Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25142
Title: Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges
Authors: Imran, M
Pili, R
Usman, M
Haglind, F
Keywords: organic Rankine cycle;control;dynamic modeling;PID;model predictive control;optimized control;non-linear control;finite volume and moving boundary;robust control
Issue Date: 22-Jul-2020
Publisher: Elsevier
Citation: Imran, M. et al. (2020) 'Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges', Applied Energy, 276, 115537, pp. 1-28. doi: 10.1016/j.apenergy.2020.115537.
Abstract: Copyright © 2020 The Authors. Organic Rankine cycle systems are suitable technologies for utilization of low/medium-temperature heat sources, especially for small-scale systems. Waste heat from engines in the transportation sector, solar energy, and intermittent industrial waste heat are by nature transient heat sources, making it a challenging task to design and operate the organic Rankine cycle system safely and efficiently for these heat sources. Therefore, it is of crucial importance to investigate the dynamic behavior of the organic Rankine cycle system and develop suitable control strategies. This paper provides a comprehensive review of the previous studies in the area of dynamic modeling and control of the organic Rankine cycle system. The most common dynamic modeling approaches, typical issues during dynamic simulations, and different control strategies are discussed in detail. The most suitable dynamic modeling approaches of each component, solutions to common problems, and optimal control approaches are identified. Directions for future research are provided. The review indicates that the dynamics of the organic Rankine cycle system is mainly governed by the heat exchangers. Depending on the level of accuracy and computational effort, a moving boundary approach, a finite volume method or a two-volume simplification can be used for the modeling of the heat exchangers. From the control perspective, the model predictive controllers, especially improved model predictive controllers (e.g. the multiple model predictive control, switching model predictive control, and non-linear model predictive control approach), provide excellent control performance compared to conventional control strategies (e.g. proportional–integral controller, proportional–derivative controller, and proportional–integral–derivative controllers). We recommend that future research focuses on the integrated design and optimization, especially considering the design of the heat exchangers, the dynamic response of the system and its controllability.
URI: https://bura.brunel.ac.uk/handle/2438/25142
DOI: https://doi.org/10.1016/j.apenergy.2020.115537
ISSN: 0306-2619
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

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