Support vector regression approach to optimize the biodiesel composition for improved engine performance and lower exhaust emissions

Abstract

The practical applicability of biodiesel is limited due to poor fuel economy and higher emissions of oxides of nitrogen. Moreover, engine characteristics vary significantly with biodiesel type, highlighting the necessity to determine the most suitable biodiesel for improved fuel economy and emissions. Choosing the most suitable biodiesel is challenging as the relationship between biodiesel composition and engine characteristics is complex. Thus, it is intended to determine the most suitable biodiesel by developing composition-based models that predict engine characteristics and use them for optimization. This study explores the applicability of support vector machine regression to correlate biodiesel composition with engine characteristics. Two additional biodiesels not used for calibration validate the developed models with an acceptable mean absolute percentage error of less than 7%. Further, the models are fed to the genetic algorithm to determine the optimal composition for lower brake specific fuel consumption (BSFC) and emissions of unburnt hydrocarbon (HC), carbon monoxide (CO), and oxides of nitrogen (NOx). The optimized biodiesel has 8% shorter chain esters and 92% longer chain esters. Sunflower biodiesel exhibits 23% higher BSFC than diesel, whereas for optimized biodiesel, it is 4% higher. The proposed biodiesel results in 42% and 2% less HC and CO emissions than diesel and are reasonably lower than other biodiesels. The optimized biodiesel results in 42% higher NOx emissions, while sunflower biodiesel exhibits 83% higher NOx emissions than diesel. Overall, the optimized biodiesel resulted in a minimum penalty in fuel economy and NOx emissions compared to other commercially available biodiesels.