Design and Development of Smart Trash Bin Prototype for Municipal Solid Waste Management

Maulana, Feisal Ramadhan; Widyanto, Theo Adhitya S.; Pratama, Yulianti; Mutijarsa, Kusprasapta

doi:10.1109/ictss.2018.8550013

Cited by 13 publications

(9 citation statements)

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“…The system identifies the waste and guides the user to deposit it in the correct container by opening the corresponding lid to indicate where to deposit it [10,43]. Waste identification is performed with image classification [10], radio-frequency identification (RFID) [43], or the sound generated by the trash bags [44].…”

Section: Physical Enablersmentioning

confidence: 99%

“…For example, visible-image-based sensors can be used for general waste classification (e.g., glass or plastic), and, combined with nearinfrared (NIR) sensors, they can be used in applications where more detailed categorization is required (e.g., PET, PS, PE, and PP) [35]. Regarding the other types of sensors, three combinations are identified: (i) capacitive sensor with inductive sensor [8,42,43], (ii) weightbased sensor with ultrasonic sensor [40], and (iii) sound-based sensor with inductive sensor [44].…”

Section: Physical Enablersmentioning

confidence: 99%

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A Systematic Literature Review of Waste Identification in Automatic Separation Systems

Arbeláez-Estrada,

Vallejo,

Aguilar

et al. 2023

Recycling

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Proper waste separation is essential for recycling. However, it can be challenging to identify waste materials accurately, especially in real-world settings. In this study, a systematic literature review (SLR) was carried out to identify the physical enablers (sensors and computing devices), datasets, and machine learning (ML) algorithms used for waste identification in indirect separation systems. This review analyzed 55 studies, following the Kitchenham guidelines. The SLR identified three levels of autonomy in waste segregation systems: full, moderate, and low. Edge computing devices are the most widely used for data processing (9 of 17 studies). Five types of sensors are used for waste identification: inductive, capacitive, image-based, sound-based, and weight-based sensors. Visible-image-based sensors are the most common in the literature. Single classification is the most popular dataset type (65%), followed by bounding box detection (22.5%). Convolutional neural networks (CNNs) are the most commonly used ML technique for waste identification (24 out of 26 articles). One of the main conclusions is that waste identification faces challenges with real-world complexity, limited data in datasets, and a lack of detailed waste categorization. Future work in waste identification should focus on deployment and testing in non-controlled environments, expanding system functionalities, and exploring sensor fusion.

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Section: Physical Enablersmentioning

confidence: 99%

Section: Physical Enablersmentioning

confidence: 99%

A Systematic Literature Review of Waste Identification in Automatic Separation Systems

Arbeláez-Estrada,

Vallejo,

Aguilar

et al. 2023

Recycling

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“…Hal ini didasarkan pada dua hal. Pertama adalah kondisi fasilitas sampah yang kurang dari segi kualitas dan kuantitas, dan yang kedua adalah pola pikir masyarakat yang masih menganggap pembuangan sampah yang paling praktis dengan membiarkannya tergeletak di pinggir jalan atau membuangnya ke sungai [3]. Padahal kebiasaan buruk ini dapat memberikan efek negatif bagi lingkungan dan bumi.…”

Section: Pendahuluanunclassified

Rancang Bangun Sistem Robot Pemilah Sampah Anorganik dengan Inductive Proximity dan LDR Sebagai Sensor

Rosiana

Perdana

2022

bits

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The existence of garbage from day to day has always increased. In 2019, waste in Indonesia reached 98 million tons. Problems that arise because management of waste that is not done well, can hinder waste management. It is necessary to create a system that can sort waste according to its type so that the grouping of waste becomes more practical and the recycling process can be done easily. In this research, inorganic waste sorting robot was created that can be used as an automatic trash can that can sort and place inorganic waste according to its type. This system works by involving an inductive proximity sensor to detect metal waste and a light dependent resistor sensor equipped with a laser to detect metal waste, paper and plastic waste. From the results of testing on the tool, the accuracy of the detection of waste types for metal waste, paper waste and plastic waste is 84%, 88% and 90%, respectively. While the average response time of the non-organic waste sorting robot in detecting each type of waste is 2.15028 seconds for metal waste, 2.15014 seconds for paper waste and 2.15028 seconds for plastic waste

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“…Slamet Kristanto Tirto Utomo et al [15] consider security and reward systems to be problems in public trash bins, and to compensate for them, propose a blockchain-based reward system consisting of a compensation system for using smart trash bins, wearable payment devices, and Ethereum smart contracts. Feisal Ramadhan Maulana et al [16] mentioned an information technology-based trash management system that handles trash management problems and developed a prototype of a trash management system for solid trash that focuses on the separation and trash collection stages.…”

Section: Related Researchmentioning

confidence: 99%

Smart Trash Bin Model Design and Future for Smart City

Choi

Seo

2021

Applied Sciences

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The trash disposal system, using standard trash bags, has been adopted by the government of the Republic of Korea (ROK) for more than two decades. This has caused a sanitary problem, as well as some secondary pollution. It is possible to solve this problem by deploying more manpower, but considering the manpower and maintenance costs that impose a heavy burden on the local governments who are experiencing tight financial situations, it would not be feasible. Thus, an Internet of Things (IoT)-based Smart Trash Separation Bin model that can reduce the cost of trash separation work has been proposed in this paper. The three efficient designs that respectively use a sensor, image processing, or spectroscope technology are presented. These IoT-based designs can bring significant merit to reducing the manpower costs, as well as the administrative cost involved.

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Design and Development of Smart Trash Bin Prototype for Municipal Solid Waste Management

Cited by 13 publications

References 0 publications

A Systematic Literature Review of Waste Identification in Automatic Separation Systems

A Systematic Literature Review of Waste Identification in Automatic Separation Systems

Rancang Bangun Sistem Robot Pemilah Sampah Anorganik dengan Inductive Proximity dan LDR Sebagai Sensor

Smart Trash Bin Model Design and Future for Smart City

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