Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM

Yaguang MEI (梅亚光); Shusen CHENG (程树森); Zhongqi HAO (郝中骐); Lianbo GUO (郭连波); Xiangyou LI (李祥友); Xiaoyan ZENG (曾晓雁); Junliang GE (葛军亮)

doi:10.1088/2058-6272/aaf6f3

Plasma Science and Technology > 2019 > 21(3): 34020-034020. > DOI: 10.1088/2058-6272/aaf6f3

Yaguang MEI (梅亚光), Shusen CHENG (程树森), Zhongqi HAO (郝中骐), Lianbo GUO (郭连波), Xiangyou LI (李祥友), Xiaoyan ZENG (曾晓雁), Junliang GE (葛军亮). Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM[J]. Plasma Science and Technology, 2019, 21(3): 34020-034020. DOI: 10.1088/2058-6272/aaf6f3

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Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM

¹ School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China
² Wuhan National Laboratory for Optoelectronics, Laser and Tera-Hertz Technology Division, Huazhong University of Science and Technology, Wuhan 430074, People’s Republic of China
³ Institute for Science and Technology Information and Strategy, Central Iron and Steel Research Institute, Beijing 100081, People’s Republic of China

Funds: This work is supported by National Natural Science Foundation of China (Grant No. 61571040).

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Received Date: August 15, 2018

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Abstract

Abstract

According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy (LIBS) has shown a great potential for rapid analysis in steel industry. Nevertheless, the accuracy and precision may be limited by complex matrix effect and self- absorption effect of LIBS seriously. A novel multivariate calibration method based on genetic algorithm-kernel extreme learning machine (GA-KELM) is proposed for quantitative analysis of multiple elements (Si, Mn, Cr, Ni, V, Ti, Cu, Mo) in forty-seven certified steel and iron samples. First, the standardized peak intensities of selected spectra lines are used as the input of model. Then, the genetic algorithm is adopted to optimize the model parameters due to its obvious capability in finding the global optimum solution. Based on these two steps above, the kernel method is introduced to create kernel matrix which is used to replace the hidden layer’s output matrix. Finally, the least square is applied to calculate the model’s output weight. In order to verify the predictive capability of the GA-KELM model, the R-square factor (R²), Root-mean- square Errors of Calibration (RMSEC), Root-mean-square Errors of Prediction (RMSEP) of GA- KELM model are compared with the traditional PLS algorithm, respectively. The results confirm that GA-KELM can reduce the interference from matrix effect and self-absorption effect and is suitable for multi-elements calibration of LIBS.

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References (20)

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