Handling model uncertainty in control of a pressure swing adsorption unit for syngas purification: A multi-model zone control scheme-based robust model predictive control strategy

The application of a robustly stabilizing model predictive control (MPC) strategy to a pressure swing adsorption (PSA) unit, hitherto unexplored in the literature, is addressed in this work. Here, a set of linear models to handle model uncertainties describes the PSA nonlinear dynamic behaviour. Each model represents a different operating condition of the PSA, generating a multi-model uncertainty description. In addition to incorporating the multi-model uncertainty, the proposed MPC control law deals with a controlled output zone tracking scheme to systematically accommodate typical cyclic steady states of a PSA process. From the industrial standpoint, economic targets on process variables (controlled outputs, manipulated inputs, or both) are also incorporated into the robust control method. A Real-Time Optimizer usually defines these targets, making up a two-layer control scheme. The effectiveness of the robust MPC is evaluated in a syngas purification process via a single bed, six-step PSA unit composed of a porous amino-functionalized titanium terephthalate MIL-125-type MOF adsorbent.

Automated summary

This paper addresses the application of a robust model predictive control strategy to a pressure swing adsorption unit. Linear models are used to handle uncertainties and describe the nonlinear behavior of the unit. The control law incorporates a multi-model uncertainty description and a tracking scheme for steady states.