Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study

Alsabt, Reema; Alkhaldi, Wadha; Adenle, Yusuf A.; Alshuwaikhat, Habib M.

doi:10.1016/j.clwas.2024.100158

Cleaner Waste Systems

2024

DOI: 10.1016/j.clwas.2024.100158

|View full text |Cite

Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study

Reema Alsabt,

Wadha Alkhaldi,

Yusuf A. Adenle

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Innovative approaches to sustainable wastewater treatment: a comprehensive exploration of conventional and emerging technologies

Shamshad,

Ur Rehman

2025

Environ. Sci.: Adv.

View full text Add to dashboard Cite

show abstract

Innovative approaches to sustainable wastewater treatment: a comprehensive exploration of conventional and emerging technologies

Shamshad,

Ur Rehman

2025

Environ. Sci.: Adv.

View full text Add to dashboard Cite

show abstract

Recent Advancements in Artificial Intelligence in Battery Recycling

Antony Jose,

Cook,

Palacios

et al. 2024

Batteries

View full text Add to dashboard Cite

Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods, often reliant on manual labor, suffer from inefficiencies and environmental harm. However, recent artificial intelligence (AI) advancements offer promising solutions to these challenges. This paper reviews the latest developments in AI applications for battery recycling, focusing on methodologies, challenges, and future directions. AI technologies, particularly machine learning and deep learning models, are revolutionizing battery sorting, classification, and disassembly processes. AI-powered systems enhance efficiency by automating tasks such as battery identification, material characterization, and robotic disassembly, reducing human error and occupational hazards. Additionally, integrating AI with advanced sensing technologies like computer vision, spectroscopy, and X-ray imaging allows for precise material characterization and real-time monitoring, optimizing recycling strategies and material recovery rates. Despite these advancements, data quality, scalability, and regulatory compliance must be addressed to realize AI’s full potential in battery recycling. Collaborative efforts across interdisciplinary domains are essential to develop robust, scalable AI-driven recycling solutions, paving the way for a sustainable, circular economy in battery materials.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study

Cited by 2 publications

References 50 publications

Innovative approaches to sustainable wastewater treatment: a comprehensive exploration of conventional and emerging technologies

Innovative approaches to sustainable wastewater treatment: a comprehensive exploration of conventional and emerging technologies

Recent Advancements in Artificial Intelligence in Battery Recycling

Contact Info

Product

Resources

About