Optimization based on genetic algorithms on energy conservation potential of a high speed SI engine fueled with butanol-gasoline blends

Bio-n-butanol is widely recognized as a renewable alternative biomass fuel, it is gradually used in internal combustion engines for scientific research to alleviate the energy crisis and environmental pollution. However, due to the difference of physical and chemical properties between n-butanol and gasoline, if n-butanol is directly applied to gasoline engines, it will certainly cause changes in engine performance. Therefore, it is of great significance to explore the optimal energy conservation potential of n-butanol application in gasoline engines. Driven by this fact, an experiment was conducted in a high speed, spark ignition (SI) engine with n-butanol blended ratio of 0% and 35% by volume to gasoline, and simulation model of GT-Power coupling with MATLAB/Simulink is built and calibrated based on the tested data. The synergistic optimization of multiple operating variables with the corresponding genetic algorithms (GA) optimization methodologies is carried out to reveal the optimal energy conservation potential of engine fueled with butanol-gasoline blends. Results show that, when the torque remains the same with respect to that of the original engine, the optimized brake specific energy consumption (BSEC) at the corresponding engine speed under full load are significantly lower than the original BSEC, the average improvement in percentage of BSEC is approximately 7.11%. By adopting the method of multi-objective genetic algorithm (MOGA), the power and economy of the engine fueled with butanol-gasoline blends can be balanced simultaneously. The engine operating parameters can be chosen that the BSEC may be small while the engine is optimally powered. Besides, it is found that the pumping loss is affected by the combined effect of intake timing and exhaust timing. The optimization procedure can be applied to calibrate the universal characteristics of engines, and the findings obtained from this study can provide guidance for better application of alternative energy in traditional thermal machines. (C) 2021 The Author(s). Published by Elsevier Ltd.