Modeling and parametric optimization of air catalytic co-gasification of wood-oil palm fronds blend for clean syngas (H2+CO) production. (26th August 2021)

• Record Type: Journal Article
• Authors: Inayat, Muddasser; Sulaiman, Shaharin A.; Inayat, Abrar; Shaik, Nagoor Basha; Gilal, Abdul Rehman; Shahbaz, Muhammad
• Is Part Of: International journal of hydrogen energy. Volume 46:Number 59(2021)
• Page Start: 30559
• Page End: 30580
• Publication Date: 2021-08-26
• Subjects: Catalytic co-gasification -- ANN -- RSM -- Portland cement -- Oil palm fronds; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2020.10.268
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: Air catalytic co-gasification of wood-OPF blend was carried out in downdraft gasifier. RSM and ANN were applied for optimization and modeling of process. Process optimized for maximum H2, CO, and low tar formation n in syngas. Portland cement was found good for H2 production and limestone for low tar yield. RSM and ANN both satisfactory validate and predict the responses.