To achieve sustainable development, it is crucial to prioritize green polygeneration systems capable of generating diverse energy outputs, including electricity, heat, and distilled water, with enhanced efficiencies, reduced costs, and environmentally friendly advantages. Therefore, this study explores an innovative integration for a solar-powered desalination system to present an eco-friendly desalination system that agrees with sustainability goals of eco-friendly clean water and net-zero energy. The proposed system utilizes the waste heat from the solar dish Stirling power engine to drive the adsorption desalination system that is combined with two ejectors, humidification-dehumidification desalination. The humidification-dehumidification utilizes the waste energy from the silica gel-based adsorption desalination system process to enhance the freshwater productivity of the designed integrated system. Comprehensive modeling for the hybrid system components has been formulated using MATLAB to assess the electrical performance and freshwater production of the proposed system. The study also expresses the effect of evaporative-condenser heat recovery in enhancing the freshwater productivity of the hybrid system. Moreover, the effects of cycle time, and evaporator pressure on the performances of the system with and without heat recovery. The findings indicate that the proposed system has the capability to generate an electrical power output of approximately 23.42 kW, achieving a solar-to-electricity efficiency of 23.40%. Moreover, the proposed system also produces a water production of 47.42 L/hr with a gained output ratio of 2.74. At the same time, the cost estimate revealed that the price of generated freshwater by the planned desalination facility utilizing the heat recovery loop was approximately 0.54 $/m3.

This work aims to assess the energy, economic, and environmental performance of a novel hybrid solar dish Bryton engine and fuel cell (SDBE-FC) system for generating green hydrogen and electricity. The system includes solar dish units, fuel cells, an electrolyzer, a converter, and a hydrogen storage tank. The study introduces a novel SDBE-FC system, replacing typical photovoltaic/fuel cell setups, to achieve higher efficiency and improved financial and environmental competitiveness. A vigorous energy-enviro-economic analysis is carried out using MATLAB/Simulink (R) to model system components. Key parameters have been analyzed, including the electrolyzer efficiency, flow rate, power, and the SDBE plant and FC stack's power, area, and efficiency. The total levelized cost of energy (LCOE) and the reduction in CO2 emissions are also calculated. Results showed that using 50 stacked fuel cells achieved an efficiency of 7%, increasing the number of staked fuel cells to 400 increased the efficiency by eightfold achieving a peak power of 297 kW. The efficiency and power output of the fuel cell decreased by 16% and 22.2%, respectively, when the operational temperature increased from 40 degrees C to 80 degrees C. Furthermore, LCOE as low as 0.218 US$/kWh was achieved using 25 SDBE units at 1100 degrees C. At 30 kW, the SDBE system produces 750 kW of power with 18.3% efficiency and occupies 4099.8 m2 as the temperature (Th) reaches to 1100 degrees C. The SDBE, with a rated power range of 15 kW-30 kW, achieved a 390.8% CO2 emissions reduction, amounting to 34,120.76 tons of CO2, when 25 SDBE units were utilized.

Ground-source heat pumps (GSHPs) harness geothermal energy efficiently but are constrained by high initial and drilling costs. To address these issues, this study explores the reduction of borehole depth and thermal resistance and optimizes the borehole heat exchangers (BHEs) design to enhance GSHP performance and reduce costs. Novel optimized-fin-shapes (rectangular, triangular, elliptical, and oval) are integrated with traditional circular U-shaped BHEs, and their performance is compared through multi-physics computational fluid dynamics (CFD) simulations. These simulations assess transient heat transfer and fluid flow, with results validated against experimental field data conducted by the authors' group. Key performance parameters such as heat flux, BHE thermal resistance, heat-to-work ratio, and pressure drop are computed and analyzed for the investigated designs of hollow-finned borehole heat exchangers (HFBHEs). The study identifies the optimal hollow-finned (HF) shape of the different cross-sectional configurations of HFBHEs and compares it with the conventional circular finless BHE. Additionally, an optimization approach is formulated by using the dimensionless shape factor to determine the optimal length ratios of the different studied HFBHE designs that achieve the maximum heat transfer efficiency. The findings reveal that both rectangular and oval HFBHE configurations outperformed the other investigated HFBHE designs, enhancing the average heat transfer rate by up to 13.91 % and 13.10 %, respectively, compared to typical circular finless BHE. Shortening the borehole depth for oval HFBHEs can significantly enhance their performance compared to circular models, raising the maximum and average heat transfer rates by 43.11 % and 29.37 %, respectively. Optimal operating parameters are identified as a 0.60 kg/s mass flow rate, and 0.40 dimensionless shape factor. These findings provide invaluable insights into HF shapes, pipe crosssectional configurations, and length ratios that affect the thermal performance of shallow U-tube BHEs in GSHPs.

Membrane desalination (MD) is an efficient process for desalinating saltwater, combining the uniqueness of both thermal and separation distillation configurations. In this context, the optimization strategies and sizing methodologies are developed from the balance of the system’s energy demand. Therefore, robust prediction modeling of the thermodynamic behavior and freshwater production is crucial for the optimal design of MD systems. This study presents a new advanced machine-learning model to obtain the permeate flux of a tubular direct contact membrane distillation unit. The model was established by optimizing a long-short-term memory (LSTM) model by an election-based optimization algorithm (EBOA). The model inputs were the temperatures of permeate and the feed flow, and the rate and salinity of the feed flow. The optimized model was compared with other optimized LSTM models by sine–cosine optimization algorithm (SCA), artificial ecosystem optimizer (AEO), and grey wolf optimization algorithm (GWO). All models were trained, tested, and evaluated using different accuracy measures. LSTM-EBOA outperformed other models in predicting the permeate flux based on different accuracy measures. LSTM-EBOA had the highest coefficient of determination of 0.998 and 0.988 and the lowest root mean square error of 1.272 and 4.180 for training and test, respectively. It can be recommended that this paper provide a useful pathway for sizing parameters selection and predicting the performance of MD systems that makes an optimally designed model for predicting the freshwater production rates without costly experiments.

This study explores the performance augmentation of a solar adsorption desalination system (SADS) powered by a hybrid solar thermal system with evacuated tubes and photovoltaic thermal (PV/T) collectors, both numerically and experimentally. The system concurrently generates both electrical power via the PV/T system and desalinated water through the SADS. The cooling of photovoltaic cells is achieved through the utilization of chilled water produced during the desalination process of the SADS, which aims to enhance the electrical efficiency of photovoltaic cells and optimize the overall utilization of the adsorption distillation cycle. The SADS is examined under different operational scenarios and thoroughly assessed in terms of specific cooling power, specific daily freshwater product, and the coefficient of performance (COP). More so, a hybrid machine learning framework integrating an adaptive network-based fuzzy inference system (ANFIS) fine-tuned through the utilization of a manta ray foraging optimization algorithm (MRFOA) is also developed to predict the performance parameters of the SADS. The experiments indicated that the modified PV/T system improved the PV efficiency by 18 % at peak time, where the yielded electrical power and electrical efficiency reached 112 W/m2 and 11.13 %, respectively. The specific freshwater product (SWP), specific cooling power (SCP), and COP of the SADS are estimated as 53.3 L/ton-cycle (6.30 m3/ton-day), 153 W/kg, and 0.25, respectively. Furthermore, Furthermore, the simulated findings showed that the deterministic coefficient and RMSE of the predictive SWP are 0.989 and 1.314 for ANFIS-MRFOA and 0.965 and 2.323 for typical ANFIS, respectively. Hence, the ANFIS-MRFOA exhibited the highest prediction accuracy and can be deemed a potent optimization tool for forecasting the energetic performance of adsorption distillation systems.

In order to overcome the drawbacks of traditional photovoltaic thermal systems, including their limited thermal power, low thermal exergy, and heat transfer fluid outlet temperature, it becomes essential to explore the optimal design configuration of the system for maximization both electricity and domestic hot water generation. Therefore, a detailed numerical modeling and comparative performance analysis on a solar photovoltaic thermal collector (PVT) are conducted under two novel structures of the cooling channels. The first system is a reference PVT collector with a classical straight zigzag-plated tube cooling channel (Case A), while the second PVT system proposes an innovative design of flow cooling channel which is divided into three equal zigzag-plated tube sections (Case B). Each section is also split into three double pass tubes and has a separate inlet and outlet in a staggered manner corresponding to the section that precedes and follows it. The two proposed PVT structures are investigated through computational fluid dynamic simulation under solar radiation values ranging from 200 to 1000 W/m2 and coolant flow rates varying between 0.001 and 0.005 kg/s using both water and air as coolant. The obtained results confirm the significant potential of the modified configuration (Case B) that yielded an improvement in the thermal efficiency by 7.23% and 15.75% for water-PVT and air-PVT system, respectively, over the reference PVT system (Case A). Also, the modified configuration (Case B) yielded an enhancement in the electrical efficiency by 4.0% and 4.6% for water-PVT and air-PVT systems, respectively. It can be concluded that dividing the cooling channel area into equally mini zigzag plate tube-shaped sections with multiple reciprocal entrances is regarded a feasible configuration for augmenting the performance of PVT collectors and maintaining a uniform coolant flow and reduced temperature distribution over the entire panel.

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35%, while the SDSE attains over 28%, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

The advancement of solar linear Fresnel reflector (LFR) systems as a viable technology for green heat and power generation has recently garnered significant attention from both researchers and authorities. Regrettably, this technology's lack of experimental performance data and operational parameters hinders its in-depth characterization, complicates modeling and design efforts, and constrains its practical applications. A large utility-scale solar LFR system with evacuated compound receiver is experimentally investigated and numerically modeled in this work. The site selection, technical design, and experimental implementation of the system are technically introduced. Moreover, the experimental performance of system is examined in the harsh outdoor conditions of Al-Khobar, Saudi Arabia and thoroughly assessed in terms of specific heating power per solar collection area, total heat power production, exit hot oil temperature, energy efficiency, and net exergy efficiency. More so, the long-term operation performance of the established LFR system, for the first time, is modeled and predicted using the relevance vector machine (RVM) modeling as a novel machine learning benchmark. Additionally, an innovative version of metaheuristic algorithms, namely simulated annealing algorithm (SAA) is incorporated with the original RVM to explore the optimum RVM hyperparameters towards maximizing the prediction accuracy. Moreover, using eight different statistical measures, the RVM-SAA model is compared with typical RVM and classical ANN. The experimental findings demonstrate the capability of the LFR system to generate approximately 62.30 kW of useful thermal power, with energy, thermohydraulic, and exergy efficiencies reaching up to 35.10 %, 25.95 % and 7.82 %, respectively. Furthermore, the operation of this new LFR system can lead to temperature levels up to 213.25 degrees C at a continuous thermal oil flow at a rate of 9.0 m3/h. 3 /h. Furthermore, the statistical analyses demonstrated the superiority of the RVM-SAA approach compared to other models investigated for predicting the system performance profiles. Specifically, RVM-SAA achieved the highest deterministic coefficient values of 0.9997 and 0.9988 and the lowest RMSE of 0.4090 and 2.5150 for predicting the system's thermal power production and the outlet oil temperature, respectively.

Recently, floating photovoltaic systems have been regarded as a promising technology for producing clean energy by utilizing the surface of water bodies, such as lakes, rivers and oceans. This study introduces a comparative experimental study and energy performance evaluation of a 1.0 kW offshore floating photovoltaic (FPV) system and a nearby traditional ground-based PV system (GPV) installed in the eastern province of Saudi Arabia. The FPV system was deployed in the Arabian Gulf, 25 m off the coast, at an average depth of 1 to 1.5 m depending on tide, wave, and current intensity. The FPV system employs a strong novel eco-friendly platform structure made of recycled buoyant materials such as engineered plastic drums and wood. This system is anchored with tension cables and concrete blocks that can withstand the changing sea water conditions. The GPV system, on the other hand, was installed 150 m inland from the shore. Real-time monthly data monitoring and assessment indicated that FPV systems outperformed GPV systems in terms of lower PV panel surface temperatures, higher power output, and panel efficiency. Based on daily average back surface temperatures, FPV system temperature was decreased by 7.5 % to 21.34 % when compared to GPV. Moreover, the FPV system efficiency was also increased by 12.2 % when compared to the GPV system. This study aims to assist in promoting the applicability of solar floating photovoltaic systems to synergistically fulfill the requirements of sustainable electricity production for the arid costal community in Saudi Arabia and similar areas.

On shore wind farms designed for hydrogen production present a promising avenue for maximizing the utilization of wind energy, achieving decarbonization and energy security objectives across various sectors. However, existing understanding of these systems, particularly in terms of economic and technical considerations, remains challenging. Hence, this paper introduces a comprehensive numerical modeling aimed at assessing the feasibility of hydrogen production from onshore wind farms for the arid costal community south of Aqaba Gulf, Saudi Arabia. This model incorporates formulas to compute wind power output, electrolyzer plant sizing, and hydrogen generation based on fluctuating wind speeds. The objective is to identify the optimal setup for a hydrogen station fueled by high wind resources, while also evaluating its economic feasibility and reliable capacity to reduce hydrogen production cost and minimize carbon emissions. The results show that the optimal Onshore WindHydrogen System (OWHS) consists of 26 units of WT (each is 6.15 MW), Alkaline Electrolyzers plant with a capacity of 120 MW, and a group of hydrogen storage tanks of total size of 300 tones. The hydrogen demand and the subsidiary electric needs are successfully met with the 160 MW of wind generation capacity installed within the system with negligible capacity shortage and unmet demand. The employed set of alkaline electrolyzers has a total rated capacity of 120 MW and can stay in operation for 7335 h/yr to produce a total annual amount of 8,214,152 kg/yr of green hydrogen. The proposed system generates hydrogen with a moderate price since the LCOH obtained is found 5.26 $/kg which falls within the global range that varies from 4 to 6 $/kg. Moreover, the CO2 mitigation of the designated OWHS reaches 3343.72 Mt. with a corresponding $133,748 carbon credit gain. The sensitivity analysis shows that the total net present cost (TNPC) is highly sensitive to wind speed, hydrogen cost, and hydrogen demand but less so to inflation and discount rates. The TNPC increases by 22 % with a 5 % decrease in wind speed, as well as reducing hydrogen costs by 25 % and 50 % decreases TNPC by 14.63 % and 29.26%, respectively. While, increasing hydrogen demand by 20% and 40% raises TNPC by 30.3 % and 51.4%, respectively. This study aims to assist stakeholders and policymakers in refining onshore wind-hydrogen systems to economically fulfill the requirements of hydrogen production for the arid costal community in Saudi Arabia.

Ground-Coupled Heat Pumps (GCHPs) are regarded as an effective technology for building energy development and residential hot water production. The essential challenges to encompass these systems in urbanized areas are the highly drilling and initial costs of GCHPs. To overcome this challenge, researchers and industries are seeking innovative solutions by focusing on optimizing the structure of ground heat exchangers (GHXs), which act as the essential component for efficient geothermal energy extraction in GCHPs. Hence, this paper introduces a comprehensive comparative numerical investigation of an innovative U-tube GHX with a hollow-finned structure for augmenting the performance of the GCHPs. This design remarkably enhances the thermal performances, reduces the thermal resistance, therefore minimizes borehole depth, reduces drilling costs, boosts GCHP's financial viability, and bolsters geothermal energy utilization. Therefore, a series of three-dimensional analyses of transient heat extraction is executed and validated using a practical field experiment performed by the authors' research group to investigate the thermal behavior of the hollow-finned U-tube GHX at different fin configurations and compare its efficacy to typical circular U-tube GHX. More so, a parametric study and sensitivity analysis are also performed to identify the influence of critical operational and design factors on the heat extraction of the proposed hollow-finned U-tube GHX compared to the typical circular GHX. The influences of groundwater flow on the transient heat extraction were also examined. Additionally, a dimensionless shape factor is introduced to assess the implications of the U-tube geometry with hollow-finned on the thermal performance of the GHXs. Furthermore, design optimization was also conducted by examining different branched-shaped hollow-fin's U-tube GHX configurations. The findings indicate that the mean heat transfer rate of hollow-finned U-tube GHX can be increased by 44.58 % compared to the conventional circular U-tube GHX. Correspondingly, the total thermal resistance of hollow-finned U-tubes GHX was reduced by 47.71 %. It also identified that the Y-shaped hollow-finned U-tube GHX was the optimal design among the investigated configurations of branched-shaped hollow-finned U-tube GHXs, demonstrating an increase of 52.64 % compared to the conventional circular U-tube GHX. Moreover, it is revealed that a lower shape factor enhances the heat transfer rate significantly. Furthermore, the optimal mass flow rate and desired number of hollow-finned were estimated as 0.80 kg/s and six fins, respectively. Conclusively, this investigation concludes that the hollow-finned U-tube GHX can greatly enhance the heat extraction rates and reduce borehole thermal resistance, which can increase the efficiency of GCHP systems.

Offshore wind power resources in the Red Sea waters of Saudi Arabia are yet to be explored. The objective of the present study is to assess offshore wind power resources at 49 locations in the Saudi waters of the Red Sea and prioritize the sites based on wind characteristics. To accomplish the set objective, long-term hourly mean wind speed (WS) and wind direction (WD) at 100 m above mean sea level, temperature, and pressure data near the surface were used at sites L1-L49 over 43 years from 1979 to 2021. The long-term mean WS and wind power density (WPD) varied between 3.83 m/s and 66.6 W/m2, and 6.39 m/s and 280.9 W/m2 corresponding to sites L44 and L8. However, higher magnitudes of WS >5 m/s were observed at 34 sites and WPD of > 200 W/m2 at 21 sites. In general, WS, WPD, annual energy yield, mean windy site identifier, plant capacity factor, etc. were found to be increasing from east to west and from south to north. Similarly, the mean wind variability index and cost of energy were observed to be decreasing as one moves from east to west and south to north in the Saudi waters of the Red Sea.

Geothermal energy showcases significant potential as a sustainable energy alternative employed for direct applications such as space heating and cooling, industrial processes, and greenhouse heating. However, despite these favorable attributes, conventional Ground-coupled Heat Pumps (GCHPs) can potentially result in significant environmental and economic consequences, including greenhouse gases and high operational costs. The Coaxial Ground Heat Exchanger (CGHE) is the chief role component for harnessing geothermal energy in GCHPs. This investigation provides comprehensive numerical modeling complemented with multi-objective optimization and seasonal life cycle assessment of CGHEs with newly developed oval-shaped (oval-CGHEs) and typical circular-shaped (circle-CGHE). The system design and dispatch of the oval-CGHEs are optimized by considering a novel optimization approach that synergistically encompasses four optimization scenarios, namely minimum Ground Heat Exchanger (GHE) number and minimum GHE length, each with multi-material topology optimization of outer tube selection criteria. The system's Life Cycle Energy Consumption (LCEC), Life Cycle Costs (LCC), and Life Cycle CO₂ emissions (LCCO₂) are introduced in the optimization stage of the oval-CGHEs and compared with typical circle-CGHE. Toward these goals, a series of three-dimensional simulations coupled with a pulse-load finite line source model is executed and validated using the authors' practical field experiment based on combined analyses, including the energy, economic, and environmental multi-criteria, to assess the different objectives and case studies. The optimization results reveal that oval-CGHEs, particularly with inner tube position optimization, consistently outperform the traditional circle-CGHEs in all the investigated scenarios. It is revealed that the most favorable scenario involves substituting traditional circle-CGHEs with oval-shaped CGHEs, utilizing a high-density polyethylene inner tube and a steel outer tube, and adopting a minimum GHE length. This approach remarkably reduces the required length of the GHEs by up to 15.00% and achieves the most substantial reductions in LCEC by up to 13.87%, LCC by up to 10.91%, and LCCO₂ emissions by up to 13.27%, respectively.

Optimizing the heat transfer and thermodynamic efficiency of Ground Heat Exchangers (GHE) is crucial for maximizing the energy efficiency of Ground-coupled Heat Pump (GCHP) systems utilized in building heating and cooling applications. This study pioneers an effective approach of both the first and second laws of thermodynamics of the newly oval-shaped coaxial GHEs (oval-CGHEs), assessing heat transfer, thermohydraulic performance, and local entropy generation rate to explore the optimum design and operational conditions for heat exchange maximization and entropy generation minimization. Utilizing a three-dimensional numerical model validated by experimental results, this study examines the trade-offs between heat transfer improvements and entropy generation rate intensification of the oval-CGHEs in comparison to the typical circular-shaped CGHE (circle-CGHE) further than exploring the effect of key operational and design parameters and performing sensitivity analysis. The results reveal that oval-CGHEs exhibit higher irreversibility as a penalty for their superior heat transfer capabilities compared to traditional circular-shaped CGHEs (circle-CGHE), which can be mitigated by optimizing the position of the inner tube. Moreover, the study underscores the criticality of maintaining a balance between enhanced heat transfer and the associated rise in irreversibility, particularly concerning the factors of mass flow rates and installation depths.

Onshore wind farms present a highly promising solution for hydrogen production to boost renewable energy integration, support decarbonization, and enhance energy security. This study offers a detailed numerical model to evaluate the potential for hydrogen production using onshore wind power in an arid coastal community located in the southern region of the Aqaba Gulf, Saudi Arabia. The proposed model emulates formulas to determine electrolyzer plant sizing, wind output power, and green hydrogen production. The goal is to develop an optimal hydrogen production plant using high wind resources, whilst assessing its economic viability, reducing production costs, and minimizing CO2 emissions. The results indicate that the optimal Onshore Wind-Hydrogen System (OWHS) comprises 26 wind turbines (WTs), each with a rated capacity of 6.15 MW, alongside Alkaline Electrolyzers with a total capacity of 120 MW and a hydrogen storage capacity of 300 tonnes. The subsidiary electricity needs and hydrogen demand are efficiently met with 160 MW of wind generation capacity, with minimal capacity shortages. The alkaline electrolyzers used have a total rated capacity of 120 MW and can operate for 7,335 hours per year, producing 8,214,152 kg of green hydrogen annually. The OWHS can produce hydrogen at a cost of 5.26 $/kg, which aligns with the global range of 4$ to 6$ per kilogram. Additionally, the system achieves CO2 mitigation of 3,344 metric tons, resulting in a net carbon credit value of 133.75$. The obtained outcomes highlight the potential advantages and cost savings of incorporating green hydrogen production into Aqaba Gulf wind projects for Saudi Arabia’s coastal community.

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

This work presents a comparative experimental investigation of the potential of low-cost metamorphic fabric materials for augmenting the distilled yield of hemispheric solar distillation systems. The impacts of two various metamorphic layers; namely, a 20 mm thick cement layer and a 20 mm thick slate layer on the distiller thermoeconomic performance are studied and compared with a conventional hemispheric solar distiller (CHSD). Three hemispheric solar stills are manufactured and tested under the same climate characteristics. Moreover, a thermoeconomic analysis of the three involved hemispherical solar stills is carried out; including cumulative distilled product, daily thermal efficiency, and cost per unit kg of the distilled product. According to the findings, the incorporation of a 20 mm thick slate layer with a saltwater depth of 10 mm into the hemispheric absorber basin with the optimal modification yields the maximal distilled product and minimal production cost of the hemispheric distillers. The daily distilled product augmented and reached 8.35 and 7.60 kg/day & sdot;m2 for the hemispheric distiller respectively with a layer of slate and cement, in comparison with that of the CHSD (5.65 kg/ day & sdot;m2). The increase in the distilled product with the use of slate and cement metamorphic layers reached 47.80 % and 34.51 %, over the CHSD, respectively. Furthermore, the thermoeconomic findings revealed that the amelioration in the daily energetic efficiency for using the slate and cement metamorphic layers is evaluated as 46.90 % and 34.04 %, respectively, compared to CHSD. Additionally, the reduction in the freshwater cost is estimated as 31.73 %, and 25.24 % over the CHSD, respectively.

This paper endeavors to utilize the numerical modeling method to evaluate the energy, economic, and environmental performances of a new hybrid PV-FC system for green hydrogen and electricity production. The proposed system consists of photovoltaic panels, fuel cells, an electrolyzer, a converter, and a hydrogen storage tank. A robust techno-enviro-economic (3E) analysis is conducted through comprehensive modeling for the system components using MATLAB/Simulink (R). In this validated model, the essential parameters have been calculated: PV plant power, area and efficiency, electrolyzer efficiency, flow rate and power, stack power, area and efficiency, total LCOE of the integrated components, and CO2 emission reduction. Moreover, the NSGA-II coupled with TOPSIS decision-making approach and Gaussian Process Regression machine learning method with selection kernel function are also utilized as a novel inclusion for the prediction and optimization of the 3E performances of this hybrid system. To obtain a multidimensional view of the optimization, six key decision variables of total stack power, fossil fuel-based generator energy, total CO2 emissions coming from hydrogen production, total FC system voltage, module area, and number of PV modules have been adopted. The optimization problem encompasses maximizing the total fuel cell stack power and carbon emission reduction, while simultaneously minimizing the total stack area and levelized cost of energy. The simulation outcomes reveal that the stack can reach its maximum output power of 350 kW when operating temperatures are between 40 degrees C and 55 degrees C and there are more than 380 cells in the stack. Also, the LCOE was found to be less than $2/kWh for solar radiation above 250 W/m(2) and PV outputs reaching 100 W. Further, Increasing FCs from 10 to 400 reduces CO2 emissions by roughly 13% at 100 degrees C. Ultimately, the optimal configuration of the system yields stack power of 1589 kW, a total stack area of 269.9 m(2), and total CO2 emission reduction of 1268 ton(CO2), respectively.

In the quest for sustainable and efficient energy solutions, hydrogen fuel cells emerge as a beacon of hope, offering a promising pathway towards a greener future. Accurate Identification of the ungiven parameters of proton exchange membrane fuel cell (PEMFC) mathematical models is indispensable for designing, managing, and simulating the practical PEMFC. In order to identify the parameters of PEMFC punctually, this paper presents a modified version of the slime mould algorithm (MSMA). In order to increase capability of the MSMA in the exploitation phase, both locally and globally, the sine-cosine technique has been utilized to boost the search capabilities. To assess the performance of MSMA, MSMA is first utilized to address ten well-known benchmark functions. The obtained results confirm that MSMA outperforms SMA on all benchmark functions. Then, MSMA is employed to solve the optimization problem of different mechanical design problems and also the MSMA provides superior performance over the standard SMA. Finally, the MSMA is used to identify the unknown parameters of four typical PEMFCs: 250W PEMFC, BCS 500W PEMFC, AVISTA SR-12 model, and the Temasek 1 kW PEMFC model. Experimental results boost the supremacy of MSMA in the PEMFC parameters extraction by comparing it with the original SMA and well-known potent optimization techniques. Furthermore, MATLAB/ Simulink is employed for advanced dynamic PEMFC modeling, facilitating a comprehensive assessment of fuel cell parameters. The validation of this dynamic PEMFC model, using MSMA-optimized parameters, establishes its practical utility in system analysis and real-world fuel cell operation, marking a significant advancement in PEMFC technology management and simulation.

Integrating air conditioning (AC) systems with thermal energy storage (TES) offers a promising solution for managing large buildings' peak load demands and energy efficiency. Predicting the performance of the AC-TES is a significant index in ensuring optimal cooling load and energy consumption. However, conventional approaches encounter challenges in achieving high levels of accuracy, reliability, and scalability. Therefore, this study introduces leveraging machine learning techniques and in-situ measurements for precise predicting the energy performance of AC-TES system in a semi-arid climate building. The study proposes a hybrid prediction strategy leveraging the benefits of machine learning and meta-heuristic optimization algorithms. The proposed approach integrates a Radial Basis Function Neural Network (RBFNN) with the Fire Hawk Optimizer (FHO) for predicting the performance parameters of the AC-TES system; including, energy consumption, cooling load, air room temperature and performance coefficient (COP). The RBFNN framework in the developed strategy is trained to identify intricate patterns and correlations within the data, while the FHO is utilized to explore the optimal hyperparameters of the RBFNN for maximizing the prediction accuracy. The proposed strategy was modeled in MATLAB software and validated using a publicly available measurements for the ACTES system. The system maintained improved cooling performance in which the average daily values of energy consumption, cooling load, air room temperature and COP are 1100 kWh/hr, 1.30 kW, 23.97 degrees C, and 3.12, respectively. The statistical outcomes depict that the developed RBFNN-FHO strategy achieved an impressive prediction accuracy of 95.81 % accuracy, a 0.94 correlation coefficient, a 0.4193 mean absolute error (MAE), and a 0.5200 root mean square error (RMSE), respectively. Furthermore, the performance of the proposed RBFNN-FHO is model, is compared with the existing machine-learning approaches utilized for predicting the dynamic performances of HVAC systems. The comparative analysis with existing techniques highlights the robustness and effectiveness of the proposed RBFNN-FHO strategy in predicting AC -TES performances.