Evolutionary Operation and Control of Chromatographic Processes

( ) A no®el generalized run-to-run control GR2R control strategy is presented for the optimization and control of nonlinear preparati®e chromatographic processes. The ( ) GR2R approach synergistically employs a hybrid both physical and empirical model to control chromatographic processes in the presence of sporadic and autocorrelated disturbances. First, parameters of the physical model through experiments are determined, and then the physical model is used to estimate initial parameters of the nonlinear ( ) empirical model Hammerstein using orthogonal forward regression. Parameters of the nonlinear empirical model are updated at the end of each run using a nonlinear recursi®e parameter estimation method. The updated empirical model is then used in the ( ) control algorithm model predicti®e control to estimate operating conditions for the next batch. Processes operating under fixed optimal conditions are compared with those operating with GR2R control for both gradient and displacement chromatography. The ( GR2R outperforms the fixed conditions in the presence of ®arious disturbances such as ) bed capacity, column efficiency, and feed load and is an effecti®e strategy for the optimization and control of complex chromatographic processes.