Online Optimization of Batch Chromatography

The crucial issue here is the handling of the inevitable discrepancies between the behavior of the plant and the predictions by the model. For example, if a mismatch of the predicted purities is observed, this can be used to modify the target values 

The first iterations w -w are needed to collect the information that is required to estimate the empirical gradients. Thereafter, the iterative improvement starts. It can be seen that despite a significant error in the chromatograms, the iterative optimization converges to the true optimum and establishes the desired purities and recoveries of the components. In recent work, this approach has been applied to continuous annular electrochromatography (Behrens and Engell, 2011). 

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