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Data-Driven Superheating Control of Organic Rankine Cycle Processes
Joint Authors
Ren, Mifeng
Zhang, Jianhua
Zhu, Zhengmao
Tian, Xiao
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-21
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
In this paper, a data-driven superheating control strategy is developed for organic Rankine cycle (ORC) processes.
Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy (QMEE) is adopted to construct the performance index of superheating control systems.
Furthermore, particle swarm optimization (PSO) algorithm is applied to obtain optimal control law by minimizing the performance index.
The implementation procedures of the presented superheating control system in an ORC-based waste heat recovery process are presented.
The simulation results testify the effectiveness of the presented control algorithm.
American Psychological Association (APA)
Zhang, Jianhua& Tian, Xiao& Zhu, Zhengmao& Ren, Mifeng. 2018. Data-Driven Superheating Control of Organic Rankine Cycle Processes. Complexity،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1134001
Modern Language Association (MLA)
Zhang, Jianhua…[et al.]. Data-Driven Superheating Control of Organic Rankine Cycle Processes. Complexity No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1134001
American Medical Association (AMA)
Zhang, Jianhua& Tian, Xiao& Zhu, Zhengmao& Ren, Mifeng. Data-Driven Superheating Control of Organic Rankine Cycle Processes. Complexity. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1134001
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134001