The experiments were conducted at different levels of infrared power, airflow, and temperature. The relationships between the input process factors and response factors’ physicochemical properties of dried garlic were optimized by a self-organizing map (SOM), and the model was developed using machine learning. Artificial Neural Network (ANN) with 99% predicting accuracy and Self-Organizing Maps (SOM) with 97% clustering accuracy were used to determine the quality characteristics of garlic. Specifically, five key areas were identified, and valuable insights were offered for optimizing garlic production and improving its overall quality. The (aw) values for the sample ranged from 0.43 to 0.48. The maximum vitamin C content was 0.112 mg/g, followed by an air temperature of 40 °C and 0.7 m/s air velocity under 1500 W/m². The total color change values increased with IR and higher air temperature but declined with higher air velocity. Also, the garlic’s flavor strength, allicin content, water activity, and vitamin C levels decreased as the IR and air temperature increased. The results demonstrated a significant impact of the independent parameters on the response parameters (P < 0.01). Interestingly, the machine learning predictions closely matched the test data sets, providing valuable insights for understanding and controlling the factors affecting garlic drying performances.

The urgent need for sustainable energy solutions drives innovation in clean fuel technologies. Oxyhydrogen (HHO) gas, produced through water electrolysis, presents a promising green energy vector. While often studied in dry-cell configurations, wet-cell electrolyzers offer advantages for efficient, scalable production but require further optimization. This study systematically investigates the design and operational parameters of a wet-cell HHO generator to maximize efficiency. Four distinct configurations (Alpha, Beta, Gamma, Delta) were constructed, varying in electrode cross-sectional area (75 × 75 mm² vs. 150 × 150 mm²), plate configuration (18 vs. 20 plates), and tested at different potassium hydroxide (KOH) concentrations (10 and 20 g/L). Performance was evaluated based on HHO flow rate, specific energy consumption, and overall system efficiency. Experimental results demonstrate that the Delta generator significantly outperformed all other designs. It achieved a peak HHO flow rate of 3.4 L/min with a specific energy consumption of 3.1 kWh·m⁻³ and a notable overall system efficiency of 59.74%. In contrast, the Alpha, Beta, and Gamma generators attained lower efficiencies of 12.7%, 23.86%, and 41.9%, respectively. The superior performance of the Delta design is attributed to its optimized combination of a larger electrode area, which reduces current density and associated overpotentials, and an effective electrode configuration that maximizes active surface area and thermal management. This study conclusively identifies optimal electrode geometry and electrolyte management as critical drivers for efficient HHO production. The optimized wet-cell electrolyzer presents a sustainable and practical technology for on-demand green hydrogen production, with direct potential applications as a combustion enhancer or a storage solution for intermittent renewable energy, contributing to the advancement of sustainable energy systems.

In the continue of meeting the energy demand as well as decreasing the CO2 emission and production costs, Egypt has expanded its power capacity of the renewable power plants. However, such expansion could lead to energy production failure due to the unreliable and unfeasible operation of such unconventional technology, if used without assessment. In this contribution, the planned expansion (till 2030) of renewable energy has been evaluated using an optimization model. The model has two phases, in which Phase-A estimates the cost and capacity of the PV, CSP, and Wind plants, while Phase-B optimizes the plants according to the required demand (i.e., the contribution of PV, CSP, and Wind). The available data (i.e., intensity, storage, and temperature effect) is used as an input to Phase-A, while the calculated cost and capacity are used as an input to Phase-B. The results are presented in terms of spatial and temporal distributions. The result successfully identified the promising renewable plants (i.e., type and location), as well as mix-installations of different renewable plants, were captured. Specifically, CSP plants contribute ≈20% of the power demand, especially in Upper Egypt, due to their higher capacities although their higher cost. Meanwhile, the PV plants are contributed ≈40% and are unpreferable for installation in Upper Egypt. Furthermore, it is recommended to install Wind plants near the Red-sea region with a ≈40% power contribution. This study is expected to insight our understanding into the planned expansion of renewable energies in Egypt; aiming to evaluate its potential.