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Abstract Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R 2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

This study presents a hierarchical forecasting approach for day-ahead energy production in distributed solar Photovoltaic (PV) systems using a tiered Nonlinear Autoregressive Exogenous (NARX) model. The methodology was applied to 52 PV systems installed at The University of Jordan, covering three prediction scales: fleet-wide, zone-specific, and site-specific. The model incorporated weather data, including solar irradiation, temperature, and humidity, to forecast the next day's energy production. Based on a new metric called the OverallMetric, fleet-wide predictions outperform the zone-specific and site-specific averages by 3.21% and 5.35%, respectively. Normalized Root Mean Square Errors (nRMSE) for fleet-wide, zone-specific, and site-specific predictions are 0.148, 0.141, and 0.137, respectively. The Correlation Coefficient (R) is above 80% for all prediction scales, with the accuracy constrained by the model's difficulty in adapting to abrupt weather changes, leading to overestimation. The model performs best when weather patterns and PV generation are consistent with previous days. This demonstrates that adapting models to the characteristics of each scale significantly improves forecast accuracy, enabling more effective macro-level planning and micro-level operational decisions.

Despite advances in enhancing the output of conventional solar stills, the pursuit of the most efficient solar distillation technique remains ongoing. Evacuated tubes, known for their superior thermal capacity compared to traditional single-basin solar stills, offer a promising solution for high-yield distillation. This study evaluates the performance of a semi-cylindrical tent-shaped solar still coupled with evacuated tubes (SCTSCET) against a conventional single-basin, single-slope solar still, used as a benchmark. Experiments conducted in Amman, Jordan, in May 2023, demonstrate that the SCTSCET significantly outperforms the conventional solar still in distilled water production. The SCTSCET achieved a daily yield of up to 9.7 liters, which is approximately 288 % higher than the 2.5 liters produced by the conventional still. This increased productivity is due to a 45.7 % enhancement in heat capacity provided by the evacuated tubes, which raised the water basin temperature to 61.4 °C, compared to 41.2 °C in the conventional still. This higher temperature facilitated a faster evaporation rate and improved water output. Additionally, the SCTSCET exhibited a 10 % higher hourly thermal efficiency and a peak exergy efficiency of 5.7 %, compared to 3.4 % for the conventional still, highlighting its superior ability to harness and utilize solar energy for distillation.

Biodiesel has emerged as a compelling substitute for conventional diesel fuel, providing a sustainable and eco-friendly choice for fueling compression ignition engines. This comprehensive study investigates the influence of biodiesel, specifically derived from sunflower oil, through the esterification method, on crucial engine performance parameters and environmental effects. The study examines the impact of varying engine torque on the performance of a single-cylinder, four-stroke compression ignition engine, encompassing parameters such as brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC), as well as exhaust emissions, including unburned hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx). Four distinct biodiesel blends (B10, B20, B30, B40) with varying sunflower oil content are systematically compared to pure diesel (B0). The engine operates at a consistent speed of 1700 rpm, while the torque undergoes controlled adjustments from 0 to 10 Nm. Subsequently, this study explores the application of an alternating model tree (AMT) machine learning algorithm to establish relationships between independent factors, specifically torque and biodiesel volume (%vol), and dependent variables, including BTE, BSFC, CO, and NOx in a combustion engine. Additionally, the study employs a multi-objective ameliorative whale optimization algorithm (AWOA) to optimize the model's output. The objective is to identify optimal values for torque and%vol that maximize engine performance (BTE) while minimizing engine emissions (CO and NOx) and reducing fuel consumption (BSFC). The optimization process yields noteworthy results, with AWOA achieving peak BTE at 29.714 %, BSFC at 0.262 kg.kWh-1, and NOx emissions at 992 ppm at torque 7.3 N.m and 13% vol. In contrast, particle swarm optimization (PSO) secured the minimum CO level at 0.123 %, with torque set at 7.6 N.m and 26% vol. The AMT models demonstrate high prediction accuracy, with coefficient of determination (R2) values exceeding 0.98.

Enhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecological revitalization. The main target of this study is to develop a robust ANFIS model to simulate the biohydrogen production process from microalgae within photobioreactors. The study focuses on enhancing hydrogen yield by optimizing three critical process parameters: sulfur concentration (%), run time (hours), and wet biomass concentration (g/L). Initially, an adaptive neuro-fuzzy inference system (ANFIS) model for biohydrogen production process is constructed based on empirical data. Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. The performance of ANFIS model in predicting hydrogen yield is assessed using root mean square error (RMSE) and coefficient-of-determination (R2) values. The obtained RMSE values for training and testing data are 2.8477 × 10−05 and 1.2807, respectively, while the corresponding R2 values are 1.0 and 0.9911 for training and testing. The introduction of fuzzy logic into the model significantly improves its predictive accuracy, as evidenced by the drop in RMSE from 10.79 with ANOVA to 0.7159 with ANFIS, representing a substantial 93.4 % decrease. The remarkable precision of the ANFIS model, indicated by its low RMSE and high R2 values, underscores the success of the modeling stage. The combination between ANFIS with the RTH technique yields impressive results, leading to a hydrogen yield enhancement of 6.87 % and 26.65 % when compared to both measured data and ANOVA.

The study aims to enhance the reliability of direct thermal energy storage (TES) using phase change materials (PCMs) and nanoparticles, ensuring sustained heat supply even during periods of low or intermittent solar radiation. The study specifically investigates a TES system comprising a double-cylindrical shell container with two distinct PCM types: petroleum jelly or petrolatum with 1 % Al2O3 nanoparticles by volume in the outer shell and wax in the inner shell. Additionally, a redesigned heat exchanger is employed to improve heating efficiency. The study examines two configurations: an open system and a closed system with a storage water tank. The deliberate strategy of augmenting PCM volume is aimed at enhancing heat transfer during both the charging and discharging phases. The performance of closed and open system configurations is compared, with a focus on highlighting the temperature profiles during the charging and discharging processes. The incorporation of 1 % Al2O3 nanoparticles in the PCMs led to a significant enhancement in efficiency, with the two-phase materials case achieving the highest efficiency of 73 %. Furthermore, the utilization of nanoparticles resulted in an improvement in efficiency of approximately 5 %. These findings demonstrate the potential of using dual PCM layers with NPs additives in direct TES systems.

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

The automotive sector is experiencing rapid evolution, with the next-generation emphasizing clean energy sources such as fuel-cell hybrid electric vehicles (FCHEVs) due to their energy efficiency, eco-friendliness, and extended driving distance. Implementing effective energy management strategies play a critical role in optimizing power flow and electrical efficiency in these vehicles. This study proposes an optimized energy management strategy (EMS) for FCHEVs. The suggested EMS introduces a hybridization between the equivalent consumption minimization strategy (ECMS) and the Artificial Hummingbird Algorithm (AHA). The Federal Test Procedure for Urban Driving (FTP-75) is employed to evaluate the performance of the proposed EMS. The results are assessed and validated through comparison with outcomes obtained by other algorithms. The findings demonstrate that the proposed EMS surpasses other optimizers in reducing fuel consumption, potentially achieving a 48.62% reduction. Moreover, the suggested EMS also yields a 15.45% increase in overall system efficiency.

The phenomenon of flow around a static or rotating circular cylinder has significant applications in various engineering fields, such as building, bridge, and structure engineering. It can be used to control flow-induced vibration, blowing or suction, moving surfaces, acoustic thriller, and synthetic jet. Additionally, there is a need to enhance heat transfer rates in many life and industrial applications, including cavity shape enhancement, heating or cooling spread inside a cavity, heat dissipation in electronic devices, thermal mixing in storage units, solar collectors, desalination, and journal bearing lubrication. This study explores the effect of a horizontal magnetic field and a rotating inner circular cylinder on mixed convection within a two-dimensional oval-shaped enclosure filled with a non-Newtonian nanofluid. The Galerkin Finite Element Method (GFEM) is utilized for analysis, with the enclosure undergoing differential heating and cooling on the inner cylinder wall and the enclosure wall, respectively. The nanofluid consists of water infused with copper nanoparticles. The study explores various simulation parameters, including the Richardson number (Ri), Hartmann number (Ha), the inclination angle of the enclosure (β), power-law index (n), circle center eccentricity (e), and nanoparticles volume fraction (φ). It has been observed that as the Ha and n index increase for different Ri values, a wavy trend in local Nusselt number (Nu) profiles along the heat source surface is observed. Additionally, the heat transfer rate decreases with increasing Ri for various combinations of Ha and n index. Under specific conditions, such as Ri = 0.001, Ha = 60, e = 0, φ = 0.09, and β = 90, the results show that, compared to a Newtonian fluid (n = 1), the heat transfer rate exhibits percentage gains of approximately 6.31% for a pseudo-plastic fluid (n = 0.6) and 8.47% for a Dilatant fluid (n = 1.4). The convective heat transfer was significantly enhanced when the eccentricity of the cylinder was positioned in the lower or upper regions of the oval-shaped enclosure, particularly at e = - 0.3. The highest heat transfer rates were observed at β = 60 and 240.

Understanding the impact of hybrid nanofluids (NF) on natural convection (NC) within complex enclosures can significantly advance the efficiency of heat transfer (HT) mechanisms. These advancements play a crucial role in various engineering applications, such as thermal systems in electronics, energy conversion, and heat exchangers. This study numerically examined the NC taking place within a sinusoidal corrugated enclosure filled with an Ag-MgO hybrid NF. This system was heated differentially by a vertical solid wall. The vertical right wall of the solid is maintained at an isothermal at a high-temperature Th, while the left vertical wall of the cavity is kept at an isothermal at a low-temperature Tc. The vertical left wall of the solid is in contact with the porous medium (PM) saturated with the cavity's hybrid NF. In contrast, the top and bottom horizontal walls are maintained adiabatic. The governing equations were solved by employing the Galerkin weighted residual finite elements approach. The porous domain is modeled by employing the Darcy-Brinkman formulation. The parameters being studied encompass the Darcy number (10−5 ≤ Da ≤ 10−2), Rayleigh number (103 ≤ Ra ≤ 106), nanoparticle volume fraction (0 ≤ ϕ ≤ 0.04), amplitude of waviness (0.05 ≤ A ≤ 0.2), and number of undulations (1 ≤ N ≤ 4). The results highlighted that the introduction of hybrid nanoparticles into the pure fluid enhances the HT rate across the parameter spectrum. The highest average Nusselt number (Nuav) is attained at Ra = 106, Da = 10−2, A = 0.2 and N = 4. The findings of the current study have practical implications for current industrial applications, particularly in the cooling of electronic devices.

The current study attempts to improve the performance of SI engines by employing two alternative acetone–gasoline mixtures. This investigation applies the Artificial Bees Colony Algorithm (ABC) to determine the optimum acetone–gasoline blends and engine speed to increase engine performance further and minimize fuel consumption. The SI engine performance of one-cylinder, four-stroke powered by neat gasoline fuel (AC0), 5% of acetone by volume (AC5), and 10 % of acetone by volume (AC10) has been investigated experimentally. Tests were carried out at speed rates from 1,000 to 3,600 rpm. The gasoline engine was integrated into an eddy-current dynamometer to evaluate the performance indexes. It was revealed that the overall performance of the gasoline engine is enhanced when acetone is blended with gasoline. The AC10 exhibited better engine brake torque (BT) and brake thermal efficiency (BTE) than pure gasoline, with 4.39 % and 6.9 %, respectively. According to the optimization findings, a 10% acetone concentration and engine speeds of 2889 rpm and 2769 rpm produced the best results in terms of BT and BTE, which were 7.776 N.m and 29.992%, respectively. However, at 2401 rpm of engine speed, a minimum BSFC of 0.2986 was reached without acetone. This result demonstrates that the ABC algorithm can precisely forecast the optimal position in terms of engine effectiveness and fuel consumption.

Oil shale (OS) can significantly enhance energy security and diversify the energy mix in countries like Jordan. However, OS extraction and utilization are always associated with negative environmental impacts. This paper investigates a novel approach of using renewable energy to further retort the OS to produce oil. In this study, Al-Lajjun and Sultani OS resources in Jordan were analyzed using a mathematical model and simulation analysis using the system advisor model (SAM) software. The study aimed for in-situ shale oil extraction from a vast shale oil reserve in Jordan, with a focus on mitigating energy usage and environmental issues. The study suggested utilizing recovered heat from the production stream along with an external source, which may include natural gas, solar thermal energy, or a combination of both, to produce the substantial amount of heat energy necessary for in situ pyrolysis of OS. The study designed five different plants using the SAM program: gas steam boiler (GB), photovoltaic (PV) with batteries, parabolic trough with thermal energy storage (TES), PV integrated with GB, and parabolic trough coupled with GB. The economic analysis, project recovery, and environmental evaluation were conducted, and the results showed that drilling wells in the Sultani area was more expensive than in the Al-Lajjun area due to the thickness of overburden. The PV with GB was the most economically advantageous option, with a total cost of $79.4 M for Al-Lajjun and $89.89 M for Sultani. However, the levelized cost of energy (LCOE) for Al-Lajjun and Sultani are 10.41 and 9.18 ¢ per kWh, respectively. Conversely, PV systems with batteries and the parabolic trough with TES demonstrated a higher level of environmental friendliness compared to other shale oil recovery systems. Among these, the systems incorporating PV with GB and parabolic trough with GB, both featuring an SF of 30%, consistently demonstrate CO2 emissions of 0.016 kg CO2 per kg of shale oil produced. In contrast, the system solely reliant on GB without any solar contribution, holding a 0% SF, exhibited elevated CO2 emissions of 0.023 kg CO2 per kg of shale oil produced. The study recommends the use of PV with GB as the most economically viable option, while PV with batteries is the more eco-friendly choice for extracting shale oil from the Al-Lajjun and Sultani OS reserves located in Jordan. The study outcomes can be useful for policymakers and investors interested in developing the shale oil industry in Jordan. However, further research is needed to evaluate the long-term environmental impact and sustainability of the proposed method.

This study aims to derive valuable insights for utilizing computational fluid dynamics (CFD) based on reynolds-averaged navier–stokes (RANS) and detached eddy simulation (DES) turbulence models (TMs) to analyze a specific radial ejector configuration known as the variable geometry radial ejector (VGRE). The VGRE features the primary nozzle and ejector duct plates with adjustable disk-like surfaces, allowing for changes in the nozzle and ejector throat areas within a single ejector. Extensive numerical investigations of the VGRE are conducted by systematically validating CFD models with experimental datasets and subsequently using the most appropriate TM to design a new radial ejector. The study reveals that the DES SST k-ω turbulence model achieves the closest agreement with experimental data, with an average entrainment ratio (ω) discrepancy of only 5 %. However, there are challenges in accurately predicting the critical compression ratio (rc*), especially under varying conditions. Based on the CFD results, the original VGRE exhibited ω values ranging from 0.16 to 0.61, rc* values between 1.5 and 3.1, and ejector efficiency (η) values between 7 % and 17 % at expansion ratio (re) values ranging from 89 to 150 for different nozzle throat separations (d) and different duct throat separations (D). Furthermore, this study presents a comprehensive investigation into predicting and optimizing ω, rc*, and η parameters using a multi-output gaussian process regression (GPR) model and a marine predators algorithm (MPA) approach. The multi-output GPR model was constructed to predict the relationships between boundary conditions (primary pressure (Pprimary) and secondary pressure (Psecondary)), geometric parameters (d and D), and the response variables (ω, rc*, and η). The model evaluation employed a 5-fold cross-validation technique to assess predictive performance, demonstrating strong predictive accuracy with low root mean square error (RMSE) and high coefficient of determination (R2) values. The optimization results revealed that the highest achieved values were ω = 0.303, rc* = 2.678, and η = 0.156, corresponding to specific parameter settings (Pprimary = 160 kPa, Psecondary = 1.8 kPa, d = 0.6 mm, and D = 2.3 mm). This study demonstrates the effectiveness of the multi-output GPR model for accurate prediction and the multi-objective MPA optimization approach for identifying optimal input parameters to maximize entrainment ratio, critical compression ratio, and ejector efficiency in fluid dynamics systems.

Acetone–gasoline fuel is one potential favorable alternative option to regular gasoline fuel. This work attempts to mitigate pollution while simultaneously improving SI engine performance with acetone (AC) proportion in fuel mixture and varying engine speed. Acetone–gasoline alternative fuel was experimentally prepared by mixing 5–10 vol.% of acetone into ordinary gasoline. The experiments were performed in a spark ignition (SI) single-cylinder, four-stroke at variable engine speed. The engine performance was measured by an eddy current dynamometer (ECD), while the exhaust engine was measured by a gas analyzer connected to the gasoline engine. The proposed strategy, the integrated combined polynomial regression with whale optimization algorithm (WOA), was implemented. The results showed that the acetone blending of 10 % with gasoline (AC10: 10 vol. % acetone + 90 vol. % gasoline) decreases carbon monoxide (CO), unburned hydrocarbon (UHC), and nitrogen oxides (NOx), emissions by 26.3%, 30.3%, and 6.6%, respectively. Furthermore, the AC10 exhibited better engine brake power (BP) than pure gasoline, with an improvement of 4.39%. According to the optimization results, the BP was enhanced by AC10 to record 2.426 kW at 2887 rpm. In addition, engine emissions reduced to the lowest levels of 142.849 ppm, 0.426%, and 1088.178 ppm, in terms of UHC, CO, and NOx, respectively. The experiment results show that the WOA optimizer can accurately predict the optimal point in SI engine performance and emissions.

Water-in-diesel (W/D) emulsion fuel is a potential alternative fuel that can simultaneously lower NOx exhaust emissions and improves combustion efficiency. Additionally, there are no additional costs or engine modifications required when using W/D emulsion fuel. The proportion of water added and engine speed is crucial factors influencing engine behavior. This study aims to examine the impact of the W/D emulsion diesel fuel on engine performance and NOx pollutant emissions using a compression ignition (CI) engine. The emulsion fuel had water content ranging from 0 to 30% with a 5% increment, and 2% surfactant was employed. The tests were performed at speeds ranging from 1000 to 3000 rpm. All W/D emulsion fuel was compared to a standard of pure diesel in all tests. A four-cylinder, four-stroke, water-cooled, direct-injection diesel engine test bed was used for the experiments. The performance and exhaust emissions of the diesel engine were measured at full load and various engine speeds using a dynamometer and an exhaust gas analyzer, respectively. The second purpose of this study is to illustrate the application of two optimizers, grey wolf optimizer (GWO) and intelligent grey wolf optimizer (IGOW), along with using multivariate polynomial regression (MPR) to identify the optimum (W/D) emulsion blend percentage and engine speed to enhance the performance, reduce fuel consumption, and reduce NOX exhaust emissions of a diesel engine operating. The engine speed and proportion of water in the fuel mixture were the independent variables (inputs), while brake power (BP), brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and NOx were the dependent variables (outcomes). It was experimentally observed that utilizing emulsified gasoline generally enhances engine performance and decreases emissions in general. Experimentally, at 5% water content and 2000 rpm, the BSFC has a minimal value of 0.258 kJ/kW·h. Under the same conditions, the maximum BP of 11.6 kW and BTE of 32.8% were achieved. According to the IGWO process findings, adding 9% water to diesel fuel and running the engine at a speed of 1998 rpm produced the highest BP (11.2 kW) and BTE (33.3%) and the lowest BSFC (0.259 kg/kW·h) and reduced NOx by 14.3% compared with the CI engine powered by pure diesel. The accuracy of the model is high, as indicated by a correlation coefficient R2 exceeding 0.97 and a mean absolute error (MAE) less than 0.04. In terms of the optimizer, the IGWO performs better than GWO in determining the optimal water addition and engine speed. This is attributed to the IGWO has excellent exploratory capability in the early stages of searching.

The natural convection within enclosures along with entropy generation minimization plays a crucial role in various applications, particularly when they involve the utilization of nanofluids and porous media. This phenomenon plays a crucial role in enhancing heat transfer, fluid flow, and overall system performance. By understanding and optimizing the natural convection and entropy generation processes, it becomes possible to improve the efficiency and effectiveness of various thermal management systems, such as heat exchangers, electronic cooling systems, and renewable energy devices. Moreover, the integration of nanofluids and porous media introduces additional complexities and opportunities for enhancing heat transfer and fluid flow characteristics within enclosures. The current study investigates entropy generation (Sgen) and natural convection in a Z-staggered cavity filled with a porous media filled with a TiO2-water nanofluid. The symmetrical enclosures with dimensions of 0.6 L × 0.5 L are considered, and the media contain a porous material saturated with TiO2-water nanofluid. The wavy left and right vertical walls of the staggered enclosure were maintained hot and cold at temperatures (Th) and (Tc), respectively. All the straight horizontal walls were considered insulated and impermeable. The fundamental equations are solved using the Galerkin Finite Element Method (GFEM), and the results are described in detail. The key result was that raising the Rayleigh number (Ra) and nanoparticle volume fraction increased heat transmission. Specifically, increasing the Rayleigh number from (Ra = 105) to (Ra = 106) leads in an 80% increase in heat transfer. However, as the density of the nanofluid increases, the highest values of streamlines decrease. Decreasing the Darcy number (Da) educed the maximum values of the streamlines and average Nusselt number (Nu). Additionally, increasing the heat generation factor (λ) from (λ=0) to (λ=5) decreases the Nusselt number by 30%. Furthermore, the most effective streamline value was achieved at an inclination angle (γ) of 60.

This study aims to improve the productivity of traditional multi-slope solar stills, which are still employed in isolated villages with no power and no clean drinking water. The conventional multi-slope solar still was equipped with a photovoltaic array system that heated the water through an electric heater submerged in the basin to increase the productivity of the conventional solar stills. This study analyzed and compared the productivity of a PV-coupled solar still (PVSS) with a solar still basin area of 0.64 m2 to that of a conventional solar still (CSS). The results showed that the productivity of the PVSS improved more than triple times (9.39–10.9 L/m2.day) during active mode compared to the CSS, which had a daytime solar still production without a PV system (passive mode) that varied between 2.2–2.34 L/m2.day. The daily efficiency of the passive mode without any additional external energy input was around 27%, which resulted in a distilled water yield of 1.4 L. However, when operated in active mode with supplementary energy inputs, such as electrical heating elements that were powered by solar panels, the daily efficiency of the solar still was approximately 44.8%, resulting in a distilled water yield of 6 liters. The payback period for PVSS was found to be two years. The main observation is that the PVSS has demonstrated its applicability for distillation improvement and a significant increase in production for the entire day when employing a clean energy source.

The use of direct thermal energy storage can improve the reliability of solar dish technology by providing a steady source of heat, even when solar radiation levels are low or intermittent. In this experimental study, a solar-thermal hybrid system that transmits light to interior photovoltaic panels through an optical fiber while producing hot household water was developed. The system employs a parabolic solar dish (PSD) with a cylindrical solar receiver designed to capture both heat and solar radiation. Fiber optics are used to transport light from the solar collector to the building as a source of illumination. To improve the system efficiency, a design of a direct storage system with phase change material (PCM) of petroleum Jelly was employed in this experimental work to heat water for a longer discharge duration. Furthermore, Al2O3 nanoparticles account for 1% of the total volume of the PCM material are added to the PCM material to improve heat transfer during heat charge and discharge. In addition, a low-cost two-axis tracking system for a PSD was developed. The study examined the efficiency of the system and analyzed the temperature profiles inside the solar receiver using a direct energy storage system. The discharge time is approximately six hours with a water temperature of more than 30 °C. The results revealed that the use of Al2O3 nanoparticles boosted thermal efficiency by around 5.68%. The proposed system could assist in solving the limited space challenges by utilizing the roof of the building.

This study examines the impact of incorporating phase change material (PCM) in photovoltaic thermal (PVT) systems on their electrical and thermal performance. Although PVT systems have shown effectiveness in converting solar energy into both electricity and heat, there is a necessity for studies to investigate how integrating PCMs can further enhance performance. The study also aims to explore the effect of solar irradiation and coolant mass flow rate on the electrical and thermal output of both PVT and PVT-PCM systems. A graphical user interface was developed within the MATLAB Simulink under the weather conditions of Amman, Jordan. The results show that the incorporation of PCM in PVT systems significantly reduces solar cell temperature and increases electrical efficiency. The highest electrical efficiency of a PVT system with PCM was found to be 14%, compared to 13.75% in a PVT system without PCM. Furthermore, the maximum achievable electrical power in a PVT system with PCM was 21 kW, while in the PVT system without PCM it was 18 kW. The study also found that increasing the coolant mass flow rate in a PVT system with PCM further reduced PV cell temperature and increased electrical efficiency, while the electrical efficiency of both the PVT and PVT-PCM systems decreases as solar incident radiation flux increases, resulting in a significant rise in cell temperature. At an increased solar radiation level from 500 W/m2 to 1000 W/m2, the electrical efficiency of the PVT configuration decreases from 13.75% to 11.1%, while the electrical efficiency of the PVT-PCM configuration falls from 14% to 12%. The findings of this study indicate that the use of PCM in PVT systems can lead to significant improvements in energy production and cooling processes. The results provide valuable information for designing and optimizing PVT-PCM systems.

A hybrid solar lighting/ thermal system utilized a parabolic solar dish as a solar collector illustrated in this paper. A two-axis tracking system was developed and employed to improve the energy output of a concentrating solar system (CSP) that produces hot water and light. During the daytime, sunlight is transmitted by fiber optics to an indoor photovoltaic PV device to generate electricity, or it can be used directly as daylighting. The heat generated is used to heat water. The findings show that the proposed system is a low-cost, high-performance approach for solar energy use in building energy efficiency, and readily available tracking components and tools. The experimental work shows that the maximum efficiency of the hybrid solar lighting/ thermal system controlled with a dual-axis solar tracking system was 32.2%.

Low-temperature heat sources are widely available in nature, they are considered to be unusable, even though the conversion of such low-grade energy into electricity (high-grade energy) is highly desirable. Thermoelectric generators (TEGs) are achieving increasing interest in converting low temperature heat into electricity. TEG suffers from low performance, improving the performance of TEG will allow there use in huge engineering applications. In this paper the effect of heat transfer rate on the performance of TEGs will be analysed under both steady and transient conditions. Enhancing heat transfer from the TEG surface will be studied using a liquid saturated porous medium. Aluminium and copper particles are used and their influences are compared to forced convection heat transfer from TEG surfaces with and without liquids. The experimental results showed that power generated with Cu particles exceeds that of Al particles with 14%. The free to forced convection power generation ratio was 26.5% for Al,36% for Cu and the enhancement of TEG performance reached 149% for liquid saturated Cu particles.

ABSTRACT: Modern innovative models have the possibility of transferring research and development (R&D) output through technology transfer from scientific and research institutions or other enterprises. The complex process of technology transfer is significantly dependent on cooperation among academia, industry, and governments (I4.0) in response to the technological developments driven together through Industry 4.0. As a result, numerous technology transfer factors must be addressed for I4.0 to become a reality. However, the abundance of literature on I4.0 and associated technologies, the key ingredients, and insights for effectively executing I4.0 technology transfer are fairly limited. This study focuses on the success factors of technology transfer for I4.0. The framework is based on systematic literature to outline significant results and factors. Furthermore, this study summarizes, analysis, and criticizes the actual models and their influential variables for I4.0 technology transfer. One of the findings of this study is the significance of cooperation between technology recipients, agents, and inventors for I4.0 technology transfer. Another impressive finding is the significance of the ecosystem component in technology transfer. Combining I4.0 technologies and open innovation is a game-changer, enabling businesses to significantly save time and cost. This article will assist decision-makers in developing policies and strategies to improve the I4.0 technology transfer process. Furthermore, this involves identifying the kind of government assistance that will help accelerate the transition to I4.0 via technology transfer.

The adoption of Proton Exchange Membrane (PEM) fuel cells (FCs) is of great significance in diverse industries, as they provide high efficiency and environmental advantages, enabling the transition to sustainable and clean energy solutions. This study aims to enhance the output power of PEM-FCs by employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) and modern optimization algorithms. Initially, an ANFIS model is developed based on empirical data to simulate the output power density of the PEM-FC, considering factors such as pressure, relative humidity, and membrane compression. The Salp swarm algorithm (SSA) is subsequently utilized to determine the optimal values of the input control parameters. The three input control parameters of the PEM-FC are treated as decision variables during the optimization process, with the objective to maximize the output power density. During the modeling phase, the training and testing data exhibit root mean square error (RMSE) values of 0.0003 and 24.5, respectively. The coefficient of determination values for training and testing are 1.0 and 0.9598, respectively, indicating the successfulness of the modeling process. The reliability of SSA is further validated by comparing its outcomes with those obtained from particle swarm optimization (PSO), evolutionary optimization (EO), and grey wolf optimizer (GWO). Among these methods, SSA achieves the highest average power density of 716.63 mW/cm2, followed by GWO at 709.95 mW/cm2. The lowest average power density of 695.27 mW/cm2 is obtained using PSO.

Jordan has set priority plans to mitigate climate change impacts, where Jordan moves towards low emissions by promoting the Electric Vehicle and Hybrid-Electric Vehicle (EV/HEV) market. Therefore, there will be a pressing need for professionals in the field of maintenance and design of EV/HEVs. Accordingly, surveys were conducted to address the market needs in Jordan. The surveys targeted main stakeholders from different geographical locations in Jordan. This study was concerned with project partners, Academic staff, and students from eight universities located in Balqa’a, Irbid, Al-Karak, Tafilah, and the capital city Amman. The responses obtained are from 8, 140, and 799 project partners, academic staff, and students, respectively. The results, including surveys on teaching and training facilities, are analyzed and discussed. In conclusion, this study presents a crucial foundation for three specialized diploma programs (vocational diploma, technical diploma, and higher diploma) and a bachelor program tailored to Jordan’s EV/HEV market needs. Through a deliberate alignment of the curriculum with the evolving demands of the EV/HEV sector, the diploma programs will foster graduates who hold the essential knowledge and skills to excel in this rapidly expanding field. Furthermore, the programs address the distinct competencies and expertise that the local market requires, ensuring that graduates are well prepared to meet industry needs. The significance of this work serves as a bridge between academia and the market, resulting in graduates who possess the knowledge and skills, that are highly sought after by prospective employers.

Disasters, extreme weather conditions and rural recreation makes the availability of a portable heating and cooling device as a must. There are many devices that work on the heating and power generation in the market. The air-conditioner which works on the heat pump principle is well known. Harsh weather conditions, natural disasters and recreation in rural areas (deserts and mountains) requires the availability of a heating and cooling device that works on well available fuel and has a low fuel consumption rate. Such a device will help in saving lives and encourage rural tourism. In this paper, a heating and cooling device is proposed for rural places, harsh weather conditions and natural disasters where no electricity is available. The device is working on the principle of heat pump (HP) to optimize its performance. The source of energy selected for this device is fossil fuels; fossil fuel is used for its convenience where no electricity is available. Low vibration low noise Stirling Engine (SE) is used to drive the heat pump that produces heating/cooling effects. Analysis of the proposed device is performed for each of its components. The results attained from analysis coincide with the results in literature. An electricity free, low vibration, silent and improved performance CHC device was attained. With suitable selected sub-devices an OHR of 2.429 was achieved by the proposed device compared to currently available devices.

Flat solar collectors are extensively utilized in various domestic and industrial applications to reduce energy consumption. In this study, an-active flat plate solar collector (FPSC) with an internal absorber tube receiver was fabricated and tested in Al-Samawa city, Iraq (latitude 31.19°N and longitude 45.17°E). The ambient temperature and incident solar radiation at the experimental location were reached 39 °C and 840 W/m2, respectively. In this study, the number of riser tubes connected to headers that are covered with a glass sheet in a conventional FPSC were replaced with a single serpentine-shaped collector tube covered with a plastic sheet. The proposed solar collector used a smooth copper tube with internal and exterior diameters of 9.5 and 12 mm, respectively, and a total length of 1000 mm. A TRNSYS model of a flat plate collector integrated with an absorber tube was developed, simulated, and validated using the experimental data. Temperature and flow rate data were obtained concurrently throughout the experiments to evaluate the performance of the fabricated solar collector. The temperature at the solar collector input stayed relatively constant at 37.7 °C, and the water flow rate remained constant at 0.75 L/min. The results indicated that the temperature at the solar collector output ranged from 52 to 61 °C, with an average of 58 °C. The efficiency of the proposed solar collector ranges from approximately 45% to 67%, with an average of 58%. Overall, the simulation results of the TRNSYS model are in excellent agreement with experimental data. The average discrepancy between the tests and simulations for temperature differential and collector efficiency is approximately 1%.

A concentrating solar power (CSP) unit was designed to work as a hybrid system to supply the required energy for heat water and high intensity light. The system consists of a parabolic solar dish that reflected light in a set of optical fiber light inside a receiver. In turn, this light was transmitted to an indoor photovoltaic (PV) panels to produce electrical energy or used directly as a source of light during the day. This study revealed the following points: (i) the increased number of fiber optics, the improved efficiency of solar panel and power generated; (ii) the efficiency of the hybrid solar system was 23.62%. (iii) when the separation distance between the fiber optics and the PV module was closed, the level of the power generated becomes higher; and finally; (v) this application is more suitable for a limited area in the roof of the building.

The use of green hydrogen as a fuel source for marine applications has the potential to significantly reduce the carbon footprint of the industry. The development of a sustainable and cost-effective method for producing green hydrogen has gained a lot of attention. Water electrolysis is the best and most environmentally friendly method for producing green hydrogen-based renewable energy. Therefore, identifying the ideal operating parameters of the water electrolysis process is critical to hydrogen production. Three controlling factors must be appropriately identified to boost hydrogen generation, namely electrolysis time (min), electric voltage (V), and catalyst amount (μg). The proposed methodology contains the following two phases: modeling and optimization. Initially, a robust model of the water electrolysis process in terms of controlling factors was established using an adaptive neuro-fuzzy inference system (ANFIS) based on the experimental dataset. After that, a modern pelican optimization algorithm (POA) was employed to identify the ideal parameters of electrolysis duration, electric voltage, and catalyst amount to enhance hydrogen production. Compared to the measured datasets and response surface methodology (RSM), the integration of ANFIS and POA improved the generated hydrogen by around 1.3% and 1.7%, respectively. Overall, this study highlights the potential of ANFIS modeling and optimal parameter identification in optimizing the performance of solar-powered water electrocatalysis systems for green hydrogen production in marine applications. This research could pave the way for the more widespread adoption of this technology in the marine industry, which would help to reduce the industry’s carbon footprint and promote sustainability.

This study aims to identify the optimal operating parameters for the carbon dioxide (CO2) capture process using a combination of artificial intelligence and metaheuristics techniques. The main objective of the study is to maximize CO2 capture capacity. The proposed method integrates fuzzy modeling with the RUNge Kutta optimizer (RUN) to analyze the impact of three operational factors: carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio. These factors are considered to maximize the CO2 capture. A fuzzy model was developed based on the measured data points to simulate the CO2 capture process in terms of the stated parameters. The model was then used to identify the optimal values of carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio using RUN. The results of the proposed method are then compared with an optimized performance using the response surface methodology (RSM) and measured data to demonstrate the superiority of the proposed strategy. The results of the study showed that the suggested technique increased the CO2 capture capacity from 6.39 to 6.99 by 10.08% and 9.39%, respectively, compared to the measured and RSM methods. This implies that the proposed method is an effective approach to maximize the CO2 capture capacity. The results of this study can be used to improve the performance of the CO2 capture process in various industrial applications.

Water-in-diesel (W/D) emulsion fuel is a potentially viable diesel fuel that can simultaneously enhance engine performance and reduce exhaust emissions in a current diesel engine without requiring engine modifications or incurring additional costs. In a consistent manner, the current study examines the impact of adding water, in the range of 5–30% wt. (5% increment) and 2% surfactant of polysorbate 20, on the performance in terms of brake torque (BT) and exhaust emissions of a four-cylinder four-stroke diesel engine. The relationship between independent factors, including water addition and engine speed, and dependent factors, including different exhaust released emissions and BT, was initially generated using machine learning support vector regression (SVR). Subsequently, a robust and modern optimization of the sea-horse optimizer (SHO) was run through the SVR model to find the optimal water addition and engine speed for improving the BT and lowering exhaust emissions. Furthermore, the SVR model was compared to the artificial neural network (ANN) model in terms of R-squared and mean square error (MSE). According to the experimental results, the BT was boosted by 3.34% compared to pure diesel at 5% water addition. The highest reduction in carbon monoxide (CO) and unburned hydrocarbon (UHC) was 9.57% and 15.63%, respectively, at 15% of water addition compared to diesel fuel. The nitrogen oxides (NOx) emissions from emulsified fuel were significantly lower than those from pure diesel, with a maximum decrease of 67.14% at 30% water addition. The suggested SVR-SHO model demonstrated superior prediction reliability, with a significant R-Squared of more than 0.98 and a low MSE of less than 0.003. The SHO revealed that adding 15% water to the W/D emulsion fuel at an engine speed of 1848 rpm yielded the optimum BT, CO, UHC, and NOx values of 49.5 N.m, 0.5%, 57 ppm, and 369 ppm, respectively. Finally, these outcomes have important implications for the potential of the SVR-SHO approach to minimize engine exhaust emissions while maximizing engine performance.

Several techniques for cooling mass concrete structures were developed in order to increase structural integrity and reduce the influence of cement hydration, which sometimes causes cracking in concrete structures, negatively affecting their durability. This research focuses on cooling system design, initial investment, and the influence of different refrigerants on cooling system performance aims in producing higher quality massive concrete. Cooling aggregates in massive concrete structures such as desert dams can be performed by employing cooled air from an air conditioning duct system or chilled water. The experimental study illustrates the relationship between the coefficient of performance COP, the evaporator temperature, cooling capacity, and refrigerant mass flow rate as a function of the evaporator temperature, cooling capacity, and refrigerant mass flow rate. The findings of the experiments were utilized to verify a numerical model developed utilizing engineering equation solver (EES) software. The performance of the vapor compression of the cooling systems was compared using alternative refrigerants, including R22, R32, and R410a at different operating conditions. This study revealed that R22 refrigerant has a higher coefficient of performance than R32 and R410A, while R32 has the highest cooling capacity among other refrigerants.

The use of a variable refrigerant flow (VRF) is being specified as an alternative solution to centralize HVAC systems in domestic building. This manuscript presents a comprehensive description of the configuration for the indoor and outdoor units of a multi-split VRF system. A case study of a hotel in Amman, Jordan was implemented. The suitable indoor and outdoor units were selected according to heating and cooling load. A sensitivity analysis of the effect of some parameters on heating and cooling loads were analyzed. Finally, the thermal human comfort indices of PMV and PPD were investigated. This study showed that even the main shortcoming of using VRF system due to its high initial cost, but this system is encouraged especially in small places due to its energy efficiency appeal at partial loads, satisfying optimum thermal human comfort, and a good substitute where scarcity of water and achieving more thermal human comfort. Keywords: VRF system, Air conditioning, Cooling, Heating, Human comfort, PMV, PPD, HAP program

Biodiesel is considered to be a promising alternative option to diesel fuel. The main contribution of the current work is to improve compression ignition engine performance, fueled by several biodiesel blends. Three metrics were used to evaluate the output performance of the compression ignition engine, as follows: brake torque (BT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE), by varying two input parameters (engine speed and fuel type). The engine speeds were in the 1200–2400 rpm range. Three biodiesel blends, containing 20 vol.% of vegetable oil and 80 vol.% of pure diesel fuel, were prepared and tested. In all the experiments, pure diesel fuel was employed as a reference for all biodiesel blends. The experimental results revealed the following findings: although all types of biodiesel blends have low calorific value and slightly high viscosity, as compared to pure diesel fuel, there was an improvement in both BT and brake power (BP) outputs. An increase in BSFC by 7.4%, 4.9%, and 2.5% was obtained for palm, sunflower, and corn biodiesel blends, respectively, as compared to that of pure diesel. The BTE of the palm oil biodiesel blend was the lowest among other biodiesel blends. The suggested work strategy includes two stages (modeling and parameter optimization). In the first stage, a robust fuzzy model is created, depending on the experimental results, to simulate the output performance of the compression ignition engine. The particle swarm optimization (PSO) algorithm is used in the second stage to determine the optimal operating parameters. To confirm the distinction of the proposed strategy, the obtained outcomes were compared to those attained by response surface methodology (RSM). The coefficient of determination (R2) and the root-mean-square-error (RMSE) were used as comparison metrics. The average R2 was increased by 27.7% and 29.3% for training and testing, respectively, based on the fuzzy model. Using the proposed strategy in this work (integration between fuzzy logic and PSO) may increase the overall performance of the compression ignition engine by 2.065% and 8.256%, as concluded from the experimental tests and RSM.

A mathematical model was developed to investigate the effect of crucial operating parameters, such as process inlet humidity ratio, inlet volume flow rate, R/P ratio (reactivation/process air flow), regenerative temperature and rotational speed on the performance of desiccant wheels in hot and humid climates, using wound silica gel and molecular sieve desiccants. The desiccant wheels were designed to operate in two modes, specifically, with 90° for reactivation and 270° for process (1:3 split) as well as with 180° for reactivation and 180° for process (1:1 split). The main results enabled to make the following observations: (i) higher regeneration air temperature leads to an improvement of dehumidification and reduction of the thermal coefficient of performance COPth, (ii) an increase in the process inlet volume flow rate causes a reduction in the process removed moisture; (iii) the greater R/P ratio is required to remove more moisture, while a lower R/P ratio implies the lower power consumption for regeneration; (iv) silica gel desiccant is preferred when greater moisture removal is required at high inlet relative humidity, while molecular sieve desiccant is preferred when low dew point is required at low inlet relative humidity. Keywords: Moisture, Desiccant, Cooling system, Silica gel, Molecular sieve, Indoor humidity

Magnetic refrigeration is a fascinating superior choice technology as compared with traditional refrigeration that relies on a unique property of particular materials, known as the magnetocaloric effect (MCE). This paper provides a thorough understanding of different magnetic refrigeration technologies using a variety of models to evaluate the coefficient of performance (COP) and specific cooling capacity outputs. Accordingly, magnetic refrigeration models are divided into four categories: rotating, reciprocating, C-shaped magnetic refrigeration, and active magnetic regenerator. The working principles of these models were described, and their outputs were extracted and compared. Furthermore, the influence of the magnetocaloric effect, the magnetization area, and the thermodynamic processes and cycles on the efficiency of magnetic refrigeration was investigated and discussed to achieve a maximum cooling capacity. The classes of magnetocaloric magnetic materials were summarized from previous studies and their potential magnetic characteristics are emphasized. The essential characteristics of magnetic refrigeration systems are highlighted to determine the significant advantages, difficulties, drawbacks, and feasibility analyses of these systems. Moreover, a cost analysis was provided in order to judge the feasibility of these systems for commercial use.

Performance assessment of a two-bed silica gel-water adsorption refrigeration system driven by solar thermal energy is carried out under a climatic condition typical of Perth, Australia. A Fourier series is used to simulate solar radiation based on the actual data obtained from Meteonorm software, version 7.0 for Perth, Australia. Two economic methodologies, Payback Period and Life-Cycle Saving are used to evaluate the system economics and optimize the need for solar collector areas. The analysis showed that the order of Fourier series did not have a significant impact on the simulation radiation data and a three-order Fourier series was good enough to approximate the actual solar radiation. For a typical summer day, the average cooling capacity of the chiller at peak hour (13:00) is around 11 kW while the cyclic chiller system coefficient of performance (COP) and solar system COP are around 0.5 and 0.3, respectively. The economic analysis showed that the payback period for the solar adsorption system studied was about 11 years and the optimal solar collector area was around 38 m2 if a compound parabolic collector (CPC) panel was used. The study indicated that the utilization of the solar-driven adsorption cooling is economically and technically viable for weather conditions like those in Perth, Australia.