Li, F., Zhang, J., Shang, C. et al. (3 more authors) (2018) Modelling of a post-combustion CO2 capture process using deep belief network. Applied Thermal Engineering, 130. pp. 997-1003. ISSN 1359-4311
Abstract
This paper presents a study on using deep learning for the modelling of a post-combustion CO 2 capture process. Deep learning has emerged as a very powerful tool in machine learning. Deep learning technique includes two phases: an unsupervised pre-training phase and a supervised back-propagation phase. In the unsupervised pre-training phase, a deep belief network (DBN) is pre-trained to obtain initial weights of the subsequent supervised phase. In the supervised back-propagation phase, the network weights are fine-tuned in a supervised manner. DBN with many layers of Restricted Boltzmann Machine (RBM) can extract a deep hierarchical representation of training data. In terms of the CO 2 capture process, the DBN model predicts CO 2 production rate and CO 2 capture level using the following variables as model inputs: inlet flue gas flow rate, CO 2 concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. A greedy layer-wise unsupervised learning algorithm is introduced to optimize DBN, which can bring better generalization than a single hidden layer neural network. The developed deep architecture network models can then be used in the optimisation of the CO 2 capture process.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier. This is an author produced version of a paper subsequently published in Applied Thermal Engineering. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | CO2capture; Chemical absorption; Deep belief network; Restricted Boltzmann 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: | 30 Apr 2018 12:27 |
Last Modified: | 20 Nov 2018 01:38 |
Published Version: | https://doi.org/10.1016/j.applthermaleng.2017.11.0... |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.applthermaleng.2017.11.078 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130222 |