AIMC Topic: Recycling

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ONDL: An optimized Neutrosophic Deep Learning model for classifying waste for sustainability.

PloS one Nov 8, 2024
Sustainability has become a key factor on our planet. If this concept is applied correctly, our planet will be greener and more eco-friendly. Nowadays, waste classification and management practices have become more evident than ever. It plays a cruci...
Deep Learning Algorithms Recycling Waste Management Conservation of Natural Resources
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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 Deep Learning Recycling China
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Improved prediction accuracy for compressive strength of recycled aggregate concrete using optimization-based algorithms and cascade forward neural network.

Journal of environmental management Oct 30, 2024
This study proposed a data driven approach to predict the compressive strength (CS) of recycled aggregate concrete (RAC) for sustainable construction using an elite single genetic optimization algorithm-based cascade forward neural network (ESGA-CFNN...
Recycling Compressive Strength Construction Materials Algorithms Neural Networks, Computer
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Deep learning approaches for classification of copper-containing metal scrap in recycling processes.

Waste management (New York, N.Y.) Oct 24, 2024
Separating copper from iron scrap is a critical operation in metal recycling and achieving this with low cost sensoric equipment like RGB cameras instead of XRF/XRT is becoming increasingly attractive. In this article, the groundwork for creating an ...
Copper Recycling Iron Deep Learning
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Prediction for the recycle of phosphate tailings in enhanced gravity field based on machine learning and interpretable analysis.

Waste management (New York, N.Y.) Sep 20, 2024
Recleaning phosphate tailings using the low-cost enhanced gravity separation method is beneficial for maximizing the recovery of phosphorus element. A machine learning framework was constructed to predict the target variables of the yield, grade, and...
Mining Gravitation Phosphates Recycling Machine Learning
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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...
Waste Management Neural Networks, Computer Recycling Deep Learning
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Machine learning constructs the microstructure and mechanical properties that accelerate the development of CFRP pyrolysis for carbon-fiber recycling.

Waste management (New York, N.Y.) Sep 10, 2024
The increasing use of carbon-fiber-reinforced plastic (CFRP) has led to its post-end-of-life recycling becoming a research focus. Herein, we studied the macroscopic and microscopic characteristics of recycled carbon fiber (rCF) during CFRP pyrolysis ...
Machine Learning Carbon Recycling Pyrolysis Carbon Fiber Plastics
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An Improved Migratory Birds Optimization Algorithm for Closed- Loop Supply Chain Network Planning in a Fuzzy Environment.

PloS one Jun 27, 2024
Recycling of used products can provide substantial economic and environmental benefits for supply chain players. However, many factors associated with the design of closed-loop supply chain networks are uncertain in their nature, including demand, op...
Birds Algorithms Animal Migration Models, Theoretical Animals Recycling Fuzzy Logic
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Potential prediction and coupling relationship revealing for recovery of platinum group metals from spent auto-exhaust catalysts based on machine learning.

Journal of environmental management Jun 24, 2024
As hazardous waste, the massive generation of spent auto-exhaust catalysts (SACs) puts enormous pressure on environmental management, but provides a rare opportunity for platinum group metals (PGMs) recycling. In this study, machine learning (ML) met...
Platinum Recycling Metals Catalysis China Machine Learning
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Image capturing, segmentation and data analysis of shredded refuse streams.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA Jun 23, 2024
Refuse sorting is an important cornerstone of the recycling industry, but ever-changing refuse compositions and the desire to increase recycling rates still pose many unsolved challenges. The digitalisation of refuse sorting plants promises to overco...
Neural Networks, Computer Refuse Disposal Solid Waste Image Processing, Computer-Assisted Recycling Data Analysis
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