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

Muddasser Inayat; Shaharin A. Sulaiman; Abrar Inayat; Nagoor Basha Shaik; Abdul Rehman Gilal; Muhammad Shahbaz

doi:10.1016/j.ijhydene.2020.10.268

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

Muddasser Inayat^*
, Shaharin A. Sulaiman
, Abrar Inayat
, Nagoor Basha Shaik
, Abdul Rehman Gilal
, Muhammad Shahbaz

^*Corresponding author for this work

Not published at VTT

Universiti Teknologi Petronas
University of Sharjah
Sukkur IBA University
Hamad bin Khalifa University

Research output: Contribution to journal › Article › Scientific › peer-review

28 Citations (Scopus)

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.

Original language	English
Pages (from-to)	30559-30580
Journal	International Journal of Hydrogen Energy
Volume	46
Issue number	59
DOIs	https://doi.org/10.1016/j.ijhydene.2020.10.268
Publication status	Published - 26 Aug 2021
MoE publication type	A1 Journal article-refereed

Funding

The authors acknowledge Universiti Teknologi PETRONAS Malaysia for the financial and technical support provided for the study. Authors also acknowledge Universal Lime Sdn Bhd, and FELCRA Nasaruddin Sdn Bhd Bota, Perak, Malaysia for providing free catalyst (dolomite and limestone) and oil palm fronds respectively for this work.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

ANN
Catalytic co-gasification
Oil palm fronds
Portland cement
RSM

Access to Document

10.1016/j.ijhydene.2020.10.268

Cite this

@article{35e384b0dcba44668f0f48993ae74456,

title = "Modeling and parametric optimization of air catalytic co-gasification of wood-oil palm fronds blend for clean syngas (H2+CO) production",

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.",

keywords = "ANN, Catalytic co-gasification, Oil palm fronds, Portland cement, RSM",

author = "Muddasser Inayat and Sulaiman, \{Shaharin A.\} and Abrar Inayat and Shaik, \{Nagoor Basha\} and Gilal, \{Abdul Rehman\} and Muhammad Shahbaz",

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month = aug,

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doi = "10.1016/j.ijhydene.2020.10.268",

language = "English",

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Modeling and parametric optimization of air catalytic co-gasification of wood-oil palm fronds blend for clean syngas (H₂+CO) production. / Inayat, Muddasser; Sulaiman, Shaharin A.; Inayat, Abrar et al.
In: International Journal of Hydrogen Energy, Vol. 46, No. 59, 26.08.2021, p. 30559-30580.

Research output: Contribution to journal › Article › Scientific › peer-review

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T1 - Modeling and parametric optimization of air catalytic co-gasification of wood-oil palm fronds blend for clean syngas (H2+CO) production

AU - Inayat, Muddasser

AU - Sulaiman, Shaharin A.

AU - Inayat, Abrar

AU - Shaik, Nagoor Basha

AU - Gilal, Abdul Rehman

AU - Shahbaz, Muhammad

PY - 2021/8/26

Y1 - 2021/8/26

N2 - 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.

AB - 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.

KW - ANN

KW - Catalytic co-gasification

KW - Oil palm fronds

KW - Portland cement

KW - RSM

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