Nonlinear identification and control of Organic Rankine Cycle systems using sparse polynomial models

peer reviewedDevelopment of a first principles model of a system is not only a time- and cost- consuming task, but often leads to model structures which are not directly usable to design a controller using current available methodologies. In this paper we use a sparse identification procedure to obtain a nonlinear polynomial model. Since this is a NP-hard problem, a relaxed algorithm is employed to accelerate its convergence speed. The obtained model is further used inside the nonlinear Extended Prediction Self-Adaptive control (NEPSAC) approach to Non- linear Model Predictive Control (NMPC), which replaces the complex nonlinear optimization problem by a simpler iterative quadratic programming procedure. An organic Rankine cycle system, characterized for presenting nonlinear time-varying dynamics, is used as benchmark to illustrate the effectiveness of the proposed combined strategies

Nonlinear identification and control of Organic Rankine Cycle systems using sparse polynomial models

Development of a first principles model of a system is not only a time- and cost-consuming task, but often leads to model structures which are not directly usable to design a controller using current available methodologies. In this paper we use a sparse identification procedure to obtain a nonlinear polynomial model. Since this is a NP-hard problem, a relaxed algorithm is employed to accelerate its convergence speed. The obtained model is further used inside the nonlinear Extended Prediction Self-Adaptive control (NEPSAC) approach to Nonlinear Model Predictive Control (NMPC), which replaces the complex nonlinear optimization problem by a simpler iterative quadratic programming procedure. An organic Rankine cycle system, characterized for presenting nonlinear time-varying dynamics, is used as benchmark to illustrate the effectiveness of the proposed combined strategies

Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges

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