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Hydrogen production and pollution mitigation: Enhanced gasification of plastic waste and biomass with machine learning & storage for a sustainable future

  • Abu Danish Aiman Bin Abu Sofian
  • , Hooi Ren Lim
  • , Kit Wayne Chew
  • , Kuan Shiong Khoo
  • , Inn Shi Tan
  • , Zengling Ma
  • , Pau Loke Show
    • University of Nottingham Malaysia
    • School of Chemistry, Chemical Engineering and Biotechnology
    • Wenzhou University
    • Yuan Ze University
    • Curtin University, Malaysia
    • Department of Chemical Engineering

    Research output: Contribution to journalReview articlepeer-review

    35 Scopus citations

    Abstract

    The pursuit of carbon neutrality confronts the twofold challenge of meeting energy demands and reducing pollution. This review article examines the potential of gasifying plastic waste and biomass as innovative, sustainable sources for hydrogen production, a critical element in achieving environmental reform. Addressing the problem of greenhouse gas emissions, the work highlights how the co-gasification of these feedstocks could contribute to environmental preservation by reducing waste and generating clean energy. Through an analysis of current technologies, the potential for machine learning to refine gasification for optimal hydrogen production is revealed. Additionally, hydrogen storage solutions are evaluated for their importance in creating a viable, sustainable energy infrastructure. The economic viability of these production methods is critically assessed, providing insights into both their cost-effectiveness and ecological benefits. Findings indicate that machine learning can significantly improve process efficiencies, thereby influencing the economic and environmental aspects of hydrogen production. Furthermore, the study presents the advancements in these technologies and their role in promoting a transition to a green economy and circular energy practices. Ultimately, the review delineates how integrating hydrogen production from unconventional feedstocks, bolstered by machine learning and advanced storage, can contribute to a sustainable and pollution-free future.

    Original languageBritish English
    Article number123024
    JournalEnvironmental Pollution
    Volume342
    DOIs
    StatePublished - 1 Feb 2024

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 4 - Quality Education
      SDG 4 Quality Education
    2. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy
    3. SDG 8 - Decent Work and Economic Growth
      SDG 8 Decent Work and Economic Growth
    4. SDG 9 - Industry, Innovation, and Infrastructure
      SDG 9 Industry, Innovation, and Infrastructure
    5. SDG 13 - Climate Action
      SDG 13 Climate Action

    Keywords

    • Biomass
    • Gasification
    • Hydrogen storage
    • Machine learning
    • Plastic waste

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