JOURNAL ARTICLE
Advancements in plasma technology for circular waste management and green hydrogen production: A review.
Published In: Journal of Renewable & Sustainable Energy, 2025, v. 17, n. 3. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Azhagu, Uthayakumar; Muniyappan, Dineshkumar; Ramanathan, Anand 3 of 3
Abstract
This article reviews plasma-based tertiary recycling as an advanced waste management technology that converts mixed and contaminated waste into high-grade energy, producing hydrogen-rich syngas and vitrified slag with lower environmental impact compared to conventional methods. It details recent advancements in plasma generation techniques—including direct current (DC), radio frequency (RF), microwave, and laser-induced plasma torches—and evaluates their techno-economic feasibility for treating diverse waste streams such as municipal solid waste, biomedical waste, plastics, and hazardous materials. The review highlights the integration of plasma gasification with internal combustion engines, gas turbines, and fuel cells for electricity generation, emphasizing lifecycle assessment results that show plasma gasification’s lower global warming potential and higher energy recovery efficiency. Additionally, it discusses the role of artificial intelligence in optimizing operational processes and predictive maintenance, and presents case studies of global plasma gasification plants, underscoring the technology’s potential to support sustainable development goals through waste reduction, renewable energy production, and circular economy practices.
Additional Information
- Source:Journal of Renewable & Sustainable Energy. 2025/05, Vol. 17, Issue 3, p1
- Document Type:Literature Review
- Subject Area:Science
- Publication Date:2025
- ISSN:1941-7012
- DOI:10.1063/5.0250350
- Accession Number:185593779
- Copyright Statement:Copyright of Journal of Renewable & Sustainable Energy is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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