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    RSM-based co-gasification of palm oil decanter cake and sugarcane bagasse: Syngas production and biochar characteristics
    (Elsevier BV, 2024-12)
    Kunmi Joshua Abioye
    ;
    Noorfidza Yub Harun
    ;
    Mohammad Yusuf
    ;
    Hesam Kamyab
    ;
    Joshua O. Ighalo
    The production of syngas (CO + H2) and biochar from biomass waste co-gasification promotes sustainable energy while addressing environmental remediation challenges. This study investigates the co-gasification of palm oil decanter cake (PODC) and sugarcane bagasse (SB) to optimize syngas production and obtain biochar in a fixed bed horizontal tube furnace reactor. Operating variables, including temperature (700–900 °C), biomass ratio (30–70 wt%), and particle size (0.25–2 mm), were optimized using Response Surface Methodology with the Box-Behnken design. Characterization analyses including Brunauer-Emmett-Teller (BET), Fourier Transformed Infrared (FTIR), and Field Emission Scanning Electron Microscopic (FESEM) analyses were conducted on the biochar. The optimal conditions yielded a syngas volume of 41.5 vol% and a biochar of 0.3 wt%, achieved at 900 °C temperature, 42 wt% PODC biomass ratio, and 2 mm particle size. BET analysis revealed a mesoporous structure biochar with surface area of 398.55 m2/g, pore volume of 0.13 cm3/g, and pore diameter of 6.49 nm. FTIR analysis indicated the presence of hydroxyl groups, carbonyl groups, aromatic compounds, and hydrocarbon structures. FESEM analysis showed well-defined pore structures on the biochar surface, with EDX analysis confirming a dominant carbon content of 83.32 wt%. These findings substantially enhance sustainable approaches in energy production, agriculture, and wastewater treatment, while effectively tackling environmental issues associated with biomass waste.
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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.