Li, Z., Ding, Z., Wang, M. orcid.org/0000-0001-9752-270X et al. (1 more author) (2018) Model-free adaptive control for MEA-based post-combustion carbon capture processes. Fuel, 224. pp. 637-643. ISSN 0016-2361
Abstract
For the flexible operation of mono-ethanol-amine-based post-combustion carbon capture processes, recent studies concentrate on model-based protocols which require underline model parameters of carbon capture processes for controller design. In this paper, a novel application of the model-free adaptive control algorithm is proposed that only uses measured input-output data for carbon capture processes. Compared with proportional-integral control, the stability of the closed-loop system can be easily guaranteed by increasing a stabilizing parameter. By updating the pseudo-partial derivative vector to estimate a dynamic model of the controlled plant on-line, this new protocol is robust to plant uncertainties. Compared with model predictive control, tuning tests of the protocol can be conducted on-line without non-trivial repetitive off-line sensitivity or identification tests. Performances of the model-free adaptive control are demonstrated within a neural network carbon capture plant model, identified and validated with data generated by a first-principle carbon capture model.
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 Fuel. 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: | Post-combustion carbon capture; Process control; Model-free adaptive control; System identification; Neural networks |
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 12:04 |
Last Modified: | 30 Mar 2019 01:42 |
Published Version: | https://doi.org/10.1016/j.fuel.2018.03.096 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.fuel.2018.03.096 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130219 |
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Filename: 2018_02_14_Ziang_Modified_Manuscript.pdf
Licence: CC-BY-NC-ND 4.0