Model Predictive Control Method of Simulated Moving Bed Chromatographic Separation Process Based on Subspace System Identification

Abstract

Simulated moving bed (SMB) chromatographic separation is a new type of separation technology based on traditional fixed bed adsorption operation and true moving bed (TMB) chromatographic separation technology, which includes inlet-outlet liquid, liquid circulation, and feed liquid separation. The input-output data matrices were constructed based on SMB chromatographic separation process data. The SMB chromatographic separation process was modeled by utilizing two subspace system identification algorithms: multivariable output-error state-space (MOESP) identification algorithm and numerical algorithms for subspace state-space system identification (N4SID), so as to obtain the 3rd-order and 4th-order state-space yield models of the SMB chromatographic separation process, respectively. The model predictive control method based on the established state-space models is used in the SMB chromatographic separation process. The influence of different control indicators on the predictive control system response performance is discussed. The output response curves of the yield models were obtained by changing the related parameters so that the yield model parameters are optimized set meanwhile. Finally, the simulation results showed that the yield models are successfully controlled based on the each control period and given yield range.