AIMC Topic: Electronic Waste

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Impact of e-waste pollutant exposure on renal injury and oxidative stress biomarkers: Evidence from causal machine learning.

Journal of hazardous materials
Global electronification has driven an unprecedented surge in electronic and electrical waste (e-waste), with approximately 75 % of this e-waste informally managed, releasing hazardous chemicals. Traditional association analyses have limited ability ...

Semi-scale stirred tank enzymatic bioleaching system for metal recovery from PCBs of end-of-life mobile phones: Process parameter optimization, predictive modelling, and economic assessment.

Waste management (New York, N.Y.)
Biocatalysts like enzymes have proven to be faster and efficient in metal bioleaching from printed circuit boards (PCBs) than microbe-mediated bioleaching. However, studies on enzymatic metal bioleaching from PCBs are mainly confined to the shake-fla...

Investigating the factors affecting the intention to separate e-waste among mobile phone repairers in an emerging economy: A hybrid structural equation modelling and artificial neural network approach.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
The growing number of mobile phone users on a global scale has led to enormous amounts of electronic waste (e-waste) being generated annually. Insufficient knowledge of e-waste separation causes individuals to dispose of e-waste along with other wast...

Enhancing e-waste management: a novel light gradient AdaBoost support vector classification approach.

Environmental monitoring and assessment
The global consequences of electronic waste significantly affect the environment and human health. Accurate classification is essential for effective recycling and management to mitigate serious environmental harm caused by improper disposal. However...

Raman spectroscopy integrated with machine learning techniques to improve industrial sorting of Waste Electric and Electronic Equipment (WEEE) plastics.

Journal of environmental management
Current industrial separation and sorting technologies struggle to efficiently identify and classify a large part of Waste of Electric and Electronic Equipment (WEEE) plastics due to their high content of certain additives. In this study, Raman spect...

Enhancing ergonomics in E-waste disassembly: the impact of collaborative robotics on muscle activation and coordination.

Ergonomics
Disassembly, as a part of the electronic waste (e-waste) management process, is a labour-intensive task. The emergence of collaborative robots (cobots) provides a robotic solution to reduce the human efforts during disassembly. This study evaluated m...

X-ray transmission imaging of waste printed circuit boards for value estimation in recycling using machine learning.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
The growing amount of electronic waste is a global challenge: on one hand, it poses a threat to the environment as it may contain toxic or hazardous substances, on the other hand it is a valuable 'urban mine' containing metals like gold and copper. T...

Classification of e-waste using machine learning-assisted laser-induced breakdown spectroscopy.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
Waste management and the economy are intertwined in various ways. Adopting sustainable waste management techniques can contribute to economic growth and resource conservation. Artificial intelligence (AI)-based classification is very crucial for rapi...

Prediction of e-waste generation: Application of modified adaptive neuro-fuzzy inference system (MANFIS).

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
An accurate estimation of generated electronic waste (e-waste) plays a pivotal role in the development of any appropriate e-waste management plan. The present study aimed to exploit modified adaptive neuro-fuzzy inference system (MANFIS) for the esti...

Fuzzy-based adaptive learning network using search and rescue optimization for e-waste management model: case study.

Environmental science and pollution research international
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 ...