Multivariable model predictive control of a catalytic reverse flow reactor

This paper is devoted to the multiple input multiple output (MIMO) model predictive control (MPC) of a catalytic reverse flow reactor (RFR). The RFR aims to reduce, by catalytic reaction, the amount of volatile organic compounds (VOCs) released in the atmosphere. The peculiarity of this process is that the gas flow inside the reactor is periodically reversed in order to trap the heat released during the reaction. The objective of this paper is to propose a solution to avoid the limitation seen in a previous study for the single input single output (SISO) control case. It was dealing with the impossibility to avoid degradation of the catalytic elements due to the excessive heating induced by the exothermic reaction. In order to overcome this issue, a ratio of the inlet (cold) gas flow is now bypassed into the central zone. This allows introducing a second manipulated variable: the dilution rate. The phenomenological model considered here for the MIMO MPC of the RFR is obtained from a rigorous first principles modeling. The resulting accurate nonlinear partial differential equation (PDE) model can be a drawback for the model based controller implementation. To overcome this issue, we use a MPC strategy previously developed: it combines a two phase approximation of the PDE model in an internal model control (IMC) structure. This strategy allows using a less accurate model and a less time-consuming control algorithm. Finally, efficiency of the control approach is shown in simulation.

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