Robust model predictive control for heat exchanger network

• Published in: Applied thermal engineering Vol. 73; no. 1; pp. 924 - 930
• Main Authors: Bakošová, Monika, Oravec, Juraj
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 05.12.2014; Elsevier
• Subjects: Applied sciences; Consumption; Control systems; Devices using thermal energy; Energy; Energy saving; Energy. Thermal use of fuels; Exact sciences and technology; Heat exchanger; Heat exchangers; Heat exchangers (included heat transformers, condensers, cooling towers); Heat transfer; Linear matrix inequalities; Mathematical models; Matlab; Networks; Optimization; Predictive control; Robust model-based predictive control; Theoretical studies. Data and constants. Metering; Uncertain system; Robust model-based predictive control; Uncertain system; Linear matrix inequalities; Energy saving; Heat exchanger; Energy conservation; Network; Models; Modeling; Energy savings; Predictive control
• DOI: 10.1016/j.applthermaleng.2014.08.023
• ISSN: 1359-4311

Optimal operation of heat exchangers represents a challenging task from the control viewpoint, due to the presence of system nonlinearities, varying process parameters, internal and external disturbances and measurement noise. Various robust control strategies were developed to overcome all these problems. The robust model predictive control (RMPC) represents one of suitable approaches. It enables to design effective control algorithms for optimization of the control performance subject to the process uncertainties and the input and output constraints. The possibility to implement the RMPC for control of a heat exchanger network is investigated in this paper, where three counter-current heat exchangers with uncertain parameters connected in series represent the controlled process. The efficiency of the advanced RMPC algorithm was verified by simulation experiments realized in the MATLAB/Simulink environment. The results confirmed that using the RMPC for the controlled process modelled as a system with uncertain parameters led to less consumption of cooling medium compared with the consumption achieved by using the optimal linear quadratic (LQ) control. •The robust and the optimal controllers control the heat exchanger network.•The robust model predictive controller is a solution of linear matrix inequalities.•Robust control assures smaller offsets and lower coolant consumption.