Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(13)
Published: June 19, 2024
Language: Английский
Environmental Technology & Innovation, Journal Year: 2023, Volume and Issue: 33, P. 103509 - 103509
Published: Dec. 29, 2023
Nowadays the pace of production and consumption is reaching environmentally unsustainable levels. In this regard, great technological advances developed in recent years are postulated as a source opportunities to boost circular economy sustainable development. This wide range possibilities offered by new technologies create more reality has aroused curiosity interest academic world, especially years. The main objective research reveal challenges that arise when incorporating objectives economy. Regarding methodology, study been partially supported using bibliometric techniques. results highlight transformative role technologies, blockchain artificial intelligence, advancing economy, with particular emphasis on community technology integration, ethical considerations, synergies, business models, burgeoning bioeconomy. We conclude promise enhanced resource efficiency, optimized supply chains, innovative improved product lifecycle management, offering profound economic environmental benefits while fostering collaborative innovation. However, these also represent address, such integrating advanced methods, ensuring chain transparency, overcoming skill gap, avoiding data centralization, adapting regulatory frameworks foster equitable growth. These some most important areas for further research, those related development employees' capabilities adaptation frameworks, they understudied gaps.
Language: Английский
Citations
91
Recycling, Journal Year: 2024, Volume and Issue: 9(4), P. 59 - 59
Published: July 15, 2024
Plastics recycling is an important component of the circular economy. In mechanical recycling, recovery high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. chemical certain types should removed as they negatively affect process. Such sortation objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical any sorting proper identification type. Spectroscopy used this end, augmented machine learning (ML) artificial intelligence (AI). Recent developments in application ML/AI are highlighted here, state art presented. Commercial equipment recyclables identified from a survey publicly available information. Automated equipment, ML/AI-based sorters, robotic sorters currently market evaluated regarding their sensors, capability sort plastics, primary application, throughput, accuracy. This information reflects rapid progress achieved plastics. However, film, dark comprising multiple polymers remains challenging. Improvements and/or new solutions forthcoming.
Language: Английский
Citations
21
International Journal on Interactive Design and Manufacturing (IJIDeM), Journal Year: 2023, Volume and Issue: 17(6), P. 2789 - 2806
Published: May 6, 2023
Language: Английский
Citations
37
Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(10), P. 29505 - 29529
Published: Sept. 13, 2023
Language: Английский
Citations
25
Waste Management, Journal Year: 2024, Volume and Issue: 183, P. 87 - 100
Published: May 11, 2024
Language: Английский
Citations
11
Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108614 - 108614
Published: May 30, 2024
This research addresses the critical challenge of disaster waste management, a growing concern exacerbated by increasing frequency and intensity natural disasters like flooding. Traditional systems often struggle with volume heterogeneity waste, highlighting need for innovative solutions. In this study, we present novel classification model integrating advanced artificial intelligence (AI) optimization techniques to streamline categorization in post-disaster environments. Our approach leverages dual ensemble deep learning framework. The first combines various image-segmentation methods, while second integrates outputs from diverse convolutional neural network architectures. A modified multiple system serves as decision fusion strategy, enhancing accuracy at both points. We rigorously evaluated our using three datasets: "TrashNet" dataset benchmarking against existing well two meticulously curated, real-world datasets collected flood-affected areas Thailand. results demonstrate that method outperforms algorithms VGG19, YoloV5, InceptionV3 general solid classification, achieving an average improvement 11.18%. Regarding specifically, achieves 96.48% 96.49% on curated datasets, consistently outperforming ResNet-101, DenseNet-121, 3.47%. These findings potential AI-enhanced revolutionize management practices. Thus, advocate such technologies into municipal policies enhance resilience optimize responses. Future will explore scaling types incorporating real-time data adaptable strategies.
Language: Английский
Citations
11
Advances in Applied Energy, Journal Year: 2025, Volume and Issue: unknown, P. 100216 - 100216
Published: Feb. 1, 2025
Language: Английский
Citations
1
Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(6)
Published: March 24, 2025
Population growth and urbanization have led to a significant increase in solid waste. However, conventional methods of treating recycling this waste inherent problems, such as low efficiency, poor precision, high cost, severe environmental hazards. To address these challenges, Artificial Intelligence (AI) has gained popularity recent years potential solution for municipal solid-waste management (MSWM). A few applications AI, based on Machine Learning (ML) Deep (DL) techniques, been used MSWM. This study reviews the current landscape MSWM, highlighting existing advantages disadvantages 69 studies published between 2018 2024 using PRISMA methodology. The ML DL algorithms demonstrate their ability enhance decision-making processes, improve resource recovery rates, promote circular economy principles. Although technologies offer promising solutions, challenges data availability, quality, interdisciplinary collaboration hinder effective implementation. paper suggests future research directions focusing developing robust datasets, fostering partnerships across sectors, integrating advanced with traditional strategies. aligns United Nations' Sustainable Development Goals (SDG), particularly Goal 11, which aims make cities inclusive, safe, resilient, sustainable. In future, can contribute making smarter, greener, more resilient techniques.
Language: Английский
Citations
1
Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 128, P. 107542 - 107542
Published: Nov. 19, 2023
Language: Английский
Citations
15
Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 247 - 268
Published: April 4, 2024
Recycling and landfilling are two of the primary means by which garbage is destroyed in context waste management. Many urban areas struggle with improper collection, transportation, disposal. This chapter depicts a competent management scheme architecture predicated on internet things. In addition, new benchmark datasets to classify waste, unified collections open-source standardized annotations for all types presented here. The faster region convolutional neural network (FRCNN) based widely used VGG-16 feature extraction from input images. detected classified into one seven different using naked mole-rat algorithm's (NMRA) hyper-parameter tuning progress classification accuracy. classifier trained unlabeled images semi-supervised manner. On test dataset, proposed method achieves an average precision 70% detection accuracy 93% classification.
Language: Английский
Citations
4