Simulated moving bed processes

B. Pynnonen, Simulated moving bed processing escape from the liigh-cost box , ]. Chromatogr. 827 143-160(1998). 

S. Nagamatsu, K. Murazumi and S. Makino, Cliiral separation of a pharmaceutical intermediate by a simulated moving bed process , ]. Chromatogr. 832 55-65 (1999). 

The UOP Ebex process has been available for license since the 1970s. This process is a rejective simulated moving bed process where the ethylbenzene is the least adsorbed member of the mixed xylenes and is recovered in high purity in the raffinate stream [47]. Other liquid phase simulated moving bed concepts selective for ethylbenzene have been considered. These would ostensibly require less adsorbent circulation per unit feed because ethylbenzene is typically at <20% concentrahon in mixed xylenes [48, 49]. A process is disclosed by Broughton [50] that produces a pure m-xylene stream along with a pure ethylbenzene stream. 

A. Kienle, et al., Improving simulated moving bed processes by cyclic modulation of the feed concentration. Chem. Engng. 

Kloppenburg E. and Gilles E.D., Automatic control of the simulated moving bed process for Cg aromatics separation using asymptotically exact input/output-linearization. J. of Process Control 9 (2000) pp. 41-50. 

IDENTIFICATION AND PREDICTIVE CONTROL OF A SIMULATED MOVING BED PROCESS... 

Subsequently, methods for the model-based design and optimization of batch and SMB (Simulated Moving Bed) processes are introduced. For this purpose dynamic simulations of an experimentally validated model are used. When a model-based approach is not affordable, as for example in the design and optimization of complex SMB processes, a short-cut design and optimization method for SMB is introduced. Finally, the performances of both batch and SMB chromatography are compared. 

This chapter gives an introduction to the concept of model predictive control and an overview of the concepts proposed for the control of simulated moving bed processes. Thereafter the benefits of a model-based optimizing control strategy for the example of a 6-column reactive SMB plant of pharmaceutical scale are presented. 

Online Optimizing Control of a Reactive Simulated Moving Bed Process 9.4.1... 

The reactive simulated moving bed process considered here is the isomerization of glucose to fructose. The plant consists of six reactive chromatographic fixed beds that are interconnected to form a closed-loop arrangement (Fricke and Schmidt-Traub, 2002). As shown in Fig. 9.3, a pure glucose solution is injected to the system at the feed line. At the extract line, a mixture of glucose and fructose, called high fructose... 

Heuer, C., Kiisters, E., Plattner, T., Seidel-Morgenstem, A. Design of the simulated moving bed process based on adsorption isotherm measurements using a perturbation method, J. Chromatogr. A, 1998, 827, 175-191. 

Kloppenburg, E., Gilles, E. D. A new concept for operating simulated moving-bed processes, Chem. Eng. Technol., 1999a, 22, 10, 813-817. 

Kiisters, E., Gerber, G., Antia, F. Enantiosepara-tion of a chiral epoxide by simulated moving bed processes, AIChEJ., 1995, 42, 154-160. 

Schramm, H., Kienle, A., Kaspereit, M., Seidel-Morgenstern, A. Improved operation of simulated moving bed processes through cyclic modulation of feed flow and feed concentration, Chem. Eng. Sci., 2003, 58, 5217-5227. 

Toumi, A., Engell, S. Optimal operation and control of a reactive simulated moving bed process, oral presentation at 7th International... 

The separation of enantiomers has become of particular importance in the production of pharmaceuticals. These separations are often difficult because the separation factors achieved on many chiral stationary phases (CSP) are low or moderate (often below 1.6) and the saturation capacities of most CSPs are low. The optimization of the experimental conditions of a separation is particularly important because most chromatographic separations of enantiomers are performed using the simulated moving bed process (SMB, see Chapter 17) and the complexity of this process, the long time that it needs to approach steady-state makes optimization by trial and error lengthy and costly. So, much attention has been devoted... 

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