Quantitative Analysis of Carbon Content in Fly Ash Using LIBS Based on Support Vector Machine Regression

Peng Chen; Chao Qi; Renwei Liu; Zhenzhen Wang; Han Luo; Junjie Yan; Jiping Liu; Yoshihiro Deguchi

doi:10.3788/AOS202242.0930003

Acta Optica Sinica, Volume. 42, Issue 9, 0930003(2022)

Quantitative Analysis of Carbon Content in Fly Ash Using LIBS Based on Support Vector Machine Regression

Peng Chen¹, Chao Qi², Renwei Liu¹, Zhenzhen Wang^1,3、*, Han Luo¹, Junjie Yan^1,3, Jiping Liu¹, and Yoshihiro Deguchi^1,3

Author Affiliations

¹School of Energy and Power Engineering, Xi′an Jiaotong University, Xi′an 710049, Shaanxi, China

²Xi′an Aerospace Propulsion Institute, Xi′an 710100, Shaanxi, China

³Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770- 8506, Japan

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Figures & Tables(10)

Fig. 1. Schematic of LIBS experimental system

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Fig. 2. LIBS spectra of sample GD_0 at different resolutions. (a) Resolution is 0.075 nm/pixel; (b) resolution is 0.012 nm/pixel

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Fig. 3. Calibration curves of carbon content in fly ash under different methods. (a) Traditional calibration method; (b) internal standard method

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Fig. 4. Effects of c and g on MSE. (a) Training results of RBF kernel function; (b) training results of polynomial kernel function

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Fig. 5. Quantitative analysis of carbon content based on characteristic spectra of internal standard elements

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Fig. 6. Quantitative analysis of carbon content based on full spectrum

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Fig. 7. Quantitative analysis of carbon content based on characteristic spectra of major elements

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Table 1. Carbon content of fly ash samples

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Table 1. Carbon content of fly ash samples

Samplenumber	Label	C contenty_C /%	Samplenumber	Label	C contenty_C/%	Samplenumber	Label	C contenty_C /%
1	GD_0	20.81	9	GE_0	45.64	17	GY_0	34.60
2	GD_2	26.54	10	GE_2	48.76	18	GY_2	38.77
3	GD_5	34.05	11	GE_5	53.01	19	GY_5	44.35
4	GD_8	40.52	12	GE_8	56.82	20	GY_8	49.27
5	GD_10	44.35	13	GE_10	59.14	21	GY_10	49.27
6	GD_15	52.59	14	GE_15	64.32	22	GY_15	52.23
7	GD_20	59.34	15	GE_20	68.74	23	GY_20	64.19
8	GD_30	69.70	16	GE_30	75.93	24	GY_30	72.83

Table 2. Information of main characteristic lines

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Table 2. Information of main characteristic lines

Element	Ionization	Wavelength W /nm	Upper level E_i /cm^-1	Lower level E_k /cm^-1
C	C I	247.86	21648.03	61981.83
Si_2	Si I	250.69	77.12	39955.05
Si_3	Si I	251.43	0	39760.28
Si_4	Si I	251.61	223.16	39955.05
Si_5	Si I	251.92	77.12	39760.28
Si_6	Si I	252.41	77.12	39683.16
Si_7	Si I	252.85	223.16	39760.28
Fe	Fe I & Fe II	274.20--275.63	415.93--8846.70	36686.16--45289.80
Mg_1	Mg II	279.55--280.30	0	35760.88--35669.31
Mg_2	Mg I	285.21	0	35051.26
Si_1	Si I	288.16	6298.85	40991.88
Al	Al I	309.27	112.06	32436.80
Ca_1	Ca II	315.89	25191.51	56839.25
Ca_2	Ca II	318.13	25414.40	56839.25

Table 3. Evaluation index of carbon content under three inputs
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Table 3. Evaluation index of carbon content under three inputs
Input R² RMSEC /% RMSEP /%
I_input1 0.860 6.21 5.66
Whole spectra 0.960 0.03 5.72
I_input2 0.986 1.79 2.57

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Peng Chen, Chao Qi, Renwei Liu, Zhenzhen Wang, Han Luo, Junjie Yan, Jiping Liu, Yoshihiro Deguchi. Quantitative Analysis of Carbon Content in Fly Ash Using LIBS Based on Support Vector Machine Regression[J]. Acta Optica Sinica, 2022, 42(9): 0930003

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