Optimization of fusel oil - Gasoline blend ratio to enhance the performance and reduce emissions

The growing demand for energy has promoted research on alcohol-based biofuels. Fusel oil is a long-chain alcohol (higher alcohol), which is a byproduct of the bioethanol synthesis process and has recently received attention as a fuel blend for compression ignition and spark-ignition engines. Although several researches have been performed for using fusel oil in internal combustion engines, it is still unclear what is the optimum fusel oil ratio in blend. The present study focuses on determining optimum fusel oil gasoline blend ratio and operation load for a spark ignition engine. The response surface method (RSM), which is a widely used optimization technique to product and evaluate the biofuels, was used to analyze the performance and emissions of a port fuel injected gasoline engine burning different blends of fusel oil and gasoline (0%, 25%, 50%, 75% and 100%) under different loads (20%, 40%, 60%, 80% and 100%) and a fixed engine speed of 2500 rpm. The most compatible model was obtained and predicted values were successfully validated with the experimental observations. All RSM models were significant at the 5% level. Optimization results suggested 25% fusel oil content with an engine load of 47.21%. According to these optimized operating conditions, the engine torque was 16.49 Nm and brake specific fuel consumption (BSFC) was 326.024 g/kWh. The emissions of CO, NOx, and UHC at this point were 0.88%, 568.3 ppm and 165.49 ppm, respectively. According to engine test results, brake thermal efficiency (BTE) and brake torque decreased while the gross BSFC increased duo to water content in fusel oil which resulted in dramatically reduced in in-cylinder temperatures. With increasing the fusel oil content from G(80)F(20) (80% gasoline-20% fuel oil) to G(0)F(100), (0% gasoline-100% fuel oil), the NOx emissions reduced by 41% while UHC and CO emissions increased about by 39% and 22%, respectively. A higher desirability value of 0.63 for regression model illustrated that RSM can be efficiently employed for modeling and optimization of engine operating parameters.