%0 Journal Article
%@ 03603199
%A Inayat, M.
%A Sulaiman, S.A.
%A Inayat, A.
%A Shaik, N.B.
%A Gilal, A.R.
%A Shahbaz, M.
%D 2021
%F scholars:14566
%I Elsevier Ltd
%J International Journal of Hydrogen Energy
%K Biomass; Blending; Catalysts; Gasification; Lime; Neural networks; Palm oil; Portland cement; Synthesis gas; Tar, Biomass Gasification; Conventional fuel; Downdraft gasifier; Optimum conditions; Parametric optimization; Response surface methodology; Syngas production; Tar contamination, Synthesis gas manufacture
%N 59
%P 30559-30580
%R 10.1016/j.ijhydene.2020.10.268
%T Modeling and parametric optimization of air catalytic co-gasification of wood-oil palm fronds blend for clean syngas (H2+CO) production
%U https://khub.utp.edu.my/scholars/14566/
%V 46
%X Syngas production from biomass gasification is a potentially sustainable and alternative means of conventional fuels. The current challenges for biomass gasification process are biomass storage and tar contamination in syngas. Co-gasification of two biomass and use of mineral catalysts as tar reformer in downdraft gasifier is addressed the issues. The optimized and parametric study of key parameters such as temperature, biomass blending ratio, and catalyst loading were made using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) on tar reduction and syngas. The maximum H2 was produced when Portland cement used as catalyst at optimum conditions, temperature of 900 °C, catalyst-loading of 30, and biomass blending-ratio of W52:OPF48. Higher CO was yielded from dolomite catalyst and lowest tar content obtained from limestone catalyst. Both RSM and ANN are satisfactory to validate and predict the response for each type of catalytic co-gasification of two biomass for clean syngas production. © 2020 Hydrogen Energy Publications LLC
%Z cited By 18