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.
Highlights
Organic Rankine cycle (ORC) processes have been widely used to utilize low-grade thermal energy [1,2,3,4]
The superheating is one of the key operating parameters involved with safety and energy efficiency; superheating control plays an important role in organic Rankine cycle (ORC) processes
It is not easy to design a high-quality superheating control system for ORC processes because ORC processes are complex in terms of nonlinearities, coupling, and stochastic disturbances
Summary
Organic Rankine cycle (ORC) processes have been widely used to utilize low-grade thermal energy [1,2,3,4]. Complexity for assessing closed-loop control performance; some control strategies have been proposed for non-Gaussian systems based on minimizing entropy of tracking error [19,20,21,22,23]. In this paper, following the recent developments on shape control of the output PDF, tracking control, and information theoretic learning using minimum error entropy principle, we cast superheating control of ORC processes into a stochastic control framework. Within this framework, a data-driven tracking control strategy is further investigated for ORC systems with non-Gaussian disturbances.
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