence angle that has a direct impact on the change in design and optical errors. For the PTC efficiencies, its average optical performance found, is 75.15% and its average thermal efficiency is 70%. Besides, the average rate of total heat loss coefficient is 7.95 W/m2K, the average rate of local concentration ratio on the receiver tube surface is 40.65 and the average rate of heat flow intensity is 36823 W/m2. The obtained results are very encouraging to exploit this effective solar technology, which will help in preserving fossil resources and progress towards sustainable development in Algeria.

This paper investigates a safety control room with 18 kW of cooling load, where a parabolic trough collector is connected to an R718 ejector air-conditioning subsystem. Engineering Equation Solver is used as a programming tool to conduct the dynamic study of the proposed system. After model validation, the optical thermal and exergy efficiency of studied solar collector, main ejector diameters, ejector mass ratio, coefficient of performance of ejector air-conditioning subsystem, and solar performance coefficient (or system thermal ratio) have been accurately determined under different operating conditions. Regarding the studied solar collector, its optical, thermal, and energy performances are acceptable, with values of 79.15 %, 67.06 %, and 19.14 %, respectively. At the best-operating conditions (Tg = 413.15 K, Te = 283.15 K, and Tc = 298.15 K), the maximum performance of the ejector air-conditioning loop was estimated at 1.193, while the system thermal ratio for the studied solar air conditioner was 0.81. For a steady-state thermal operation, a hot storage tank was incorporated between the studied solar collector and the ejector air-conditioning subsystem with two different storage liquids (Therminol VP-1 oil and Hitec XL molten salt); the daily maximum storage temperature reached was thus 424.15 K.

The current work employs two modern ensemble machine learning algorithms, Matern 5/2 Gaussian process regression (GPR) and quadratic support vector regression (SVR), to model-predict the thermal conductivity, specific heat, and viscosity of novel Ionic liquid-MXene hybrid nanofluids. The data for model development was obtained in the lab at various temperatures and mass concentrations. The obtained thermal conductivity value for the pure aqueous Ionic liquid (IL) solution at 20 °C was 0.443 W/m·K which rose to 0.82 W/m·K due to the inclusion of 0.5 wt% MXene nanomaterial. The achieved result for the specific heat capacity of the pure aqueous IL solution is 1.985 J/g·K, while this value enhances up to 2.374 J/g·K due to the inclusion of the highest loading concentration (0.5 wt%) of MXene nanomaterial. The acquired data was divided into two portions, with 70% of the data utilized for model training and 30% (hold-out) data used for model testing. Several statistical indicators were used to evaluate the GPR and SVR-based prognostic models created for specific heat, viscosity, and thermal conductivity. For SVM models, the correlation coefficient (R) ranged from 0.9741 to 0.9958, while for GPR-based models, it ranged from 0.9942 to 0.9998. The inaccuracy in the prediction model was quantified using root mean squared error, which ranged from 0.0129 to 0.0398 for SVM-based models and 0.004 to 0.105 for GPR-based models. The predictive effectiveness of the models was tested using Willmott's Index of Agreement, which was 0.9833 to 0.999 for SVM-based models and 0.9834 to 0.9999 for GPR-based models, respectively. The close to unity regression coefficient, low model error, and good prediction effectiveness illustrate the robustness of both SVM and GPR prognostic models. However, the GPR outperformed the SVM on all the statistical indices. The experiment results were also used to compute the Mouromtseff number (<4) and Figure of merit (>1). The results showed that the studied nanofluids can successfully substitute water in specified applications for solar energy.