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Modelling of a Post-combustion CO2 Capture Process Using Neural Networks

Lookup NU author(s): Dr Fei Li, Dr Jie ZhangORCiD, Dr Eni OkoORCiD



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


This paper presents a study of modelling post-combustion CO2 capture process using bootstrap aggregated neural networks. The neural network models predict CO2 capture rate and CO2 capture level using the following variables as model inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. The developed neural network models can then be used in the optimisation of the CO2 capture process.

Publication metadata

Author(s): Li F, Zhang J, Oko E, Wang M

Publication type: Article

Publication status: Published

Journal: Fuel

Year: 2015

Volume: 151

Pages: 156-163

Print publication date: 01/07/2015

Online publication date: 24/02/2015

Acceptance date: 09/02/2015

Date deposited: 09/02/2015

ISSN (print): 0016-2361

ISSN (electronic): 1873-7153

Publisher: Elsevier Ltd


DOI: 10.1016/j.fuel.2015.02.038


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