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Robust modelling development for optimisation of hydrogen production from biomass gasification process using bootstrap aggregated neural network

Lookup NU author(s): Dr Hannah Kargbo, Dr Jie ZhangORCiD, Professor Anh Phan

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

In this study, a robust model using bootstrapped aggregated neural network (BANN) was developed for optimising operating conditions of a two-stage gasification for high carbon conversion, high hydrogen yield and low CO₂. The developed BAAN model predicted accurately (R² of 0.999) the gas composition and the 95% confidence bounds for model predictions on unseen validation data indicated good prediction reliability for various feedstock. The BANN was also used to predict the optimum operating condition for hydrogen production from waste wood (1^st stage temperature of 900˚C, 2^nd stage temperature of 1000˚C, steam/carbon molar ratio of 5.7) to achieve high hydrogen (71-72 mol%), gas yield (98-99 wt%) and low CO₂ (17-18 mol%). The optimal conditions were tested in the laboratory and the experimental results agreed well with the predicted data with an error of 0.01-0.05. Sensitivity analysis revealed that an increase in temperatures for both stages and high steam/carbon ratio favoured the H₂ production and carbon conversion.

Publication metadata

Author(s): Kargbo HO, Zhang J, Phan AN

Publication type: Article

Publication status: Published

Journal: International Journal of Hydrogen Energy

Year: 2023

Volume: 48

Issue: 29

Pages: 18012-10828

Print publication date: 05/04/2023

Online publication date: 31/12/2022

Acceptance date: 09/12/2022

Date deposited: 19/06/2023

ISSN (print): 0360-3199

ISSN (electronic): 0360-3199

Publisher: Elsevier

URL: https://doi.org/10.1016/j.ijhydene.2022.12.110

DOI: 10.1016/j.ijhydene.2022.12.110

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