Optimization of gasoline engine operating parameters fueled with DIPE-gasoline blend: Comparative evaluation between response surface methodology and fuzzy logic expert system

Abstract

Engine performance and emission characteristics of diisopropyl ether (DIPE)–gasoline blends were evaluated for several loads using a single-cylinder, four-stroke, multi-fuel variable compression ratio engine. The engine was operated with five test fuels: DIPE0 (neat gasoline), DIPE10 (90% gasoline + 10% DIPE), DIPE15 (85% gasoline + 15% DIPE), DIPE20 (80% gasoline + 20% DIPE), and DIPE25 (75% gasoline + 25% DIPE) by volume. The responses such as brake thermal efficiency (BTE), specific fuel consumption (SFC), carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NOx) were optimized using response surface methodology (RSM) and fuzzy logic expert system (FLES) considering compression ratio (CR), brake power (BP), and DIPE blend percentage as input variables. The developed RSM model provided a significant fit with higher R2 (correlation coefficient) values. The RSM models showed better performance than the FLES model. The optimized responses such as BTE, SFC, CO, HC, and NOx were 26.9%, 0.378 kg/kW h, 0.0135% by volume, 152.66 ppm, and 3465 ppm, respectively. The optimized results were validated with the experimental results, and the error percentage for all the responses were low. Thus, the developed RSM models gave better results than FLES and can predict and optimize engine performance and emission characteristics.