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Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network

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



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


A two-stage gasification has been proven as an effective and robust approach for converting low-valued and/or highly heterogeneous materials i.e. waste, into hydrogen and/or syngas due to its tight control and flexibility in operation. As the gas yield and gas properties depend upon materials and operating conditions, the interactions of operating conditions should not be ignored. However, these have not been able to fully capture experimentally. In this work, an artificial neural network model was developed and validated using experimental data to predict and optimise the gasification process thereby reducing time and costs in developing and testing. The model can predict accurately gas composition and yield corresponding to the variations at the output with a correlation R2 > 0.99. The developed neural network model was then applied for optimising operating conditions of the two-stage gasification for high carbon conversion, high hydrogen yield and low carbon dioxide in nitrogen and carbon dioxide environments. The predicted conditions were tested, and the results agreed well with experimental data. For example, at the optimum operating conditions (900˚C for the 1st stage and 1000oC for the 2nd stage with a steam/carbon ratio of 3.8 in nitrogen and 5.7 in carbon dioxide environments), the gas yield, hydrogen and carbon dioxide were 96.2 wt%, 70 mol% and 16.4 mol% for nitrogen environment and 97.2 wt%, 66 mol% and 12 mol% for carbon dioxide environment.

Publication metadata

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

Publication type: Article

Publication status: Published

Journal: Applied Energy

Year: 2021

Volume: 302

Print publication date: 15/11/2021

Online publication date: 20/08/2021

Acceptance date: 30/07/2021

Date deposited: 24/09/2021

ISSN (print): 0306-2619

ISSN (electronic): 1872-9118

Publisher: Elsevier


DOI: 10.1016/j.apenergy.2021.117567


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Funder referenceFunder name
PhD studentship (Scholarship reference number: LCS-2018-603)