• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.1
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• pp.85-91
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• 2021

Aggregate waste separator with rotating separating net was designed for applying classification process of construction waste. In order to evaluate the performance of the aggregate waste separator, according to the type of waste, standardized waste samples are prepared using acrylic. The appropriate operating point was evaluated by the classification efficiency and misclassification rate of recycled aggregate according to the control frequency of the blower operating and inlet position of the separating net. The classification efficiency at the operating point of the aggregate waste separator was evaluated through flow analysis assuming recycled aggregate and waste sample as particles. As a result of the performance test, when the distance. between the conveyor belt and the inlet was 0.2m, the classification efficiency was 95%, but the misclassification rate of recycled aggregate was 2% or more, which satisfies the classification efficiency and the misclassification rate of less than 2%. The operating point was shown at a control frequency of 58Hz at a suction distance of 0.254m. As a resu lt of flow analysis, there was no misclassification of recycled aggregate. In order to redu ce constru ction waste in the existing recycled aggregate production process, adsorption system using a rotating separating net that can be operated as an installation type was built.

• Journal of the Korea Organic Resources Recycling Association
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• v.25 no.2
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• pp.77-90
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• 2017

In order to promote recycling of animal and plant residues, it is necessary to prepare detailed statistics on the sources, generation amount and the state of disposal so that waste recycling companies and enterprises can obtain the information easily. Also, the recycling methods specified in the law should be appropriate. For this, the study reviewed the appropriateness of detailed classification of animal and plant residues and permitted recycling methods in the Enforcement Regulations of the Waste Management Act of Korea. For improvement of the detailed classification, the study conducted literature review on European and Japanese ones. Additionally, we visited slaughterhouses of livestock and poultry, vegetable oils manufacturing companies, starches and glucose or maltose manufacturing companies, which generate the waste and recycle the waste, to grasp the status of recycling in Korea. Based on the results, the study proposes improvement measures for the detailed classification and the permitted recycling types in the law.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2002.11a
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• pp.179-182
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• 2002

Today, augmentation of industrial wastes with industrial development demands diminution and recycling technical development for industrial wastes reduction. A statistical research of industry and constructional wastes as a request of the times can achieve the conservation of resource and the protection of environment. The ultimate object of the study is not only diminution and recycling of industrial wastes but also the degree of self-sufficiency in resource and the attainment of comfortable life environment, which can the accomplish the resource circulation system and make progress into the environmentally advanced country. The object of this investigation is industrial classification, a waste discharge quantity, a waste sort, waste disposal of industrial wastes in Yeung-nam area. The investigation of special quality in industrial wastes can be used to establish a wastes management policy and a disposition method .

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2008.05a
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• pp.70-71
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• 2008

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.421-426
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• 2001

The pneumatic classification and acidic leaching behaviors of phosphor sludge have been examined to establish the recycling system of rare earth components contained in waste fluorescent lamp. At first, separation characteristic of rare earth components and calcium phosphate in phosphor sludge was investigated by pneumatic classification. After pneumatic classification of phosphor sludge, rare earth components were leached in various acidic solutions and sodium hydroxide solution. For recovery of soluble component in leaching solution, rare earth components were separated as hydroxide and oxalate precipitations. The experimental results obtained are summarized as follows: (1) In classification process, rare earth components in phosphor sludge were concentrated to 29.3% from 13.3%, and its yield was 32.9%. (2) In leaching process, sulfuric acid solution was more effective one as a leaching solvent of rare earth component than other solutions. Y and Eu components in phosphor sludge were dissolved in sulfuric acid solution of 1.5 k㏖/㎥, and other rare earth components were rarely dissolved in leaching solution. Leaching degrees of Y and Eu were respectively 92% and 98% in the following optimum leaching conditions; sulfuric acid concentration is 1.5 k㏖/㎥ , leaching temperature 343 K, leaching time 3.6 ks and pulp concentration 30 kg/㎥. (3) Y and Eu components of phosphor sludge contained in waste fluorescent lamp were, effectively recovered by three processes of pneumatic classification, sulfuric acid leaching and oxalate precipitation methods. Their recovery was finally about 65 %, and its purity was 98.2%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.199-206
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• 2023

This study utilizes deep learning algorithms to automatically classify construction waste into three categories: wood waste, plastic waste, and concrete waste. Two models, VGG-16 and ViT (Vision Transformer), which are convolutional neural network image classification algorithms and NLP-based models that sequence images, respectively, were compared for their performance in classifying construction waste. Image data for construction waste was collected by crawling images from search engines worldwide, and 3,000 images, with 1,000 images for each category, were obtained by excluding images that were difficult to distinguish with the naked eye or that were duplicated and would interfere with the experiment. In addition, to improve the accuracy of the models, data augmentation was performed during training with a total of 30,000 images. Despite the unstructured nature of the collected image data, the experimental results showed that VGG-16 achieved an accuracy of 91.5%, and ViT achieved an accuracy of 92.7%. This seems to suggest the possibility of practical application in actual construction waste data management work. If object detection techniques or semantic segmentation techniques are utilized based on this study, more precise classification will be possible even within a single image, resulting in more accurate waste classification

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2018.11a
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• pp.486-487
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• 2018

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2017.10a
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• pp.35-36
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• 2017

• Resources Recycling
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• v.14 no.2
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• pp.3-9
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• 2005

A study on the air and gravity separation has been performed for the removal of chlorine containing materials from ASR of end-of-life vehicles. The gravity separation was also conducted on waste plastics collected from ASR. In this work, ASR were previously shredded to pass through 8 mm sieve prior to separation tests and the gravity separation of waste plastics was conducted for three different particle sizes. The two-stage air classification was conducted with the range of air flow rate of 9~20 M$^3$/hr at first stage and 25~34 M$^3$/hr at second stage, respectively. The fraction of overflow product was remarkably increased in the 2nd stage air classification because of high air flow rate while that of underflow product obtained from 1st stage air classification was found to be 62~66%. From the results of gravity separation on waste plastics, it was also found that the amount of the float product was much greater than sink product. It is believed that the gravity separation may be used very efficiently for the removal of calorine bearing materials from waste plastics.

• Design & Manufacturing
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• v.16 no.4
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• pp.60-66
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• 2022

Recently, with the development of industry and the improvement of living standards, various wastes are generated along with the production of various products. Most of these wastes are used as containers for products, and plastic or aluminum is used. Various attempts are being made to automate the classification of these wastes due to the high labor cost, but most of them are solved by manpower due to the geometrical shape change due to the nature of the waste. In this study, in order to automate the waste sorting task, Deep Learning technology is applied to a robot system for waste sorting and a vision system for waste sorting to effectively perform sorting tasks according to the shape of waste. As a result of the experiment, a Deep Learning parameter suitable for waste sorting was selected. In addition, through various experiments, it was confirmed that 99% of wastes could be selected in individual & group image learning. It is expected that this will enable automation of the waste sorting operation.