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The need for accurate solar energy forecasting is paramount as the global push towards renewable energy intensifies. We aimed to provide a comprehensive analysis of the latest advancements in solar energy forecasting, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. The novelty of this review lies in its detailed examination of ML and DL models, highlighting their ability to handle complex and nonlinear patterns in Solar Irradiance (SI) data. We systematically explored the evolution from traditional empirical, including machine learning (ML), and physical approaches to these advanced models, and delved into their real-world applications, discussing economic and policy implications. Additionally, we covered a variety of forecasting models, including empirical, image-based, statistical, ML, DL, foundation, and hybrid models. Our analysis revealed that ML and DL models significantly enhance forecasting accuracy, operational efficiency, and grid reliability, contributing to economic benefits and supporting sustainable energy policies. By addressing challenges related to data quality and model interpretability, this review underscores the importance of continuous innovation in solar forecasting techniques to fully realize their potential. The findings suggest that integrating these advanced models with traditional approaches offers the most promising path forward for improving solar energy forecasting. [ABSTRACT FROM AUTHOR]

The CaCO3 is being studied for its application in thermal energy storage. However, it has drawbacks of slow reaction rate during calcination and incomplete reversible carbonation which limit its use. In this paper, SiO2 has been studied as a dopant for CaCO3 to improve its cyclic performance. The CaCO3 samples were loaded with different concentrations of SiO2 and its effect on the thermal energy density of CaCO3 was determined. Afterwards, the effect of the dopant on the heat storage process of the synthesized composite along with kinetics of decarbonation reaction was investigated. Cyclic tests were performed to determine the reusability of the material. It was found that the addition of dopant helped to increase the rate of decarbonation reaction, thereby making the heat storage process more efficient as compared to pure CaCO3. The activation energy values are 255.9 kJ/mol, 280.1 kJ/mol, 244.9 kJ/mol, and 234.8 kJ/mol for 5%, 15%, 30%, and 0% doped SiO2 samples, respectively. Furthermore, thermal energy storage density increases when the amount of dopant decreases in the samples such as the 30% and 5% doped samples have gravimetric energy storage densities of 339.85 J/g and 759.24 J/g, respectively. It was observed that the large quantities (15% and 30%) of dopant had introduced a new phase of Ca3SiO5 during CaCO3 decomposition. [ABSTRACT FROM AUTHOR]

The current work applies L9 (33) Orthogonal Array (OA) of Taguchi design to obtain an optimum combination of nature of fuel, speed of engine, and load for DI- CI diesel engine that is alternatively fueled with waste cooking oil (WCO) based pure biodiesel (B100) & 20% blend of biodiesel with neat diesel (B20) with an objective to achieve maximum reduction in smoke and NOx emissions and to enhance the output parameters like engine's heat release, cylinder pressure and brake power with the conceivable dwindling of brake specific fuel consumption of the engine at various load conditions. The optimal input parameters (nature of fuel as B100, speed as 2300 rpm and load as 100%) were specified by the grey relational analysis in such a way that the preferred output results are successfully accomplished. Furthermore, analysis of variance (ANOVA) technique showed that the nature of fuel is the major leading factor with 44.28% impact on the output parameters. The experimental and ANN simulated results at the predicted optimal combination ensured the significant improvements of output response factors, thus validating the use of Grey-Taguchi method in reducing emissions and improving combustion and performance simultaneously. [ABSTRACT FROM AUTHOR]

Summary: Proton exchange membrane fuel cell is an energy conversion technology with an excellent potential to replace fossil fuel–based internal combustion engines. Evenly distributed Pt on conductive support is commonly used as an electrocatalyst. This catalyst support material is a key component of proton exchange membrane fuel cell as it greatly affects the cost, durability, and electrochemical activity of fuel cells. Although the carbon‐based support materials have evolved in the last few decades, there is still need to explore other alternatives as the corrosion of carbon is inevitable under the harsh environments within the catalyst layer of proton exchange membrane fuel cells. Moreover, the performance of noncarbon supports is also not satisfactory. Therefore, the advent of hybrid support materials, which are electrochemically stable and cost‐effective, is required. The hybrid supports exhibiting the characteristics of contributing component, or even showing synergistic effect, would circumvent the shortcomings associated with individual components. This review introduces the recent advances in hybrid support materials, including carbonaceous and noncarbonaceous one; discusses the pros and cons of different support materials; and highlights the improved properties of hybrid supports as compared with the individual components. [ABSTRACT FROM AUTHOR]

Selective catalytic reduction (SCR) is now an established NOx removal technology for industrial flue gas as well as for diesel engine exhaust gas. However, it is still a big challenge to develop a novel low-temperature catalyst for NH3-SCR of NOx, especially at a temperature below 200°C. In the past few years, many studies have demonstrated the potential of iron (Fe)-based catalysts as low-temperature catalysts for NH3-SCR of NOx. Herein, we summarize the recent progress and performance of Fe-based catalysts for low-temperature NH3-SCR of NOx. Catalysts are divided into three categories: single FexOy, Fe-based multimetal oxide, and Fe-based multimetal oxide with support catalysts. The catalytic activity and selectivity of Fe-based catalysts are systematically analyzed and summarized in light of some key factors such as activation energy, specific surface area, morphology, crystallinity, preparation method and precursor, acid sites, calcination temperature, other metal dopant/substitute, and redox property of catalysts. In addition, H2O/SO2 tolerance and the NH3-SCR reaction mechanism over Fe-based catalysts, including Eley-Rideal and Langmuir-Hinshelwood mechanism, are emphasized. Lastly, the perspectives and future research directions of low-temperature NH3-SCR of NOx are also proposed. [ABSTRACT FROM AUTHOR]

The selective catalytic reduction of NO with NH3 at low temperatures has been investigated with natural iron ore catalysts. Four iron ore raw materials from different locations were taken and processed to be used as catalysts. The methods of X-ray diffraction (XRD), X-ray fluorescence (XRF), Brunauer-Emmett-Teller (BET), X-ray photoelectron spectroscopy (XPS), hydrogen temperature-programmed reduction (H2-TPR), ammonia temperature-programmed desorption (NH3-TPD), scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) were used to characterize the materials. The results showed that the sample A (comprised mainly of α-Fe2O3 and γ-Fe2O3), calcined at 250 °C, achieved excellent selective catalytic reduction (SCR) activity (above 80% at 170–350 °C) and N2 selectivity (above 90% up to 250 °C) at low temperatures. Suitable calcination temperature, large surface area, high concentration of surface-adsorbed oxygen, good reducibility, lots of acid sites and adsorption of the reactants were responsible for the excellent SCR performance of the iron ore. However, the addition of H2O and SO2 in the feed gas showed some adverse effects on the SCR activity. The FT-IR analysis indicated the formation of sulfate salts on the surface of the catalyst during the SCR reaction in the presence of SO2, which could cause pore plugging and result in the suppression of the catalytic activity. [ABSTRACT FROM AUTHOR]

Selective catalytic reduction (SCR) is now an established NOx removal technology for industrial flue gas as well as for diesel engine exhaust gas. However, it is still a big challenge to develop a novel low-temperature catalyst for NH3-SCR of NOx, especially at a temperature below 200°C. In the past few years, many studies have demonstrated the potential of iron (Fe)-based catalysts as low-temperature catalysts for NH3-SCR of NOx. Herein, we summarize the recent progress and performance of Fe-based catalysts for low-temperature NH3-SCR of NOx. Catalysts are divided into three categories: single FexOy, Fe-based multimetal oxide, and Fe-based multimetal oxide with support catalysts. The catalytic activity and selectivity of Fe-based catalysts are systematically analyzed and summarized in light of some key factors such as activation energy, specific surface area, morphology, crystallinity, preparation method and precursor, acid sites, calcination temperature, other metal dopant/substitute, and redox property of catalysts. In addition, H2O/SO2 tolerance and the NH3-SCR reaction mechanism over Fe-based catalysts, including Eley-Rideal and Langmuir-Hinshelwood mechanism, are emphasized. Lastly, the perspectives and future research directions of low-temperature NH3-SCR of NOx are also proposed. [ABSTRACT FROM AUTHOR]