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Recycling

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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.)
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...

Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production.

Chemosphere
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...

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.)
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...

An intelligent identification and classification system of decoration waste based on deep learning model.

Waste management (New York, N.Y.)
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...

Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild.

Journal of environmental management
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...

Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the...

Multi-modal deep learning networks for RGB-D pavement waste detection and recognition.

Waste management (New York, N.Y.)
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...

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning.

Environmental science and pollution research international
Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of waste generated require the development of automated waste management models. The development of smart ...

Agro-industrial waste management employing benefits of artificial intelligence.

Environmental science and pollution research international
By 2050, the world's population is predicted to reach over 9 billion, which requires 70% increased production in agriculture and food industries to meet demand. This presents a significant challenge for the agri-food sector in all aspects. Agro-indus...