Efficient Moving Horizon State and Parameter Estimation for the Varicol SMB Process

In this paper, a moving horizon state and parameter estimation (MHE) scheme for the Varicol process is presented. The Varicol process is an extension of the Simulated Moving Bed (SMB) process that realizes non-integer column distributions over the separation zones by an asynchronous switching of the inlet and outlet ports (the ports are shifted individually). These additional degrees of freedom can be used to yield an improvement in economical performance compared to SMB operation. The proposed estimation scheme is based on a rigorous SMB model that incorporates rigorous chromatographic columns and port switching. The absence of model simplifications allows the extension of the estimation scheme to the more complex Varicol process. The goal of the estimation scheme is to reconstruct the full state of the system, i.e. the concentration profiles along all columns, and to identify critical model parameters in the presence of noisy measurements. The estimation is based on measurements of the concentrations of the components at the two outlet ports (which are asynchronously switched from one column to the next) and at one fixed location between two columns. The state estimation scheme utilizes a deterministic model within the prediction horizon. State noise is only considered in the state and in the parameters up to the beginning of the horizon. By applying a multiple-shooting method and a real-time iteration scheme for solving the resulting optimization problem, the computation times are reduced and the scheme can be applied online. A numerical simulation for an enantiomer separation system with nonlinear adsorption isotherm is presented.