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A method for identifying the standard deviation of characteristic peak intensities in laser plasma spectra of particle flow

A technology of laser plasma and standard deviation, which is applied in the field of plasma spectroscopy, can solve the problems of invalid spectrum not being eliminated and effective spectrum being mistakenly eliminated, and achieve the effect of simplifying the process of identifying spectral data, improving reliability, and improving accuracy

Active Publication Date: 2019-04-09
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

Both of these two identification methods have more effective spectra that are mistakenly rejected, and at the same time, there are more invalid spectra that have not been rejected

Method used

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  • A method for identifying the standard deviation of characteristic peak intensities in laser plasma spectra of particle flow
  • A method for identifying the standard deviation of characteristic peak intensities in laser plasma spectra of particle flow
  • A method for identifying the standard deviation of characteristic peak intensities in laser plasma spectra of particle flow

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Embodiment Construction

[0019] The present invention will be further described in detail below in conjunction with the embodiments and drawings. It should be pointed out that if there are processes or parameters that are not specifically described below, those skilled in the art can refer to the prior art.

[0020] by figure 1 It can be seen from the flowchart shown in the present invention that a method for identifying particle flow laser plasma spectra using the standard deviation of the characteristic peak intensity includes the following steps:

[0021] 1) First, place the powder sample (just as an example, the particle flow sample used is a fly ash sample) in a laser-induced breakdown spectroscopy measurement system (such as a laser-induced breakdown spectrometer), so that the sample is in the system as a particle flow flow. A pulsed laser is used to directly excite the particle stream, and 1500 spectral data are collected.

[0022] 2)Since the content of Si element is the highest in the fly ash sampl...

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Abstract

The invention discloses a method for identifying a laser plasma spectrum of grain flow through standard deviation of characteristic peak strength.According to the method, a laser-induced breakdown spectrum detection system is utilized for obtaining laser plasma spectrum data of a grain flow sample, a standard deviation value of the strength of characteristic peak pixel points of a selected element spectral line is calculated, and whether the spectrum data is valid or invalid is identified according to a set threshold value.Distribution information of characteristic peaks of the element spectral line in the laser plasma spectrum is utilized comprehensively, the process of identifying the spectrum data is simplified, and the accuracy of invalid spectrum removal is improved.

Description

Technical field [0001] The invention relates to a method for identifying spectrum data of a laser induced breakdown spectroscopy to directly measure a particle flow sample, in particular to a method for identifying a particle flow laser plasma spectrum by using a characteristic peak intensity standard deviation. Background technique [0002] In the process industry, the elemental composition measurement of materials mainly uses on-site sampling, and then is sent to the laboratory for sample preparation and offline analysis. The detection results are lagging, and it is difficult to meet the automatic control requirements based on online / fast measurement results in the process industry. [0003] In recent years, laser-induced breakdown spectroscopy has been applied to the direct measurement of the elemental composition of powder samples in the state of particle flow. Compared with the traditional powder compacting or stacking state, the measurement in the particle flow state will cau...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/71
CPCG01N21/718
Inventor 姚顺春徐嘉隆白凯杰卢志民殷可经陆继东
Owner SOUTH CHINA UNIV OF TECH
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