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Classification and discrimination of coal ash by laser-induced breakdown spectroscopy(LIBS) coupled with advanced chemometrics methods

Abstract

The classification and identification of coal ash contributes to recycling and reusing of metallurgical waste. This work explores the combination of laser-induced breakdown spectroscopy(LIBS) technique and independent component analysis-wavelet neural network(ICA-WNN) for the classification analysis of coal ash. A series of coal ash samples were compressed into pellets and prepared for LIBS measurement. At first, principal component analysis(PCA) was used to identify and remove abnormal samples in order to optimize the training set for WNN model. And then, ICA was employed to select and optimize input variables for WNN model. The classification of coal ash was carried out by WNN model with optimized model parameters(the number of hidden neurons (NHN), the number of iterations (NI), the learning rate (LR) and the momentum) and input variables optimized by ICA. Under the optimized WNN model parameters, the coal ash samples for test sets were identified and classified by WNN and artificial neural networks(ANN) model, and WNN model shows a better classification performance. It was confirmed that the LIBS technique coupled with WNN method is a promising approach to achieve the online analysis and process control of coal industry.

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Publication details

The article was received on 16 Jun 2017, accepted on 03 Aug 2017 and first published on 03 Aug 2017


Article type: Paper
DOI: 10.1039/C7JA00218A
Citation: J. Anal. At. Spectrom., 2017, Accepted Manuscript
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    Classification and discrimination of coal ash by laser-induced breakdown spectroscopy(LIBS) coupled with advanced chemometrics methods

    T. Zhang, C. Yan, J. Qi, H. Tang and H. Li, J. Anal. At. Spectrom., 2017, Accepted Manuscript , DOI: 10.1039/C7JA00218A

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