Solid Waste Classification Research Articles

AUTORECYCLER: Prototype based on artificial vision to automate the material classification process (Plastic, Glass, Cardboard and Metal)

Environmental protection has gained greater importance over time due to the negative impact and irreversible consequences that have occurred worldwide and stem from pollution. One of the great challenges faced in different parts of the world is the inadequate management and classification of solid waste. In order to contribute to tackling this issue, this paper proposes an automated sorting system based on artificial vision which allows recognition and separation of recyclable materials (Plastic, Glass, Cardboard and Metal) through a webcam connected in real time to the Nvidia® Jetson Nano™ 2 GB programming board, which has a convolutional neural network (CNN) trained for the proper classification of waste. The system had a 95 % accuracy in separating plastic, 96 % in glass and metal, and 94 % in cardboard. With this in mind, we conclude it contributes to the recycling effort, which has an impact on the reduction of environmental pollution worldwide.

Characteristics of reactive nitrogen emissions from solid waste and its environmental impact in the Yellow River Basin, China

ABSTRACT One of the topics of concern on a global scale is the source, fate, and environmental effects of solid waste nitrogen (N). Quantitative calculation and evaluation of solid waste N (SWN) generation and its reactive N (Nr) emission characteristics, as well as their environmental effects in the basin ecosystem, are essential for the sustainable management of SWN and the improvement of the ecological environment. The accounting framework for the production of SWN and associated Nr emission was established using the Yellow River Basin of Henan section as an example. The findings revealed that SWN, with an annual average of 265.3 Gg N yr−1, grew by 17.2% over the previous ten years. The largest contributors were domestic waste N (82.5%) and livestock and poultry waste N (12.9%), with the remaining sources making up roughly 4.5%. The majority of the sources of Nr emissions were NH3 (63.2%), NOx (18.9%), and Nr lost to groundwater (12.3%), whereas N2O contributed just 5.7%. To mitigate Nr emissions from solid waste in the basin, enhancing solid waste classification, recycling, and composting treatment while reducing dumping and landfilling is vital. The study offers a scientific basis for the management and prevention of SWN in the basin.

System simulation and multi-objective optimization methodology for sustainable municipal solid waste classification management: A case study in China

Cities worldwide are actively implementing waste classification policies to develop efficient waste management systems that address the growing challenges of waste disposal and promote sustainable development. However, selecting tailored waste classification solutions is challenging due to the intricate interrelationships within the system and the complex trade-offs between different management objectives. This study presents a comprehensive methodology that simulates the entire waste classification management system by integrating the stages of waste generation, source classification, and final disposal, all linked by waste quantities and properties. A multi-objective optimization model is employed to identify optimal technical pathways that balance economic, environmental, and energy objectives. This methodology was applied in Zhangjiagang, a medium-sized city in China, revealing that economically optimal solutions focusing on landfill and composting as disposal pathways could exacerbate environmental impacts, with indicators deteriorating by 23.1 %–419.1 % except for freshwater eutrophication potential. In contrast, a multi-objective optimization solution prioritizing incineration and anaerobic digestion achieves environmental and energy benefits valued at 18.7 USD/t, with a modest increase in economic costs of 5.7 USD/t compared to the base year. The unconstrained optimal scenarios achieve net negative environmental costs and recover 2.0 × 106 MJ of energy. The study recommends moderate and dynamically adjusted kitchen waste separation rates depending on the stage of implementation. Additionally, the findings reveal that the “lock-in” effects of mismatched disposal facilities can significantly escalate costs. In conclusion, this study underscores the importance of systematic design and multi-objective optimization, offering a comprehensive methodology for cities aiming to enhance their waste classification strategies.

The way out for urban household solid waste classification in China: From the perspective of multi-agent based behavior simulation

Rapid industrialization and urbanization drive China's massive urban household solid waste (HSW) generation. Classification and resource recycling of HSW is an effective way to address the “garbage-sieged city” dilemma and promote “urban mineral resources” exploitation. Despite efforts since 2000, HSW sorting and recycling remain at 30%. This study develops a multi-agent based behavior simulation model for HSW classification and recycling, incorporating the interactions and decision-making of key agents, including residents, enterprises, and the government. Taking Suzhou, China as a case study, field surveys were conducted, and behavioral decision-making functions were constructed to simulate micro-behavioral responses and macro-system evolution under different policy incentives, and to predict the implementation effect of domestic waste management approaches, provides the basis for decision-making. The results demonstrate that a combination of waste metering charges, recycling subsidies, and enhanced education program can effectively regulate agent behavior and optimize the system. One year after the implementation of the simulation, the number of “unclassification” residents has dropped from 1.85 million to 250,000 decreased by 86%; the number of “classification deposition” residents has declined from 1.28 million to 1.08 million, reduced by 16%; the number of “selling after classification” residents has risen from 1.05 million to 2.81 million, increased by 63%; the daily waste output per capita has decreased from 1.21 kg to 1.04 kg, reduced by 14%; the recovery rate of resources has been raised from 33.4% to 66.5%. The implementation effects of other policy scenarios are ranked as follows: the metering charge > the recycling subsides > the enhanced education program. The findings provide a basis for decision-making in HSW management. Concerning the metering charge and the recycling subsidies, they can benefit positive interactions encouraged by such economic stimulus. At the same time, it is suggested to strengthen classification and recycling infrastructure, and push forward the integration of HSW classification recycling.

MSW-Net: A hierarchical stacking model for automated municipal solid waste classification

ABSTRACT Efficient solid waste management is crucial for urban health and welfare in the midst of fast industrialization and urbanization. In this changing environment, government authorities have a significant role in addressing and reducing the effects of solid waste. While waste separation at the source simplifies processes, manual sorting is a consequence of ignorance in numerous regions, which endangers the health of waste pickers. This study addresses the challenges by introducing the MSW-Net model, a hierarchical stacking model designed for the automated classification of municipal solid waste (MSW). Customized Convolutional Neural Network (custom CNN) and Bayesian-Optimized MobileNet models serve as the base models, with Gradient Boosting employed as the meta-classifier. The MSW-Net model, as proposed, exhibits exceptional performance, attaining 99%, 95%, and 96.43% accuracy rates over training, validation, and testing, respectively. Additionally, the model achieves precision, recall, and F1 scores of 96.42%, 96.43%, and 96.42% during the testing phase. Therefore, the proposed MSW-Net model performs better than the other existing models in sorting the waste. This could also aid the municipal authorities in classifying the waste with minimal human intervention. Implications: The MSW-Net model, featuring a hierarchical stacking approach with custom CNN and Bayesian-Optimized MobileNet base models, and Gradient Boosting as the meta-classifier, achieves remarkable accuracy in automated municipal solid waste classification. With performance metrics of 99% accuracy in training, 95% in validation, and 96.43% in testing, alongside precision, recall, and F1 scores around 96.42%, the MSW-Net model significantly outperforms existing models. This advancement promises to aid municipal authorities in efficient waste management, reducing reliance on manual sorting and thereby improving the health and safety of waste pickers.

Comparison on Environmental Benefits of Municipal Solid Waste Management in Suzhou Before and After Classification

In order to systematically understand the urban environmental benefit improvement of municipal solid waste （MSW） classification, based on the disposal data of MSW before and after the MSW classification in Suzhou from 2017 to 2021, the environmental impact potential （EIP） of the MSW collection-transportation-disposal process was calculated, and the environmental benefits of the MSW integrated management in Suzhou to 2035 were predicted. After the MSW classification in Suzhou at the end of 2019, the EIP （in terms of PET2000, the same below） of the per unit weight of MSW was reduced by 18.38% from 2.34×10-13 t-1 in 2017 to 1.91×10-13 t-1 in 2021. The environmental benefits of the MSW integrated management could be improved by classification. Based on the Suzhou MSW removal and transportation situation in 2021, different classification and disposal scenarios were established to calculate. It was found that after the classification effect showed gradient improvement, and the disposal capacity matched accordingly, the environmental benefits of MSW were further improved. Under the planning disposal capacity scenario of "zero waste to landfill", the EIP and the total carbon emissions of per unit weight of MSW should be reduced by 23.96% and 30.73%, respectively, compared with the actual situation in 2021. Based on the linear model of population and economic development level of Suzhou, it is expected that the annual production of MSW in Suzhou will be increased to 6.965 million tons in 2035. Under the background of continuous improvement of MSW classification and continuous optimization of city appearance and environment in Suzhou, based on the status quo of terminal disposal capacity in Suzhou, the EIP of per unit weight of MSW after improving the efficiency of classification by 2035 was predicted to be 1.54×10-13 t-1, the total EIP would be 1.05×10-6, and the total carbon emissions would increase to 3.80 million tons. Under the ideal scenario of expanding the scale of waste disposal, "zero landfill" of raw MSW, and full resource utilization of food waste, the EIP of per unit weight of MSW in 2035 was predicted to be 1.28×10-13 t-1, and the total EIP and the total carbon emissions would be 8.69×10-7 and 3.23 million tons, respectively, which was approximately 5.65% and 1.23% less than the actual scenario in 2021, respectively. The EIP and carbon emissions of MSW integrated management could be controlled better by the coordinated promotion of classified collection and transportation and quality disposal.

Optimization-driven artificial intelligence-enhanced municipal waste classification system for disaster waste management

This research addresses the critical challenge of disaster waste management, a growing concern exacerbated by the increasing frequency and intensity of natural disasters like flooding. Traditional waste systems often struggle with the volume and heterogeneity of disaster waste, highlighting the need for innovative solutions. In this study, we present a novel disaster waste classification model integrating advanced artificial intelligence (AI) and optimization techniques to streamline waste categorization in post-disaster environments. Our approach leverages a dual ensemble deep learning framework. The first ensemble combines various image-segmentation methods, while the second integrates outputs from diverse convolutional neural network architectures. A modified artificial multiple intelligence system serves as a decision fusion strategy, enhancing accuracy at both ensemble points. We rigorously evaluated our model using three datasets: the “TrashNet” dataset for benchmarking against existing methods, as well as two meticulously curated, real-world datasets collected from flood-affected areas in Thailand. The results demonstrate that our method outperforms existing algorithms like VGG19, YoloV5, and InceptionV3 in general solid waste classification, achieving an average improvement of 11.18%. Regarding disaster waste specifically, our model achieves 96.48% and 96.49% accuracy on the curated datasets, consistently outperforming ResNet-101, DenseNet-121, and InceptionV3 by an average of 3.47%. These findings demonstrate the potential of our AI-enhanced model to revolutionize disaster waste management practices. Thus, we advocate integrating such technologies into municipal waste management policies to enhance resilience and optimize disaster responses. Future research will explore scaling the model to diverse disaster types and incorporating real-time data for adaptable waste management strategies.

Impacts of a municipal solid waste classification policy on carbon emissions: case study of Beijing, China

National and local governments implement municipal solid waste classification policies to reduce waste disposal and minimize environmental pollution. Beijing started implementing its classification of municipal solid waste policy in May 2020. This study evaluates the impact of Beijing’s household waste classification policy on carbon emissions during collection, transportation, and treatment. The policy’s introduction reduced the number of trash bins, influenced transportation modes, and altered waste treatment emissions. We found a notable reduction in average net carbon emissions post-policy implementation (2403 kg CO2eq per metric ton of waste) compared with pre-policy (3584 kg CO2eq per metric ton of waste). If Beijing reaches its 2025 waste target, these emissions will decrease further to 1760 kg CO2eq per metric ton of waste. Translating these findings into monetary terms confirms this policy’s environmental and economic efficacy.

Awareness and Behavior of Residents on the Classification of Household Solid Waste in Trungmon, Vietnam

The classification of household solid waste (CHSW) in rural areas and areas beginning to urbanize in developing countries is very important. This not only helps reduce environmental pollution but also helps recycle and utilize resources effectively, while creating a healthy and sustainable living environment for the community. The pressure from household solid waste in large cities is an alarming signal that it is necessary to classify household solid waste in rural areas as soon as the urbanization process begins to accelerate strongly. Clearly, research to survey people's awareness and behavior about CHSW in Trung Mon commune, Yen Son district, Tuyen Quang province, Vietnam using sociological survey method through questionnaires is imperative. The findings revealed that residents have a good awareness of the CHSW policy and the majority consider classification necessary. Noticeably, attention is needed on encouraging the use of more environmentally friendly products and increasing education to reinforce awareness and positive habits in waste management to better CHSW practices. By building upon existing research and addressing the unique context of the study area, this research endeavors to provide insights and recommendations for enhancing CHSW practices in similar settings globally.

Assessing the current status, classification, collection, and transportation of domestic solid waste in Hai Hau district, Nam Dinh province

The article presents the current status, classification, collection, and transportation of domestic solid waste in Hai Hau district, Nam Dinh province. The research results show that the volume of domestic solid waste generated in Hai Hau district, Nam Dinh province is 147.468 tons/day and the average generation coefficient is 0.53 kg/person/day. The main source of waste generation is the daily activities of households, markets, and commercial centres... The composition of domestic solid waste includes organic matter (about 74.64%), recyclable inorganic matter (about 14.32%), non-recyclable inorganic matter (about 10.36%), and a small amount of hazardous solid waste (about 0.68%). Regarding the classification of domestic solid waste at source, people are aware and have classified it themselves but still do not fully understand the meaning and benefit of classifying solid waste at source. Regarding collection and transportation, people actively participate in the environmental cleaning programs, apply solutions to minimise domestic solid waste, and coordinate with the unit in charge of collecting and transporting domestic solid waste. The amount of domestic solid waste generated by 2030 is forecasted to be 156,144 tons/day, equivalent to 56,992.62 tons/year.

Determine greenhouse gas emissions from landfills and suggest a household solid waste classification system in Dong Hoi, Quang Binh province

Determine greenhouse gas emissions from landfills and suggest a household solid waste classification system in Dong Hoi, Quang Binh province

Intelligent system for solid waste classification using combination of image processing and machine learning models

ABSTRACT Solid waste is a major issue in all cities around the world. Classification and segregation of solid waste prior to reuse, recycle or recover is an important step towards sustainable waste management. Traditional manual sorting of solid waste is a labour-intensive process that may pose health risks to the workers. Currently, automated classification of solid waste using machine learning techniques is widely applied. This study aims to develop an automated waste classification model by testing traditional and deep machine learning models. To achieve that, both open and generated datasets were used in the model training and testing. The study results showed relatively low prediction capability of the traditional machine learning models like Random Forest (RF) and Support Vector Machine (SVM) as compared to the deep machine learning Convolutional Neural Network (CNN). The testing of the three models on a combined data set of Trashnet with local garbage data set resulted in accuracy of 62.5% for SVM, 72.0% for RF and 92.7% for CNN. JONET deep learning model has been developed using a combination of pre-trained base model (DenseNet 201) with a new architicture that contains a fully connected layer in the classification stage with 1024 neurons. The model is capable to identify six classes of solid waste items with various accuracies. When tested on the Trashnet dataset, the accuracy was 96.06%, while testing on the local garbage dataset gave an accuracy of 94.40%. JONET has been tested also on multi object images which gave an acceptable prediction accuracy.

Long-term implications of municipal solid waste (MSW) classification on emissions of PCDD/Fs and other pollutants: Five-year field study in a full-scale MSW incinerator in southern China

Understanding how municipal solid waste (MSW) classification influences the physiochemical properties and incineration emission characteristics of MSW is imperative for sustainable waste management. To investigate how long-term changes in the composition and physical properties of MSW resulting from MSW classification affect the emissions of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and multiple flue gas pollutants, a five-year experiment (2018–2022) was conducted in a full-scale MSW incinerator in Shenzhen City, China. After MSW classification, the food waste in MSW was substantially reduced while the paper, textile, and wood components were notably increased. MSW classification also remarkably decreased the water and chlorine contents of MSW while concomitantly increasing the wet-based calorific value, sulfur content, and nitrogen content. Consequently, the PCDD/Fs emissions declined from 106.7 ng I-TEQ/t MSW in 2018 to 26.0 ng I-TEQ/t MSW in 2022. Moreover, a process-tracing experiment demonstrated noticeable decreases in PCDD/F synthesis in the post-combustion area, with mass-reduction efficiencies of 23.6 %–95.4 %. In a correlation analysis, the PCDD/F emissions were significantly positively correlated with food waste, water content, and chlorine and negatively correlated with the wet-based calorific value and nitrogen content. Furthermore, MSW classification reduced the emissions of conventional flue gas pollutants (SO2, CO, and HCl) and multiple heavy metals (total sum of Sb, As, Pb, Cr, Co, Cu, Mn, and Ni). In summary, the long-term study demonstrated that MSW classification can feasibly separate incombustible waste, improve combustion quality, and reduce pollutant emissions from sources. These findings offer practical guidance for promoting MSW classification.

The impact of “Internet plus” enterprises on municipal solid waste classification and social welfare: An example from China

Following rapid economic development and urbanization, the classification of municipal solid waste (MSW) has become an urgent problem. This study analyzes the multiple subjects, including government, residents, and “Internet plus” MSW classification enterprises (IPMCE), and proposes a dynamic evolutionary collaborative framework for the coproduction of public goods to address the government and market failure during the supply of MSW classification as a public good. Using China as an example, this study constructs a multi-agent equilibrium simulation model to analyze this theoretical framework and investigate how to improve the related policies. The simulation results reveal that the collaborative and coproduction of public goods facilitated by IPMCE can improve classification rates and social welfare. Moreover, government subsidies for the research and development (R&D) expenditures of IPMCE are shown to further enhance this effect. In addition, the social welfare growth rate exceeds the subsidy growth rate, indicating that the subsidy has a leverage effect. Although this study has some limitations, such as the fact that the simulation model setting cannot fully reproduce the real situation, it provides policy recommendations on how to better utilize IPMCE in MSW classification as well as strategies for solving similar problems in other regions or public goods.

Education for Sustainability: A Data-Driven Methodological Proposal for the Strengthening of Environmental Attitudes in University Students and Their Involvement in Policies and Decision-Making

Introduction: in the context of global environmental challenges, university education emerges as a fundamental pillar to cultivate proactive attitudes towards sustainability. This research not only seeks to influence individual perceptions, but also the ability of these students to contribute significantly to policies and decision-making processes related to the environment.Objective: implement a methodological proposal that uses educational events, artistic events and social responsibility projects to strengthen attitudes towards environmental sustainability in university students.Method: a quantitative approach and a pre-experimental design were used, applying pre-test and post-test to students from five majors at a public university. The intervention was based on four thematic axes: classification and selection of solid waste, rational use of water, efficient use of electrical energy and university safety.Results: the results revealed positive changes in student attitudes, with significant increases in solid waste classification (from 28 % to 72 %), university safety (from 32 % to 75 %), rational use of water (from 34 % % to 76 %) and energy efficiency (from 43 % to 82 %). In addition, a strengthening of continuous environmental knowledge was observed by 46 %, representing an increase of 81 %.Conclusions: these findings suggest that universities can play a crucial role in promoting environmental educational policies that train professionals committed to nature and future generations, thus contributing to the construction of a paradigm that integrates ethics and socio-environmental responsibility

INFLUÊNCIA DO PROCEDIMENTO DE AJUSTE GRANULOMÉTRICO DE AMOSTRAS DE CERÂMICA VERMELHA AGREGADA COM RESÍDUOS DE ROCHAS ORNAMENTAIS PARA CLASSIFICAÇÃO CONFORME CRITÉRIOS DA ABNT

INFLUENCE OF THE PROCEDURE FOR GRANULOMETRIC SAMPLE ADJUSTMENT OF RED CERAMIC AGGREGATED WITH DIMENSION STONE WASTES FOR CLASSIFICATION ACCORDING TO THE ABNT CRITERIA’S. In order to clarify questions about the granulometric preparation of samples for the classification of solid wastes according to the Brazilian technical standard (NBR 10004/04), solubilization tests were carried out on red ceramic specimens made with conventional clay aggregated with ornamental rock residues. Samples were prepared by fragmentation and milling to verify the effects on metals releasing to the extractor medium. The specimens were made with 80% clay, 20% residue and 10% (m/m) moisture and sintered at 1000 °C. The data show that ceramics made with conventional clay, when discarded, will produce non inert wastes, as well as the ceramics made with dimension stone wastes added to the conventional raw material, according to the granulometry of its particles. After all, the metals concentration in the solubilization test are influenced by the sample granulometry. This paper shows that the method of granulometric adjustment should be detailed in the technical standard for solubilization tests (NBR 10006/04), in order to ensure the reliability of the method for classification of solid wastes.

Deep Learning Models based Classification of Solid Waste

Deep Learning Models based Classification of Solid Waste

Prediction of municipal solid waste treatment and disposal in high cold and high altitude area based on system dynamics: a case study of Lhasa

At present, the high cold and high altitude areas of China continue to face challenges in achieving high rates of resource utilization for the municipal solid waste (MSW). In this paper, for the first time, a prediction method based on system dynamics (SD) for the treatment and disposal of municipal solid waste in high cold and high altitude areas is proposed and applied to Lhasa City. The model simulate and predict the proportion of treatment methods (including landfill, incineration, kitchen waste disposal, recycling) and the rate of resource utilization (other disposal methods except landfill) of municipal solid waste under Natural Tendency and regulation schemes (Strengthened Municipal Solid Waste Classification Scheme and Municipal Solid Waste Classification and Treatment Scheme). The results show that strengthening municipal solid waste classification and treatment can significantly enhance the resource utilization rate of municipal solid waste in Lhasa, with an expected rate of 100% by 2026. This outcome has the potential to promote sustainable and healthy development of society, economy and environment of a city.

Modelling of African Vulture Optimization Algorithm with Deep Learning-based Object Classification for Intelligent Manufacturing Systems

Abstract Intelligent manufacturing system incorporates a number of sensors including IoT devices, cameras, and scanners, for capturing real-time data about the manufacturing process. Based on their physical properties, colours, dimensions, or other relevant characteristics, these sensors can be used to track and identify waste objects. Waste object classification in intelligent manufacturing includes the usage of recent systems and technologies to detect and classify waste materials or objects produced during the manufacturing process. The objective is to enable effective waste management and recycling practices, optimizing resource utilization and reducing environmental impact. Manual waste classification is a laborious and expensive task, which results in the development of automatic waste classification models using artificial intelligence (AI) techniques. It remains a challenging process due to the significant variations in the solid waste because of varying shapes, colours, and sizes. Therefore, recent advances in deep learning (DL) methods can be employed to accomplish the solid waste classification process. The study introduces a chaotic African vulture optimization algorithm with a deep learning-based solid waste classification (CAVOA-DLSWC) system. The CAVOA-DLSWC technique aims to automatically detect waste objects and classify them into different categories using DL models. In the presented CAVOADLSWC approach, two major processes are involved such as object classification and detection. For the object detection method, the CAVOA-DLSWC technique uses a lightweight RetinaNet model with CAVOA based hyperparameter tuning process. The CAVOA is derived by integrating the chaotic concepts into the initial iteration values of the AVOA. Once the waste objects are identified, the classification process can be performed by the use of convolutional long short-term memory (CLSTM) network. The experimental values of the CAVOA-DLSWC approach can be tested employing the solid waste database including diverse kinds of waste objects. The comparative results show the remarkable performance of the CAVOA-DLSWC method over other techniques.

Research on Service Design of Garbage Classification Driven by Artificial Intelligence

This paper proposes a framework for AI-driven municipal solid waste classification service design and management, with an emphasis on advancing sustainable urban development. This study uses narrative research and case study methods to delve into the benefits of AI technology in waste classification systems. The framework includes intelligent recognition, management strategies, AI-based waste classification technologies, service reforms, and AI-powered customer involvement and education. Our research indicates that AI technology can improve accuracy, efficiency, and cost-effectiveness in waste classification, contributing to environmental sustainability and public health. However, the effectiveness of AI applications in diverse city contexts requires further verification. The framework holds theoretical and practical significance, offering insights for future service designs of waste management and promoting broader goals of sustainable urban development.