Administrative Divisions Al-Mustaqbal Energy Research Center
We are delighted to invite you to join us for the 3rd International #Sustainability #Week Festival, an event dedicated to advancing sustainability and innovation. This festival also celebrates two significant milestones: the 15th #Anniversary of the University’s Establishment and the 2nd Anniversary of the Transformation from Al-Mustaqbal College to Al-Mustaqbal University. Event Duration: April 16 – April 24, 2025 Location: Al-Mustaqbal University International Conferences: - April 16-17: 1st International Conference on Applications of Future Technologies for Sustainable Education (1st #ICFSET) - April 19: Future Media Dialogue Forum – 4th Edition - April 22: 2nd Al-Mustaqbal International Conference on Sciences and Health Techniques (2nd #MICSHT) - April 23: Conference on Poverty and Sustainable Development in Iraq - April 24: Conclusion of Events Activities and Events: - Case Studies and Voluntary Campaign Exhibitions - Art Exhibitions and Performances - Graduation Project Exhibitions - Innovation Showcases - Sustainability Excellence Awards Ceremony. We look forward to your participation in this momentous event, where we will celebrate and explore innovations for a sustainable future. Don’t miss this exciting opportunity to engage with global experts and share in the achievements of Al-Mustaqbal University!
The rising energy demand of the building sector has led to a critical need for innovative solutions to curb its 37 % share of global CO₂ emissions in 2020. This study introduces a novel hybrid energy system integrating solar photovoltaic-thermal (PVT) panels with a biofuel-driven boiler, designed to meet residential heating, cooling, and electricity needs. Dynamic simulations were conducted using TRNSYS to evaluate the system s real-world performance across varying climatic conditions. Optimization using the greywolf algorithm achieved a balance among three competing objectives: thermodynamic efficiency, environmental sustainability, and economic viability. The system delivered a thermal efficiency of 28.5 %, a levelized energy cost of 0.163 $/kWh, and a CO₂ emission index of 121 kg/MWh, reflecting significant improvements of 13.5 %, 23.1 %, and 15.4 %, respectively, over the baseline design. Unlike prior studies, this work uniquely combines dynamic transient modeling with multi-objective optimization for a solar-biofuel energy system, offering actionable insights into sustainable energy integration for the building sector. These findings underscore the system s potential to enhance energy performance while reducing environmental and economic costs. https://www.sciencedirect.com/science/article/abs/pii/ S2352710225001512
Abstract In current work, simulations of the freezing process within a tank featuring plus sign-shaped fins were conducted utilizing the Galerkin method. The focus was on unsteady heat transfer based on conduction, solving two coupled equations. The primary material in this investigation is water, which is strategically combined with CuO nanoparticles to improve cold energy preservation efficiency. The simulations are influenced by two critical factors: the shape and concentration of the nanoparticles. Considering these factors adds complexity to the study, facilitating a comprehensive exploration of their impacts on solidification within the plus sign-shaped fin container. Validation of the code against previous benchmarks has shown good accuracy. For water alone, the cold storage time is 1433 s, which significantly reduces to 1049.19 s with the introduction of nanoparticles. The substantial decrement in freezing time is observed with the augmentation of both ϕ and “m” (shape factor), approximately 7 % and 27 %, respectively. https://www.sciencedirect.com/science/article/pii/ S2590123025002178
This research aims to address the critical need for sustainable cooling systems in greenhouses, particularly relevant in mitigating global warming impacts and enhancing food security worldwide. The urgency becomes more pronounced in locations experiencing high ambient temperature and humidity. The study introduces an innovative cooling system integrating Phase Change Material, a desiccant wheel, and an absorption chiller, powered by solar and biomass energy. This novel system aims to efficiently regulate temperature and humidity in greenhouse environments. The performance of this system is examined in Abu Dhabi, Doha, and Riyadh during the summer months, utilizing TRNSYS software for a medium-scale greenhouse model. Additionally, a comprehensive Life Cycle Assessment is carried out to quantify the environmental impacts of the proposed system. Results indicate that in Abu Dhabi, the system yields a Coefficient of Performance (COP) of 1.108, effectively maintaining indoor climate conditions. Similarly, Doha and Riyadh exhibit COPs of 1.015 and 0.827, respectively. In terms of solar energy utilization, Abu Dhabi demonstrates a solar fraction of 40.4, corresponding to the lowest Global Warming Potential (GWP) at 0.106 kg CO2eq per 1 kW of provided cooling capacity. Conversely, Riyadh records the highest GWP at 0.149 kg CO2eq, followed by Doha at 0.118 kg CO2eq. The Energy Payback Time (EPBT) for the system in Abu Dhabi is calculated to be 3.96 years, the shortest among the examined cities. In comparison, Doha and Riyadh present longer EPBTs of 4.48 and 5.83 years, respectively. These findings suggest that the proposed system offers a viable and environmentally friendly alternative to conventional greenhouse cooling approaches. https://www.sciencedirect.com/science/article/abs/pii/ S2352152X24034571
There s a growing emphasis on adopting eco-friendly energy sources to mitigate greenhouse gas emissions and foster sustainability. While renewable energy sources like solar power offer numerous benefits, they have some limitations. Additionally, storing electricity generated from solar panels can be costly and challenging due to limitations in battery technology and storage capacity. Therefore, phase change material-based thermal energy storage systems offer a promising solution to these challenges. These systems also play a crucial role in enhancing buildings energy efficiency and sustainability. The dimensionality of these systems affects their performance. Traditional systems often rely on fixed dimensions, leading to non-uniform thermal conditions within the storage medium. To address these challenges, adopting a compact-latent heat storage (C-LHS) mechanism was recommended herein. Besides, some fins were inserted into the C-LHS system to alter the heat transfer dynamics within the device. Three of the critical parameters of the fins were changed to examine their impact on the charging time. Artificial neural networks and genetic algorithms were employed to determine the optimal positioning of fins to minimize the duration of material to get both part (melt fraction of 0.8) and full (melt fraction of 1) melting capacities. Here, the primary aim was to present a predictive model capable of accurately forecasting the charging time of the material. In all the presented finned samples, the entire PCM melted in less than 15,110 s (4 h and 12 min) and was able to fully utilize the energy absorption capacity in latent form, whereas in the non-finned system, only 68.6 % of the material managed to melt within the entire duration of the investigation (5 h). After analyzing the data, two optimal configurations of OS1 and OS2 with the minimum time for melt fractions of 0.8 and 1 were introduced. In the OS2 configuration, it took 16,200 s (4 h and 30 min) to absorb 4929 kJ of energy. The non-finned sample absorbed only 3250 kJ of energy during the same period. In General, OS2 achieved total energy absorption 51.6 % faster than the non-finned sample. Therefore, the introduced system has the potential to increase absorbed energy in the rest of the daylight hours to further amounts by increasing the number of units and employing a larger volume of material. https://www.sciencedirect.com/science/article/abs/pii/ S2352152X24035527
This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system s design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources. https://www.sciencedirect.com/science/article/pii/ S2214157X24012413
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