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다변량 통계와 심층신경망 분석을 이용한 폐금속 회수효율성 향상 연구

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

최정현|위대현|윤상미
한국자원공학회 (The Korean Society of Mineral and Energy Resources Engineers)|2019.10|한국자원공학회지 (Journal of the Korean Society of Mineral and Energy Resources Engineers)|제56권 제5호|pp. 535-547

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키워드

다변량분석PCBDeep neural network심층신경망LIBSMultivariate analysis인쇄회로기판레이저유도붕괴분광법
주제분류자원공학

초록

본 연구는 인쇄회로기판(PCB)의 RAM시료를 대상으로 선별을 통한 주요 금속의 회수율을 제고하기 위해레이저 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 구분이되는지 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.

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.

참고문헌 (42)

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