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The conversion of different waste materials into biogas through anaerobic digestion process has been presented in different literatures. However, studies about the application of these biomass energy resources are limited. The thermodynamic assessment of a neoteric biomass-integrated comprehensive energy system configuration is studied. This study aims to investigate the multigeneration performance of the novel energy system and compare its thermodynamic efficiencies with two different input sources. This study will provide an insight into the better choice of biomass-based thermal input source for thermodynamic modeling. The system is developed to generate power, cooling, hot water, and hydrogen with a steam (power) cycle, absorption system, hot water tank, and polymer electrolyte membrane electrolyzer. With the aim to compare biomass from two different sources, biogas generated from rice husk and sugarcane bagasse is integrated as the thermal input and energetic/exergetic performance of the system presented in this study. The overall energy and exergy efficiencies of the system are 34.83% and 12% for rice husk biomass integrated system, and 68.29%, and 21.85% for sugarcane bagasse-integrated system. The highest exergy destruction was recorded at the boiler stage of the comprehensive energy system. [ABSTRACT FROM AUTHOR]

The recent effects of climate change and rising global warming levels have increased the need to transition towards clean energy. The use of multi-energy systems is one of the potential solutions to these issues, as validated in the literature. The production of hydrogen from cleaner sources has an integral role in decarbonizing the industrial, building, and transportation sectors. Hence, this study proposes novel multi-energy systems that can produce hydrogen from wind resources. The study is novel as it developed an innovative multi-energy system configuration and also considers hydrogen production as a means to utilize excess wind power production. The intermittency of wind resources has been a major drawback in using this renewable energy as the singular source for multi-generation systems. The multi-energy configuration developed and analyzed in this study proposed a solution for this by integrating a regenerative reheat biomass integrated power cycle as an auxiliary system for the multi-generation system (Wind-Bio-MGS). This system is modeled to produce electricity, heating, hot water, and hydrogen. The energy, exergy, and exergoeconomic approach is adopted in this study to evaluate the steady-state performance of the system, while the levelized cost of electricity (LCOE), levelized cost of heating (LCOh), and levelized cost of Hydrogen (LCOH) are also computed. A multi-objective optimization of the overall exergy efficiency and total product unit cost is presented. The parametric analysis of the energy systems is included to show the sensitivity of different parameters to changes and validate the robustness of the modeled system. The results show that the integration of hydrogen with wind-based multi-generation systems is a viable means of reducing carbon emissions and global warming. The overall energy and exergy efficiencies of the Wind-Bio-MGS system are 69.13% and 31.16%, respectively. The LCOE, LCOh, and LCOH for the system, respectively, are 0.02828 $ kWh−1, 0.004038 $ kWh−1, and 1.311 $ kg−1 s−1. [ABSTRACT FROM AUTHOR]

The growing interest in hybrid nanofluids is due to the synergistic effects of nanoparticles, which could give them better heat transfer properties as compared to base fluids, and conventional nanofluids. Several factors like temperature and volume fraction have been used in explaining the behaviours of hybrid nanofluids, however, the need to investigate comprehensively, the mixing ratios of hybrid nanofluids, remains a critical research area in the development of efficient nanofluids as heat transfer fluids. In this study, three mixture ratios of 1:1:1 (33.3% Al 2 O 3 , 33.3% ZnO, 33.3% Fe 3 O 4), 1:2:1 (25% Al 2 O 3 , 50% ZnO, 25% Fe 3 O 4), and 1:1:2 (25% Al 2 O 3 , 25% ZnO,50% Fe 3 O 4) ternary hybrid nanofluid (THNF) are synthesized at volume concentrations of 0.5%, 0.75%, 1%, and 1.25%. All experiments were carried out at a temperature range between 25 °C-65 °C. The effect of temperature, volume concentration, mixture ratio, are examined, as well as the development of a machine learning model for accurate prediction. The thermal conductivity and dynamic viscosity behaviour of the THNF were investigated. The result showed that temperature and volume concentration significantly affected the thermophysical properties of the fluid. The optimum Thermal Conductivity enhancement (TCE) was retrieved for the 1:1:1 THNF, at 36.018%. The 2:1:1, and 1:2:1 mixture ratios had a 32.92%, and 31.68% TCE respectively. At 1% volume concentration, the optimum TCE (as compared with water) for the mono, hybrid, and THNF measured are 18.98%, 28.58%, and 32.45% respectively. It is seen that the least viscosity was recorded for the 1:1:1 mixture ratio (0.001 Pa.s), while the highest viscosity was measured for the 2:1:1 THNF mixture ratio (0.021 Pa. s). The Gaussian process regression (GPR) gave an excellent prediction showing an R2 value of 0.9656, and 0.934 for the thermal conductivity and dynamic viscosity prediction respectively. In terms of application to solar thermal systems, the low viscosity of 1:1:1 Al 2 O 3 –ZnO-Fe 3 O 4 THNF makes that a low-pressure drop and pump work is required in practical applications of the 1:1:1 Al 2 O 3 –ZnO-Fe 3 O 4 THNF. [Display omitted] • Least viscosity was recorded for the 1:1:1 mixture ratio. • Optimum Thermal Conductivity enhancement was retrieved for the 1:1:1 THNF. • Optimum prediction was retrieved using the machine learning algorithm. • Highest viscosity was measured for the 2:1:1 THNF mixture ratio (0.021 Pa. s). [ABSTRACT FROM AUTHOR]

Hybrid nanofluids have been known to provide perceptibly increased heat transfer characteristics over conventional fluid, and so have been experimentally applied to solar collector applications. Recently, attention has grown towards three-particle hybrid nanocomposites, to investigate their increased thermophysical properties for heat transfer applications. This study numerically investigates the practical application of CuO-MgO-TiO2 ternary nanofluids in a photovoltaic thermal (PV/T) collector. The proposed model explores the effect of volume fraction, solar irradiation, and mass flow rate on the performance of the collector system. The result showed an improved electrical efficiency of 13.54%. 58.38% and 15.68% thermal efficiency and energy efficiency respectively were also gotten, calculated at an optimum volume fraction of 0.01. The maximum cell temperature drop was 8.24 °C measured at a mass flow rate of 0.1 kg s−1. The study also concludes that a maximum increase of 11.14% of the total PV/T system was achieved by the use of ternary nanofluid. The study also investigated the pressure drop and pumping power of the PV/T collector with and without nanofluid. The result showed that a lower pressure drop and pumping power for the CuO–MgO–TiO2 ternary nanofluid which makes it more desirable as compared to the Al2O3–ZnO hybrid nanofluid. Finally, it is shown that the performance of the PV/T collector depends highly on the volume fraction of the ternary nanofluid. [ABSTRACT FROM AUTHOR]

Unlike steam and gas cycles, the Kalina cycle system can utilize low-grade heat to produce electricity with water-ammonia solution and other mixed working fluids with similar thermal properties. Concentrated photovoltaic thermal systems have proven to be a technology that can be used to maximize solar energy conversion and utilization. In this study, the integration of Kalina cycle with a concentrated photovoltaic thermal system for multigeneration and hydrogen production is investigated. The purpose of this research is to develop a system that can generate more electricity from a solar photovoltaic thermal/Kalina system hybridization while multigeneration and producing hydrogen. With this aim, two different system configurations are modeled and presented in this study to compare the performance of a concentrated photovoltaic thermal integrated multigeneration system with and without a Kalina system. The modeled systems will generate hot water, hydrogen, hot air, electricity, and cooling effect with photovoltaic cells, a Kalina cycle, a hot water tank, a proton exchange membrane electrolyzer, a single effect absorption system, and a hot air tank. The environmental benefit of two multigeneration systems modeled in terms of carbon emission reduction and fossil fuel savings is also studied. The energy and exergy efficiencies of the heliostat used in concentrating solar radiation onto the photovoltaic thermal system are 90% and 89.5% respectively, while the hydrogen production from the two multigeneration system configurations is 10.6 L/s. The concentrated photovoltaic thermal system has a 74% energy efficiency and 45.75% exergy efficiency, while the hot air production chamber has an 85% and 62.3% energy and exergy efficiencies, respectively. Results from this study showed that the overall energy efficiency of the multigeneration system increases from 68.73% to 70.08% with the integration of the Kalina system. Also, an additional 417 kW of electricity is produced with the integration of the Kalina system and this justifies the importance of the configuration. The production of hot air at the condensing stage of the photovoltaic thermal/Kalina hybrid system is integral to the overall performance of the system. Image 1 • Development of two innovative multigeneration systems. • Energy and exergy analysis of two solar-based CPVT multigeneration system with/without a Kalina system. • Kalina cycle intergation with the CPVT increased electricity production by 14.15%. • 68.73 % multigeneration energy efficiency for configuration 1 and 70.08% for configuration 2. [ABSTRACT FROM AUTHOR]

The main aim of this study is to review researches that have been carried out on solar assisted HVAC systems. The objective is to design a basic solar assisted air conditioning system that uses CO2 as a refrigerant. The performance analysis of the system is done with EES and Copeland Scroll software. Research about the solar-assisted HVAC system has advanced as some studies reveal solar-assisted system that can operate for 24 h. In this kind of system, the storage is important and takes a sizeable amount of space and cost in the design. Also, LiBr is one of the most commonly used efficient absorber in absorption refrigeration for HVAC. The details of all the findings and the performance of the system designed is given in the paper. [ABSTRACT FROM AUTHOR]

Renewable energy- based multigeneration systems have been proposed as a viable means of achieving a net-zero future as these systems can decarbonize the power and energy sector simultaneously. Although there has been significant development in this research field, the large sizes of the existing systems in literature are a major setback in the adoption/implementation of these systems. Furthermore, the unavailability of solar and wind energy during some hours daily makes solar or wind energy-based multigeneration systems undesirable in many applications. Considering the urgency/importance of attaining a net-zero emission future, this study presents a novel multi-energy system that can bridge the existing gaps in literature. Therefore, the thermodynamics, economic, and environmental analysis novel CO2-based geothermal micro-multi-energy system that is hybridized with a concentrated solar photovoltaic/thermal system and wind turbine is presented in this paper. The proposed energy system presents an innovative method that can be effectively used to decarbonize future energy production. The multi-energy system is modeled to produce electricity, refrigeration effect, space heating, hydrogen, and hot water. The economic study in this paper also analyzed the Levelized cost of electricity (LCOE), Levelized cost of cooling (LCOC), and Levelized cost of hydrogen (LCOH). A multi-objective optimization operation is also carried out on the modeled system in order to determine the optimal exergy efficiency and total product unit cost. The steady-state performance analyses showed that the overall energetic and exergetic efficiencies of the system are 48,61% and 88.31% and these efficiencies can be as high as 51.76% and 95.08% respectively when the system is optimized with reference to exergy efficiency. The LCOE, LCOC, and LCOH at the optimal total product unit cost are 0.04529 $/kWh, 0.004564 $/kWh, and 28.86 $/kg. [ABSTRACT FROM AUTHOR]

Solar energy technologies represent a viable alternative to fossil fuels for meeting increasing global energy demands. However, to increase the production of solar technologies in the global energy mix, the cost of production should be as competitive as other sources. This study focuses on the implementation of machine learning for estimating the thermophysical properties of nanofluids for nanofluid-based solar energy technologies as this would make the synthesis of nanofluids cost-effective. The prediction of thermal conductivity has gained a lot of research attention, whereas, the viscosity of nanofluids has less concentration of studies. The accurate prediction of the viscosity of hybrid nanofluids is important in estimating the heat transfer performance of nanofluids as regards their pump power requirements and convective heat transfer coefficient in several applications. The rigor of experimentations of hybrid nanofluids has necessitated the need for developing efficient and robust machine learning models for accurately estimating the viscosity of hybrid nanofluids for solar applications. Several studies were aimed at developing a predictive model for the viscosity of nanofluids; however, these models are limited to specific types of nanofluids. This study is aimed at developing a robust machine learning algorithm for predicting the viscosity of several hybrid nanofluids from reliable experimental data (700 datasets) culled from literature. This study implements a novel optimizable Gaussian process regression (O-GPR), which have not been previously used in this area, and compares the result with other commonly used machine learning algorithms like, Boosted tree regression (BTR), Artificial neural network (ANN), support vector regression (SVR), to accurately predict the viscosity of a wide range of Newtonian-based hybrid nanofluid. The input parameters used in training the machine learning models were temperature (T), volume fraction (VF), the acentric factor of the base fluid (ACF), nanoparticle size (NS), and nanoparticle density (ND). The prediction performance of the machine learning algorithms was tested using statistical metrics and was compared with theoretical models. The O-GPR model showed superior predictive performance with an R2 of 0.999998 and an MSE of 0.0002552. The study conclusively states that the high accuracy prediction of thermophysical properties of nanofluid using robust machine learning models makes the design of nanofluid-based solar energy technologies more cost-effective. [ABSTRACT FROM AUTHOR]

Decade research on nanofluids have solidified the claim that they have excellent heat transfer properties. The properties of mono nanofluids can be optimized by varying the volume concentration of the nanoparticle, however, there is a limit to this due to the challenge of the trade-off of the net negative of viscosity increase. In resolving this limitation, hybrid nanofluids have been synthesized by researchers to maximize the unique properties of different types of nanoparticles. The early days of hybrid nanofluid experimentations consisted of two-particle nanofluids, however, in recent years, a new class of working fluids has been discovered and extensively explored, consisting of three solid nanoparticles dispersed in a conventional fluid. These fluids are referred to as 'ternary nanofluid, 'ternary hybrid nanofluid' or 'trihybrid nanofluids'. This paper gives a comprehensive review of all studies that have synthesized this new class of nanofluids. The major focus in this study was given to their synthesis methods, characterizations, stability tests, thermophysical properties (thermal conductivity, specific heat capacity, and viscosity). This study also reviews the heat transfer applications of ternary nanofluids. The ternary nanofluids are shown to significantly increase the thermal conductivity and heat transfer properties of base fluids. The type and sizes, as well as mixture ratio of nanoparticles are important factors that determine the effectiveness of ternary nanofluids. Several areas still need to be covered for proper understanding of the hybridization effects of three particle nanofluids, like the effect of combining different nanoparticle types, particle sizes, nanoparticle shape, and base fluids. [ABSTRACT FROM AUTHOR]

In this study, two wind-solar-based polygeneration systems namely CES-1 and CES-2 are developed, modeled, and analyzed thermodynamically. CES-1 hybridizes a heliostat based CSP system with wind turbines while CES-2 integrates heliostat-based CPVT with wind turbines. This study aims to compare the production and thermodynamics performance of two heliostat based concentrated solar power technologies when hybridized with wind turbines. The systems have been modeled to produce, freshwater, hot water, electricity, hydrogen, and cooling with different cycles/subsystems. While the overall objective of the study is to model two polygeneration systems with improved energy and exergy performances, the performances of two solar technologies are compared. The wind turbine system integrated with the comprehensive energy systems will produce 1.14 MW of electricity and it has 72.2% energy and exergy efficiency. Also, based on the same solar energy input, the performance of the heliostat integrated CPVT system (CES-2) is found to be better than that of the CSP based system (CES-1). The polygeneration thermal and exergy efficiencies for the two systems respectively are 48.08% and 31.67% for CES-1; 59.7% and 43.91% for CES-2. Also, the electric power produced by CES-2 is 280 kW higher in comparison to CES-1. • Design of two comprehensive energy system to achieve better energy and exergy performance. • Thermodynamic modeling of two systems for hydrogen, hot water, electricity, and cooling production. • Comparison of heliostat CPVT and CSP system performance with the same input solar energy. [ABSTRACT FROM AUTHOR]

The deployment of photovoltaic technology in global electricity generation has reduced the dependence on fossil fuels and the adverse effect of global warming climate changes. However, the variability of PV output due to its reliance on meteorological parameters hinders the total maximization of the technology. In this paper, the interdependence of meteorological parameters on accurate estimation of the specific yield of the PV system is examined using machine learning algorithms. The analysis uses 2 years of meteorological data in a 10-min interval. The meteorological parameters used as input are global solar radiation (GR), wind speed (WS), wind direction (WD), the standard deviation of wind direction (WSD), air temperature (AT), and relative humidity (RH), while the output variable is the PV specific yield (PVSY). This study uses a Python-based “all regressor” machine learning algorithm, to analyze their accuracy for the estimation analysis at different input feature space. The accuracy of the models and algorithms is analyzed using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R2). The best performance in the two feature space was retrieved for AT, and GR with MSE and MAE values of 0.113 and 0.252, respectively. The best performance in the three feature space was retrieved for WSD, AT, and GR with MSE and MAE values of 0.109 and 0.255, respectively. The results show that the most accurate PVSY estimation was for the four input feature space with input variables of WS, AT, GR, and RH. The K-nearest neighbor (KNN) proved to be the most appropriate algorithm for this feature space with an RMSE and MAE score of 0.101 and 0.266, respectively. The study also shows that the most significant meteorological inputs in PV yield estimation are air temperature and global solar radiation. The study also noted that a large feature space adds uncertainties to the training process. Finally, the paper concludes that the sunshine-based parameters are key inputs for high-quality PV yield estimation regardless of the number of input combinations. It is also concluded that, in this study, the most accurate regression algorithms for estimation are the Least Absolute Shrinkage and Selection Operator (LASSO), KNN, Elastic Net (EN), Decision Tree regressor (DT), and Extra Tree regression (ET). [ABSTRACT FROM AUTHOR]

• Analyzing wind-hydrogen production synergy in multi-energy systems. • Modelling three renewable energy-based multi-energy systems' configurations. • Modelling multi-energy systems' production of electricity, cooling, hydrogen, and hot water. • Levelized cost of electricity as low as 0.03653 $/kWh for wind-hydrogen production. • Energy and exergy efficiency ranges from 55.26% and 78.73% and 27.05% to 58.51%. With rising temperatures, extreme weather events, and environmental challenges, there is a strong push towards decarbonization and an emphasis on renewable energy, with wind energy emerging as a key player. The concept of multi-energy systems offers an innovative approach to decarbonization, with the potential to produce hydrogen as one of the output streams, creating another avenue for clean energy production. Hydrogen has significant potential for decarbonizing multiple sectors across buildings, transport, and industries. This paper explores the integration of wind energy and hydrogen production, particularly in areas where clean energy solutions are crucial, such as impoverished villages in Africa. It models three systems: distinct configurations of micro-multi-energy systems that generate electricity, space cooling, hot water, and hydrogen using the thermodynamics and cost approach. System 1 combines a wind turbine, a hydrogen-producing electrolyzer, and a heat pump for cooling and hot water. System 2 integrates this with a biomass-fired reheat-regenerative power cycle to balance out the intermittency of wind power. System 3 incorporates hydrogen production, a solid oxide fuel cell for continuous electricity production, an absorption cooling system for refrigeration, and a heat exchanger for hot water production. These systems are modeled with Engineering Equation Solver, and analyzed based on energy and exergy efficiencies, and on economic metrics like levelized cost of electricity (LCOE), cooling (LCOC), refrigeration (LCOR), and hydrogen (LCOH) under steady-state conditions. A sensitivity analysis of various parameters is presented to assess the change in performance. Systems were optimized using a multi-objective method, with maximizing exergy efficiency and minimizing total product unit cost used as objective functions. The results show that System 1 achieves 79.78 % energy efficiency and 53.94 % exergy efficiency. System 2 achieves efficiencies of 55.26 % and 27.05 % respectively, while System 3 attains 78.73 % and 58.51 % respectively. The levelized costs for micro-multi-energy System 1 are LCOE = 0.04993 $/kWh, LCOC = 0.004722 $/kWh, and LCOH = 0.03328 $/kWh. For System 2, these values are 0.03653 $/kWh, 0.003743 $/kWh, and 0.03328 $/kWh. In the case of System 3, they are 0.03736 $/kWh, 0.004726 $/kWh, and 0.03335 $/kWh, and LCOR = 0.03309 $/kWh. The results show that the systems modeled here have competitive performance with existing multi-energy systems, powered by other renewables. Integrating these systems will further the sustainable and net zero energy system transition, especially in rural communities. [ABSTRACT FROM AUTHOR]

Unlike previous studies where models/methods used in determining the carbon emission are presented, in this paper, a detailed analysis of the causes, trends, and solutions to carbon emission in Africa is presented. Economic development plays a crucial role in the well-being of a country/continent, thereby, affecting energy consumption. The impact of economic development on Africa's carbon (CO 2) emissions trend is first investigated. After which, three neural network models are developed to predict the future trend of total CO2 emission in the continent. Then, the use of renewable energy (RE) sources for power generation is analyzed/proposed as a viable solution for CO2 emission reduction in Africa. Finally, the impact of battery electric vehicles (BEVs) integration and hydrogen production in maximizing RE production in Africa's largest economy is analyzed. Secondary data of the economic indicators for twenty-five different African countries have been used to justify the effect of economic development on their carbon emission. From the results of the analyses, gross national income and carbon emissions in all sectors were found to be significantly positively correlated. That is, as national wealth across Africa increases, carbon emissions in the continent increase. Also, the predicted total annual CO2 emission showed that most countries will witness an increase in total CO2 emission by 2022 in comparison to 2018. The proposed RE-based method for power generation showed that the CO2 emission from the power industry can be reduced to zero for an African country. Nevertheless, the use of BEVs and the production of hydrogen will be integral in achieving this. • Integration of EVs and hydrogen production to maximize RE integration. • The impact of GNI and other economic indicators on CO2 emission. • Development of three neural network models for CO2 emission prediction. • The sole use of RE for electricity generation in Africa. [ABSTRACT FROM AUTHOR]

• Steady-state and transient analysis of a comprehensive energy system. • Energy and exergy analysis a wind-biomass integrated system for multigeneration. • Overall exergy and energy efficiencies of 25.32% and 34.07%. • The energy equivalence of the required biomass fuel required to power the system for a year is 189.24 GWh. • Total renewable electricity production is 33.35 GWh yearly. One of the main challenges in renewable energy integrated multigeneration and comprehensive system researches is the inability to study the transient performance analysis of the system. In this study, a novel wind-biomass comprehensive energy system for power, cogeneration, trigeneration, and multigeneration is proposed. This system is analyzed in both steady-state and transient conditions using energy and exergy approaches. Rice husk is considered as the raw material for biomass fuel. The comprehensive system is modeled to produce electricity, freshwater, hot water, and hydrogen with a steam cycle, an organic Rankine cycle, a double stage refrigeration system, a proton exchange membrane electrolyzer, and a hot water chamber. Electric vehicles are integrated with the system in time-based process modeling to ensure load stability and maximize electricity production. The overall exergy and energy efficiencies of the proposed comprehensive system increase from 24.91% and 27.42% while producing electricity to, 26.08% and 35.22% when multigenerating. Based on the transient analysis, this system is capable of meeting the energy demand of a rice-producing factory, campus, and its surrounding community. The process modeling also shows the hourly performance of the system and its load stability. [ABSTRACT FROM AUTHOR]

Summary: Solar photovoltaic‐thermal (PV/T) collectors, are hybrid collectors used to convert solar radiation into usable thermal and electrical energy. Recently, the field of research on PV/T is has focused on improving the efficiency of the PV/T collector by replacing the conventional heat transfer fluids (HTFs) with nanofluids. This article investigates the effect of hybrid nanofluids mixture ratio on the useful energy and overall efficiency of a PV/T collector operating with Al2O3‐ZnO water nanofluid as the HTF. Experiments to measure the thermophysical properties of the hybrid nanofluids were conducted for various temperatures, volume concentrations, and mixture ratios, furthermore, accurate correlation models were proposed. Metrological data and energy output readings collected from the PV solar farm at Cyprus International University were used to validate our model. The study observed that at the optimum mixture ratio (0.47 of Al2O3 in the hybrid), the electrical, thermal, and exergy efficiencies of the PV/T collector are 13.8%, 55.9%, and 15.13% respectively. Also, the cell temperature drops by 21% when the mass flow rate is 0.1 kg/s as compared to when it is 0.01 kg/s. Finally, the study concludes that by using the Al2O3‐ZnO hybrid nanofluid an overall peak thermal efficiency of 91% can be attained, and this represents a 34% enhancement in the collector's performance when compared to water. [ABSTRACT FROM AUTHOR]

The performance analysis of a novel multi-generation (MG) system that is developed for electricity, cooling, hot water and hydrogen production is presented in this study. MG systems in literature are predominantly built on a gas cycle, integrated with other thermodynamic cycles. The aim of this study is to achieve better thermodynamic (energy and exergy) performance using a MG system (without a gas cycle) that produces hydrogen. A proton exchange membrane (PEM) utilizes some of the electricity generated by the MG system to produce hydrogen. Two Rankine cycles with regeneration and reheat principles are used in the MG configuration. Double effect and single effect absorption cycles are also used to produce cooling. The electricity, hot water, cooling effect, and hydrogen production from the multi-generation are 1027 kW, 188.5 kW, 11.23 kg/s and 0.9785 kg/h respectively. An overall energy and exergy efficiency of 71.6% and 24.5% respectively is achieved considering the solar parabolic trough collector (PTC) input and this can increase to 93.3% and 31.9% if the input source is 100% efficient. The greenhouse gas emission reduction of this MG system is also analyzed. • Multi-generation with two Rankine cycles instead of a Brayton cycle in combination with other cycles. • The use two Rankine cycles, two absorption cycles, and a PEM to produce electricity, cooling, hot water and hydrogen. • Multi-generation system is powered with solar parabolic collectors. • Overall system energy and exergy efficiencies are 71.6% and 24.5% respectively. [ABSTRACT FROM AUTHOR]

This study presents an experimental nanoparticle synthesis and the numerical analysis of a parabolic trough collector (PTC) operating with olive leaf synthesized TiO2/water nanofluid. The PTC is modeled after the LS-2 collector for various operating conditions. An analysis of the heat transfer and entropy generation in the PTC is carried out based on the first and second laws of thermodynamics for various parameters of nanoparticle volumetric concentration (0 ≤ φ ≤ 8%), mass flow rate (0.1 ≤ m ≤ 1.1kg/s), and inlet temperatures (350-450 K) under turbulent flow regime. The effect of these parameters is evaluated on the Nusselt number, thermal losses, heat convection coefficient, outlet temperature, pressure drop, entropy generation rate, and Bejan number. The results show that the values of the Nusselt number decrease with higher concentrations of the nanoparticles. Also, the addition of nanoparticles increases the heat convection coefficient of the nanofluid compared to water. The thermal efficiency of the system is improved with the use of the new nanofluid by 0.27% at flow rates of 0.1kg/s. The entropy generation study shows that increasing the concentration of nanoparticles considerably decreases the rate of entropy generation in the system. It is also observed that increasing the volumetric concentration of nanoparticles at low mass flow rates has minimal effect on the rate of entropy generation. Finally, a correlation that provides a value of mass flow rate that minimizes the entropy generation rate is also presented for each values of inlet temperature and nanoparticle volumetric concentration. [ABSTRACT FROM AUTHOR]

Transition in the energy systems are intricate processes of change that have political, social, and technical components. The Middle East and North Africa (MENA) region is a particularly complicated setting for these transformations, primarily due to the region's highly varied and variable energy resources, infrastructure, and economic and political frameworks. This study considers grid-integrated renewable for decarbonization of the MENA region. In this study, a 3 kWp grid-connected photovoltaic/thermal system is proposed for a four-person family in the MENA region. The performance of the proposed hybrid PV/T system is simulated and compared for the major cities in eighteen (18) MENA countries. Economic and environmental analysis of the hybrid system was also carried out. The simulation results reveal that the maximum reference yield of 2035 kWh was measured in Kuwait-Kuwait City, while the maximum final yield of 1786 kWh was measured in Morocco-Casablanca. In addition, the electrical efficiency in the region is found to be between 11.11% and 13.48%. The study also revealed that the optimal module efficiency was between January and February, while the least module efficiency was between July and August for all the cities considered. The proposed system in Morocco-Casablanca displays the highest annual specific yield of 1878 kWh/kWp. The maximum electricity to the grid amongst the cities considered in this study was recorded for Morocco-Casablanca with 3612 KW, while the least was measured for Tunisia-Tunis, with 2795 KW. The Net Present Value (NPV) analysis showed that Morocco-Casablanca, and Qatar-Doha are forerunners in terms of profitability if the proposed system is considered. The NPV values of the system for both cities were estimated to be $56,291 and $25,483, respectively. A maximum annual CO 2 reduction of 1009 kg was calculated for UAE-Abu Dhabi while it was found that the annual total CO 2 reduction in the entire region can go up to 15838 kg. Prediction analysis was also carried out to estimate the reference yield, final yield and electrical efficiency of the proposed system. The results showed that the artificial neural network model gave superior results when compared to the Gaussian process regression (GPR) model. Following the results of the current study, it can be confidently suggested that countries in the MENA region should explore the installations of hybrid PV/T systems for residential homes to actualize their commitment to the Paris agreement in reducing greenhouse emissions in the region. [Display omitted] [ABSTRACT FROM AUTHOR]

• Volume concentration have an inverse relationship with SHC of hybrid nanofluids. • Nanoparticle size also affects SHC, though this remains scarcely researched. • Machine learning models give better estimation of SHC than classical models. • There is continuous enhancement of the SHC of hybrid NFs at high temperature. Nanofluids have gained tremendous research interests in diverse fields of study due to their improved properties, especially as heat transfer fluids. Numerous studies have revealed the characteristics, limitations, and applicability of single type nanoparticles and the attributes of their colloidal mixtures. The discovery of the synergistic effect of nanoparticles, as relating to their improved thermophysical properties have been further explored in experimental and numerical studies of hybrid nanofluids. While the major focus has been on thermal conductivity, and viscosity, another extremely important property that gets little study attention is the specific heat capacity, which is a key thermal property in energy systems. Proper understanding of the specific heat capacity (SHC) of hybrid nanofluids is a sine qua non for proper decisions for heat transfer and heat storage applications. The crux of this review study is to give a detailed and comprehensive review of the specific heat capacity of hybrid nanofluids. The synthesis, characterization, and stability of hybrid nanofluids are presented. Furthermore, the different effects like temperature, volume concentration, mixture ratio, and particle size that influence the SHC behaviour of hybrid nanofluids are analyzed in this study. A compilation and discussion on correlation and machine learning models developed for hybrid nanofluids are also presented. Results suggest that volume concentration have an inverse relationship with the SHC of hybrid nanofluids, an increase in temperature improves the SHC of hybrid nanofluids, and this is more significant at temperatures above 50 ℃. Finally, although scarcely researched, the SHC is also affected by the size of the nanoparticles. [ABSTRACT FROM AUTHOR]

The utilization of solar thermal collectors has increased significantly in the last decade. The heat transfer fluids that is used in the design of these systems has notable effects on their performance. The recent discoveries of hybridized nanofluids, give a promising approach for investigating the performance of parabolic trough collectors (PTC), especially at different design conditions and environmental parameters. This study investigates the thermodynamic performance of a Kalina cycle incorporated with a parabolic trough collector that utilizes ternary nanofluid as a heat transfer fluid. The use of mono, hybrid and ternary nanofluids (THNF) in the parabolic trough collector is examined based on their impact on the efficiency of thermal system. The nanofluids considered are Al 2 O 3 , Al 2 O 3 –Fe, Al 2 O 3 –ZnO, and CuO–MgO–TiO 2. The performance of integrated cycle based on the effect of solar irradiation and volume fraction of the nanofluids are studied and compared with conventional fluids namely: thermal VP1 oil, DowThermal oil, and Salt (7NaNO 3 , 40NaNO 2 , 53KNO 2). In addition, an optimization study is conducted using genetic algorithm to maximize the net power output of the Kalina cycle and the exergetic efficiency of overall system. The results showed that performance of the system which uses nanofluids as a working fluid was higher than the systems using conventional fluids. Furthermore, the results demonstrate that maximum net power output of the system is recorded when Al 2 O 3 , Al 2 O 3 –Fe, Al 2 O 3 –ZnO, and CuO–MgO–TiO 2 nanoparticles are used. The power outputs were evaluated as 59.72 kW, 56.64 kW, 54.54 kW, and 61.21 kW respectively. The optimization study reveals that the optimum exergetic efficiency when Al 2 O 3 , Al 2 O 3 –Fe, Al 2 O 3 –ZnO, and CuO–MgO–TiO 2 nanofluids are used can reach up to 21.0%, 17.2%, 10.8%, and 25.1% respectively. • The effects of nanofluids on PTC/kalina is examined. • Effects of volume fraction, solar radiation and temperature is analyzed. • Optimum exergy efficiency measured is for CuO-MgO-TiO2 at 25.1%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Development of two innovative energy systems for sustainability in agricultural farms. • Thermodynamic comparison of thermal storage and solid oxide fuel cell for energy storage. • Design of solar-based multigeneration systems for 24-hours energy production. • Overall energetic and exergetic efficiencies are 55.37% and 53.70% for MGS-TS, and 51.12% and 46.82% for MGS-SOFC. • Sensitivity analysis of system's performance considering variation in dispatch rates. Agriculture and its corresponding farms are an integral part of every society/country. In recent years, the energy consumption by farms has risen as sophisticated technologies (that require a high amount of energy) are used for the pre-processing/storage of farm produce. In a bid to advance the transition toward cleaner energy consumption in farms, the thermodynamic assessment of two innovative high-performance solar-based multigeneration systems is presented in this study. Based on the identified most crucial energy needs in farms, the multigeneration systems are designed to produce electrical power, cooling/refrigeration effect, hydrogen, and domestic hot water. In comparison to existing literature, this study is novel as it analyzes the daily performance of the multigeneration systems based on the integration/comparison of two storage techniques. While thermal storage is integrated with one multigeneration system (MGS-TS), the power-to-hydrogen conversion and use of the hydrogen in a solid oxide fuel cell is considered as the storage mechanism in the second system (MGS-SOFC). To analyze the daily performance of the systems, solar parabolic trough collectors are used to produce the input thermal energy based on a 6-hour availability timeframe per day, and the storage systems are designed to power the system for the remaining 18 h. The multigeneration systems developed are analyzed using the energy/exergy approach while the economic costs of the systems are also compared. The effect of different crucial parameters including, ambient temperature, discharge periods, dispatch rates, etc. on key variables related to the systems is investigated. Based on the performance assessment of the two high-performance systems modeled in this study, the overall energetic and exergetic efficiencies are 55.37% and 53.70% for MGS-TS, and 51.12% and 46.82% for MGS-SOFC. The total daily electricity, cooling/refrigeration effect, and hot water useful outputs from the two multigeneration systems are 61,630 kWh/day, 7,939.1 kWh/day, and 125,178 Liters/day for MGS-TS; and 34,597 kWh/day, 15,504 kWh/day, and 129,686 L/day for MGS-SOFC respectively. While the performances of the two systems differ slightly, the unique advantages of each are embedded in their useful energy products. [ABSTRACT FROM AUTHOR]

The amelioration of photovoltaic (PV) and photovoltaic/thermal (PV/T) systems have garnered increased research interest lately, more so due to the discovery of the thermal property augmentation of nanofluids. The overarching goal of this study is to conduct a comparative analysis of mono, hybrid, and ternary hybrid nanofluids utilized as fluids for heat transfer applications and particularly as cooling mediums in PV/T applications. Al2O3, ZnO, Al2O3-ZnO, and Al2O3-ZnO-Fe3O4 nanofluids are synthesized at 1% volume concentration using the two-step method. The zeta potential tests carried out showed that the fluids have high stability. The numerical model developed in this study was validated using real data culled from Cyprus International University. The findings in this study showed that the Al2O3-ZnO-Fe3O4 ternary hybrid nanofluid and ZnO mono nanofluid were more efficient heat transfer fluids for the PV/T system. The optimum relative electrical PV/T efficiency against that of the PV is 8.13% while the electrical and thermal enhancement recorded in this study was 1.79% and 19.06%, respectively, measured for the ternary hybrid nanofluid based PV/T system. This present study shows that despite the limitation of pumping power and pressure drop associated with nanofluid in thermal systems, the close performance evaluation criterion values as compared with water is positive for practical utilization of nanofluid in PV/T systems. [ABSTRACT FROM AUTHOR]

Nanofluids are known to have immense potential for heat transfer applications because of their unique thermophysical properties when compared to the conventional heat transfer fluid. Predicting the thermophysical features like thermal conductivity has posed a challenge to their application. This article addresses some of the challenges posed in their prediction by using data sets from several experimental research on various hybrid nanofluids to train an intelligent neural network. The thermal conductivity of hybrid nanofluids is predicted using seven different input variables namely, volume concentration, temperature, the acentric factor of the base fluid, nanoparticle bulk density, mixture ratio of particles, the thermal conductivity, and size of nanoparticles. 715 experimental data points from studies using different hybrid nanoparticles are used in developing a multi-layer perceptron artificial neural network (ANN) and support vector regression (SVR) models. The performance validation of the models is computed using the mean square error (MSE) and the coefficient of determination (R 2 ). The performance result showed an R 2 value of 0.99997 and 0.99788 in the validation phase of the ANN and SVR model, respectively. This indicates that the models are capable of accurately predicting the thermal conductivity of hybrid nanofluids over a wide range of hybrid nanoparticle combinations. Finally, a universal formula using MLP-ANN for predicting the thermal conductivity of hybrid nanofluids is presented. [ABSTRACT FROM AUTHOR]

• Energy and exergy analysis a CPVT and wind turbine integrated comprehensive system. • Integration of biogas powered systems as a backup system and modelling based on four scenarios. • Overall energy efficiency considering all the case studies, ranges from 64.91% to 71.06%. • Also, overall exergy efficiency ranges from 31.80% to 53.81%. • Maximum CO2 emission reduction in comparison to coal fired system is 2680.4 kg/h. One of the challenges faced by renewable energy (RE) powered multigeneration system is the unavailability of RE resources at different times. In this research, a novel comprehensive energy system powered with wind turbines, concentrated photovoltaic/thermal and biogas is modelled and analyzed using energy and exergy approach. Multiple useful products such as electricity, hot air, hydrogen, fresh water, cooling effect and hot water will be produced by the system. Four different operational scenarios namely; CPVT/wind, wind/biogas, CPVT/biogas, and biogas only are considered for this comprehensive energy system. Thermal energy storage is incorporated with the system, in order to save thermal energy that will be used during hours without solar radiation. Furthermore, the CO2 emissions saved, in comparison with the use of different fossil fuel sources is analyzed. The maximum productions from the comprehensive system are: 3.4 MW of electricity; 12.41 L/sec of Hydrogen; 279.4 kW of cooling effect; 17.546 kg/s of hot air; 144.18 L/min of hot water and 10.31 L/min of fresh water. The overall energetic efficiency of the system considering all the case studies ranges from 64.91% to 71.06% while the exergetic efficiencies ranges from 31.80% to 53.81%. The CO2 emission analysis results show that the system is environmentally friendly. [ABSTRACT FROM AUTHOR]