AIMC Topic: Waste Management

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A hybrid classification and evaluation method based on deep learning for decoration and renovation waste in view of recycling.

Waste management (New York, N.Y.) Nov 4, 2024
The escalating volume of decoration and renovation waste (D&RW) amid the rapid urbanization in China has posed significant challenges for the effective recycling of this waste stream, primarily due to the difficulty of accurately assessing its precis...
Waste Management Recycling Deep Learning China
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Innovations in plastic remediation: Catalytic degradation and machine learning for sustainable solutions.

Journal of contaminant hydrology Oct 24, 2024
Plastic pollution is an extreme environmental threat, necessitating novel restoration solutions. The present investigation investigates the integration of machine learning (ML) techniques with catalytic degradation processes to improve plastic waste ...
Machine Learning Environmental Restoration and Remediation Waste Management Catalysis Plastics
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Can " Zero waste city" policy promote green technology? Evidence from econometrics and machine learning.

Journal of environmental management Oct 13, 2024
The promotion of green technology innovation (GTI) is regarded as an effective way to protect the environment and achieve sustainable development. The "Zero waste city" construction pilot policy (ZWCP), is an important policy for the promotion of was...
Sustainable Development Waste Management Machine Learning Cities
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Optimizing waste handling with interactive AI: Prompt-guided segmentation of construction and demolition waste using computer vision.

Waste management (New York, N.Y.) Sep 24, 2024
Optimized and automated methods for handling construction and demolition waste (CDW) are crucial for improving the resource recovery process in waste management. Automated waste recognition is a critical step in this process, and it relies on robust ...
Image Processing, Computer-Assisted Construction Materials Algorithms Waste Management Artificial Intelligence
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Lightweight deep learning model for underwater waste segmentation based on sonar images.

Waste management (New York, N.Y.) Sep 14, 2024
In recent years, the rapid accumulation of marine waste not only endangers the ecological environment but also causes seawater pollution. Traditional manual salvage methods often have low efficiency and pose safety risks to human operators, making au...
Deep Learning Recycling Neural Networks, Computer Waste Management
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Machine vision-based detection of forbidden elements in the high-speed automatic scrap sorting line.

Waste management (New York, N.Y.) Aug 30, 2024
Highly efficient industrial sorting lines require fast and reliable classification methods. Various types of sensors are used to measure the features of an object to determine which output class it belongs to. One technique involves the use of an RGB...
Metallurgy Algorithms Neural Networks, Computer Copper Machine Learning Image Processing, Computer-Assisted Waste Management
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Exploring artificial intelligence role in improving service building engagement in sorting.

Waste management (New York, N.Y.) Aug 22, 2024
Waste management researchers have identified that the correct disposal of solid waste is better addressed upstream, where people properly sort their solid waste. Sorting solid waste is a practice that requires a behaviour friendly to sorting and will...
Solid Waste Waste Management Refuse Disposal Humans Artificial Intelligence
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MSW-Net: A hierarchical stacking model for automated municipal solid waste classification.

Journal of the Air & Waste Management Association (1995) Jul 15, 2024
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 s...
Models, Theoretical Bayes Theorem Solid Waste Waste Management Neural Networks, Computer Refuse Disposal Cities
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Higher heating value estimation of wastes and fuels from ultimate and proximate analysis by using artificial neural networks.

Waste management (New York, N.Y.) May 30, 2024
Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes...
Wood Manure Refuse Disposal Heating Waste Management Waste Products Animals Neural Networks, Computer Solid Waste Sewage
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Identifying ESG types of Chinese solid waste disposal companies based on machine learning methods.

Journal of environmental management May 25, 2024
In the context of China's efforts to combat climate change and promote sustainable development, the solid waste treatment industry's environmental, social, and corporate governance (ESG) performance is receiving significant attention. To comprehensiv...
Refuse Disposal Solid Waste Waste Management Machine Learning China
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