Data-Driven Superheating Control of Organic Rankine Cycle Processes. (21st October 2018)

• Record Type: Journal Article
• Authors: Zhang, Jianhua; Tian, Xiao; Zhu, Zhengmao; Ren, Mifeng
• Other Names: Na Jing Guest Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-10-21
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/4154019
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.