Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H2) production
Journal
International Journal of Hydrogen Energy
ISSN
0360-3199
Date Issued
2024-09
Author(s)
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
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.
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.