Method for recognizing slag variety by combining with laser-induced breakdown spectroscopy based on least squares support vector machine

A laser-induced breakdown and support vector machine technology, applied in the field of spectral analysis, can solve problems such as time-consuming, limited real-time rapid analysis, and inability to quickly obtain steel product quality information, so as to overcome interference, improve prediction accuracy, and tolerate degree of effect

Inactive Publication Date: 2015-06-10
NORTHWEST UNIV
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Problems solved by technology

At present, the conventional methods for slag analysis include chemical analysis, atomic fluorescence spectrometry (XRF), atomic emission spectrometry (AES), inductively coupled plasma-optical emission spectro

Method used

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  • Method for recognizing slag variety by combining with laser-induced breakdown spectroscopy based on least squares support vector machine
  • Method for recognizing slag variety by combining with laser-induced breakdown spectroscopy based on least squares support vector machine

Examples

Experimental program
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Embodiment 1

[0020] The LIBS system used in this example includes a dual-wavelength Q-switched single-pulse Nd:YAG laser, an optical system, an adjustable three-dimensional sample stage, an echelle spectrometer (ARYELLE-UV-VIS, LTB400, German) and a computer. The laser energy is 80mJ, 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 5 Hz, and the spectral range is 220nm-800nm.

[0021] Three kinds of slags (blast furnace slag, converter slag, and flat furnace slag) were selected, and a total of 30 slag samples were used. For the convenience of testing, each slag sample was ground to 200 mesh with a ball mill, and then each sample was pressed into a thin sheet about 2mm thick, with a pressure of 400MPa and last for 5min.

[0022] The LIBS signals of different slag samples were collected by laser-induced breakdown spectroscopy. Randomly select 50 measurement points for each slag sample, obtain a measuremen...

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Abstract

The invention provides a method for recognizing slag variety by combining with a laser-induced breakdown spectroscopy (LIBS) analysis technology based on a least squares support vector machine. The method comprises the following steps: optimizing the least squares support vector machine parameter (radical basis kernel function-gamma and rho2) by adopting grid global optimization and half-off cross validation; building a least squares support vector machine classification model; and realizing reorganization of the slag variety by combining the classification model with the LIBS technology. A research shows that a least squares support vector machine modeling approach has a good prediction effect; a novel modeling method is provided for mode recognition of the LIBS technology; and the method can be applied to recognition, recovery and reutilization of metallurgical waste materials.

Description

technical field [0001] The invention relates to a method for identifying slag types based on a least square support vector machine combined with a laser-induced breakdown spectrum, and belongs to the technical field of spectral analysis. Background technique [0002] In the steelmaking industry, a large number of by-products exist in the form of slag and deposits. The slag produced by the world slag industry amounts to almost 50 million tons per year. As an important by-product of the slag industry, slag plays a decisive role in ensuring the smooth progress of steelmaking operations, steel quality and metal recovery. Different types of steelmaking furnaces produce different slags. Each type of slag has its own unique chemical, mineral and physical properties, however its main components include calcium oxide, silicon dioxide, aluminum oxide, magnesium oxide, iron oxide, etc. Sorting of slag facilitates recycling and reuse of metallurgical waste. The reuse of slag is main...

Claims

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Application Information

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IPC IPC(8): G01N21/63
Inventor 李华张天龙汤宏胜
Owner NORTHWEST UNIV
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