Research on Deep Sorting Approach Based on Infrared Spectroscopy for High-Value Utilization of Municipal Solid Waste

Bin HU; Hao FU; Wen-bin WANG; Bing ZHANG; Fan TANG; Shan-wei MA; Qiang LU

doi:10.3964/j.issn.1000-0593(2022)05-1353-08

Spectroscopy and Spectral Analysis, Volume. 42, Issue 5, 1353(2022)

Research on Deep Sorting Approach Based on Infrared Spectroscopy for High-Value Utilization of Municipal Solid Waste

Bin HU1,1; 2;... Hao FU1,1;, Wen-bin WANG1,1;, Bing ZHANG1,1; 2;, Fan TANG3,3; *;, Shan-wei MA1,1; 2; and Qiang LU1,1; 2; *; |Show fewer author(s)

Author Affiliations

¹1. School of New Energy, North China Electric Power University, Beijing 102206, China

³3. School of Artificial Intelligence, Jilin University, Changchun 130012, China

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Figures & Tables(11)

Fig. 1. Original infrared spectra of cellulose (a), vinyl polymers (b), woods (c) and low-value wastes (d)

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Fig. 2. SNV pretreated infrared spectra of cellulose (a), vinyl polymers (b), woods (c) and low-value wastes (d)

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Fig. 3. MCS pretreated infrared spectra of cellulose (a), vinyl polymers (b), woods (c) and low-value wastes (d)

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Fig. 4. DC/Smooth pretreated infrared spectra of cellulose (a), vinyl polymers (b), woods (c) and low-value wastes (d)

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Fig. 5. The first (a) and second (b) principal component load analysis spectra

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Table 1. Residual waste materials

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Table 1. Residual waste materials

高值化类别	材料名称
纤维素类	打印纸、草纸、一次性纸杯、棉布、烟头
烯类聚合物	方便面包装盒、食品包装袋、快餐包装纸、奶茶杯、腈纶标签
木竹类	竹扇、落叶、干树枝、木质铅笔、一次性筷子
低值类	棒骨、陶瓷、贝壳

Table 2. The principal component eigenvalues and variance contribution of SNV, MSC and DC/smooth datasets

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Table 2. The principal component eigenvalues and variance contribution of SNV, MSC and DC/smooth datasets

预处理方法	主成分	Z₁	Z₂	Z₃	Z₄	Z₅	Z₆	Z₇	Z₈
	特征值	208.35	102.04	50.50	39.22	32.71	14.99	6.91	4.15
SNV	方差贡献率/%	43.7	21.4	10.6	8.2	7.1	3.1	1.5	0.9
	累计方差贡献率/%	43.7	65.1	75.7	83.9	91.0	94.1	95.6	96.5
	特征值	861.19	429.45	207.22	161.67	107.20	47.83	36.77	24.15
MSC	方差贡献率/%	44.6	22.2	10.7	8.4	5.6	2.5	1.9	1.2
	累计方差贡献率/%	44.6	66.8	77.5	85.9	91.5	94.0	95.9	97.1
	特征值	149.73	80.39	16.76	14.34	6.66	4.98	3.28	2.50
DC/Smooth	方差贡献率/%	52.2	28.0	5.8	5.0	2.3	1.8	1.1	0.9
	累计方差贡献率/%	52.2	80.2	86.0	91.0	93.3	95.1	96.2	97.1

Table 3. Comparison of classification model accuracies (based on 72×8 dataset)

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Table 3. Comparison of classification model accuracies (based on 72×8 dataset)

预处理方式	分类准确率/%				均值 /%	均方根误差/%
预处理方式	PNN	GRNN	RDF	SVM	均值 /%	均方根误差/%
未预处理	78.7	85.7	85.7	81.5	82.9	3.4
SNV	88.7	90.1	88.7	90.1	89.4	0.8
MSC	87.3	90.1	87.3	88.7	88.4	1.3
DC/Smooth	95.8	87.5	95.8	97.2	94.1	4.4
均值^*	90.6	89.2	90.6	92.0	-	-
均方根误差^*	4.6	1.5	4.6	4.6	-	-

Table 4. Comparison of classification model accuracies (based on 72×5 dataset)

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Table 4. Comparison of classification model accuracies (based on 72×5 dataset)

预处理方式	分类准确率/%				均值 /%	均方根误差/%
预处理方式	PNN	GRNN	RDF	SVM	均值 /%	均方根误差/%
未预处理	77.8	84.7	84.7	87.5	83.6	4.1
SNV	95.8	100.0	88.9	90.3	93.8	5.1
MSC	98.6	88.9	90.2	91.7	92.4	4.3
DC/Smooth	100.0	94.4	95.8	95.8	96.5	2.4
均值^*	98.1	94.4	91.6	92.6	-	-
均方根误差^*	2.1	5.6	3.6	2.8	-	-

Table 5. Comparison of classification accuracies for the four kinds of residual wastes (based on 72×8 DC/Smooth dataset)

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Table 5. Comparison of classification accuracies for the four kinds of residual wastes (based on 72×8 DC/Smooth dataset)

垃圾类别	DC/Smooth				均值 /%	均方根误差/%
垃圾类别	PNN	GRNN	RDF	SVM	均值 /%	均方根误差/%
纤维素类	95.0	90.0	100.0	95.0	95.0	4.1
烯类聚合物	95.0	80.0	90.0	95.0	90.0	7.1
木竹类	95.0	90.0	95.0	100.0	95.0	4.1
低值类	100.0	91.6	100.0	100.0	97.9	4.2

Table 6. Comparison of classification accuracies for the four kinds of residual wastes (based on 72×5 DC/Smooth dataset)

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Table 6. Comparison of classification accuracies for the four kinds of residual wastes (based on 72×5 DC/Smooth dataset)

垃圾类别	DC/Smooth				均值 /%	均方根误差/%
垃圾类别	PNN	GRNN	RDF	SVM	均值 /%	均方根误差/%
纤维素类	100.0	100.0	95.0	95.0	97.5	2.9
烯类聚合物	100.0	90.0	95.0	90.0	93.8	4.8
木竹类	100.0	90.0	95.0	100.0	96.3	4.8
低值类	100.0	100.0	100.0	100.0	100.0	0.0

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Bin HU, Hao FU, Wen-bin WANG, Bing ZHANG, Fan TANG, Shan-wei MA, Qiang LU. Research on Deep Sorting Approach Based on Infrared Spectroscopy for High-Value Utilization of Municipal Solid Waste[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1353

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Paper Information

Category: Research Articles

Received: Mar. 1, 2021

Accepted: --

Published Online: Nov. 10, 2022

The Author Email:

DOI:10.3964/j.issn.1000-0593(2022)05-1353-08

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Residual waste materials

Table 1. Residual waste materials

Table 2. The principal component eigenvalues and variance contribution of SNV, MSC and DC/smooth datasets

Table 2. The principal component eigenvalues and variance contribution of SNV, MSC and DC/smooth datasets

Table 3. Comparison of classification model accuracies (based on 72×8 dataset)

Table 3. Comparison of classification model accuracies (based on 72×8 dataset)

Table 4. Comparison of classification model accuracies (based on 72×5 dataset)

Table 4. Comparison of classification model accuracies (based on 72×5 dataset)

Table 5. Comparison of classification accuracies for the four kinds of residual wastes (based on 72×8 DC/Smooth dataset)

Table 5. Comparison of classification accuracies for the four kinds of residual wastes (based on 72×8 DC/Smooth dataset)

Table 6. Comparison of classification accuracies for the four kinds of residual wastes (based on 72×5 DC/Smooth dataset)

Table 6. Comparison of classification accuracies for the four kinds of residual wastes (based on 72×5 DC/Smooth dataset)

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