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.

中文翻译：

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激光诱导击穿光谱法（LIBS）结合先进的化学计量学方法对煤灰进行分类和鉴别

粉煤灰的分类和识别有助于冶金废物的回收和再利用。这项工作探索了激光诱导击穿光谱技术（LIBS）技术和独立成分分析小波神经网络（ICA-WNN）的结合，用于粉煤灰的分类分析。将一系列粉煤灰样品压缩成颗粒，并准备进行LIBS测量。首先，使用主成分分析（PCA）识别和消除异常光谱，以优化WNN模型的训练集。然后，使用ICA为WNN模型选择和优化输入变量。使用优化的模型参数（隐藏神经元数（NHN），迭代数（NI），学习率（LR）和动量）以及由ICA优化的输入变量。在优化的WNN模型参数下，利用WNN和人工神经网络（ANN）模型对测试集的粉煤灰样品进行了识别和分类，WNN模型具有较好的分类性能。已经证实，LIBS技术与WNN方法相结合是实现煤炭行业在线分析和过程控制的一种有前途的方法。