• Resources Recycling
• /
• v.25 no.1
• /
• pp.31-39
• /
• 2016

Small household appliances such as electric rice cooker, a vacuum cleaner, an electric fan, etc. are diverse and complex due to the materials and components and waste streams from the manufacturing processes. In the present study, physical characterization of small e-wastes was analyzed on major items including electric rice cooker after manual dismantling. Small household appliances is an important potential source of waste plastic, however, recycling plastics from small e-waste is still unusual. The present communication gives results of separation processes on black plastics and the limitations of these sorting processes in used small household appliances.

• Resources Recycling
• /
• v.25 no.2
• /
• pp.17-24
• /
• 2016

This study aims at evaluating environmental impacts on recycling process of used small household appliances. The recycling process mainly consists of manual dismantling, crushing and various sorting processes to effectively recover valuable resources and to minimize environmental impact. In this study, life-cycle assessment (LCA) methodology is applied to analyze major environmental parameters such as GWP, ADP, POCP, EP, etc. One of the major impact categories on the weight basis in the recycling process is global warming (GWP) 57.1%, next to ADP 35.4% and POCP 4.8%, respectively. As a result of environmental impact on recovery of valuable resources/ton, the GWP of plastics for ABS is highest (33.7%) compared to ferrous metals (9.4%). The effects of environmental and economical benefit are also analyzed to compare with the amount of virgin materials to be recycled by recycled materials. In addition, recycled materials are also more economical in comparison to virgin materials due to the environmental avoiding effect by recycling. In conclusion, the key environmental issues related to the recycling of e-wastes are analyzed and therefore, the effective recycling process will contribute to mitigate global warming potential in the near future.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.6
• /
• pp.477-484
• /
• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.

• Resources Recycling
• /
• v.26 no.6
• /
• pp.73-83
• /
• 2017

Used small household appliances have a wide variety of product types and component materials, and contain high percentage of black plastics. However, they are not being recycled efficiently as conventional sensors such as near-infrared ray (NIR), etc. are not able to detect black plastic by types. In the present study, an automatic sorting system was developed based on laser-induced breakdown spectroscopy (LIBS) to promote the recycling of waste plastics. The system we developed mainly consists of sample feeder, automatic position recognition system, LIBS device, separator and control unit. By applying laser pulse on the target sample, characteristic spectral data can be obtained and analyzed by using CCD detectors. The obtained data was then treated by using a classifier, which was developed based on artificial intelligent algorithm. The separation tests on waste plastics also were carried out by using a lab-scale automatic sorting system and the test results will be discussed. The classification rate of the radial basis neural network (RBFNNs) classifier developed in this study was about > 97%. The recognition rate of the black plastic by types with the automatic sorting system was more than 94.0% and the sorting efficiency was more than 80.0%. Automatic sorting system based on LIBS technology is in its infant stage and it has a high potential for utilization in and outside Korea due to its excellent economic efficiency.