A Predication Analysis of the Factors Influencing Minimum Ignition Temperature of Coal Dust Cloud Based on Principal Component Analysis and Support Vector Machine

摘要

To investigate the effect of different proximate index on minimum ignition temperature(MIT) of coaldust cloud, 30 types of coal specimens with different characteristics were chosen. A two-furnace automaticcoal proximate analyzer was employed to determine the indexes for moisture content, ash content, volatilematter, fixed carbon and MIT of different types of coal specimens. As the calculated results showed thatthese indexes exhibited high correlation, a principal component analysis (PCA) was adopted to extractprincipal components for multiple factors affecting MIT of coal dust, and then, the effect of the indexes foreach type of coal on MIT of coal dust was analyzed. Based on experimental data, support vector machine(SVM) regression model was constructed to predicate the MIT of coal dust, having a predicating errorbelow 10%. This method can be applied in the predication of the MIT for coal dust, which is beneficialto the assessment of the risk induced by coal dust explosion (CDE).