This innovative article provides a detailed description of the successful biosynthesis of zinc nanoparticles (ZnO-NPs) using an aqueous extract of Algerian Date Syrup, also known as molasses. A meticulous process was carried out to determine the optimal calcination temperature for ZnO-NPs, a crucial step in the preparation of these nanoparticles. The study was further extended by creating ZnO/rGOx nanocomposites through a hydrothermal method, varying the concentrations of reduced graphene oxide (rGO) at 5%, 10%, and 15%. The characteristics of the nanocomposites were thoroughly explored, encompassing chemical, optical, and morphological aspects, using sophisticated analysis techniques such as scanning electron microscopy (SEM), UV-visible diffuse reflectance spectroscopy (UV DRS), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). These analyses provided an in-depth understanding of the structure and properties of the nanocomposites. The centerpiece of this study is the evaluation of the photocatalytic degradation capacity of ZnO-NPs and ZnO/rGOx nanocomposites. These materials have demonstrated their ability to act as cost-effective and environmentally friendly photocatalysts for wastewater treatment. Experiments on methylene blue degradation under UV irradiation were conducted, yielding impressive results: a degradation efficiency of 86.6% was achieved in 140 min using 1 g/L of ZnO-NPs, and this rate reached 100% with the ZnO/rGO catalyst in the same time frame, highlighting its superiority as a photocatalyst. Furthermore, this study examined the variables affecting the photocatalysis experiment, including the solution’s pH and the amount of catalyst. The results revealed that the ZnO/rGO photocatalyst reached its optimal efficiency under neutral pH conditions and at a concentration of 1 g/L, providing crucial information for practical use of these materials. This enriched article highlights the promising potential of ZnO-NPs and ZnO/rGOx nanocomposites as efficient photocatalysts for methylene blue degradation, paving the way for significant environmental applications in wastewater treatment.

This study investigates the degradation kinetics of polycyclic aromatic hydrocarbons (PAHs) in contaminated soil using hydrogen peroxide (H₂O₂) and the Fenton process (H₂O₂/Fe²⁺). The effect of oxidant concentration and the Fenton molar ratio on PAH decomposition efficiency is examined. Results reveal that increasing H₂O₂ concentration above 25 mmol/samples leads to a slight increase in the rate constants for both first- and second-order reactions. The Fenton process demonstrates higher efficiency in PAH degradation compared to H₂O₂ alone, achieving decomposition yields ranging from 84.7% to 99.9%. pH evolution during the oxidation process influences PAH degradation, with alkaline conditions favoring lower elimination rates. Fourier-transform infrared (FTIR) spectroscopy analysis indicates significant elimination of PAHs after treatment, with both oxidants showing comparable efficacy in complete hydrocarbon degradation. The mechanisms of PAH degradation by H₂O₂ and the Fenton process involve hydroxyl radical formation, with the latter exhibiting greater efficiency due to Fe²⁺ catalysis. Gaussian process regression (GPR) modeling accurately predicts reduced concentration, with optimized ARD-Exponential kernel function demonstrating superior performance. The Improved Grey Wolf Optimizer algorithm facilitates optimization of reaction conditions, yielding a high degree of agreement between experimental and predicted values. A MATLAB 2022b interface is developed for efficient optimization and prediction of C/C₀, a critical parameter in PAH degradation studies. This integrated approach offers insights into optimizing the efficiency of oxidant-based PAH remediation techniques, with potential applications in contaminated soil remediation.

Total alkalinity (TAC) plays a pivotal role in buffering acid–base fluctuations and maintaining pH stability in aquatic ecosystems. This study presents a data-driven approach to model TAC using decision tree regression, applied to a comprehensive dataset of 454 water samples collected in diverse aquatic environments of the Médéa region, Algeria. Twenty physicochemical parameters, including concentrations of bicarbonates, hardness, major ions, and trace elements, were analyzed as input features. The decision tree algorithm was optimized using the Dragonfly metaheuristic algorithm coupled with 5-fold cross-validation. The optimized model (DT_DA) demonstrated exceptional predictive performance, with a correlation coefficient R of 0.9999, and low prediction errors (RMSE = 0.3957, MAE = 0.3572, and MAPE = 0.4531). External validation on an independent dataset of 68 samples confirmed the model’s robustness (R = 0.9999; RMSE = 0.4223; MAE = 0.3871, and MAPE = 0.4931). The tree structure revealed that total hardness (threshold: 78.5 °F) and bicarbonate concentration (threshold: 421.68 mg/L) were the most influential variables in TAC determination. The model offers not only accurate predictions but also interpretable decision rules, allowing the identification of critical physicochemical thresholds that govern alkalinity. These findings provide a valuable tool for anticipating pH instability and guiding water quality management and protection strategies in freshwater ecosystems.

This study investigates the performance of a solar-powered organic Rankine cycle (ORC) coupled with a vapor compression cycle (VCC) for simultaneous power generation and cooling applications in the region of Ghardaïa (Southern Algeria). The analysis focuses on the energy and exergy performance of five low-GWP refrigerants: R134a, R1234ze(E), R600a (isobutane), R152a, and R1234yf, under realistic operating conditions. In addition, key parameters such as turbine inlet temperature and pressure, VCC condensing and evaporation temperatures, and the VCC/ORC mass flow rate ratio were analyzed to evaluate their impact on net power output, cooling capacity, coefficient of performance (COP), and exergy efficiency. Results indicate that R600a provides the highest net power output (7.08 kW) and exergy efficiency (9.87%), making it ideal for power-dominant applications. R152a, on the other hand, excels in cooling performance, achieving a COP of 6.10. R1234ze(E) emerges as the most environmentally sustainable option, offering a balanced performance with 8.02% exergy efficiency and a negligible GWP of 7. Exergy analysis reveals that the solar collector is the primary source of irreversibility, with 58 kW of exergy destruction, followed by the ORC and VCC subsystems. These findings offer valuable insights for designing sustainable cogeneration systems in arid regions, highlighting the trade-off between thermodynamic performance and environmental impact when selecting the optimal working fluid for ORC-VCC applications.