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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.
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
URL: https://doi.org/10.1016/j.apenergy.2021.117567
DOI: 10.1016/j.apenergy.2021.117567
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