Method for measuring coal property on line based on neural network

Abstract

The invention relates to a method for measuring coal property on line based on a neural network, which is used for a laser-induced plasma spectrum coal property detection device. The method comprises the following steps of: acquiring the spectra of calibration samples by using a laser-induced plasma system, extracting the principal components of a spectral intensity matrix through principal component analysis (PCA) or partial least square discriminant analysis (PLS-DA), and dividing the calibration samples into different categories on a principal component score plot; and respectively establishing a neural network model for the calibration sample in each category, and training, wherein spectral intensity data are taken as the input of the neural network, and the concentration of each element is taken as the output. For an unknown sample to be detected, the category to which the unknown sample belongs is determined through the PCA or PLS-DA, and the spectral intensity data of the sample to be detected are input into the trained neural network model in the category so as to obtain the concentration of each element. The method can realize the multi-element measurement of the coal property and reduce errors caused by different categories of coal, has high anti-jamming capability and adaptability and improves the accuracy of laser-induced plasma spectrum measurement simultaneously.

Description

technical field [0001] The invention relates to an online measurement method for coal quality. Specifically, the basic principle of the method is Laser Induced Plasma Spectroscopy (LIBS), on the basis of coal classification using Principal Component Analysis (PCA) or Partial Least Squares Discriminant Analysis (PLS-DA), the application of artificial neural The meta-network model is used for rapid quantitative analysis of coal quality. Background technique [0002] In coal-consuming units such as power plants, in order to control the production process in real time, it is necessary to know the specific composition of the coal on the belt conveyor in time, so as to adjust the relevant production parameters according to the change of the coal composition. For example, in coal-fired power plants, coal costs account for 80% of the total cost, so it is crucial to understand and study the impact of factors such as coal quality on production efficiency. This requires that the coal...

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Problems solved by technology

[0006] The purpose of the present invention is to design a method for online measurement of coal quality characteristics based on neural networks, which can be used in laser-induced plasma spectroscopy, in view of the disadvantages of large investment, insufficient precision, or inability to realize full-element analysis in the current coal quality online analysis technology. Use on the system

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