Experimental investigation, ANN modeling, and TOPSIS optimization of gasoline-alcohol blends for minimizing tailpipe emissions of a motorcycle

Abstract

Two-wheelers contribute more than 32% of global pollution; particularly motorcycle idling and revving operations induce severe emissions in metropolitan cities. The current study conducts both experimental and modeling assessments of a motorcycle operated with alcohol fuel blends (10%, 20%, and 55% by vol.) with gasoline, to optimize the idling and revving emissions. An artificial neural network is modeled to predict the tailpipe emissions with blend percentage, and vehicle speeds as the model inputs. The measured and predicted data are used for optimizing the alcohol blends at various speeds using a multi-objective optimization based technique for order preference by similarity to ideal solution. The experimental results reveal that the maximum HC and CO emissions are observed at higher speeds of 3000 rpm, whereas the peak NO is noted in the range of 3000–3500 rpm for gasoline operation. The maximum percentage error variation with the measured and predicted values is 12.5% for CO, 4.2% for CO2, 1.1% for HC, and 1.7% for NO emissions. The optimized blend varies from M15 to M50 for methanol-gasoline and E15 to E55 for ethanol-gasoline blend for the speed range of 1000–3500 rpm. Hence, the developed models provide a novel approach for the prediction and optimization of motorcycle tailpipe emissions for light alcohol fuel blends.