This study aims to enhance the thermal and moisture insulation properties of asphalt by incorporating eco-friendly organic and inorganic additives. Physical modification was performed using sawdust and calcium carbonate (CaCO3), while aluminum chloride (AlCl3) was used as a catalyst for chemical modification. Key tests included thermal conductivity, water absorption, FTIR spectroscopy, and mechanical property evaluations. Thermal conductivity decreased from 0.243 W/m & centerdot; degrees C (original asphalt) to 0.124 W/m & centerdot; degrees C in the chemically modified sample. Water absorption also significantly declined in modified specimens, particularly those containing CaCO3 and AlCl3.These modifications improved insulation efficiency and reduced permeability. The use of natural, low-cost additives enhances environmental sustainability, making the modified asphalt suitable for infrastructure applications in severe climates.
Financial aid and subsidies for electricity are intended to mitigate the impact of energy bills on low-income households. Unfortunately, these subsidies can represent a significant financial burden for governments. By eliminating them, states can reduce their budget deficit and reallocate those funds to other priorities, such as education or health. Several methods and alternative solutions to avoid or reduce dependence on electricity subsidies can be considered, but they will need to be adapted to the specific context of each country or region. Promoting energy efficiency will help consumers to reduce their electricity consumption and offset price rises. The integration and investment in renewable energy are essential to diversify energy sources and reduce dependence on fossil fuels. These two actions can effectively reduce the need and dependence on subsidies by decreasing demand. Moreover, by implementing more rational aid programs instead of subsidizing fossil electricity for low-income households, it will be highly possible to encourage states to stabilize their budgets acceptably by reallocating funds to other priorities. This study aims to evaluate the energy and economic performance of the Algerian buildings. This paper quantitatively analyzes the financial and energy efficiency of buildings and, rehabilitation projects of single-family houses in all of Algeria's climatic regions. An assessment of the building's overall energy balance was the basis of the investigation method. According to the obtained results, the current state subsidy system will not have to be fully maintained. Eliminating or reducing electricity subsidies is a complex process that requires a thoughtful and gradual approach. It must be carried out gradually and accompanied by protective measures for vulnerable households. To avoid negative impacts on the purchasing power and well-being of citizens, these measures must be based, firstly, on specific and limited investments and financial aid according to climatic regions, unlike what they were at the beginning (unlimited); secondly, on the judicious integration of renewable energies; and thirdly, on strengthening energy efficiency. From a financial point of view, subsidies are significantly lower for lower cooling energy needs. By adopting rehabilitation measures, unsubsidized energy bills are lower than those corresponding to a conventional home without thermal rehabilitation and with subsidized bills. Subsidizing the thermal rehabilitation procedure, or subsidizing thermal rehabilitation, a suitable solar water heater and, a stand-alone, optimized and well-sized photovoltaic system at the same time, can be a good alternative for effectively reducing the need for and dependence on subsidies by reducing demand or getting rid of these subsidies altogether. The most suitable regions for financial assistance, ranked according to merit, are M'sila, Na & acirc;ma, Biskra, Bechar and, lastly, the Drabla climatic region.
Proton exchange membrane fuel cells (PEMFCs) represent a promising renewable energy technology that converts chemical energy from hydrogen and oxygen into electrical energy. Accurate mathematical modeling and precise parameter identification are essential for optimizing PEMFC performance and control. This study proposes a novel hybrid meta-heuristic algorithm, the mutated puma optimizer (Mu-PO), which integrates a mutation operator from differential evolution to enhance the exploration and exploitation capabilities of the conventional puma optimizer, enabling it to escape local minima and reach global optima in fewer iterations. A sum of squared error (SSE)-based objective function is formulated to minimize the discrepancy between estimated and experimental voltages. The proposed method identifies seven unknown parameters for three commercial PEMFC models (250 W, SR-12, and NedStack PS6), achieving SSE values of 0.6419, 1.0566, and 2.0791, respectively. Notably, Mu-PO attains these low SSE values in fewer than 50 iterations for all models, demonstrating rapid convergence. Comparative analysis using statistical indicators (minimum, mean, maximum, and standard deviation of SSE) confirms that Mu-PO outperforms well-established optimization algorithms in terms of convergence speed, stability, and accuracy. Furthermore, validation under dynamic operating conditions, including variations in pressure and temperature, demonstrates consistent and reliable parameter identification, highlighting the robustness and practical applicability of the proposed approach for PEMFC modeling and optimization.
This paper presents one of the most important renewable energy candidates, analyzing its viability in light of significant requirements and recent global events, including health and political factors. This candidate is wind energy, which has recently gained wide attention. During this review, we will introduce wind energy technologies, discuss their present development, explore current research challenges and opportunities (current status), and outline future prospects. Furthermore, the top countries in the 19th century (the beginning) and the present top five countries for wind energy capacity in 2021. The primary finding of the proposed study is that the future of this energy is open and promising. Therefore, researchers are working to design wind turbines that can withstand and operate in the most difficult conditions to make their devices and systems more cost-effective and competitive with fossil fuel energy systems.
Accurate identification of photovoltaic (PV) cell and module parameters remains a fundamental yet challenging task, particularly as model complexity increases from five to nine unknown parameters. In this study, the parameter extraction problem is rigorously formulated as a nonlinear optimization task and addressed using a novel hybrid metaheuristic algorithm, termed the Shuffled Frog Leaping-Shuffled Complex Evolution (SFL-SCE) method. The proposed approach synergistically integrates the population-based social learning mechanism of the Shuffled Frog Leaping Algorithm (SFL) with the robust global search and refinement capabilities of Shuffled Complex Evolution (SCE), thereby achieving an effective balance between exploration and exploitation. The SFL-SCE algorithm minimizes the root-mean-square error (RMSE) between measured and simulated current-voltage characteristics and is systematically applied to three widely used PV technologies: the RTC-France silicon solar cell, the polycrystalline Photowatt-PWP201 module, and the monocrystalline STM6-40/36 module. For each device, parameter identification is performed under one-diode, two-diode, and three-diode modelling frameworks, encompassing increasing levels of physical fidelity and computational complexity. Experimental data are employed throughout to ensure practical relevance and robustness. The performance of the proposed algorithm is comprehensively evaluated against its constituent algorithms (SFLA and SCE) as well as several state-of-the-art hybrid optimization techniques reported in the literature. Comparative results demonstrate that SFL-SCE consistently achieves superior accuracy, enhanced reliability, and faster convergence, as evidenced by lower minimum, mean, and maximum RMSE values, reduced standard deviation, and improved convergence behavior across all test cases. These findings confirm the effectiveness of the proposed hybridization strategy and establish SFL-SCE as a powerful and reliable tool for high-precision PV model parameter identification.
