The effect of time correlation function, Sauter mean diameter and asymmetry ratio on machine learning classifiers for particle discrimination by using light scattering

Abstract

This study illustrates that different particle types can be accurately discriminated using a novel and simple detector cell. ML models for the classification purpose were trained with scattered light at different wavelengths. Different smokes of wood, polyurethane, sunflower oil and paraffin, PAO, DEHS aerosols were introduced to the measurement cell. Measurements for each material were repeated ten times at five different test scenarios. The measurement range is up to 1.42% obs/m. For particle analysis, asymmetry ratio, Sauter mean diameter information and TCF were derived from the scattered light. NN, RFC, SVC and KNN classifiers were trained with different features to investigate the effect of each feature and a comparison study on the classification performances of ML models was carried out. ML models which include all features, identified smoke of wood and polyurethane with high accuracies. According to the F1 scores, the top two classifiers were determined as NN and SVC. The F1 scores of the proposed best models ranged from 71.0% to 100.0%. TCF data generally improve the classification performance. Proposed best ML models are capable of discriminating smoke types at the early stage of fire and can be tailored to new particle types and provide excellent quantitative accuracy.