Air catalytic biomass (PKS) gasification in a fixed-bed downdraft gasifier using waste bottom ash as catalyst with NARX neural network modelling. (2nd November 2020)

• Record Type: Journal Article
• Authors: Shahbaz, Muhammad; Taqvi, Syed Ali Ammar; Inayat, Muddasser; Inayat, Abrar; Sulaiman, Shaharin A.; McKay, Gordon; Al-Ansari, Tareq
• Is Part Of: Computers & chemical engineering. Volume 142(2020)
• Publication Date: 2020-11-02
• Subjects: Air gasification -- Catalyst loading -- NARX neural network -- Time series modelling -- Higher heating value; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.107048
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Study on air catalytic gasification of PKS using downdraft fixed-bed gasifier. Effect of temperature, air flowrate, catalyst were evaluated on gas composition. Time series ANN modelling approach to predict the gaseous flowrate. Good agreement found amongst developed NARX neural networks model and experiment data. 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 R 2 ≥ 0.99.