Rapid Classification and Quantification of Coal by Using Laser-Induced Breakdown Spectroscopy and Machine Learning

نویسندگان

چکیده

Coal is expected to be an important energy resource for some developing countries in the coming decades; thus, rapid classification and qualification of coal quality has impact on improvement industrial production reduction pollution emissions. The traditional methods proximate analysis are time consuming labor intensive, whose results will lag combustion condition coal-fired boilers. However, laser-induced breakdown spectroscopy (LIBS) assisted with machine learning can meet requirements detection multi-element quality. In this work, 100 samples from 11 origins were divided into training, test, prediction sets, clustering models, regression models established performance different application scenarios. Among them, cluster several clusterings only by spectra; classify labels categories; model give quantitative indicators. Cross-validation was used evaluate performance, which helped select optimal parameters each model. showed that K-means could effectively divide four clusters similar within class but between classes; naive Bayesian distinguish according probability distribution function, its accuracy reach 0.967; partial least squares reduce influence multivariate collinearity prediction, root mean square error ash, volatile matter, fixed carbon 1.012%, 0.878%, 1.409%, respectively. built provided a reference selection LIBS when applied qualification.

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2023

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app13148158