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Author: search.filters.author.Abdullah A. Al-KahtaniLanguage: search.filters.language.English

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    Exploring waste-derived catalysts for sustainable biodiesel production: a path towards renewable energy
    (Springer Science and Business Media LLC, 2024-07-17)
    T. Sathish
    ;
    Sivamani Selvaraju
    ;
    N. Ahalya
    ;
    Ashok Kumar
    ;
    Abhishek Agarwal
    Fossil fuels have a high energy density, meaning they contain a significant amount of energy per unit of volume, making them efficient for energy production and transport. Biodiesel is especially becoming a fossil fuel alternative and a key part of renewable energy. Several types of waste from homes, markets, street vendors, and other industrial places were collected and transesterified with Ni-doped ZnO nanoparticles for this study. These included castor oil, coffee grounds, eggshells, vegetable oil, fruit peels, and soybean oil. The Ni-doped ZnO’s were then calcined at 800 °C. The maximum conversion rate found in converting fruit peel waste into biodiesel is about 87.6%, and it was 89.6% when the oil-to-methanal ratio was about 1:2 and the reaction time was 140 min. This is the maximum biodiesel production compared to other wastes. Moreover, using vegetable oil with nanocatalyst, the maximum biodiesel production rate of about 90.58% was recorded with 15% catalyst loading, which is the maximum biodiesel production compared with the other wastes with nanocatalyst. Furthermore, at 75 °C and a concentration of catalyst of about 15% the maximum biodiesel production obtained by using castor oil is about 92.8%. It has the highest biodiesel yield compared with the yield recorded from other waste. The catalyst also demonstrated great stability and reusability for the synthesis of biodiesel. Using waste fruit peels with Ni-doped ZnO helps to progress low-cost and ecologically friendly catalyst for sustainable biodiesel production.
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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.