Abstract
The presented contribution concerns the model predictive control (MPC) and moving horizon estimation (MHE) of a catalytic fixed-bed reactor model. Rigorous modeling for these systems leads to systems of (transport) partial differential equations. Following a so-called early lumping approach for the model predictive control and estimation yields high-dimensional systems of ordinary differential equations and therefore the need to solve large-scale dynamic optimization problems online. It is shown how a tailored gradient method and efficient numerical integration can be combined to solve the concerned optimization methods in a time-efficient way.
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