AI Medical Compendium Journal:
Waste management (New York, N.Y.)

Showing 21 to 30 of 68 articles

AI-based plastic waste sorting method utilizing object detection models for enhanced classification.

Waste management (New York, N.Y.)
The export ban on plastic waste by China has brought domestic plastic recycling to the forefront of environmental concerns, with sorting being a crucial step in the recycling process. This study assessed the performance of advanced AI models, Mask R-...

Evaluating drivers of PM air pollution at urban scales using interpretable machine learning.

Waste management (New York, N.Y.)
Reducing urban fine particulate matter (PM) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM will enable the development of targeted strategies to reduce PM levels. This stud...

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.)
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...

Deep learning approaches for classification of copper-containing metal scrap in recycling processes.

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

Optimizing waste handling with interactive AI: Prompt-guided segmentation of construction and demolition waste using computer vision.

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

Prediction for the recycle of phosphate tailings in enhanced gravity field based on machine learning and interpretable analysis.

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

Co-firing characteristic prediction of solid waste and coal for supercritical CO power cycle based on CFD simulation and machine learning algorithm.

Waste management (New York, N.Y.)
The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO (S-CO) circulating fluidized bed (CFB) can effectively deal with domestic waste, promote social and environmental benefits, improve the coal conversion rate, a...

Lightweight deep learning model for underwater waste segmentation based on sonar images.

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

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.)
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 vision-based detection of forbidden elements in the high-speed automatic scrap sorting line.

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