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Modelling of a post-combustion CO₂ capture process using neural networks

Li, Fei; Zhang, Jie; Oko, Eni; Wang, Meihong


Fei Li

Jie Zhang

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Dr Eni Oko
Lecturer in Chemical Engineering

Meihong Wang


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. Simulated CO₂ capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO₂ capture process.

Publication Date Jul 1, 2015
Journal Fuel
Print ISSN 0016-2361
Electronic ISSN 1873-7153
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 151
Pages 156-163
APA6 Citation Li, F., Zhang, J., Oko, E., & Wang, M. (2015). Modelling of a post-combustion CO₂ capture process using neural networks. Fuel, 151, 156-163.
Keywords CO₂ capture; Chemical absorption; Neural networks; Data-driven modelling
Publisher URL
Additional Information Author's accepted manuscript of article published in: Fuel, 2015, v.151 : The 10th European Conference on Coal Research and its Applications


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