Robust Model Predictive Control of Heat Exchangers in Series

Abstract

This study investigates using of robust model based predictive control (MPC) algorithms for optimal operating of heat exchangers in series from the stability and economic viewpoints. For the advanced controller design, the influence of uncertain parameters was taken into account. In order to design the robust MPC, the optimization problem with constraints was formulated in the form of linear matrix inequalities and then the convex optimization problem was solved using the semidefinite programming. The designed robust MPC strategies were based on the worst-case optimization and on the additional control input saturation. We investigated a case study with two various significant disturbances in the temperature of the input stream in the heat exchangers in series. Results revealed that the robust MPC improved control performance and ensured energy savings during the heat exchanger network operation.