INDEPENDENT COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES; NEURAL-NETWORK WNN; QUANTITATIVE-ANALYSIS; RANDOM FOREST; FLY-ASH; WAVELET-TRANSFORM; UNBURNED CARBON; SIGNAL; MODEL

The classification and identification of coal ash contributes to recycling and reuse of metallurgical waste. This work explores the combination of the 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 measurements. At first, principal component analysis (PCA) was used to identify and remove abnormal spectra in order to optimize the training set for the WNN model. And then, ICA was employed to select and optimize input variables for the WNN model. The classification of coal ash was carried out by using the 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 using WNN and artificial neural network (ANN) models, and the WNN model shows a better classification performance. It was confirmed that the LIBS technique coupled with the WNN method is a promising approach to achieve the online analysis and process control of the coal industry.