A Coal Ash Type Recognition Method Based on Wavelet Neural Network Algorithm

A wavelet neural network and coal ash technology, applied in the field of spectral analysis, can solve the problems of radiation, insensitive detection of light elements, and limit the application of rapid analysis, and achieve the effect of reducing parameter fluctuation and improving accuracy.

Active Publication Date: 2020-10-02
NORTHWEST UNIV
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Problems solved by technology

[0003] At present, the commonly used coal ash analysis methods include chemical analysis, atomic absorption spectroscopy, X-ray fluorescence spectroscopy, instantaneous neutron activation method and inductively coupled plasma mass spectrometry, etc. The detection of light elements is not sensitive, and there will be radiation, etc. These shortcomings limit the application of rapid analysis

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  • A Coal Ash Type Recognition Method Based on Wavelet Neural Network Algorithm
  • A Coal Ash Type Recognition Method Based on Wavelet Neural Network Algorithm

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Embodiment 1

[0018] The following takes the modeling and classification of three different types of coal ash samples as an example, combined with the attached figure 1 and examples to further illustrate the operation process of the present invention, but the present invention is not limited to this example.

[0019] The LIBS system used in this example includes a dual-wavelength Q-switched single-pulse Nd:YAG laser, an optical system, a movable sample stage, an echelle spectrometer (ARYELLE-UV-VIS, LTB150, German) and a computer. The laser energy is 61mJ, the fundamental frequency light wavelength is 1064 nm, the pulse width is 10 ns, the delay time is 1.5 μs, the repetition frequency is 10 Hz, and the spectral range is 220 nm-800 nm.

[0020] The coal ash samples used in this example are based on coal ash standard samples (GSB06-2119-2007, GSB06-2121-2007, GSB06-2122-2007), SiO 2 、Al 2 o 3 , Fe 2 o 3 , CaO, MgO, MnO 2 and TiO 2 Seven kinds of oxides were mixed according to the cont...

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Abstract

The invention discloses a coal ash type identification method based on wavelet neural network algorithm combined with laser induced breakdown spectrum technology. The laser-induced breakdown spectrometer is used to collect the spectral data of the coal ash thin slice samples at different measurement points, the independent component analysis is used to filter the characteristic variables of the training set data, and the gradient descent method is used to optimize the parameters of the wavelet neural network model. Predict the class of an unknown coal ash sample. This method effectively extracts the difference information between classes, and at the same time eliminates the noise information irrelevant to the analysis variables, reduces the adverse effects caused by parameter fluctuations and matrix effects during the experiment, and improves the accuracy of the classification results.

Description

technical field [0001] The invention relates to a method for identifying coal ash types. Specifically, it utilizes laser-induced breakdown spectrum technology and a wavelet neural network algorithm to realize the discrimination and analysis of coal ash types, and belongs to the technical field of spectral analysis. Background technique [0002] In recent years, long-term and large-scale regional pollution weather in my country in winter, especially the phenomenon of smog has become commonplace and has become a hot issue of global concern. Air pollution threatens human health and ecological balance, and is one of the biggest environmental challenges facing mankind in the 21st century. The main cause of air pollution, especially smog, comes from the combustion of coal. If the coal ash particles of the combustion product are not processed, they will be transformed into the most harmful components of air pollution particles, thereby affecting human health. Therefore, the recove...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/71G06N3/04G06N3/08
CPCG01N21/718G06N3/04G06N3/08
Inventor 张天龙李华汤宏胜
Owner NORTHWEST UNIV
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