Abstract
Based on machine learning models,an approach for the type recognition of oxygenated additives(ester isomers,i.e.,methyl butyrate,methyl crotonate,ethyl acrylate,and ethyl acrylate)via optical diagnostics was proposed.By utilizing optical diag-nostic methods flame features were extracted,and three models including random forest(RF),artificial neural network(ANN),and support vector machine(SVM),were employed to establish the relationship between flame images and oxygenated additives.Moreover,the impact of multiple factors on model performance,including image compression,dataset size,and feature number was also investigated.The images of flame obtained from inverse diffusion flame under four different oxyge-nated additives and various combustion conditions were used as examples to examine the effectiveness of the proposed approach.Results indicated that the accuracy of the recognition of ester isomers by the proposed approach exceeded 90％.Furthermore,it is observed that image compression had minimal impact on prediction accuracy but significantly reduced processing time.Different types of features contributed to predicting the type of ester isomers variously,and all models exhibited improved accuracy with an increased number of features.The number of samples significantly affected model accuracy.The investigation of feature missing and insufficient training samples suggested that ANN and RF models were more suitable for cases with many missing features,while SVM was more suitable for dealing with small samples.
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