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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]

It is vital, following the Kyoto Protocol, to find environmentally benign and energy-efficient refrigerants, consequently boosting the coefficient of performance (COP). Refrigeration systems are used extensively in the industrial, home, and commercial sectors for cooling, heating, food preservation, and cryogenic purposes. Researchers have successfully employed the application of nanoparticles in cooling systems to achieve improved enhancement, reliability, and efficiency of refrigeration systems because of their higher heat transfer and thermophysical capabilities. The function of numerous variables, however, makes the experimental technique appear to be costly and time-consuming to carry out. This study was, therefore, designed to numerically simulate the performance assessment of a nanoparticle-enhanced Cascaded Vapor Compression Refrigeration Cycle (CVCRC). The focus of this paper is on four distinct SiO2 nanoparticle nanorefrigerants and their pure fluids: two HFCs as well as two fourth-generation refrigerants (HFOs), namely; R12, R134a, R1234yf, and R-1234ze (E). The results show that adding nanoparticles to the pure refrigerant improves COP, and the highest values were achieved with the R1234ze(E)/SiO2 mixture. Increasing the mass concentration of the nanoparticles leads to an increase in the refrigeration effect, an increase in COP, and a reduction in compressor work. Although R125 had the lowest compressor work of 47.12 kW when SiO2 nanoparticles are introduced, however, is not suitable for refrigeration because of its high GWP values. R1234ze has the second-lowest compressor work of 59.58 kW with the addition of SiO2, it is consequently more energy efficient and can be used in its place as it has a GWP of 6, among other benefits. [ABSTRACT FROM AUTHOR]

In this article, by using the notion of quantum calculus, we define a new type Szász–Mirakjan operators based on the q-integers. We derive a recurrence formula and calculate the moments Φ n , q (t m ; x) for m = 0 , 1 , 2 and the central moments Φ n , q ((t − x) m ; x) for m = 1 , 2 . We give estimation for the first and second-order central moments. We present a Korovkin type approximation theorem and give a local approximation theorem by using modulus of continuity. We obtain a local direct estimate for the new Szász–Mirakjan operators in terms of Lipschitz-type maximal function of order α. Finally, we prove a Korovkin type weighted approximation theorem. [ABSTRACT FROM AUTHOR]

Cyprus continues to be one of the European Union's most energy import-dependent countries. It is of worthy note that Cyprus is also the only European Union member state whose electricity grid is not connected to the European power network, as it uses an independent network that relies on local production. The high cost of electricity has been identified as a top problem, and officials on the island are looking at several ways to lower electricity rates. Increasing the amount of renewable energy is one of the main options being looked at. Furthermore, in a bid to meet the European Union target of 40% renewable energy electricity generation, Cyprus needs to fully utilize the vast renewable energy potential in the region. In this study, the integration of dominant renewable energy sources (solar, wind, and concentrated solar power) to the thermal power run grid system is simulated. Also, a 100% renewable energy electricity generation for 2050 is also modelled. The analysis is done, for a projected 8.3TWh/year of electricity demand, in the ENERGYPLAN simulation environment. The deterministic factors considered in this study are the power production and critical excess electricity production. Eight (8) case scenarios were developed in this study for comparative discourse of most feasible electricity generation to cut down on carbon dioxide emission in Cyprus. The optimized analysis reveals that the best case scenario for electricity generation for 2050 is Photovoltaic system integrated with the oil powered plants. This system (Photovoltaic/Oil) would generate 1.68TWh/year of renewable energy electricity. For 100% renewable energy electricity generation, the most feasible option is the Photovoltaic/Wind/storage system. This system would have an installed 4000 MW PV capacity, 7500 MW wind capacity and 30GWh storage capacity. This study gains significance as the results will give valuable technical and economic parameters that can inform policy changes in the region. [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]

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]