Administrative Divisions Al-Mustaqbal Energy Research Center
Researchers are exploring innovative solutions for thermal energy storage to address the challenges posed by intermittent renewable sources, enhance energy efficiency, and contribute to the global shift towards cleaner and more sustainable energy practices. In the pursuit of an optimal system to improve the heat release/absorption efficiency of phase change materials (PCMs), a unique shell and tube latent heat storage system with four rectangular fins was designed. The melting and solidification behaviors of the material in this device were examined by manipulating the tube s position within the shell and fins around the tube. Six different cases were considered such as case A (tube in the center of the shell), case B (tube at the top of the shell), case C (tube at the bottom of the shell), case D (tube in the center of the shell with fins on its sides), case E (tube at the top of the shell with fins located in its bottom section), and case F (tube at the bottom of the shell with fins located in its top section). Cases D and E were the best options for absorbing and releasing heat in the shortest time. However, it should be noted that case F was faster during the melting process and dropped behind in the final stages. The authors proposed that if achieving a balanced result without incurring additional costs is essential, case D is a suitable option since it offers reasonable performance in melting and solidification processes. However, suppose researchers and developers of energy storage systems are seeking higher performances where heat absorption and release occur much more rapidly. In that case, it is suggested to construct one of the cases, E or F, and implement a rotational mechanism to enable access to the other case. Based on the outcomes, cases D and F needed 6235 s and 7552 s, respectively, to fully melt. While all cases even required more than 3 h to solidify 80 % of the PCM. The complete melting speed of Case F is 21.12 % faster than that of Case D. Additionally, the time required for 50 % solidification is 14.79 % faster for Case E compared to Case D. During 3 h, this system could absorb 1172 kJ of energy (cases D and F) and release 893 kJ of energy (cases D and E). https://www.sciencedirect.com/science/article/abs/pii/ S2352152X24014658?via%3Dihub
Abstract Entropy generation and convection heat transfer in a partially porous chamber with different side wall temperatures using CuO–H2O have been investigated. The importance of this issue is wide application of the results in solar collectors, thermal extrusion systems, heat exchangers, bio-medicine, nuclear waste disposal, etc. The innovation of the present work is related to the investigation of fluid and heat fields and entropy generation by using a matrix with subordination of porosity to the vertical axis and permeability, thermal conductivity, and viscositywith subordination of porosity. To obtain accurate results, the two-phase mixture model was used, and thermal conductivity and viscosity of nanofluid were simulated by experimental models by temperature and volume fraction dependence. Governing equations are solved by the FVM. The main findings indicate that the best and worst optimization factor will occur in the porous matrix ε = ε(y2) and ε = -ε(y2), respectively, which is 113 % and 86 % of NH of the homogeneous matrix, respectively. Also increasing the filling of the cavity, highly improves NH, so that the NH will reach from 1.19 to 1.82 with the increase of S from 0.25 to 1. https://www.sciencedirect.com/science/article/pii/ S2214157X24005161?via%3Dihub
The present work proposes an innovative system for decarbonizing the energy mix and accelerating the worldwide green transition process. The system is driven by a biomass digester integrated with the supercritical carbon dioxide cycle for power generation and a multi-effect desalination unit for drinkable water production. At the heart of this concept is additional hydrogen injection through a proton exchange membrane electrolyzer based on photovoltaic panels. The suggested innovative model s techno-environmental, sustainability, and economic aspects are assessed and compared with a similar system without hydrogen injection. Then, a comparative multi-criteria optimization is applied to find the most optimal conditions from various facets based on the genetic algorithm with machine learning techniques. Afterward, the system’s performance at different optimal conditions is analyzed and compared by evaluating the most significant techno-economic, environmental, and sustainability indicators. The parametric assessment comparing different models indicates that the proposed novel model, including increased hydrogen injection, surpasses the basic system in terms of performance efficiencies, emissions, and energy costs. In the first optimization scenario, the proposed method demonstrates robustness by achieving higher water production of 1456 kg/s, a lower total cost of 118 $/h, and a higher net power of 1.1 MW than the design condition. When considering the sustainability index, energy cost, and emission metric as the optimization objective, their values are altered from 0.81 to 0.85, 92.5 $/MWh to 89.7 $/MWh, and 64.2 kg/MWh to 53.6 kg/MWh. The results further show that when prioritizing the sustainability index, energy cost, and emission as objectives, all components perform better from the energy conversion quality aspect compared to the scenario where water production, total cost, and net power are the optimization objectives. Finally, it is observed that the combustion chamber and solar panels are the worst components from the irreversibility aspect because of the highest exergy destruction rate. https://www.sciencedirect.com/science/article/abs/pii/ S0957582024004786?via%3Dihub
Announcement of Participation Certificate in the European Energy Centre Course The European Energy Centre has announced the awarding of a Certificate of Participation to the Director of the Al-Mustaqbal Energy Research Center, Professor Salwan Obaid Waheed, in recognition of his successful completion of the seminar on "Renewable Energy Management Techniques" held in March 2025. This seminar represents a vital opportunity to discuss the latest trends and technologies in the field of renewable energy management, contributing to a better understanding of how to improve energy efficiency. Attendance at such events also supports the achievement of global goals related to sustainable energy, underscoring the Centre's commitment to fostering innovation and research in this critical field. The seminar brought together experts and specialists from various sectors, allowing for the exchange of ideas and experiences, and helping to build a strong network of professionals dedicated to enhancing sustainability in the energy industry.
This study delineates the development of a solar energy system that leverages concentrated solar power (CSP) technology to supply both electricity and potable water for residential applications. The proposed thermal architecture uniquely integrates heliostat solar fields with a dual-loop power generation cycle, augmented by a seawater desalination system that employs reverse osmosis (RO) membranes. To bolster electricity production, a thermoelectric generator (TEG) has been incorporated into the system s design framework. A comprehensive analysis of the system has been performed, encompassing thermodynamic and economic evaluations. Furthermore, a parametric analysis has been executed to investigate the effects of critical parameters on the system s operational efficiency. The efficacy of the system was rigorously assessed through a case study that examined its capabilities for daily production outputs. This research, grounded in the analytical projections from Saudi Arabia and the favorable environmental conditions characteristic of the region, explores the operational performance of the system within this specific geographical context. The primary objective of this inquiry is to determine the ideal operational parameters by employing multi-criteria optimization methods tailored to the established system. Variations in compressor pressure ratios were found to significantly affect the performance of the Brayton cycle and the exergetic efficiency of the system, with optimal economic efficiency being realized at a specific pressure ratio. Furthermore, increasing the inlet temperatures in the organic Rankine cycle has been shown to improve system efficiency up to a certain limit, beyond which potential reliability issues could arise. The case study demonstrated that electricity generation peaks during the summer months, particularly in June, aligning with a high volume of freshwater production totaling 264,530 m³. The optimization efforts achieved an exergetic efficiency of 17.69 % and an overall cost of $359.58 per hour. https://www.sciencedirect.com/science/article/pii/ S2214157X24015946
As the global energy demand continues to rise, there is an urgent need to improve the efficiency and sustainability of power generation systems. This study integrated a modified supercritical carbon dioxide (S-CO2) and multi-effect desalination (MED) units to recover residual heat from a gas turbine cycle (GTC) in two stages, significantly enhancing electricity production while reducing the environmental footprint of the GTC. The significance of this study lies in its comprehensive approach, combining thermodynamic, environmental, and thermoeconomic analyses alongside thorough sensitivity evaluations. A triple optimization framework was implemented to optimize the system s performance, focusing on key metrics such as exergy efficiency, CO2 reduction rates, and levelized energy cost, utilizing the NSGA-II and the TOPSIS decision-making method in MATLAB software. Economic viability was assessed through a net present value (NPV) analysis, demonstrating substantial profitability. Finally, a comparison study of the devised system CO2 emissions rate was performed for different renewable energy sources. A specific application of the devised system is its capacity to generate 1.415 m³/h of distilled water while generating 1441 kW of electricity. Sensitivity analysis identified the combustion chamber temperature as the most critical design parameter, with a sensitivity index of 0.328. The optimum economic indicators showed marked improvement, with the NPV increasing from 2.371 M$ to 10.75 M$ and the payback period decreasing from 13.28 years to 7.18 years. https://www.sciencedirect.com/science/article/pii/ S2214157X24015235
The principal aim of this article is to optimize the thermal and electrical efficiency of a geothermal combined heat and power system through metaheuristic particle swarm optimization (PSO) method. The objective of this research is to conduct a thorough analysis of the incorporation of metaheuristic PSO technique, with a specific emphasis on the potential advantages and obstacles associated with the utilization of metaheuristic approaches in improving the effectiveness of geothermal energy systems. The utilization of a double-flash geothermal system in conjunction with a transcritical carbon dioxide Rankine cycle is utilized for the co-generation of electricity and thermal energy. The research utilized a PSO method to enhance power generation, heating capacity, and overall system efficiency. The PSO algorithm was employed to determine the optimum operational parameters for a pressure level of 820 kPa and a pressure ratio of 1.59, leading to the maximization of power output to 2591.4 kW The PSO algorithm effectively identified the optimal operational parameters as a pressure of 820 kPa and a pressure ratio of 1.59, resulting in the achievement of a peak power output of 2591.4 kW. The methodology has determined that a pressure of 916.4 kPa and a pressure ratio of 1.5 represent the optimal parameters for achieving a maximum heating capacity of 12329.1 kW. https://www.sciencedirect.com/science/article/pii/ S2214157X24013741
Utilizing the capabilities of artificial intelligence can lead to the development of energy systems and power supply chain that are more efficient, sustainable, and resilient. The integration of machine learning techniques within these systems provides substantial benefits and is essential for enhancing overall performance. As the global community confronts challenges like climate change and rising energy demands, machine learning will play an increasingly vital role in defining the future of energy systems. This research examines how effective regression-based machine learning techniques are for analyzing and optimizing the performance of a geothermal combined heat and power system. It focuses on creating both linear and quadratic models to assess electricity generation, heat production, and the efficiency of the entire system. The evaluation of these models is performed through residual analysis and R-squared statistics. Results indicate that quadratic models surpass linear ones, with linear model achieving an R-squared value of 88.56 % for power generation, while the quadratic model reaches an impressive R-squared level of 99.88 %. Furthermore, the study demonstrates that quadratic machine learning models hold significant promise for optimizing system performance, shown by desirability metrics exceeding 0.99. This research highlights the importance of regression-based machine learning methods in analyzing and improving geothermal combined heat and power systems. https://www.sciencedirect.com/science/article/abs/pii/ S0360544224033723