Liu, K, Li, K orcid.org/0000-0001-6657-0522, Lin, M et al. (1 more author) (2017) Control of Organic Rankine Cycle for waste heat recovery based on an optimized predictive model. In: Proceedings of the 36th Chinese Control Conference. CCC 2017: 36th Chinese Control Conference, 26-28 Jul 2017, Dalian, China. IEEE , pp. 9349-9354. ISBN 978-1-5386-2918-5
Abstract
The Organic Rankine Cycle (ORC) is a promising technique to recover low grade waste heat, and thus helps to improve the overall thermal efficiency of a process and reduce the environmental impact of large consumption of fossil fuels. A proper control strategy is a key for the safe and efficient operation of the ORC systems. In this paper, the key components in the ORC system are introduced first. Then a constrained generalized predictive control (CGPC) is designed to control the process. A controlled auto-regressive integrated moving average (CARIMA) model is used as the online self-tuning predictive model for the GPC controller, while the structure of the CARIMA model is optimized by the fast recursive algorithm (FRA). Simulation results confirm that the developed ORC control strategy is capable of achieving desirable set-point tracking performance, while also has a satisfactory disturbance rejection capability.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Organic Rankine Cycle; Waste heat recovery; Constrained generalized predictive control; Fast recursive algorithm |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Nov 2018 12:21 |
Last Modified: | 06 Mar 2019 14:31 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.23919/ChiCC.2017.8028847 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139081 |