Li, F., Zhang, J., Oko, E. et al. (1 more author) (2017) Modelling of a post-combustion CO2 capture process using extreme learning machine. International Journal of Coal Science & Technology, 4 (1). pp. 33-40. ISSN 2095-8293
Abstract
This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine (ELM). ELM randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables: inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flow rate, lean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Keywords: | CO2 capture; Neural networks; Data-driven modelling; Extreme learning machine |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Apr 2017 14:32 |
Last Modified: | 10 Apr 2017 14:32 |
Published Version: | http://dx.doi.org/10.1007/s40789-017-0158-1 |
Status: | Published |
Publisher: | SpringerOpen and Springer Verlag (Germany) and China Coal Society |
Refereed: | Yes |
Identification Number: | 10.1007/s40789-017-0158-1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:114842 |
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