Neural Network-based Identification and Mpc Control of Smb Chromatography

In this contribution, the identification and control of nonlinear SMBchromatographic processes are discussed. Instead of using the physical manipulated process variables, the flow rates of extract, desorbent, and recycle, and the switching time directly, a new set of input variables ( -factors) is employed as control inputs to reduce input/output couplings. A new measure of the front positions of the axial concentration profiles is used as outputs. Multi-layer neural network models are identified for this nonlinear MIMO system. The identified model is used in a model predictive control algorithm. In this algorithm a parameter varying linear model is employed which avoids the on-line computation of the nonlinear optimization problem. The simulation results show that the identified model gives a very good approximation of the process models and the LPVMPC scheme has a good control performance.