Data-Driven Superheating Control of Organic Rankine Cycle Processes
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 sys...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4154019 |
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| _version_ | 1849404862451679232 |
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| author | Jianhua Zhang Xiao Tian Zhengmao Zhu Mifeng Ren |
| author_facet | Jianhua Zhang Xiao Tian Zhengmao Zhu Mifeng Ren |
| author_sort | Jianhua Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a0792fc6364490ebf948a9ab9e5dddb |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9a0792fc6364490ebf948a9ab9e5dddb2025-08-20T03:36:52ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/41540194154019Data-Driven Superheating Control of Organic Rankine Cycle ProcessesJianhua Zhang0Xiao Tian1Zhengmao Zhu2Mifeng Ren3State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing 102206, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaIn 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.http://dx.doi.org/10.1155/2018/4154019 |
| spellingShingle | Jianhua Zhang Xiao Tian Zhengmao Zhu Mifeng Ren Data-Driven Superheating Control of Organic Rankine Cycle Processes Complexity |
| title | Data-Driven Superheating Control of Organic Rankine Cycle Processes |
| title_full | Data-Driven Superheating Control of Organic Rankine Cycle Processes |
| title_fullStr | Data-Driven Superheating Control of Organic Rankine Cycle Processes |
| title_full_unstemmed | Data-Driven Superheating Control of Organic Rankine Cycle Processes |
| title_short | Data-Driven Superheating Control of Organic Rankine Cycle Processes |
| title_sort | data driven superheating control of organic rankine cycle processes |
| url | http://dx.doi.org/10.1155/2018/4154019 |
| work_keys_str_mv | AT jianhuazhang datadrivensuperheatingcontroloforganicrankinecycleprocesses AT xiaotian datadrivensuperheatingcontroloforganicrankinecycleprocesses AT zhengmaozhu datadrivensuperheatingcontroloforganicrankinecycleprocesses AT mifengren datadrivensuperheatingcontroloforganicrankinecycleprocesses |