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Author: search.filters.author.Kunmi Joshua AbioyeSubject: search.filters.subject.Waste management

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    Trends and Advancements in Utilization of Biomass Waste for Gasification: A Bibliometric Review
    (Wiley, 2025-11-24)
    Kunmi Joshua Abioye
    ;
    Noorfidza Yub Harun
    ;
    Abdoulmohammad Gholamzadeh Chofreh
    ;
    HESAM, KAMYAB  
    ;
    Mohammad Yusuf
    Biomass waste gasification is widely recognized as a sustainable and efficient method for converting organic waste into valuable energy, making it a focal point in global research. This study conducts a bibliometric analysis of publications related to this field, focusing on articles indexed in the Elsevier Scopus database from 1977 to 2023 using search terms “biomass waste,” “biomass residue,” “waste biomass,” and “gasification”. Initially, 981 articles were identified, with subsequent refined analyses narrowing the focus to 592 publications, using VOSviewer for in‐depth examination. The analysis revealed that the year 2023 saw the highest publication count with 73 articles, followed by the year 2022 and the year 2020, with 61 and 54 articles, respectively. China, the USA, and India emerged as the leading contributors, accounting for 9.68%, 7.07%, and 6.75% of the total publications, respectively. Top institutions by citations are the University of Saskatchewan (259), Hamad Bin Khalifa University (169), and Paul Scherrer Institut (113). The most prolific researchers in the field include Gulyurtlu, I., Cabrita, I., and Dalai, Ajay K., with citation counts of 1296, 1290, and 1020, respectively. The journals Energy, Fuel, and Energies were identified as authors most preferred publishing choice with 26, 23, and 22 publications, respectively. The keywords “Gasification,” “Biomass,” and “Syngas” were the most frequently occurring, with 194, 147, and 52 occurrences. Keyword analysis also revealed five thematic clusters. These findings offer a detailed overview of the research landscape in biomass waste gasification, emphasizing key contributors, emerging trends, and thematic areas, providing valuable insights for guiding future research in this domain.
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    Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H2) production
    (Elsevier BV, 2024-09)
    Kunmi Joshua Abioye
    ;
    Noorfidza Yub Harun
    ;
    Ushtar Arshad
    ;
    Suriati Sufian
    ;
    Mohammad Yusuf
    Syngas (CO + H2) production through biomass gasification offers a promising and sustainable alternative to conventional fuels. This study investigates the co-gasification of palm oil decanter cake (PODC) and Alum Sludge (AS), utilizing response surface methodology (RSM) and artificial neural network (ANN) techniques to optimize and predict syngas production. Conducted in a fixed bed horizontal reactor, the experiment investigates temperature, airflow rate, and particle size as input parameters. Results revealed that optimal condition of 900 °C temperature, 10 mL/min airflow rate, and 2 mm particle size yielded the highest syngas production at 39.48 vol%. The RSM showed an R2 value of 0.9896, whereas ANN network revealed an overall R2 value of 0.971. Both models demonstrated strong alignment with experimental data and the modelled equation. This research demonstrates the effective use of statistical modelling to enhance the efficiency and effectiveness of syngas production, thereby fostering advancements in sustainable energy production.