TY  - JOUR
Y1  - 2021///
JF  - International Journal of Hydrogen Energy
A1  - Inayat, M.
A1  - Sulaiman, S.A.
A1  - Inayat, A.
A1  - Shaik, N.B.
A1  - Gilal, A.R.
A1  - Shahbaz, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097072733&doi=10.1016%2fj.ijhydene.2020.10.268&partnerID=40&md5=9eb50e551f9dd6f573bdefd1823b67cd
VL  - 46
N2  - 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
IS  - 59
ID  - scholars14566
KW  - Biomass; Blending; Catalysts; Gasification; Lime; Neural networks; Palm oil; Portland cement; Synthesis gas; Tar
KW  -  Biomass Gasification; Conventional fuel; Downdraft gasifier; Optimum conditions; Parametric optimization; Response surface methodology; Syngas production; Tar contamination
KW  -  Synthesis gas manufacture
PB  - Elsevier Ltd
SN  - 03603199
EP  - 30580
AV  - none
N1  - cited By 18
SP  - 30559
TI  - Modeling and parametric optimization of air catalytic co-gasification of wood-oil palm fronds blend for clean syngas (H2+CO) production
ER  -