Accurate Quantitative Analysis of Valuable Components in Zinc Leaching Residue Based on XRF and RBF Neural Network

Yuan LI; Yao SHI; Shao-yuan LI; Ming-xing HE; Chen-mu ZHANG; Qiang LI; Hui-quan LI

doi:10.3964/j.issn.1000-0593(2022)02-0490-08

Spectroscopy and Spectral Analysis, Volume. 42, Issue 2, 490(2022)

Accurate Quantitative Analysis of Valuable Components in Zinc Leaching Residue Based on XRF and RBF Neural Network

Yuan LI^{1、1; 2;}, Yao SHI^{2、2; *;}, Shao-yuan LI^{1、1; *;}, Ming-xing HE^3、3;, Chen-mu ZHANG^2、2;, Qiang LI^2、2;, and Hui-quan LI^{2、2; 4;}

Author Affiliations

¹1. Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China

²2. CAS Key Laboratory of Green Process and Engineering, National Engineering Laboratory for Hydrometallurgical Cleaner Production Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China

³3. School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China

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Figures & Tables(9)

Fig. 1. The technical roadmap of the experiment

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Fig. 2. Working curve of Cu (a), Pb (b), Zn (c), Cd (d), As (e) in the leaching residue

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Fig. 3. The accuracy of the model's prediction results changes with the target error
(a): Precision; (b): Acouracy

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Fig. 4. The prediction results of the RBF neural network model for the five target elements in the leaching residue B, C, and D samples
(a):Cu; (b): Pb; (c): Zn; (d): Cd; (e): As

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Table 1. Measurement parameters of XRF
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Table 1. Measurement parameters of XRF
项目管压
/kV 管流
/μA 测量时间
/s 测量元素范围
/Z^*
参数 49 80 200 16~92(S~U)

Table 2. Measurement parameters of ICP-OES
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Table 2. Measurement parameters of ICP-OES
项目发射
功率
/kW 载气等离子
气流量/
(L·min^-1) 辅助
气流量/
(L·min^-1) 雾化器
流量/
(L·min^-1) 校准
类型
参数 1.0 氩气 15 1.5 0.75 线性

Table 3. Test results by XRF of leaching residue samples to be tested (mg·kg^-1)

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Table 3. Test results by XRF of leaching residue samples to be tested (mg·kg^-1)

序号	样品	Cu	Pb	Zn	Cd	As
1	1浸出渣B	8 984	59 937	195 034	3 395	5 733
2	2浸出渣B	8 851	55 501	186 111	3 036	5 247
3	3浸出渣B	8 928	57 184	189 343	3 125	5 072
4	4浸出渣B	8 978	59 671	193 890	3 341	5 899
5	5浸出渣B	8 976	59 785	194 275	3 325	5 958
6	1浸出渣C	8 928	53 639	194 745	1 998	5 750
7	2浸出渣C	8 897	54 273	193 379	2 377	5 612
8	3浸出渣C	8 839	53 123	191 248	2 322	5 826
9	4浸出渣C	8 790	52 498	188 829	2 270	4 484
10	5浸出渣C	8 834	52 775	191 841	2 353	5 585
11	1浸出渣D	7 989	69 391	180 724	2 008	5 213
12	2浸出渣D	8 166	70 913	183 582	1 993	4 752
13	3浸出渣D	7 918	70 227	181 094	1 926	4 470
14	4浸出渣D	8 255	74 085	185 452	1 818	4 269
15	5浸出渣D	7 906	68 751	179 103	1 917	3 878

Table 4. Evaluation index of XRF working curve method

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Table 4. Evaluation index of XRF working curve method

序号	样品	组分	基准值/ (mg·kg^-1)	检测结果均值/ (mg·kg^-1)	评价指标
序号	样品	组分	基准值/ (mg·kg^-1)	检测结果均值/ (mg·kg^-1)	RE/%	SD	RSD/%
		Cu	9 497	8 943.4	5.8	56.3	0.6
		Pb	56 493	58 415.6	4.1	1 986.1	3.4
1	浸出渣B	Zn	177 301	191 730.6	8.1	3 851.3	2.0
		Cd	2 688	3 244.4	20.7	155.1	4.8
		As	5 549	5 581.8	6.2	399.1	7.1
		Cu	8 997	8 857.6	3.1	54.7	0.6
		Pb	52 684	53 261.6	1.2	707.9	1.3
2	浸出渣C	Zn	175 875	192 008.4	9.2	2 240.7	1.2
		Cd	1 878	2 264	20.6	154.0	6.8
		As	5 578	5 451.4	5.6	549.8	10.1
		Cu	7 463	8 046.8	7.8	156.0	1.9
		Pb	70 417	70 673.4	2.0	2 075.8	2.9
3	浸出渣D	Zn	166 023	181 991	9.6	2 513.0	1.4
		Cd	1 718	1 932.4	12.5	75.4	3.9
		As	4 939	4 516.4	10.8	502.9	11.1

Table 5. Evaluation index of XRF combined with RBF neural network method

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Table 5. Evaluation index of XRF combined with RBF neural network method

序号	样品	组分	基准值/ (mg·kg^-1)	检测结果均值/ (mg·kg^-1)	评价指标
序号	样品	组分	基准值/ (mg·kg^-1)	检测结果均值/ (mg·kg^-1)	RE/%	SD	RSD/%
		Cu	9 497	9 487.8	-0.09	31.0	0.33
		Pb	56 493	56 490.3	0.00	18.6	0.03
1	浸出渣B	Zn	177 301	177 325.8	0.01	48.6	0.03
		Cd	2 688	5 547.7	0.58	34.3	1.27
		As	5 549	5 581.8	-0.02	6.5	0.12
		Cu	8 897	8 604.47	0.13	17.1	0.20
		Pb	52 684	52 723.15	0.07	95.2	0.18
2	浸出渣C	Zn	175 875	175 819.2	-0.03	279.1	0.16
		Cd	1 878	1 885.809	0.42	25.6	1.36
		As	5 578	5 589.818	0.22	32.6	0.58
		Cu	7 463	7 477.377	0.19	53.0	0.71
		Pb	70 417	70 433.33	0.02	132.6	0.19
3	浸出渣D	Zn	166 023	166 212.6	0.11	305.7	0.18
		Cd	1 718	1 714.995	-0.19	32.6	1.90
		As	4 939	4 968.57	0.59	70.5	1.42

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Yuan LI, Yao SHI, Shao-yuan LI, Ming-xing HE, Chen-mu ZHANG, Qiang LI, Hui-quan LI. Accurate Quantitative Analysis of Valuable Components in Zinc Leaching Residue Based on XRF and RBF Neural Network[J]. Spectroscopy and Spectral Analysis, 2022, 42(2): 490

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