Response surface methodology application on lubricant oil degradation, performance, and emissions in SI engine: A novel optimization of alcoholic fuel blends

Abstract

For evaluating the significance of renewable alternative fuels for optimized engine performance and lower emissions, methanol has been extensively utilized as a blend with gasoline in spark-ignition engines. However, rare attempts have been rendered to examine the consequence of methanol–gasoline fuel blends (M6, M12, and M18) on lubricant oil operating for a longer period in engines. The highest and least decrease of 9.62% and 6.68% in kinematic viscosity (KV) was observed for M0 and M18, respectively. However, the flash point (FP) of degraded lubricant oil for M6, M12, and M18 was 3%, 5%, and 7% higher than that of M0, respectively. Total acid number (TAN) and ash content of degraded lubricant oil for M18 were the highest among M0, M6, and M12. An inclusive optimization of engine performance, emissions, and lubricant oil properties has been made for various methanol–gasoline fuel blends at distinct operating conditions by employing the response surface methodology (RSM) technique. RSM-based optimization portrayed the composite desirability value of 0.73 for 2137.13 watt brake power (BP), 6.08 N-m torque, 0.37 kg/kwh brake-specific fuel consumption, 22.10% brake thermal efficiency, 4.02% carbon monoxide emission, 7.15% carbon dioxide emission, 134.12 ppm hydrocarbon emission, 517.02 ppm nitrogen oxides emission, 12.44 cst KV, 203.77°C FP, 2.23 mg/g KOH TAN, and 2.65%wt ash content as responses for fuel blend M8 at 3400 rpm and higher loading condition. RSM predicted results demonstrated significant compliance with empirical findings, with absolute percentage error (APE) below 5% for each response. However, the highest APE of 4.68% was obtained for FP owing to inefficient desirability as a consequence of manual testing. The least APE of 1.57% was obtained for torque because of the highest desirability. Overall, the RSM predicted results of the designed models are effective and viable. RSM technique was found to be effective for the optimization of the broader engine characteristics spectrum.