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A Cloud-Based Disassembly Planning Approach towards Sustainable Management of WEEE

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Abstract
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Waste Electrical and Electronic Equipment (WEEE) is becoming an important and challenging waste stream in terms of quantity and toxicity. Developing technical solutions for sustainable management of WEEE is becoming a global trend. Disassembly planning plays an important role in End-of-Life treatment of WEEE. Effective disassembly planning can improve the recovery rate and reduce the environmental impact of WEEE significantly. Targeting sustainable WEEE management, this paper aims to propose a cloud-based approach for disassembly planning. The approach provides a comprehensive and standardized service-oriented environment for distributed information sharing, disassembly modeling, evaluation and optimization.

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This paper investigates the regulations, recycling and treatment of WEEE (waste electrical and electronic equipment) in China. An online survey about Chinese households’ treatment of WEEE is conducted. Optimization models are used to compare the performances of WEEE treatment in two different recycling networks. In the first network, WEEE is collected and sent by recycling stations to licensed WEEE recycling and treatment centers for testing and dismantling. In the second network, WEEE are tested and dismantled at small recycling workshops in residential districts, and then parts/components that require further processing are sent to licensed WEEE recycling and treatment centers. The performances of the two networks are analyzed with linear programming models. The results indicate that the second model is more effective with lower cost and higher recycling efficiency.

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Waste electrical and electronic equipment (WEEE) which contains various valuable and harmful materials is an inevitable waste in modern society. In order to resolve the pollution problems associated with WEEE treatment, a WEEE management system has been established in China. The main role of importers and manufacturers of electrical and electronic equipment (EEE) is to pay the treatment fees to facilitate the WEEE recycling in China. The announced treatment and subsidy fee is given by set, not by the weight of WEEE. There is no lesser green treatment fee for the producers which can produce environmentally friendly EEE in China. Also, the recovery of refrigerants from the foaming agent of refrigerators is not required in China. In total, 45 million sets of recycled WEEE were certified in 2020, a year that contains the most updated data. Among them, 48%, 14%, 20%, 10% and 8% are for TV, refrigerator, washing machine, computer and air conditioners, respectively. The spatial analysis indicates that the WEEE recycling activities are mainly concentrated on the mid-east and east regions of China. It also can be concluded that the certified amount of each province has higher positive correlation with provincial population than provincial GDP per capita and green recovery rate. It also clearly notes that the amount of recycled air conditioner is the lowest for each province. Thus, more effort should be conducted to increase the recycling of scrapped air conditioner in China.

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  • Book Chapter
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23 - WEEE management in China
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  • Research Article
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Comprehensive knowledge of built-in batteries in waste electrical and electronic equipment (WEEE) is required for sound and save WEEE management. However, representative sampling is challenging due to the constantly changing composition of WEEE flows and battery systems. Necessary knowledge, such as methodologically uniform procedures and recommendations for the determination of minimum sample sizes (MSS) for representative results, is missing. The direct consequences are increased sampling efforts, lack of quality-assured data, gaps in the monitoring of battery losses in complementary flows, and impeded quality control of depollution during WEEE treatment. In this study, we provide detailed data sets on built-in batteries in WEEE and propose a non-parametric approach (NPA) to determine MSS. For the pilot dataset, more than 23 Mg WEEE (6500 devices) were sampled, examined for built-in batteries, and classified according to product-specific keys (UNUkeys and BATTkeys). The results show that 21% of the devices had battery compartments, distributed over almost all UNUkeys considered and that only about every third battery was removed prior to treatment. Moreover, the characterization of battery masses (BM) and battery mass shares (BMS) using descriptive statistical analysis showed that neither product- nor battery-specific characteristics are given and that the assumption of (log-)normally distributed data is not generally applicable. Consequently, parametric approaches (PA) to determine the MSS for representative sampling are prone to be biased. The presented NPA for MSS using data-driven simulation (bootstrapping) shows its applicability despite small sample sizes and inconclusive data distribution. If consistently applied, the method presented can be used to optimize future sampling and thus reduce sampling costs and efforts while increasing data quality.

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Apprise of Used Electrical and Electronic Equipment in Residential Houses of Citizens of Yazd City and Determination of Their Knowledge, Attitude and Practice Regarding Electronic and Electrical Waste Management
  • Dec 20, 2018
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  • Mahboubeh Davoudi + 7 more

Introduction: The production of Waste Electrical and Electronic Equipment (WEEE) has grown rapidly in recent years. It is necessary to determine the amount of them for its effective management. The aim of this study was to determine the amount of WEEE stored in the houses of Yazd citizens and the level of Knowledge, Attitude and Practice (KAP) of citizens regarding WEEE management. Materials and Methods: This descriptive, cross-sectional study was carried out using random sampling on 300 Yazdian citizens. To determine the amount of WEEE stored in houses and the level of KAP of people regarding WEEE, was used a researcher-made questionnaire whose validity and reliability were confirmed and data analysis was performed using nonparametric tests in SPSS. Results: The amount of WEEE in the study population was 21.8 metric tons was obtained. Of these, the highest amount of waste was related to the refrigerator with 3.9 metric tons and the highest number of used equipment stored was related to cell phone, with 285 units. The levels of knowledge (with a mean score of 5.06 ± 2.5), attitude (with a mean score of 43.37 ± 5.21), and practice (with a mean score of 10.71 ± 2.95) were respectively in moderate, good and moderate conditions. Conclusion: Given planning by the Waste Organization leads to job creation, access to valuable raw materials, and environmental protection. With increasing knowledge about the proper use of electronic and electrical equipment, their useful life can be increased and the process of their conversion to waste can be extended.

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