Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 142, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2020.107048
Keywords
Air gasification; Catalyst loading; NARX neural network; Time series modelling; Higher heating value
Funding
- Hamad Bin Khalifa Univesity, Doha, Qatar
- University Teknologi PETRONAS, Malaysia
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The air gasification of Palm Kernel Shells (PKS) using coal bottom ash (CBA) as a catalyst has been per-formed in a fixed-bed gasifier. The impact of three process parameters, namely, temperature (575-775 degrees C), air flowrate (1.5-45 litter/min) and catalyst loading (0-30wt.%) has been investigated on the product gas yield. The composition of the H 2 product is computed to be a maximum of 28 vol.% at 875 degrees C. The air flowrate has a direct relation with H-2 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 R-2 >= 0.99. (C) 2020 Elsevier Ltd. All rights reserved.
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