[en] In this paper the performance of Model Predictive
Control (MPC) and PID based strategies to optimally recover waste heat using Organic Rankine Cycle (ORC) technology is investigated. First the relationship between the evaporating temperature and the output power is experimentally evaluated, concluding that for some given heat source conditions there exists an optimal evaporating temperature which maximizes the energy production. Three different control strategies MPC and PID based are developed in order not only to maximize energy production but to ensure safety conditions in the machine. For the case of the MPC, the Extended Prediction Self-Adaptive Control (EPSAC) algorithm is considered in this study as it uses input/output models for prediction, avoiding the need of state estimators, making of it a suitable tool for industrial applications. The experimental results obtained on a 11kWe pilot plant show that the constrained EPSAC-MPC outperforms PID based strategies, as it allows to accurately regulate the evaporating temperature with a lower control effort while keeping the superheating in a safer operating range.
IEA, "Industrial excess heat recovery technologies & applications, " Technical report, Industrial Energy-related Technologies and Systems (IETS), Tech. Rep., 2010.
A. Verneau, "Waste heat recovery by organic fluid rankine cycle, " in In Proceedings from the First Industrial Energy Technology Conference, Houston, TX, 1979, pp. 940-952.
F. Casella, T. Mathijssen, P. Colonna, and J. Van Buijtenen., "Dynamic modeling of organic rankine cycle power systems, " Journal of Engineering for Gas Turbines and Power, vol. Vol 135, pp. 042 310-042 310-12, 2013.
D. Wei, X. Lu, Z. Lu, and J. Gu., "Performance analysis and optimization of organic rankine cycle (orc) for waste heat recovery, " J. Energy Conversion and Management, vol. Vol. 48, pp. 1113-1119, 2007.
S. Quoilin, R. Aumann, A. Grill, A. Schuster, and V. Lemort., "Dynamic modeling and optimal control strategy for waste heat recovery organic rankine cycles, " Applied Energy, vol. Vol. 88, pp. 2183-2190, 2011.
G. Hou, R. Sun, G. Hu, and J. Zhang., "Supervisory predictive control for evaporator in organic rankine cycle (orc) system for waste heat recovery, " in Proceedings of the 2011 International Conference on Advanced Mechatronics Systems, 2011, pp. 306-311.
V. Lemort, A. Zoughaib, and S. Quoilin., "Comparison of control strategies for waste heat recovery organic rankine cycle systems, " In Sustainable Thermal Energy Management in the Process Industries International Conference (SusTEM2011), 2011.
J. Zhang, Y. Zhou, Y. Li, G. Hou, and F. Fang., "Generalized predictive control applied in waste heat recovery power plants, " Applied energy, vol. 102, pp. 320-326, 2013.
A. Hernandez, A. Desideri, C. Ionescu, S. Quoilin, V. Lemort, and R. D. Keyser., "Increasing the efficiency of organic rankine cycle technology by means of multivariable predictive control. " in Proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC 2014), 2014.
E. F. Camacho and C. Bordons., Model Predictive Control, 2nd ed. Springer-Verlag, London,, 2004, vol. 405 pages.
R. De Keyser, Model based Predictive Control for Linear Systems. Invited chapter in UNESCO EoLSS. Oxford (6. 43. 16. 1), 2003.
L. Wang, Model Predictive Control System Design and Implementation Using MATLABR, ser. Advances in Industrial Control. Springer, 2010.
L. Ljung, System identification: Theory for the user. Prentice-Hall, 2007.
R. De Keyser and C. Ionescu., "A frequency response tool for cacsd in matlab, " in IEEE International Symposium on Computer Aided Control Systems Design, Munich, 2006, pp. 2275-2280.
