Abstract
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.
| Original language | English |
|---|---|
| Article number | 107048 |
| Journal | Computers and Chemical Engineering |
| Volume | 142 |
| DOIs | |
| Publication status | Published - 2 Nov 2020 |
| MoE publication type | A1 Journal article-refereed |
Funding
The authors pleased to acknowledge the financial support and facilities provided by Hamad Bin Khalifa Univesity , Doha, Qatar and University Teknologi PETRONAS , Malaysia.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 13 Climate Action
Keywords
- Air gasification
- Catalyst loading
- Higher heating value
- NARX neural network
- Time series modelling
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