Administrative Divisions Al-Mustaqbal Energy Research Center
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
Solar-driven photocatalysis depicts an effective efficiency for producing hydrogen via water splitting, as well as for CO2 reduction and the degradation of hazardous pollutants in aqueous environment, playing a vital role in addressing the challenges associated with the environmental and energy crises. This study presents a synthesis of a Strontium Titanate/Zinc Oxide/Graphitic Carbon Nitride photocatalyst via an integrated sonochemical and wet impregnation method. The photocatalytic efficiency of various content (10 %, 20 %, 30 %, and 40 % wt%) of 40 wt% Titanate/Zinc Oxide loaded on Graphitic Carbon were investigated, with the 30 % loading demonstrating optimal performance. A complete identification of the synthesized materials was carried out using XPS, XRD, BET, TEM, DRS, SEM, EIS, Photocurrent, ESR, PL, and Mott–Schottky analyses. The BET scrutiny indicated a type IV isotherm with H2 hysteresis, representing a mesoporous construction. The improved photocatalytic activity of optimized composite was due to its structural features facilitating efficient electron-hole separation and charge transfer. RSM optimization identified the key parameters for achieving high Cefixime (CFX) degradation efficiency (96 %) at 29.91 mg/L CFX concentration, pH 4.55, 78 min of reaction time, and 0.65 g/L catalyst dosage. LC-MS analysis unveiled two proposed pathways for CFX photodegradation, and trapping experiments and ESR analysis highlighted superoxide and hydroxyl radicals as significant contributors. The synthesized photocatalyst exhibited excellent stability and reusability over five cycles. Hydrogen production showed an initial increase with Strontium Titanate/Zinc Oxide loading, peaking at 645.62 μmol g−1 h−1 at 30 wt%, before declining due to potential electron trapping effects. Notably, the SrZg-30 photocatalyst demonstrated a significantly higher CO production rate (0.97 μmol g−1 h−1) compared to SrTiO3/ZnO and g-C3N4 components. This work offers a viable approach to developing eco-friendly photocatalysts to tackle pressing environmental and energy crises. https://www.sciencedirect.com/science/article/abs/pii/
This paper presents a ground-breaking design for a multigeneration system capable of simultaneously producing electricity, hydrogen, and cooling loads. This research advances sustainable energy systems by introducing an innovative design that optimally utilizes waste heat and integrates biomass gasification with advanced thermodynamic cycles. It also provides a model for future studies on carbon emission reduction and improved efficiency. The proposed system effectively harnesses waste heat from the Brayton cycle to drive the supercritical carbon dioxide cycle, steam Rankine cycle, absorption refrigeration, and proton exchange membrane electrolyzer. This approach improves overall efficiency and offers a promising solution for integrated energy generation. Additionally, employing the sCO2 cycle provides high thermal efficiency, cost-effectiveness, and lower environmental impacts compared to traditional power generation methods. Extensive evaluations, including techno-economic and environmental analyses, confirm the system s practicality and potential for future commercial application. Additionally, a parametric investigation of five essential design parameters provides important insights into the system s performance and flexibility. Analysing the proposed system determined that the gasifier-Bryton unit has the highest irreversibility and cost rate among other subsystems. A novel approach combining artificial neural networks (ANN) with a non-dominated sorting genetic algorithm II (NSGA-II) has been developed to optimize the system, substantially reducing computational time and costs associated with system performance analysis. According to the three-objective optimization, the system in the optimal operating mode provides 45.89 kg/h of hydrogen with an exergy efficiency of 33.15 % and a total cost rate of 159.5 $/h. After the optimization process, significant achievements have been observed, including a 5.02 % improvement in exergy efficiency, an increase of 7.29 kg/h of hydrogen production, and a decrease of 0.1 ton/MW in the CO2 emission index. https://www.sciencedirect.com/science/article/pii/ S2214157X24016204
This research aims to analyze and enhance a tri-generation energy system that utilizes geothermal energy for producing power from heat extracted from the geothermal source, along with freshwater and heating. To achieve this, the study employed the central composite design method from response surface methodology. The system comprises a double-flash geothermal configuration, a desalination component, and a transcritical carbon dioxide Rankine cycle. A Pareto analysis was performed to pinpoint and rank the most critical factors affecting the system s performance, while residual analysis assessed the precision of the central composite design approach. The analysis of variance offered a numerical evaluation of how processing parameters influenced power generation, heating output, freshwater yield, and overall system efficiency. In the end, the central composite design method facilitated the optimization of system performance. Findings from the Pareto analysis indicated that the geothermal source temperature had the greatest influence on power production, hot water output, and overall efficiency, while the mass flow rate of the geothermal source primarily affected freshwater generation. Under optimal operating conditions, the system was able to generate a power output of 1191.8 kW, produce 64.89 kg/s of hot water, generate 4.94 kg/s of freshwater, and achieve an efficiency of 59.96 %. This study illustrates the significance of transforming heat extracted from the geothermal source into valuable energy and emphasizes the advantages of integrated systems for converting heat extracted from the geothermal source into power, heating, and freshwater. https://www.sciencedirect.com/science/article/abs/pii/ S0960148124023346
Heat transfer enhancement inside a constant temperature pipe via clockwise and counter-clockwise twisted triangle turbulators, applicable in IC engine system