How to Quickly Characterize Construction and Demolition Wastes? Traditional and Advanced Portable Solutions in Comparison DOI
Alessandra Mobili, Maria Teresa Calcagni, Gian Marco Revel

et al.

Published: June 12, 2024

Language: Английский

The hidden concept and the beauty of multiple “R” in the framework of waste strategies development reflecting to circular economy principles DOI
Antonis A. Zorpas

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 952, P. 175508 - 175508

Published: Aug. 15, 2024

Language: Английский

Citations

26

Advancing Textile Waste Recycling: Challenges and Opportunities Across Polymer and Non-Polymer Fiber Types DOI Open Access
Mehrdad Seifali Abbas‐Abadi, Brecht Tomme, Bahman Goshayeshi

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(5), P. 628 - 628

Published: Feb. 26, 2025

The growing environmental impact of textile waste, fueled by the rapid rise in global fiber production, underscores urgent need for sustainable end-of-life solutions. This review explores cutting-edge pathways waste management, spotlighting innovations that reduce reliance on incineration and landfilling while driving material circularity. It highlights advancements collection, sorting, pretreatment technologies, as well both established emerging recycling methods. Smart collection systems utilizing tags sensors show great promise streamlining logistics automating pick-up routes transactions. For automated technologies like near-infrared hyperspectral imaging lead way accurate scalable separation. Automated disassembly techniques are effective at removing problematic elements, though other pretreatments, such color finish removal, still to be customized specific streams. Mechanical is ideal textiles with strong mechanical properties but has limitations, particularly blended fabrics, cannot repeated endlessly. Polymer recycling-through melting or dissolving polymers-produces higher-quality recycled materials comes high energy solvent demands. Chemical recycling, especially solvolysis pyrolysis, excels breaking down synthetic polymers polyester, potential yield virgin-quality monomers. Meanwhile, biological methods, their infancy, natural fibers cotton wool. When methods not viable, gasification can used convert into synthesis gas. concludes future hinges integrating sorting advancing solvent-based chemical technologies. These innovations, supported eco-design principles, progressive policies, industry collaboration, essential building a resilient, circular economy.

Language: Английский

Citations

2

Emerging Trends in Sustainable Building Materials: Technological Innovations, Enhanced Performance, and Future Directions DOI Creative Commons
Ali Akbar Firoozi, Ali Asghar Firoozi, D.O. Oyejobi

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103521 - 103521

Published: Nov. 24, 2024

Language: Английский

Citations

13

Towards built environment Decarbonisation: A review of the role of Artificial intelligence in improving energy and Materials’ circularity performance DOI Creative Commons
Bankole Awuzie, A.B. Ngowi, Douglas Aghimien

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 319, P. 114491 - 114491

Published: June 28, 2024

Mitigating climate change challenges in the built environment through decarbonisation of energy and construction materials remains a pressing challenge. The circular economy (CE) has been identified as critical pathway to achieving this objective. CE promotes efficient use resources, extending their lifecycle minimising environmental impact using plethora methods. link between becomes evident when intertwined relationship materials, energy, is considered. By reducing waste ensuring continuous significantly lowers carbon emissions. This approach inherently aligned with overarching goals agenda. emergence digital technologies such artificial intelligence (AI) continued transform how activities are conducted improved. However, utility AI models engendering actualisation agenda improved performance within context under-researched. study addresses knowledge-practice gap, scientometric scoping analysis relevant peer-reviewed grey literature. Findings from revealed explored separately decarbonisation. Yet, studies exploring relation circularity for remain scant. narrative review further usefulness driving optimal levels across various economic sectors, including decision making which turn, encourages responsible producer consumer behaviour performance.

Language: Английский

Citations

5

Harnessing Robotics for Sustainable Waste Management: Insights from the UK And African Cities DOI Creative Commons

Anya Adebayo Anya

Published: Jan. 10, 2025

Waste management remains one of the most pressing global challenges, exacerbated by rapid urbanization, population growth, and limited infrastructure, particularly in developing regions such as Africa. This paper explores role robotics artificial intelligence (AI) advancing sustainable waste management, drawing comparative insights from United Kingdom (UK) African cities. It examines how technologies are transforming systems, with a particular focus on sorting, collection optimization, recycling efforts. In UK, applications have contributed to nuclear construction demolition demonstrating potential automation reduction. cities, emerging trends AI-powered mobile apps smart bins offer scalable solutions tailored region’s unique including urbanization resource constraints. Through review policies, technological innovations, challenges both contexts, this identifies strategies for integrating into management. underscores importance collaboration between governments, technology companies, communities, highlights capacity-building initiatives that can help cities effectively adopt robotic technologies. The study offers recommendations advocating scalable, cost-effective emphasizing need public awareness local expertise. Ultimately, demonstrates leveraging contribute more practices, improving environmental health supporting urban development across diverse contexts.

Language: Английский

Citations

0

Utilizing Artificial Intelligence and Machine Learning for Enhanced Recycling Efforts DOI
Nikita Kandpal,

Nishant Singhal,

Harsh Vardhan Lavaniya

et al.

Advances in environmental engineering and green technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 65 - 82

Published: Jan. 16, 2025

One industry that has benefitted largely from the integration of Artificial Intelligence (AI) and machine learning (ML) in its processes is recycling, providing significant advancements waste management towards sustainability environmental conservation. This chapter highlights application AI ML various streams (plastic, electronic food, paper, textile, metal etc. wastage). These systems use AI-powered image recognition sorting to better separate materials, helping increasing efficiency chemical recycling technologies; meanwhile algorithms enable cleaner for handling chemicals material recovery. Increased precision removal valuable components via automated disassembly predictive analytics. Using helped increase operational efficiency, resources recovery but also shown clear contributions environment overall ensure sustainable future ahead.

Language: Английский

Citations

0

Recent advances in recycling and upcycling of hazardous plastic waste: A review DOI

Shahrani Anuar,

Abu Hassan Nordin, Siti Muhamad Nur Husna

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 124867 - 124867

Published: March 12, 2025

Language: Английский

Citations

0

Automated recognition of contaminated construction and demolition wood waste using deep learning DOI Creative Commons
A. Madini Lakna De Alwis, Milad Bazli, Mehrdad Arashpour

et al.

Resources Conservation and Recycling, Journal Year: 2025, Volume and Issue: 219, P. 108278 - 108278

Published: April 4, 2025

Language: Английский

Citations

0

AI and Machine Learning for Optimizing Waste Management and Reducing Air Pollution DOI
Kuldeep Singh Rautela,

Manish Kumar Goyal,

Rao Y. Surampalli

et al.

Journal of Hazardous Toxic and Radioactive Waste, Journal Year: 2025, Volume and Issue: 29(3)

Published: April 21, 2025

Language: Английский

Citations

0

Machine learning assisted optimization of polyoxometalate catalyzed lignin oxidation and depolymerization through reverse design DOI
Jiemin Zheng, Yuan Gao,

Keqing Li

et al.

Resources Conservation and Recycling, Journal Year: 2025, Volume and Issue: 220, P. 108337 - 108337

Published: May 1, 2025

Language: Английский

Citations

0