(148g) Online Cycle to Cycle Optimizing Control of Varicol and Simulated Moving Bed Processes

Simulated Moving Bed (SMB) and Varicol are well established technologies for continuous chromatographic separation especially for enantiomers¹. However, optimum SMB or Varicol operation is a challenge and currently these processes are operated sub-optimally to ensure robustness. Consequently, control and automation of SMB is receiving increasing attention lately to exploit the full economic potential of these processes^2-8. These control schemes require accurate physical data i.e., adsorption isotherm, column voidage, and dispersion parameters' which are difficult and time consuming to measure and may change over time due to aging of the stationary phase.

We have developed an SMB control scheme which guarantees the fulfilment of product and process specifications and optimizes the economics of the process at the same time^9-15. Besides, this controller requires only minimal information such as adsorption behaviour at infinite dilution and average void fraction of the columns. Recently, experimental validation of this optimizing controller has been demonstrated through separation of nucleosides, a system with linear isotherm. This was based on continuous online monitoring and frequent controlling (64 times per cycle) of the SMB unit. However, accurate online monitoring of the SMB unit is a key challenge for the efficient implementation of any control scheme especially for chiral systems due to a number of technical and system specific constraints and to the presence of impurities. A more pragmatic approach would be to develop a control scheme that makes use of existing and accurate monitoring schemes currently used in the industry such as HPLC analysis over every cycle ('cycle to cycle'). However, this imposes additional loads on the controller since it gets less frequent information, i.e. once in a cycle, from the plant to take necessary control actions. Therefore, evaluation of the performance of this optimizing controller under cycle to cycle information is important for implementing it to industry and using the full economic benefits of this.

This paper reports experimental validation of the 'cycle to cycle' control scheme in a laboratory scale SMB unit for the separation of a binary mixture of nucleosides exhibiting linear isotherm. The effectiveness of the 'cycle to cycle' control scheme is also shown by several simulations of SMB and Varicol processes under nonlinear chromatographic conditions. The results illustrate that the 'cycle to cycle' controller is able to meet the products' purity specifications and operate the process optimally with minimal information regardless of the disturbances that might take place during the operation.

1. Juza M, Mazzotti M, Morbidelli M. Simulated moving-bed chromatography and its application to chirotechnology. Trends in Biotechnology. 2000;18:108-118. 2. Kloppenburg E, Gilles ED. Automatic control of the simulated moving bed process for C-8 aromatics separation using asymptotically exact input/output-linearization. Journal of Process Control. 1999;9:41-50. 3. Song I, Lee S, Rhee H, Mazzotti M. Identification and predictive control of a Simulated moving bed process: Purity control. Chemical Engineering Science. 2006;61:1973-1986. 4. Schramm H, Kaspereit M, Kienle A, Seidel-Morgenstern A. Simulated moving bed process with cyclic modulation of the feed concentration. Journal of Chromatography A. 2003;1006:77-86. 5. Wang C, Klatt KU, Dunnebier G, Engell S, Hanisch F. Neural network-based identification of SMB chromatographic processes. Control Engineering Practice. 2003;11:949-959. 6. Klatt KU, Hanisch F, Dunnebier G. Model-based control of a simulated moving bed chromatographic process for the separation of fructose and glucose. Journal of Process Control. 2002;12:203-219. 7. Toumi A, Engell S. Optimization-based control of a reactive simulated moving bed process for glucose isomerization. Chemical Engineering Science. 2004;59:3777-3792. 8. Song I-H, Amanullah M, Erdem G, Mazzotti M, Rhee H-K. Experimental implementation of identification-based optimizing control of a Simulated Moving Bed process. Journal of Chromatography A. 2006;1113:60-73. 9. Abel S, Erdem G, Amanullah M, Morari M, Mazzotti M, Morbidelli M. Optimizing control of simulated moving beds - experimental implementation. Journal of Chromatography A. 2005;1092:2-16. 10. Abel S, Erdem G, Mazzotti M, Morari M, Morbidelli M. Optimizing control of simulated moving beds - linear isotherm. Journal of Chromatography A. 2004;1033:229-239. 11. Erdem G, Abel S, Amanullah M, Morari M, Mazzotti M, Morbidelli M. Automatic control of simulated moving beds - Experimental verification. Adsorption. 2005;11:573-577. 12. Erdem G, Abel S, Morari M, Mazzotti M, Morbidelli M. Automatic control of simulated moving beds II: Nonlinear isotherm. Industrial & Engineering Chemistry Research. 2004;43:3895-3907. 13. Erdem G, Abel S, Morari M, Mazzotti M, Morbidelli M. Online optimization based feedback control of simulated moving bed chromatographic units. Chemical and Biochemical Engineering Quarterly. 2004;18:319-328. 14. Erdem G, Abel S, Morari M, Mazzotti M, Morbidelli M, Lee JH. Automatic control of simulated moving beds. Industrial & Engineering Chemistry Research. 2004;43:405-421. 15. Erdem G, Amanullah M, Morari M, Mazzotti M, Morbidelli M. Optimizing control of an experimental simulated moving bed unit. AIChE Journal. 2006;52:1481-1494.