AIMC Topic: Recycling

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Zero-shot and few-shot multimodal plastic waste classification with vision-language models.

Waste management (New York, N.Y.)
The construction sector is a large consumer of plastic, generating substantial volumes of plastic waste. Effective recycling of this waste requires accurate classification, as different plastic materials undergo distinct recycling processes to retain...

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...

Efficient and anti-interference plastic classification method suitable for one-shot learning based on laser induced breakdown spectroscopy.

Chemosphere
Efficient recycling of plastics is critical for environmental sustainability. In this work, an efficient and anti-interference method for plastic classification based on one-shot learning and laser-induced breakdown spectroscopy (LIBS) was proposed. ...

Identifying plastic materials in post-consumer food containers and packaging waste using terahertz spectroscopy and machine learning.

Waste management (New York, N.Y.)
Accurate identification of plastic materials from post-consumer food container and packaging waste is crucial for enhancing the purity and added value of recycled materials, thereby promoting recycling and addressing the issue of plastic pollution. H...

A machine-learning approach to optimize nutritional properties and organic wastes recycling efficiency conversed by black soldier fly (Hermetia illucens).

Bioresource technology
Suboptimal nutrition in organic waste limits the growth of black soldier fly (BSF) larvae, thereby reducing biowaste recycling efficiency. In this study, weight gain data from BSF larvae fed diets with distinct nutrient compositions were used to buil...

Contaminant detection in flexible polypropylene packaging waste using hyperspectral imaging and machine learning.

Waste management (New York, N.Y.)
Flexible plastic packaging (FPP) constitutes one of the largest post-consumer plastic streams processed in recycling facilities. To address the key challenges of its sorting and quality control, this study developed and tested a classification proced...

Identification and information acquisition of high-value construction solid waste combined millimeter-wave radar and convolutional neural networks.

Waste management (New York, N.Y.)
The accumulation of construction solid waste (CSW) leads to the waste of land resources and environmental pollution, becoming a significant social problem. Identifying the amount of high-value CSW is essential for assessing the value of accumulated C...

Prototype of AI-powered assistance system for digitalisation of manual waste sorting.

Waste management (New York, N.Y.)
Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for effective recycling and progress toward a circular economy. Achieving this transformation requires higher sorting intensity through inte...

A systematic review of plastic recycling: technology, environmental impact and economic evaluation.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
In this systematic review, advancements in plastic recycling technologies, including mechanical, thermolysis, chemical and biological methods, are examined. Comparisons among recycling technologies have identified current research trends, including a...

Machine learning-based automated waste sorting in the construction industry: A comparative competitiveness case study.

Waste management (New York, N.Y.)
This article presents a comparative analysis of the circularity and cost-efficiency of two distinct construction material recycling processes: ML-based automated sorting (MLAS) and conventional sorting technologies. Empirical data was collected from ...