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Interest in biodiesel as a diesel fuel substitute has increased due to the growing need for sustainable energy sources. The blends of biodiesel, such B20, have become more popular because they can lessen the need for fossil fuels and greenhouse gas emissions. The blends of biodiesel, however, may pose problems with emissions, performance, and combustion efficiency. The objective of this study is to investigate the effects of blending ethanol (C2H6OH) and aluminium oxide (Al2O3) into B20 biodiesel blend in order to improve engine performance. The study examines the effects of adding C2H6OH (5% of vol.) and Al2O3 (75 ppm) to the B20 biodiesel mix on its essential features and combustion. To fully assess the performance and emissions characteristics of the single cylinder diesel engine, experimental evaluations include a wide range of engine operating loads. The findings show that adding C2H6OH to the B20 blend increases its volatility and oxygen content, which promotes better ignition and combustion characteristics. Additionally, adding Al2O3 nanoparticles to the blend shows promise for improving combustion efficiency by enhancing fuel atomization and lowering soot emission. The synergy of adding both Al2O3 and C2H6OH to B20 significantly reduces CO, HC, and smoke levels of the diesel engine by 33.04, 28.13, and 12.88%, respectively. The results of this study offer important new information about how C2H6OH and Al2O3 additives might improve the B20 biodiesel blend's emissions performance and combustion efficiency, increasing the fuel's potential as a greener alternative for the transportation industry. [ABSTRACT FROM AUTHOR]

In recent decades, the significance of power generation from renewable sources has emerged as a pivotal advancement for environmental sustainability. This study endeavors to thoroughly explore potential renewable energy sources to comprehensively evaluate their viability in regions characterized by average energy consumption patterns. The primary objective is to estimate the on-site potential for a techno-economic investigation of renewable energy sources tailored for a village setting through optimized models. The research aspires to propose a blueprint for transforming a village into a self-sustainable entity by integrating onsite hybrid renewable energy sources. The investigation forecasts that, for the village's annual energy demand of 1267 MWh, the hybrid energy generation potential could reach 1355 MWh annually, resulting in substantial savings of approximately $46 057 annually. Utilizing the HOMER optimization model for techno-economic assessment reveals an optimal surplus energy generation of 88.2 MWh per year and an annual profit of about $4614, achieving an optimum balance in generation costs. The proposed approach anticipates surplus power generation and foresees reduced energy costs, ultimately contributing to realizing a self-sustainable village. This transformative initiative is poised to alleviate the strain on the conventional grid infrastructure, concurrently addressing environmental concerns. The findings emphasize the potential for self-sustaining energy solutions to meet local demand efficiently and make significant strides in reducing the environmental footprint of rural communities. [ABSTRACT FROM AUTHOR]

The current work analyses the thermal (η th) and effective efficiency (⁠|${\eta}_{\mathrm{eff}}$|⁠) of a solar thermal air collector (STAC) that has an arc-shaped dimple as a roughness geometry on the absorber plate. Nusselt number (Nu) and friction factor (ff) were computed for roughness geometry during the testing, which was done on STAC. Additionally, for different roughness values, the correlations for Nu and ff were developed and further used in this study. The temperature rise parameter and a parametric design are used to assess these efficiencies. The influence of design variables on STAC performance is analyzed using a numerical model based on thermal and effective evaluations. During the investigation, parameters such as relative roughness height (e / D h) varied from 0.021 to 0.036, relative roughness pitch (p / e) from 10 to 20, arc angle (α) from 45 to 60°, temperature rise parameter from 0.003 to 0.02 and Reynolds number (Re) from 3000 to 48 000 at a constant solar intensity (I  = 1000 W/m2). The η th and |${\eta}_{\mathrm{eff}}$| are observed to be 85% and 78%, respectively, at the optimum values of roughness parameters, i.e. e / D h = 0.036, p / e  = 10, and α  = 60°. The curves have been plotted between each of the roughness parameters and Re in order to evaluate the best η th and |${\eta}_{\mathrm{eff}}$|. The research emphasizes the usefulness of MATLAB for STAC analysis and optimization, roughness parameters of the suggested collector design, by integrating simulation and experimental data. [ABSTRACT FROM AUTHOR]

In this paper, the impact of different input variables on the performance and emission features of a pongamia pinnata and rapeseed oil biodiesel with n-Butanol additive were investigated, statistically analyzed, and optimized by employing the powerful response surface methodology (RSM) based design of experiment (DOE) techniques. The vegetable oils (pongamia pinnata and rapeseed oils) were transesterified and their corresponding methyl esters were blended with diesel and n-Butanol at blend ratios 10:84:6, 10:78:12, 20:74:6 and 20:68:12. The samples were tested on a direct injection CI engine at a rated speed of 1500 rpm and standard CR of 17.5:1 at different loads. In each test, performance and emission parameters were measured. Expert machine learning (ML) methods were used to forecast these features. In addition, polynomial equations were developed for each blend using regression techniques and compared with an artificial intelligence technique. It was observed that the engine performance increased as biodiesel and additive weight percentage increased. Regardless of the loads placed on the engine and the blend ratios, the use of PPME and RSME combined with n-Butanol blends demonstrated a clear decrease in NOx compared to diesel (7.07% for P20B12 and 6.58% for R20B12). As per the trend, it is seen that the percentage reduction in CO2 emissions is greater with high percentage increase of n-Butanol in the tested sample irrespective of loads applied on the engine (2.95% more P20B12 for as compared to P20B6). For the emission characteristics, ANN demonstrated a range of 87.92% to 98.83% prediction accuracy while that of regression varies from 81.4% to 98.8% for all the samples of PPME blended biodiesel. [ABSTRACT FROM AUTHOR]