The Development of an Automated Waste Segregator

Accumulation of waste is a major global concern, and recycling is considered one of the most effective methods to solve the problem. However, recycling requires proper segregation of waste according to waste types. This paper develops an automatic waste segregator, capable of identifying and segregating six types of wastes; metal, paper, plastic, glass, cardboard, and others. The proposed system employs Convolutional Neural Network (CNN) technology, specifically the Inception-v3 architecture, as well as two physical sensors; weight and metal sensors, to classify and segregate the waste. Overall classification accuracy of the system is 86.7%. Classification performance of the developed waste segregator has been evaluated further using the precision and recall; with high precision obtained for cardboard, metal, and other waste types, and high recall for metal and glass. These results demonstrate the applicability of the developed system in effectively segregating waste at source, and thereby, reducing the need for the commonly labor-intensive segregation at waste facility. Deploying the system has the potential of reducing waste management problems by assisting recycling companies in sorting recyclable waste, through automation

The Development of an Automated Waste Segregator

Accumulation of waste is a major global concern, and recycling is considered one of the most effective methods to solve the problem. However, recycling requires proper segregation of waste according to waste types. This paper develops an automatic waste segregator, capable of identifying and segregating six types of wastes; metal, paper, plastic, glass, cardboard, and others. The proposed system employs Convolutional Neural Network (CNN) technology, specifically the Inception-v3 architecture, as well as two physical sensors; weight and metal sensors, to classify and segregate the waste. Overall classification accuracy of the system is 86.7%. Classification performance of the developed waste segregator has been evaluated further using the precision and recall; with high precision obtained for cardboard, metal, and other waste types, and high recall for metal and glass. These results demonstrate the applicability of the developed system in effectively segregating waste at source, and thereby, reducing the need for the commonly labor-intensive segregation at waste facility. Deploying the system has the potential of reducing waste management problems by assisting recycling companies in sorting recyclable waste, through automation

Mobile application to identify recyclable materials

This dissertation proposes a system to help the consumer recycle efficiently. The system is composed by a mobile application that can capture images of waste and classify their category through the usage of a machine learning model. Furthermore, this application can communicate with a server to update the model with new improved versions and also upload the images to the server in order to contribute to the creation of more precise model versions. The system has been validated by a fully working prototype. Although the proof of concept has been achieved, with some types of waste items correctly categorized, the machine learning model produced is not precise enough to be used in real-life scenarios, that is, for any type of waste. The main contributions of this study are a compendium of information in the area of computer vision and machine learning to categorize waste, and a working prototype system that utilizes crowdsourcing and machine learning elements to help the consumer recycle more efficiently.Nesta dissertação é proposto um sistema para ajudar o consumidor a reciclar eficientemente. O sistema é composto por uma aplicação móvel que captura imagens de lixo e classifica a sua categoria usando um modelo de aprendizagem automática. Consegue também comunicar com um servidor para atualizar o modelo com versões melhoradas e enviar as imagens para o servidor para contribuir para a criação de modelos mais precisos. Foi demonstrado através de um protótipo totalmente funcional que o sistema proposto funciona. Algumas imagens de lixo foram categorizadas correctamente, mas o modelo de aprendizagem automática produzido durante este projeto não é preciso o suficiente, em qualquer categoria de lixo, para usar em cenários da vida real. As principais contribuições deste estudo são um compêndio de informação na área de visão de computador e aprendizagem automática para categorizar lixo, e um sistema protótipo funcional que utiliza elementos de contribuição colaborativa e aprendizagem automática para ajudar o consumidor a reciclar mais eficientemente

Critical review of real-time methods for solid waste characterisation: Informing material recovery and fuel production

Waste management processes generally represent a significant loss of material, energy and economic resources, so legislation and financial incentives are being implemented to improve the recovery of these valuable resources whilst reducing contamination levels. Material recovery and waste derived fuels are potentially valuable options being pursued by industry, using mechanical and biological processes incorporating sensor and sorting technologies developed and optimised for recycling plants. In its current state, waste management presents similarities to other industries that could improve their efficiencies using process analytical technology tools. Existing sensor technologies could be used to measure critical waste characteristics, providing data required by existing legislation, potentially aiding waste treatment processes and assisting stakeholders in decision making. Optical technologies offer the most flexible solution to gather real-time information applicable to each of the waste mechanical and biological treatment processes used by industry. In particular, combinations of optical sensors in the visible and the near-infrared range from 800 nm to 2500 nm of the spectrum, and different mathematical techniques, are able to provide material information and fuel properties with typical performance levels between 80% and 90%. These sensors not only could be used to aid waste processes, but to provide most waste quality indicators required by existing legislation, whilst offering better tools to the stakeholders

Identification of residues deposited outside of the deposition equipment, using video analytics

In areas where waste production is excessive, sometimes improper deposition occurs around the garbage equipment, requiring more effort from the waste collection teams. In this dissertation an image recognition system is proposed for the detection and classification of waste outside the existing waste disposal equipment. The main motivation is to facilitate the work of waste collection in the city of Lisbon, which is done by the teams of the Lisbon Waste Collection Centers. In order to help the waste collection planning, the collection team inspectors in partnership with the Lisbon City Council created a repository with several datasets, which they named, 'LxDataLab'. The collected images go through the pre-processing process and finally are submitted to waste detection and classification, through deep learning networks. In this sense, a classification and identification method using neural networks for image analysis is proposed: the first approach consisted in training a deep learning convolutional neural network (CNN) specifically developed to classify residues; in a second approach a CNN was trained using a pre-trained MobileNetV2 model, which only the last layer was trained. The training in this approach was faster compared to the previous approach, as were the performance values in detecting the class and the amount of residues in the images. The hit rate for the classification of the selected debris varied between 80%, for test set. After the detection and classification of the residues in the images are recognized, annotations are generated on the images.Nas áreas onde a produção de resíduos é excessiva, por vezes ocorre a deposição indevida em torno dos equipamentos de deposição de lixo, exigindo mais esforço por parte das equipas de recolha destes resíduos. Nesta dissertação é proposto um sistema de reconhecimento de imagem para a deteção e classificação de resíduos fora dos equipamentos de deposição existentes para o mesmo. A principal motivação é facilitar o trabalho de recolha dos resíduos na cidade de Lisboa. De forma a possibilitar o desenvolvimento de algoritmos que possam vir a ser úteis na automatização de tarefas em diferentes áreas de intervenção, a Câmara Municipal de Lisboa criou um repositório, denominado ‘LxDataLab’, contendo vários conjuntos de dados. Estes dados, por sua vez são submetidos a um processo pré-processamento e por fim são submetidas para deteção e classificação dos resíduos. Assim é proposto um método de classificação e identificação de resíduos utilizando redes neuronais para análise de imagens: a primeira abordagem consistiu no treino de uma rede neuronal convolucional de aprendizagem profunda (CNN) desenvolvida especificamente para classificar resíduos; numa segunda abordagem foi treinada uma CNN utilizando um modelo pré-treinado MobileNetV2. Nesta última abordagem, o treino foi mais rápido em relação à abordagem anterior, e o desempenho na deteção da classe e da quantidade de resíduos nas imagens foi superior. A taxa de acerto para as classes de resíduos selecionadas variou nos 80% para o conjunto de teste. Após a deteção e classificação dos resíduos nas imagens são geradas anotações nas mesmas

Unlocking system transitions for municipal solid waste infrastructure:A model for mapping interdependencies in a local context

Rapid global urbanization, urban renewal and changes in people's lifestyles have led to both an increase in waste generation and more complex waste types. In response to these changes, many local governments have invested in municipal solid waste infrastructure (MSWI) to implement circular strategies. However, matching and bridging the costly and logistically complex MSWI with the dynamic social context is a central challenge. In this paper we aim to explore the interdependencies between MSWI and the local social system, and then conceptualize and empirically validate the systemic nature of MSWI. We first review the current MSW treatment methods, corresponding infrastructure, and the challenges facing them. Then, we interrogate system-oriented concepts and use two key insights to set up a conceptual model for mapping the interdependencies in a MSWI system (MSWIS). Finally, a case study of the Dutch city of Almere is used to empirically validate the MSWIS model and identify the social systems that contribute to the development of the MSWIS. The analysis reveals that the development of MSWIS is beyond the municipality's control: efficient resource recovery facilities established by businesses under market rules and waste reuse facilities constructed by social organizations/individuals based on their own needs are key pieces of the puzzle to complete the MSWIS. This highlights the ability of the framework to capture interdependencies that go further than just the formal municipal sphere of influence.</p

Color Analysis and Image Processing Applied in Agriculture

Color and appearance are perhaps the first attributes that attract us to a fruit or vegetable. Since the appearance of the product generally determines whether a product is accepted or rejected, measuring the color characteristics becomes an important task. To carry out the analysis of this key attribute for agriculture, it is recommended to use an artificial vision system to capture the images of the samples and then to process them by applying colorimetric routines to extract color parameters in an efficient and nondestructive manner, which makes it a suitable tool for a wide range of applications. The purpose of this chapter is to give an overview on recent development of image processing applied to color analysis from horticultural products, more specifically the practical usage of color image analysis in agriculture. As an example, quantitative values of color are extracted from Habanero Chili Peppers using image processing; the images from the samples were obtained using a desktop configuration of machine vision system. The material presented should be useful for students starting on the field, as well as for researchers looking for state-of-the-art studies and practical applications

Determination of the recovered-fiber content in paperboard samples by applying mid-infrared spectroscopy

Paperboard is widely used in different applications, such as packaging and graphic printing, among others. Consumption of recycled paper is growing, which has led the paper-mill packaging industry to apply strict quality controls. This means that it is very important to develop methods to test the quality of recycled products. In this article, we focus on determining the recoveredfiber content of paperboard samples by applying Fourier transform mid-infrared (FT-MIR) spectroscopy in combination with multivariate statistical methods. To this end, two very fast, nondestructive approaches were applied: classification and quantification. The first approach is based on classifying unknown paperboard samples into two groups: high and low recovered-fiber content. Conversely, under the quantification approach, the content of recovered fiber in the incoming paperboard samples is determined. The experimental results presented in this article show that the classification approach, which classifies unknown incoming paperboard samples, is highly accurate and that the quantification approach has a root mean square error of prediction of about 4.1Peer ReviewedPostprint (author's final draft

Public awareness of circular economy in southern Poland : case of the Malopolska region

In the transition to the circular economy (CE) model in Europe, increasing public awareness is one of the major driving forces. This paper presents the results of an evaluation of public awareness and attitudes about CE in the Malopolska region of southern Poland. The data used in this study was collected by random distribution of questionnaires in the Malopolska region and interviews with 430 respondents. Malopolska was chosen for research because the region has significant economic and social potential, but features serious environmental problems - primarily air pollution. As environmental protection has become an important aspect for regional and local policy, the CE concept has already begun to be promoted. The questionnaires distributed to residents were divided into three areas: (1) knowledge and attitudes about CE, (2) CE-related behaviour, and (3) future development of CE in the region. The results show that the CE concept was well recognized mainly by the younger generation, which is more familiar with CE-related behaviours like waste segregation and buying recycled and remanufactured goods. The findings additionally indicate that sharing and collaborative economy practices are becoming popular among residents due to the belief that such services create more economic, environmental, and social benefits for users. People's awareness of the CE concept also has a positive correlation with their educational level, such individuals believing that the CE model could, in the future, be implemented in the region. However, this requires time and additional economic and educational resources