All Issue

2019 Vol.56, Issue 5 Preview Page

Research Paper

Improvement of Waste Metal Recovery Efficiency Using Multivariate Statistical and Deep Neural Network Analysis

다변량 통계와 심층신경망 분석을 이용한 폐금속 회수효율성 향상 연구

Sangmi Yoon1
Jung Hyun Choi1
Daehyun Wee1*
윤 상미1
최 정현1
위 대현1*
1이화여자대학교 환경공학과
* Corresponding Author
October 2019. pp. 535-547
PDF XML Citation
Abstract

In this study, we collected spectral data using laser induced breakdown spectrometry. Our results were processed by a principle component analysis (PCA) and a deep neural network to improve the separation efficiency of metals from RAM samples of a printed circuit board (PCB). The spectra were collected from 294 spots on the sample surface and subsequently divided into three groups, i.e., Black (K), Yellow (Y), and Green (G) by visual inspection of the surface color. We identified the specific wavelengths that were usable as separation criteria as well as the main elemental composition of each part by comparing the PCA and scanning electron microscopy-electron dispersive spectroscopy results. We also confirmed the possibility of automatic separation with a deep neural network whose separation accuracy was more than 98%. Our results can be used to create a new automatic separation process for waste electronic electrical equipment.

Keywords
LIBS
PCB
Multivariate analysis
Deep neural network

본 연구는 인쇄회로기판(PCB)의 RAM시료를 대상으로 선별을 통한 주요 금속의 회수율을 제고하기 위해 레이저 유도붕괴 분광기(LIBS)로 스펙트럼 데이터를 얻어 주성분분석(PCA)과 심층신경망 기법을 적용했다. 레이저를 RAM 시료 표면294 지점에 조사해 스펙트럼을 얻고 기판을 검정(K), 노랑(Y), 초록(G)로 구분해 데이터가 구분이 되는지 살펴보았다. PCA 분석과 SEM-EDS 분석결과의 비교를 통해 각 부분의 선별 가능성과 기준이 될 수 있는 파장, 포함된 주요 금속 조성비를 확인하였다. 추후 공정 자동화를 위해 심층신경망을 통한 각 부분 자동 분류 가능성을 타진하였고 98% 이상의 분류 정확도를 보았다. 따라서 본 연구를 통해 폐전기전자제품(WEEE)을 폐금속 회수 공정에 투입하기 전에 이를 선별할 수 있는 새로운 방법론을 만드는 데 활용할 수 있을 것으로 기대된다.

키워드
레이저유도붕괴분광법
인쇄회로기판
다변량분석
심층신경망
References
1
Aguirre, M.Á., Hidalgo, M., Canals, A., Nóbrega, J.A., and Pereira-Filho, E.R., 2013. Analysis of waste electrical and electronic equipment (WEEE) using laser induced breakdown spectroscopy (LIBS) and multivariate analysis. Talanta, 117, 419-424.
10.1016/j.talanta.2013.09.04624209362
2
Anzano, J., Bonilla, B., Montull‐Ibor, B., and Casas‐González, J., 2011. Plastic identification and comparison by multivariate techniques with laser‐induced breakdown spectroscopy. J. Appl. Polym. Sci., 121(5), 2710-2716.
10.1002/app.33801
3
Aquino, F.W.B., Santos, J.M., Carvalho, R.R.V, Coelho, J.A.O., and Pereira-Filho, E.R., 2015. Obtaining information about valuable metals in computer and mobile phone scraps using laser-induced breakdown spectroscopy (LIBS). RSC Adv., 5(82), 67001-67010.
10.1039/C5RA07609A
4
Awasthi, S., Kumar, R., Devanathan, A., Acharya, R., and Rai, A. K., 2017. Multivariate methods for analysis of environmental reference materials using laser-induced breakdown spectroscopy. Anal. Chem. Res., 12, 10-16.
10.1016/j.ancr.2017.01.001
5
Brunner, P.H., 2011. Urban mining a contribution to reindustrializing the city. J. Ind. Ecol., 15(3), 339-341.
10.1111/j.1530-9290.2011.00345.x
6
Carvalho, R.R.V., Coelho, J.A.O., Santos, J.M., Aquino, F.W. B., Carneiro, R.L., and Pereira-Filho, E.R., 2015. Laser- induced breakdown spectroscopy (LIBS) combined with hyperspectral imaging for the evaluation of printed circuit board composition. Talanta, 134, 278-283.
10.1016/j.talanta.2014.11.01925618668
7
Charles, R.G., Douglas, P., Hallin, I.L., Matthews, I., and Liversage, G., 2017. An investigation of trends in precious metal and copper content of RAM modules in WEEE: Implications for long term recycling potential. Waste Manag., 60, 505-520.
10.1016/j.wasman.2016.11.01827890594
8
Clegg, S.M., Sklute, E., Dyar, M.D., Barefield, J.E., and Wiens, R.C., 2009. Multivariate analysis of remote laser- induced breakdown spectroscopy spectra using partial least squares, principal component analysis, and related techniques. Spectrochim. Acta Part B At. Spectrosc., 64(1), 79-88.
10.1016/j.sab.2008.10.045
9
Cremers, D.A., Yueh, F.Y., Singh, J.P., and Zhang, H., 2006. Laser‐induced breakdown spectroscopy, elemental analysis. Encycl. Anal. Chem., On the Web.,
10.1002/ 9780470027318.a0708.
10
Cui, J., and Forssberg, E., 2003. Mechanical recycling of waste electric and electronic equipment: A review. J. Hazard. Mater., 99(3), 243-263.
10.1016/S0304-3894(03)00061-X
11
Filzmoser, P., Liebmann, B., and Varmuza, K., 2009. Repeated double cross validation. J. Chemom., 23(4), 160-171.
10.1002/cem.1225
12
Gaudiuso, R., Dell'Aglio, M., Pascale, O.D., Senesi, G.S., and Giacomo, A.D., 2010. Laser induced breakdown spectroscopy for elemental analysis in environmental, cultural heritage and space applications: A review of methods and results. Sensors, 10(8), 7434-7468.
10.3390/s10080743422163611PMC3231154
13
Goodfellow, I., Bengio, Y., and Courville, A., 2016. Deep learning, MIT press, Massachusetts, USA, 800p.
14
Gurell, J., Bengtson, A., Falkenström, M., and Hansson, B.A. M., 2012. Laser induced breakdown spectroscopy for fast elemental analysis and sorting of metallic scrap pieces using certified reference materials. Spectrochim. Acta Part B At. Spectrosc., 74, 46-50.
10.1016/j.sab.2012.06.013
15
Haider, Z., Munajat, Y.B., Kamarulzaman, R., and Shahami, N., 2015. Comparison of single pulse and double simultaneous pulse laser induced breakdown spectroscopy. Anal. Lett., 48(2), 308-317.
10.1080/00032719.2014.940532
16
He, Y., Liu, X., Lv, Y., Liu, F., Peng, J., Shen, T., Zhao, Y., Tang, Y., and Luo, S., 2018. Quantitative analysis of nutrient elements in soil using single and double-pulse laser-induced breakdown spectroscopy. Sensors, 18(5), 1526.
10.3390/s1805152629751689PMC5982673
17
Hu, C. and Zhou, L., 2016. Failure analysis for micro-short circuit between two pins in printed circuit board assembly. 2016 11th ICRMS., IEEE, Hangzhou, China, p.1-4.
10.1109/ICRMS.2016.8050164
18
In, K. J. and Sip, J. S., 1996. Electron Microscopy for Polymers (TEM , SEM , STEM). Polym. Sci. Tech., 7(4), 437-444.
19
Kim, G.C., 2003. Introduction to Integrated Circuit Design (1st Ed.), Green Press, Seoul, Korea, 445p.
20
Kim, J.H., Kim, B.J., Jung, H.W., amd Shin, S.R., 2018. Environmental Statistics and Data analysis (Revised Ed.), Hannarae Publishing co., Seoul, Korea, 496p.
21
Lal, B., Zheng, H., Yueh, F.Y., and Singh, J.P., 2004. Parametric study of pellets for elemental analysis with laser- induced breakdown spectroscopy. Appl. Opt., 43(13), 2792- 2797.
10.1364/AO.43.00279215130021
22
Li, J., Lu, H., Guo, J., Xu, Z., and Zhou, Y. 2007. Recycle technology for recovering resources and products from waste printed circuit boards. Environ. Sci. Technol., 41(6), 1995-2000.
10.1021/es061824517410796
23
Li, J., Lu, J., Lin, Z., Gong, S., Xie, C., Chang, L., Yang, L., and Li, P., 2009. Effects of experimental parameters on elemental analysis of coal by laser-induced breakdown spectroscopy. Opt. Laser Technol., 41(8), 907-913.
10.1016/j.optlastec.2009.03.003
24
Luque-García, J.L., Soto-Ayala, R., and De Luque Castro, M.D., 2002. Determination of the major elements in homogeneous and heterogeneous samples by tandem laser-induced breakdown spectroscopy-partial least square regression. Microchem. J., 73, 355-362.
10.1016/S0026-265X(02)00124-8
25
Martin, M.Z., Labbé, N., Rials, T.G., and Wullschleger, S.D., 2005. Analysis of preservative-treated wood by multivariate analysis of laser-induced breakdown spectroscopy spectra. Spectrochim. Acta Part B At. Spectrosc., 60(7-8), 1179-1185.
10.1016/j.sab.2005.05.022
26
MathWorks, 2019.08.07., https://kr.mathworks.com/help/deep learning/index.html?lang=en
27
National Assembly Research Service, 2009. Problems and Improvement for Recycling of Rare Metals, NARS pending issue report vol. 43, Seoul, Korea, 34p.
28
National Research Council for Economics, Humanities and Social Sciences, 2010. Study on Institutional and Technical Measures to Promote Recycling of Metals from Waste Electric and Electronic Equipments, Research Report 10-02-86, Seoul, Korea, 131p.
29
Noel, C., 1990. the Origins of Kriging. Math. Geol., 22(3), 47-55.
10.1007/BF00889887
30
Park E.K, Jung, B.B., Choi, T.J., Choi, W.Z., and Oh, S.K., 2017. Automatic sorting system based on laser-induced technology for black plastics from used small household appliances. Proceedings of the 2017 Autumn Conference of The Korea Society of Waste Management, the Korea Society of Waste Management, Incheon, Korea, p. 12.
31
Putnam, R.A., Mohaidat, Q.I., Daabous, A., and Rehse, S.J., 2013. A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification. Spectrochim. Acta Part B At. Spectrosc., 87, 161-167.
10.1016/j.sab.2013.05.014
32
Raele, M.P., De Pretto, L.R., and Zezell, D.M., 2017. Soldering mask laser removal from printed circuit boards aiming copper recycling. Waste Manag., 68, 475-481.
10.1016/j.wasman.2017.07.01928739025
33
Seoul Resource Center, 2019.08.07., http://www.srcenter.kr.
34
Shin, S.H., Lee, J.P., Moon, Y.M., Choi, J.H., and Jeong, S.H., 2018. Classification of metal scraps using laser induced breakdown spectroscopy. J. Korean Inst. Resour. Recycl., 27(1), 31-37.
35
Shlens, J., 2014. A tutorial on principal component analysis, Cornell University.
36
Thomsen, V., Schatzlein, D., and Mercuro, D., 2003. Limits of detection in spectroscopy. Spectroscopy, 18(12), 112-114.
37
Tognoni, E. and Cristoforetti, G., 2016. Signal and noise in laser induced breakdown spectroscopy: An introductory review. Opt. Laser Technol., 79, 164-172.
10.1016/j.optlastec.2015.12.010
38
Tuncuk, A., Stazi, V., Akcil, A., Yazici, E. Y., and Deveci, H., 2012. Aqueous metal recovery techniques from e-scrap: Hydrometallurgy in recycling. Miner. Eng., 25(1), 28-37.
10.1016/j.mineng.2011.09.019
39
Tunsu, C., Petranikova, M., Gergorić, M., Ekberg, C., and Retegan, T., 2015. Reclaiming rare earth elements from end- of-life products: A review of the perspectives for urban mining using hydrometallurgical unit operations. Hydrometallurgy, 156, 239-258.
10.1016/j.hydromet.2015.06.007
40
Varmuza, K. and Filzmoser, P. 2009. Introduction to multivariate statistical analysis in chemometrics (1st Ed.). Vol 1., CRC press, Boca Raton, Florida, USA, 336p.
41
Yu, K.Q., Zhao, Y.R., Liu, F., and He, Y., 2016. Laser-Induced breakdown spectroscopy coupled with multivariate chemometrics for variety discrimination of soil. Sci. Rep., 6, 27574.
10.1038/srep2757427279284PMC4899786
42
Zhao, N., Wu, Z.S., Zhang, Q., Shi, X.Y., Ma, Q., and Qiao, Y. J., 2015. Optimization of parameter selection for partial least squares model development. Sci. Rep., 5, 11647.
10.1038/srep1164726166772PMC4499800
Information
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 56
  • No :5
  • Pages :535-547
  • Received Date :2019. 09. 05
  • Revised Date :2019. 10. 15
  • Accepted Date : 2019. 10. 25
  • DOI :https://doi.org/10.32390/ksmer.2019.56.5.535
Journal Informaiton Agriculture and Life Sciences Research Institute Journal of the Korean Society of Mineral and Energy Resources Engineers
Online Submission
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • KCI 문헌 유사도 검사
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close