TY - JOUR
T1 - Air catalytic biomass (PKS) gasification in a fixed-bed downdraft gasifier using waste bottom ash as catalyst with NARX neural network modelling
AU - Shahbaz, Muhammad
AU - Taqvi, Syed Ali Ammar
AU - Inayat, Muddasser
AU - Inayat, Abrar
AU - Sulaiman, Shaharin A.
AU - McKay, Gordon
AU - Al-Ansari, Tareq
N1 - Publisher Copyright:
© 2020
PY - 2020/11/2
Y1 - 2020/11/2
N2 - The air gasification of Palm Kernel Shells (PKS) using coal bottom ash (CBA) as a catalyst has been performed in a fixed-bed gasifier. The impact of three process parameters, namely, temperature (575–775 °C), air flowrate (1.5–45 litter/min) and catalyst loading (0–30 wt.%) has been investigated on the product gas yield. The composition of the H2 product is computed to be a maximum of 28 vol.% at 875 °C. The air flowrate has a direct relation with H2 production. The catalysts used have demonstrated a positive impact on the carbon conversion efficiency, showing the increase in carbon-containing gases in the product gas due to the increases in gas yield. A Non-linear Autoregressive Network with exogenous inputs (NARX) neural network has been used to predict the gaseous flowrate dynamically in order to improve gasification performance. The predicted results from the NARX network demonstrate good agreement with the experimental study with R2 ≥ 0.99.
AB - The air gasification of Palm Kernel Shells (PKS) using coal bottom ash (CBA) as a catalyst has been performed in a fixed-bed gasifier. The impact of three process parameters, namely, temperature (575–775 °C), air flowrate (1.5–45 litter/min) and catalyst loading (0–30 wt.%) has been investigated on the product gas yield. The composition of the H2 product is computed to be a maximum of 28 vol.% at 875 °C. The air flowrate has a direct relation with H2 production. The catalysts used have demonstrated a positive impact on the carbon conversion efficiency, showing the increase in carbon-containing gases in the product gas due to the increases in gas yield. A Non-linear Autoregressive Network with exogenous inputs (NARX) neural network has been used to predict the gaseous flowrate dynamically in order to improve gasification performance. The predicted results from the NARX network demonstrate good agreement with the experimental study with R2 ≥ 0.99.
KW - Air gasification
KW - Catalyst loading
KW - Higher heating value
KW - NARX neural network
KW - Time series modelling
UR - http://www.scopus.com/inward/record.url?scp=85089153631&partnerID=8YFLogxK
U2 - 10.1016/j.compchemeng.2020.107048
DO - 10.1016/j.compchemeng.2020.107048
M3 - Article
AN - SCOPUS:85089153631
SN - 0098-1354
VL - 142
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 107048
ER -