Optimization of biodiesel injection parameters based on support vector machine

Abstract

For the running diesel engine, spray-atomization, mixed-combustion, and thermal-power conversion processes are inseparable, which causes difficulty to investigate atomization effect separately. This study was conducted to improve the atomization efficiency of the soybean fatty acid methyl ester (SFAME) in engine, to achieve the minimum effective specific fuel consumption in specific engine working conditions, the different injection parameters combination were explored on the influence of effective specific fuel consumption at elevated fule temperature. The effective specific fuel consumption prediction model was established based on support vector machine (SVM). With small samples, the intrinsic functional relationship was determined and the best injection parameters were validated under seven different experimental conditions. The study results have shown that the engine's spray-thermal-power conversion process could be simulated accurately by using SVM. It will be more favorable to improve application effect of biodiesel in the engine to select the fuel temperature as injection parameters which influence atomization effect. Furthermore, using enumeration-verification methods to simulate the parameters might save a lot of resources as compared to the similar experiments.