Prediction-Correction Method for Optimization of Simulated Moving Bed Chromatography

A systematic algorithm for simulated moving bed (SMB) chromatography process development that utilizes dynamic optimization, transient experimental data, and parameter estimation to arrive at optimal operating conditions is described. These operating conditions ensure both high purity constraints and optimal productivity are satisfied. This algorithm proceeds until the SMB process is optimized without manual tuning. In a case study, it has been shown with a linear isotherm system that the optimal operating conditions can be reached in only two changes of operating conditions following the proposed algorithm. Another case study with a linear isotherm system has shown that the algorithm is robust to optimize the SMB even if there is significant model mismatch at first. (C) 2012 American Institute of Chemical Engineers AIChE J, 59: 736-746, 2013