AIMC Topic: Recycling
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Multi-modal deep learning networks for RGB-D pavement waste detection and recognition.
Waste management (New York, N.Y.)
Feb 6, 2024
To create a clean living environment, governments around the world have hired a large number of workers to clean up waste on pavements, which is inefficient for waste management. To better alleviate this problem, relevant scholars have proposed sever...
Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild.
Journal of environmental management
Jan 1, 2024
The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting process...
An intelligent identification and classification system of decoration waste based on deep learning model.
Waste management (New York, N.Y.)
Dec 19, 2023
Efficient sorting and recycling of decoration waste are crucial for the industry's transformation, upgrading, and high-quality development. However, decoration waste can contain toxic materials and has greatly varying compositions. The traditional me...
Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling.
Waste management (New York, N.Y.)
Mar 28, 2023
Waste recycling is a critical issue for environment pollution management while garbage classification determines the recycling efficiency. In order to reduce labor costs and increase garbage classification capacity, a machine vision system is establi...
Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production.
Chemosphere
Aug 14, 2022
The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable energy resources in the upcoming years. Recycling sources from waste could efficiently ease the risk...
Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review.
Waste management (New York, N.Y.)
Jun 24, 2022
Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could...
Fuzzy-based adaptive learning network using search and rescue optimization for e-waste management model: case study.
Environmental science and pollution research international
Oct 1, 2021
In recent days, the expansion of e-waste disposal should be increased due to environmental hazards, contamination of groundwater, an unconcerned consequence on marine life, human health, and decrease in the fertility of the soil. The majority of the ...
Digitalization of waste management: Insights from German private and public waste management firms.
Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
Jun 30, 2021
Policymakers, practitioners, and scholars have long-lauded digital technologies, such as smart waste containers or artificial intelligence for material recognition and robotic automation, as key enablers to more effective and efficient waste manageme...
A European household waste management approach: Intelligently clean Ukraine.
Journal of environmental management
Jun 10, 2021
The European-wide environmental obstacles of inefficient and unsustainable recycling systems and flows constrain household waste (HW) management, endangering the circular economy. The European 2020 strategy and ongoing environmental disasters indicat...
Research on the process of small sample non-ferrous metal recognition and separation based on deep learning.
Waste management (New York, N.Y.)
Mar 28, 2021
Consumption of copper and aluminum has increased significantly in recent years; therefore, recycling these elements from the end-of-life vehicles (ELVs) will be of great economic value and social benefit. However, the separation of non-ferrous materi...
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