Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H2) production

Kunmi Joshua Abioye; Noorfidza Yub Harun; Ushtar Arshad; Suriati Sufian; Mohammad Yusuf; Ahmad Hussaini Jagaba; Joshua O. Ighalo; Abdullah A. Al-Kahtani; Hesam Kamyab; Ashok Kumar; Chander Prakash; Jude A. Okolie; Hussameldin Ibrahim

doi:10.1016/j.ijhydene.2024.06.397

Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H₂) production

Kunmi Joshua Abioye^*, Noorfidza Yub Harun^*, Ushtar Arshad, Suriati Sufian, Mohammad Yusuf, Ahmad Hussaini Jagaba, Joshua O. Ighalo, Abdullah A. Al-Kahtani, Hesam Kamyab, Ashok Kumar, Chander Prakash, Jude A. Okolie, Hussameldin Ibrahim

^*Corresponding author for this work

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

10 Scopus citations

Abstract

Syngas (CO + H₂) production through biomass gasification offers a promising and sustainable alternative to conventional fuels. This study investigates the co-gasification of palm oil decanter cake (PODC) and Alum Sludge (AS), utilizing response surface methodology (RSM) and artificial neural network (ANN) techniques to optimize and predict syngas production. Conducted in a fixed bed horizontal reactor, the experiment investigates temperature, airflow rate, and particle size as input parameters. Results revealed that optimal condition of 900 °C temperature, 10 mL/min airflow rate, and 2 mm particle size yielded the highest syngas production at 39.48 vol%. The RSM showed an R² value of 0.9896, whereas ANN network revealed an overall R² value of 0.971. Both models demonstrated strong alignment with experimental data and the modelled equation. This research demonstrates the effective use of statistical modelling to enhance the efficiency and effectiveness of syngas production, thereby fostering advancements in sustainable energy production.

Original language	English
Pages (from-to)	200-214
Number of pages	15
Journal	International Journal of Hydrogen Energy
Volume	84
DOIs	https://doi.org/10.1016/j.ijhydene.2024.06.397
State	Published - 26 Sep 2024
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2024 Hydrogen Energy Publications LLC

Keywords

Artificial neural network
Biomass waste
Co-gasification
Response surface methodology
Syngas production
Waste management

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Fuel Technology
Condensed Matter Physics
Energy Engineering and Power Technology

UN SDGs

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

Access to Document

10.1016/j.ijhydene.2024.06.397

Cite this

Abioye, K. J., Harun, N. Y., Arshad, U., Sufian, S., Yusuf, M., Jagaba, A. H., Ighalo, J. O., Al-Kahtani, A. A., Kamyab, H., Kumar, A., Prakash, C., Okolie, J. A., & Ibrahim, H. (2024). Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H₂) production. International Journal of Hydrogen Energy, 84, 200-214. https://doi.org/10.1016/j.ijhydene.2024.06.397

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title = "Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H2) production",

abstract = "Syngas (CO + H2) production through biomass gasification offers a promising and sustainable alternative to conventional fuels. This study investigates the co-gasification of palm oil decanter cake (PODC) and Alum Sludge (AS), utilizing response surface methodology (RSM) and artificial neural network (ANN) techniques to optimize and predict syngas production. Conducted in a fixed bed horizontal reactor, the experiment investigates temperature, airflow rate, and particle size as input parameters. Results revealed that optimal condition of 900 °C temperature, 10 mL/min airflow rate, and 2 mm particle size yielded the highest syngas production at 39.48 vol%. The RSM showed an R2 value of 0.9896, whereas ANN network revealed an overall R2 value of 0.971. Both models demonstrated strong alignment with experimental data and the modelled equation. This research demonstrates the effective use of statistical modelling to enhance the efficiency and effectiveness of syngas production, thereby fostering advancements in sustainable energy production.",

keywords = "Artificial neural network, Biomass waste, Co-gasification, Response surface methodology, Syngas production, Waste management",

author = "Abioye, {Kunmi Joshua} and Harun, {Noorfidza Yub} and Ushtar Arshad and Suriati Sufian and Mohammad Yusuf and Jagaba, {Ahmad Hussaini} and Ighalo, {Joshua O.} and Al-Kahtani, {Abdullah A.} and Hesam Kamyab and Ashok Kumar and Chander Prakash and Okolie, {Jude A.} and Hussameldin Ibrahim",

note = "Publisher Copyright: {\textcopyright} 2024 Hydrogen Energy Publications LLC",

year = "2024",

month = sep,

day = "26",

doi = "10.1016/j.ijhydene.2024.06.397",

language = "English",

volume = "84",

pages = "200--214",

journal = "International Journal of Hydrogen Energy",

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Abioye, KJ, Harun, NY, Arshad, U, Sufian, S, Yusuf, M, Jagaba, AH, Ighalo, JO, Al-Kahtani, AA, Kamyab, H, Kumar, A, Prakash, C, Okolie, JA & Ibrahim, H 2024, 'Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H₂) production', International Journal of Hydrogen Energy, vol. 84, pp. 200-214. https://doi.org/10.1016/j.ijhydene.2024.06.397

Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H₂) production. / Abioye, Kunmi Joshua; Harun, Noorfidza Yub; Arshad, Ushtar et al.
In: International Journal of Hydrogen Energy, Vol. 84, 26.09.2024, p. 200-214.

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

TY - JOUR

T1 - Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H2) production

AU - Abioye, Kunmi Joshua

AU - Harun, Noorfidza Yub

AU - Arshad, Ushtar

AU - Sufian, Suriati

AU - Yusuf, Mohammad

AU - Jagaba, Ahmad Hussaini

AU - Ighalo, Joshua O.

AU - Al-Kahtani, Abdullah A.

AU - Kamyab, Hesam

AU - Kumar, Ashok

AU - Prakash, Chander

AU - Okolie, Jude A.

AU - Ibrahim, Hussameldin

PY - 2024/9/26

Y1 - 2024/9/26

N2 - Syngas (CO + H2) production through biomass gasification offers a promising and sustainable alternative to conventional fuels. This study investigates the co-gasification of palm oil decanter cake (PODC) and Alum Sludge (AS), utilizing response surface methodology (RSM) and artificial neural network (ANN) techniques to optimize and predict syngas production. Conducted in a fixed bed horizontal reactor, the experiment investigates temperature, airflow rate, and particle size as input parameters. Results revealed that optimal condition of 900 °C temperature, 10 mL/min airflow rate, and 2 mm particle size yielded the highest syngas production at 39.48 vol%. The RSM showed an R2 value of 0.9896, whereas ANN network revealed an overall R2 value of 0.971. Both models demonstrated strong alignment with experimental data and the modelled equation. This research demonstrates the effective use of statistical modelling to enhance the efficiency and effectiveness of syngas production, thereby fostering advancements in sustainable energy production.

AB - Syngas (CO + H2) production through biomass gasification offers a promising and sustainable alternative to conventional fuels. This study investigates the co-gasification of palm oil decanter cake (PODC) and Alum Sludge (AS), utilizing response surface methodology (RSM) and artificial neural network (ANN) techniques to optimize and predict syngas production. Conducted in a fixed bed horizontal reactor, the experiment investigates temperature, airflow rate, and particle size as input parameters. Results revealed that optimal condition of 900 °C temperature, 10 mL/min airflow rate, and 2 mm particle size yielded the highest syngas production at 39.48 vol%. The RSM showed an R2 value of 0.9896, whereas ANN network revealed an overall R2 value of 0.971. Both models demonstrated strong alignment with experimental data and the modelled equation. This research demonstrates the effective use of statistical modelling to enhance the efficiency and effectiveness of syngas production, thereby fostering advancements in sustainable energy production.

KW - Artificial neural network

KW - Biomass waste

KW - Co-gasification

KW - Response surface methodology

KW - Syngas production

KW - Waste management

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U2 - 10.1016/j.ijhydene.2024.06.397

DO - 10.1016/j.ijhydene.2024.06.397

M3 - Article

AN - SCOPUS:85201518676

SN - 0360-3199

VL - 84

SP - 200

EP - 214

JO - International Journal of Hydrogen Energy

JF - International Journal of Hydrogen Energy

ER -