Waste Classification Research Articles

AI-Powered Waste Classification Using Convolutional Neural Networks (CNNs)

AI-Powered Waste Classification Using Convolutional Neural Networks (CNNs)

Game Analysis of Household Waste Classification Behavior and Government Charge Mode: A Case Study of China

Game Analysis of Household Waste Classification Behavior and Government Charge Mode: A Case Study of China

FedADC: Federated Average Knowledge Distilled Mutual Conditional Learning (FedADC) for Waste Classification

FedADC: Federated Average Knowledge Distilled Mutual Conditional Learning (FedADC) for Waste Classification

SMARTSORT: YOLOv4-driven Smart Sorting for Household Waste Management

The current market offers smart bins, incorporating automatic door openings as a notable design feature for household cleanliness. While advanced and versatile waste management devices exist for business applications, the Philippines faces limited access to these innovations. The discrepancy is compounded by existing challenges in waste segregation techniques that demand attention and improvement. This project aims to address these issues by developing a system that automates the separation of household waste into three distinct categories: paper, plastic, and metal. Utilizing an Arduino Mega microcontroller, YOLOv4-based waste classification, and various models, the system offers efficient and hands-free waste disposal. Key features include real-time notifications, three-succession waste disposal, and LED bar indicators. The system’s potential lies in streamlining waste segregation, user-friendly design, and versatile applications beyond residential use. SMARTSORT aims to contribute to sustainable waste management practices.

A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification

A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification

AIWLO-WMO Model Based an Artificial Intelligence to Smart Waste Management for Smart Cities Environment

Smart waste management is one of the world's massive challenges, either in the advanced or started economies. Regularly, the technologies of artificial intelligence obtained recognition in presenting different computer techniques to solving smart waste challenge. The smart waste management of specified problems, events and doubts and partial statistics were capable for AI. Although this task did operate lots of findings, very few assessments proved the impact of AI to determine numerous difficulties of intelligent organization of waste. Perfect assessment of waste amount and condition is explained to Smart waste management technique development and model. Currently, heavy quantity of waste resources has become substantial enhanced with increasing population. Appropriate management of waste resources becomes necessary to decrease environmental degradation and recover value of life in smart cities. Smart Waste management supports to gather and heal waste resources from society. Suitable arrangement of waste objects needs the model of automated waste category models based on artificial intelligence and image processing-based methods. The aim of this paper, an automated artificial intelligence with world larva optimization supports waste management and organization (AIWLO-WMO) model is proposed for smart cities environment. The proposed model mainly develops a RetinaNet built object finding segment to recognize the presence of smart waste objects in the views. To better the category operation, Adagrad optimizer has used. To confirming the invented outcome of the AIWLO-WMO method, complete testing is performed on normal dataset and the found rates implied the authority of AIWLO-WMO model throughout other methods with expanded accuracy of 99.62%

Sustainability Assessment of Construction and Demolition Waste Material

Construction and demolition waste (C&DW) constitutes a substantial portion of waste generated during construction activities, ranging from 15 to 30% globally. This waste category poses environmental threats, impacting health and the environment, particularly in Nigeria. This research investigates the environmental and economic implications of C&DW in the Nigerian construction industry, systematically addressing types, causes, and disposal methods. The study explores potential reuse through Circular Economy (CE) strategies. The literature review encompasses CE principles and Construction Waste Management in Nigeria. Employing quantitative research methods, questionnaires were administered to a selected group, revealing respondents' recognition of economic benefits from proper waste management. However, a lack of incentives and facilities for diverse disposal methods emerged as a significant challenge. Recommendations include government regulations, incentives for recycling facilities, and educational initiatives emphasizing the advantages of a circular economy. Emphasis is placed on effective information dissemination to stakeholders. This research contributes valuable insights for industry professionals, policymakers, and government entities, offering a foundation for sustainable waste management practices in the Nigerian construction sector.

Uncertainties about waste using an online survey and review approach: Environmentalist perceptions, household waste compositions and views from media and science

Abstract Waste generation and subsequent plastic pollution pose a major threat to both human and environmental health. Furthering our understanding of waste at individual levels can inform future waste reduction strategies, education and policies. This study explores the components and perceptions among individuals using survey data combined with a mini-review. An online Qualtrics survey was distributed pre-COVID-19 following a global social media challenge, Futuristic February, which directed participants to collect their nonperishable waste during February 2020. Participants were asked about their waste generation, perceptions toward waste and plastic pollution issues, and environmental worldview using the New Ecological Paradigm (NEP) scale (n = 50). We also conducted a mini-review of eight waste and plastic pollution statements from our survey in both popular media and scientific journal articles. Survey results indicated participants had an overall pro-ecological worldview (M = 4.32, SD = 0.88) and reported cardboard and paper (66%) as the most commonly occurring nonperishable waste category. Across categories, food packaging was the most common waste type. Participants were most uncertain about statements focusing on bioplastic or biodegradable plastic, respectively (44% and 30%), while the statement on microplastic toxicity obtained 100% mild or strong agreement among participants. Uncertainty for reviewed statements varied depending on the topic and group. Popular media and scholarly articles did not always agree, possibly due to differences in communication of uncertainty or terminology definitions. These results can inform future policy and educational campaigns around topics of misinformation.

Optimal route selection using network analysis in terms of time, cost and fuel savings: The case of İskenderun, Türkiye

Solid waste generation has increased significantly as a result of increasing population and living standards. In this context, variables such as waste classification, amount produced, collection and transportation status, disposal method, socioeconomic status of the region include important stages in system planning. It is important to optimize these variables, waste collection route, time, cost, environmental impacts and examine the current situation. The cost of collection and processing accounts for roughly 65-80% of the total cost of the solid waste management system managed by local governments. Therefore, by optimization studies, the trash collection-transport route can significantly reduce this rate. Depreciation and fuel expenditures can be significantly reduced as a result of improvements in operational costs. Denizciler neighborhood was selected as an example of all district characteristics within the study's scope when the socio-economic, demographic, and topographic conditions of the Iskenderun district of Hatay province were evaluated. The ESRI ARCGIS software's tools have been used to test the optimization path. A 421-meter shorter route was discovered using network analysis. Considering the current route, which is 14340 meters long, a 3% improvement has been achieved with the analysis made. Garbage collection was done using the newly developed route in the study's final section. As a result, the viability of the network analysis enhancement has been evaluated in practice, and it has been discovered that time and fuel savings are realized in terms of cost.

Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems

BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.

Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems

BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.

HOSPITAL WASTE – LEGISLATIVE FRAMEWORK AND MANAGEMENT DIFFERENTIATIONS IN AUSTRIA, BELGIUM, GERMANY AND GREECE

In this paper, the differences regarding hospital waste management, specifically regarding the legal and technical situation and the definition of hazardous hospital waste in four member states (Austria, Belgium, Germany and Greece) are presented. All countries follow the legal guidelines of the European Union, but the technical situation, especially the possibilities for thermal waste disposal in incineration plants are different. Also, the definition and categories of hazardous medical wastes is different in the aforementioned four EU member states. The annual production amounts of medical waste in selected countries are presented, as well as criteria that lead to their classification as hazardous medical waste.

The Waste Sorting Education Media: An Innovation with Android-based Game

Knowledge of waste classification and segregation is very important considering that currently Indonesia is one of the largest plastic waste contributor countries in the world. Games that can be modified for educational purposes are educational games, especially in waste sorting. This study aims to develop an android-based waste sorting educational game (with the types of waste that appear in the school environment), as well as how to sort this android-based waste. Educational games are very appropriate as educational or learning media. Researchers use a research and development design or Research and Development Design (R&D) with the Borg and Gall model. In this initial study, it was carried out up to the small-scale user trial stage. The collection of teacher response data as a media user is carried out using an application usability instrument, namely the System Usability Scale (SUS). The research was conducted in 4 elementary schools. The educational game Sorting Trash was developed using Construct 3, Canva and Pixlr. This game is operated with an Android-based smart phone so that it is easily accessible to the wider community. The results of the validity test by media experts and material experts show that the results of the Android-based Sort Waste educational game are very valid, so they are very suitable for use as educational media for elementary school students.

COMPARATİVE ANALYSİS OF THE CLASSİFİCATİON OF RECYCLABLE WASTES

The classification of recycling wastes is of great importance both environmentally and economically. Correct classification of recyclable wastes such as packaging wastes increases the efficiency of the recycling process. This classification process can be done according to the raw material type, colour, shape, size and source of the waste. Correct classification of recycling wastes also provides economic benefits by ensuring more efficient use of resources. The traditional waste classification method involves manually sorting waste into different categories. This method requires a lot of labour and is time consuming. The traditional waste classification method is also prone to human error, which can lead to contamination of recyclable materials. Deep neural networks can quickly identify different types of recyclable materials by analysing images of waste materials. Thus, it can increase efficiency and reduce pollution by sorting them appropriately. In this study, an experimental study was carried out on a data set consisting of 6 classes and 2527 images under the name of "Garbage classification". In this study, a comparative analysis was carried out using the Convolutional Neural Network architectures Resnet101, Convnext and Densenet121. As a result of this study, Resnet101 architecture was more successful than other architectures with an accuracy rate of 98.41%.

Healthcare Workers' Knowledge about the Segregation Process of Infectious Medical Waste Management in a Hospital.

Any hospital's primary goal is to restore human health and save lives through health services provided to patients, but at the same time, hazardous wastes are produced. Inconsistent management of unsafe wastes might cause adverse effects and other issues for workers, the environment, and public health. Segregation is considered the critical stage in successful medical waste management. Mixing hazardous medical waste with non-hazardous medical waste will be avoided by correctly applying practices at the segregation stage. This study aimed to assess personnel's knowledge about infectious medical waste and segregation practices used at six wards in Nicosia General Hospital. An analytical cross-sectional study was conducted, and data were collected through a structured self-administered questionnaire. The Statistical Package of Social Science (SPPS) version 25 was used with a minimum statistical significance of α = 0.05. The study population was nurses, nurse assistants, ward assistants, and cleaners working at the study wards. Out of 191 questionnaires, 82 were received, with a response rate of 42.93%. Most participants were female (72%) and nurses (85.4%). Participants had moderate knowledge about infectious medical waste management and good knowledge regarding segregation practices applied in their ward. Segregation was not carried out as it should have been, since most participants stated that infectious medical waste was mixed with non-hazardous medical waste. The number of correct answers the participants gave regarding the colour-coding of different medical waste categories was 67.5%, and only four answered correctly to all questions. Although participants knew segregation practices and the colour-coding process applied to medical waste, they did not use them satisfactorily. They applied methods regarding segregation without specific training, knowledge and guidance. Due to the issue's importance, training programs must be implemented and performed.

Ex-Situ and In-Situ bioremediation strategies and their limitations for Solid Waste Management: A Mini-Review

The term "waste management" refers to any trash that isn't in a gaseous or liquid state, although it also includes container - based gaseous and gaseous waste. Solid waste generated trash, agricultural residues, industrial garbage, ashes from thermal plants, and toxic materials are the principal categories of solid waste. Biological treatment is well-defined as the process of biologically degrading organic wastes in controlled circumstances to a benign state or to concentrations lower then regulatory concentration limits. Because biological treatment is only effective when conditions are favorable for microbial growth and activity. There are basically two types of remediation in situ and ex situ remediation. In situ remediation have landfill, aerobic composting, anaerobic digestion. Ex-situ remediation has biopile and bioreactors. But there are the limitations for the bioremediation. For example, bioremediation is only possible with biodegradable chemicals.

Mitigating Environmental Threats: An Intervention Study on Household Waste Management in Pasirkaliki Village

The lack of awareness regarding the management of household waste, particularly hazardous and toxic waste, poses a significant threat to environmental pollution. This lack of awareness is exacerbated by the accumulation and mixing of such waste with other categories of household waste. This study aims to mitigate the impact of household waste through an assistance mechanism designed to alter waste management behavior. Employing a quasi-experimental method, the research was conducted within the community of Pasirkaliki Village, North Cimahi. The study revealed significant changes in both the knowledge and behavior of the community towards waste management. Post-intervention, there was a notable increase of 16.6% in the community's knowledge compared to pre-intervention measurements. Furthermore, individuals who initially exhibited poor knowledge of waste types showed a remarkable 55.6% increase in understanding. The enhanced knowledge positively correlated with changes in behavior, evidenced by the increased community agreement on sorting waste based on its categories. This behavioral change was attributed to the intervention, supported by a p-value of 0.002. The results underscore the effectiveness of the assistance provided, which involved identifying community tendencies and employing direct involvement to enable individuals to experience the benefits firsthand through education.

Community Participation in Managing Waste and Waste Reduction Models in Baumata and Penfui Housing

The aim of this study is to analyze the role and society in managing waste and reducing waste from households. This research is descriptive, describing the role and society in the management of household garbage, and further treating the role of the community in order to obtain the appropriate methods in reducing household waste. The subject of the study is a family member who has grown up one in each household of houses built by developers in the district of Naimata and Penfui of 72 houses. Role data collection and society, waste management, utilization and waste reduction by interviewing and observing, monthly waste data measured volume of waste referring to SNI 19-3964-1994 (2). The role as well as the society in the management of garbage which includes the category both 28% and not good 72%, the category of waste which includes 21% and is not good 79%, the society that exploits the waste 24% and does not exploit the waste 76%, which reduces the waste 22% and which does not reduce the waste 77%. The monthly amount of organic waste is 1 litre/day/house, and the monthly volume is 4 litres/house/day. From the results of FGD obtained waste management method is organic waste processed into compost, waste that has economic value given to the rulers and recycle. Results of crosstab roles as well as societies with waste management, waste utilization and waste reduction there is a correlation of roles and society in garbage management. The role as well as the society in managing garbage is still low, this affects the hygiene of the environment in which the waste is not properly managed can become a pathogen and become a source of infectious diseases and pollution to the environment.

Yolov3 Tabanlı Atık Tespit Sistemleri

Today, the increase in the number of people, advances in industry and technology cause an increase in the number of wastes generated with the acceleration of production. It is important for the future of our country and the world that these wastes are more easily identified and recycled. In the process of recycling wastes, the classification of wastes as well as their collection requires costly energy and manpower. Wastes are basically separated into paper, plastic, glass and metal. Various studies have been carried out to complete these processes in a shorter and easier way with technologies such as artificial intelligence, deep learning and image processing. In this study, a dataset of paper, plastic and food and beverage wastes that are common in the environment was created. In this dataset, paper cups, plastic water bottles and fast food wastes were detected from different locations in nature and photographed. These images were labeled and trained and tested with YoloV3 in deep learning algorithms. In addition, in order to compare the performance of the new dataset, studies were conducted on a global dataset used in the literature. As a result of the studies, it was observed that it was successful in classifying the newly created dataset and the global dataset.

MAIN ASPECTS OF THE IMPLEMENTATION OF THE LAW ON WASTE MANAGEMENT IN EUROPEAN LEGISLATION

The article discusses the main aspects of the implementation of the Law of Ukraine «On Waste Management» adopted on July 9, 2023, which launches waste management reform and approximates Ukrainian legislation to EU legislation. A list of key innovations of this law is provided. The introduction and application of the List of Waste and the Procedure for Waste classification adopted by this law are analyzed. A list of normative legal acts aimed at implementing the provisions of the framework Law «OnWaste Management» is provided. The article provides examples of the hierarchy of the structure of global waste and the classification-identification of waste management in some countries of the world and Ukraine. The Law of Ukraine "On Waste Management" brings our legislation closer to the EU legislation, and also initiates the reform of waste management. The Framework Law "On Waste Management" implements the basic principles of waste management into our legislation in the context of the European integration movement of Ukraine, but it cannot solve all urgent issues at the same time.