Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

Abstract

Application of machine learning algorithms for the predictionof flame temperature in small-scale burner fueled with ethanol-diesel fuel blends IntroductionAs a light petroleum product, diesel fuel is widely used in ships, large-scale vehicles, and locomotives in railway. However, contaminants such as oxides of nitrogen (NOx) and particulate matter (PM) are generated with the use of diesel fuel. It was found that diesel fuel can be partially replaced by oxygenated fuels, reducing contaminant emissions from combustion. Consequently, many researchers have devoted their attention to the study of oxygenated fuels, such as methanol, ethanol and biodiesel (Hansen et al., 2005;Moon et al., 2010;Yao et al., 2010). Ethanol contains oxygen in 34.7wt% and its required air is theoretically nearly half as diesel when burned (Lu et al., 2021). Therefore, diesel fuel blended with ethanol can make combustion more fully, which reduces soot emissions.Ethanol-diesel fuel blends requires appropriate equipment to achieve efficient combustion. The small-scale burner is the key equipment of small-electromechanical system and this burner has the advantage of high energy density. However, in the small scale, many new problems and challenges arise, such as incomplete combustion, unstable flame, and large heat loss (