Recent developments of control strategies for organic Rankine cycle (ORC) systems

Abstract

This is a repository copy of Recent developments of control strategies for organic Rankine cycle (ORC) systems.

Data‐Driven Superheating Control of Organic Rankine Cycle Processes

“…The main advantage of QMEE is that it can decrease the computational complexity compared with MEE. When calculating IP 2 e k in MEE based on sliding window, whose width is N, the computational complexity is O N 2 due to the existence of inner summation; meanwhile, IP Q 2 e k can be calculated according to (5) adopting the quantizing error samples, which reduces the number of inner summations. , which are used to estimate IP Q 2 e k at instant k, can be collected using a sliding window whose width is N.…”

“…Some efforts have been made to develop superheating control algorithms for ORC processes [5][6][7][8][9][10][11][12][13][14][15]. In [6], traditional PID controller was applied to control the superheating of an ORC-based waste heat recovery process by manipulating pump flow rate.…”

Optimal tuning of model predictive controllers for organic Rankine cycle systems recovering waste heat from heavy-duty vehicles

“…One of the main challenges for the waste heat recovery are the large and rapid fluctuations of the mass flow rate and temperature of the waste heat caused by the unsteady driving conditions of the truck. In order to cope with these, increasing efforts have been dedicated to the design of the ORC unit considering the dynamic behavior of the ORC system and to the development of control strategies for the ORC unit [7][8][9]. Jimenez-Arreola et al [10] studied the dynamic behavior of two types of ORC evaporators subjected to fluctuating waste heat.…”

Control of Supercritical Organic Rankine Cycle based Waste Heat Recovery System Using Conventional and Fuzzy Self-tuned PID Controllers

“…Therefore, the proposed evaporator model based on fuzzy logic was developed in this research to save computation time and make it suitable for real-time applications. The control in real-time ORC systems is always challenging because it has to deal with the dynamic conditions of the heat source, which has many constraints and uncertainties that must be taken into account in control system simulations [15]. To address these challenges, different control strategies such as conventional PID and advanced controllers, e.g.…”