Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H2) production-Reference-Cited by-同舟云学术

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

Published:2024-09 Issue: Volume:84 Page:200-214
ISSN:0360-3199
Container-title:International Journal of Hydrogen Energy
language:en
Short-container-title:International Journal of Hydrogen Energy

Author:

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

Funder

Universiti Teknologi PETRONAS

King Saud University

Publisher

Elsevier BV

Reference76 articles.

1. Electrochemical hydrogen storage materials: state-of-the-art and future perspectives;Xu;Energy & Fuels,2024

2. Predicting and optimizing syngas production from fluidized bed biomass gasifiers: a machine learning approach;Kim;Energy,2023

3. Technology projection in biofuel production using agricultural waste materials as a source of energy sustainability: a comprehensive review;Sikiru;Renew Sustain Energy Rev,2024

4. Life cycle cost analysis of power generation from underground coal gasification with carbon capture and storage (CCS) to measure the economic feasibility;Feng;Resour Policy,2024

5. Development of a biomass pyrolyser for sawdust and rice husks conversion to energy;Ishola;J. of Agric. Eng. and Technol.,2021

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