Soft-constrained robust model predictive control of a plate heat exchanger: Experimental analysis

Abstract Real processes with heat exchange have usually complex behaviour and are energy intensive. In practical applications, the process variables are always bounded, and it is suitable to include these boundaries into the controller design. The soft-constrained robust model predictive controller has been designed to improve the control performance and energy consumption in comparison with the robust model predictive control with only hard constraints. Experimental application of soft-constrained robust model predictive control (SCR MPC) for a laboratory plate heat exchanger is presented in this paper. The plate heat exchanger is a non-linear process with asymmetric dynamics and is modelled as a system with parametric uncertainties. The controlled variable is the temperature of the heated fluid at the outlet of the heat exchanger and the manipulated variable is the volumetric flow rate of the heating fluid. The actuator is a peristaltic pump and the influence of the linear and non-linear actuator characteristics on the control performance is also investigated. The set-point tracking using SCR MPC is studied for the laboratory plate heat exchanger in an extensive case study. The experimental results confirmed the improvement of the control responses and reduction of energy consumption by introducing the soft constraints into MPC design.

[1]  Ján Mikleš,et al.  Process Modelling, Identification, and Control , 2010 .

[2]  Junghui Chen,et al.  PLS-based multi-loop robust H2 control for improvement of operating efficiency of waste heat energy conversion systems with organic Rankine cycle , 2017 .

[3]  Monika Bakosova,et al.  Soft constraints in the robust MPC design via LMIs , 2016, 2016 American Control Conference (ACC).

[4]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[5]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[6]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[7]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[8]  Michal Fratczak,et al.  Simplified dynamical input–output modeling of plate heat exchangers – case study , 2016 .

[9]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[10]  Michal Fratczak,et al.  Practical validation of the effective control of liquid-liquid heat exchangers by distributed parameter balance-based adaptive controller , 2018 .

[11]  Juraj Oravec,et al.  Experimental investigation of alternative robust model predictive control of a heat exchanger , 2016 .

[12]  Béla G. Lipták,et al.  Instrument engineers' handbook , 2012 .

[13]  Libor Kudela,et al.  Potential of predictive control for improvement of seasonal coefficient of performance of air source heat pump in Central European climate zone , 2018, Energy.

[14]  Wang Jingcheng,et al.  Min-max MPC for LPV systems subject to actuator saturation by a saturation-dependent Lyapunov function , 2013, Proceedings of the 32nd Chinese Control Conference.

[15]  Miroslav Fikar,et al.  Lab of Things: A Network-Based I/O Services for Laboratory Experimentation , 2017 .

[16]  Ting Ma,et al.  Design and optimization of a novel high temperature heat exchanger for waste heat cascade recovery from exhaust flue gases , 2018, Energy.

[17]  Alfredo Núñez,et al.  Dynamic simulator and model predictive control of an integrated solar combined cycle plant , 2016 .

[18]  Alberto Bemporad,et al.  Robust model predictive control: A survey , 1998, Robustness in Identification and Control.

[19]  Jizhen Liu,et al.  Closed-loop optimization control on fan speed of air-cooled steam condenser units for energy saving and rapid load regulation , 2017 .

[20]  Marian Trafczynski,et al.  Robust model predictive control and PID control of shell-and-tube heat exchangers , 2018, Energy.

[21]  Juraj Oravec,et al.  Advanced Robust MPC Design of a Heat Exchanger: Modeling and Experiments , 2017 .

[22]  Jiří Jaromír Klemeš,et al.  Heat transfer improvement, energy saving, management and pollution reduction , 2018, Energy.

[23]  Yujie Dong,et al.  Multi-layer perception based model predictive control for the thermal power of nuclear superheated-steam supply systems , 2018 .

[24]  Manfred Morari,et al.  Robust constrained model predictive control using linear matrix inequalities , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[25]  Miroslav Fikar,et al.  Soft-Constrained Alternative Robust MPC: Experimental Study , 2017 .

[26]  Juraj Oravec,et al.  Robust model predictive control of a plate heat exchanger , 2018 .

[27]  Robert Grabarczyk,et al.  Energy saving potential and the efficacy of using different control strategies for the heat exchanger network operation , 2018 .

[28]  Mariusz Markowski,et al.  On-line control of the heat exchanger network under industrial constraints , 2018 .