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Real-Time Optimization of Organic Rankine Cycle Systems by Extremum-Seeking Control ?

机译:寻求极值控制的有机朗肯循环系统的实时优化

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In this paper, the optimal operation of a stationary sub-critical 11 kW el organic Rankine cycle (ORC) unit for waste heat recovery (WHR) applications is investigated, both in terms of energy production and safety conditions. Simulation results of a validated dynamic model of the ORC power unit are used to derive a correlation for the evaporating temperature, which maximizes the power generation for a range of operating conditions. This idea is further extended using a perturbation-based extremum seeking (ES) algorithm to identify online the optimal evaporating temperature. Regarding safety conditions, we propose the use of the extended prediction self-adaptive control (EPSAC) approach to constrained model predictive control (MPC). Since it uses input/output models for prediction, it avoids the need for state estimators, making it a suitable tool for industrial applications. The performance of the proposed control strategy is compared to PID-like schemes. Results show that EPSAC-MPC is a more effective control strategy, as it allows a safer and more efficient operation of the ORC unit, as it can handle constraints in a natural way, operating close to the boundary conditions where power generation is maximized.
机译:在本文中,从能源生产和安全条件两方面,研究了用于废热回收(WHR)应用的固定亚临界11 kW el有机朗肯循环(ORC)单元的最佳运行。经过验证的ORC动力装置动态模型的仿真结果可用于得出蒸发温度的相关性,从而在一定范围的工作条件下最大化发电量。使用基于扰动的极值搜寻(ES)算法进一步扩展了此想法,以在线确定最佳蒸发温度。关于安全条件,我们建议将扩展预测自适应控制(EPSAC)方法用于约束模型预测控制(MPC)。由于它使用输入/输出模型进行预测,因此无需状态估计器,从而使其成为工业应用的合适工具。将所提出的控制策略的性能与类似PID的方案进行比较。结果表明,EPSAC-MPC是一种更有效的控制策略,因为它可以使ORC单元更安全,更有效地运行,因为它可以自然方式处理约束,在接近发电最大化的边界条件下运行。

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