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Optimization of Enantiomeric Gas Separation by Simulated Moving Bed Chromatography and Pressure Swing Adsorption

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Kawajiri,  Y.
Physical and Chemical Foundations of Process Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Georgia Inst. of Technology, School of Chem. & Biomol. Eng., Atlanta, USA;

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Seidel-Morgenstern,  A.
Physical and Chemical Foundations of Process Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Bentley, J., Kawajiri, Y., Huang, Q., Eic, M., & Seidel-Morgenstern, A. (2010). Optimization of Enantiomeric Gas Separation by Simulated Moving Bed Chromatography and Pressure Swing Adsorption. Talk presented at 2010 AIChE Annual Meeting. Salt Lake City, USA. 2010-11-07 - 2010-11-12.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-8E8F-8
Abstract
Separation of enantiomers by gas-phase adsorption processes was investigated. We compare two continuous gas adsorption processes: gas phase simulated moving bed (GC-SMB) chromatography [1] and pressure swing adsorption (PSA) processes. The simulated moving bed process, which is widely used as a liquid adsorption technique, consists of multiple columns connected to each other in a loop with continuous feed/desorbent supply and product withdrawal, simultaneously performing the cyclic port switching operation at a regular interval; this mimics the counter-current operation. On the other hand, the PSA process performs several operations, which include feeding, pressurization, depressurization and product withdrawal. For these two different adsorption processes, a systematic comparison was made using advanced numerical optimization techniques. As a case study, we consider the separation of the racemic mixture of enflurane, an inhalation anesthetic that is commonly administered to patients during surgery [1]. GC-SMB and PSA processes were described by a rigorous mathematical model, which consists of a set of partial differential algebraic equations. Using this model, a systematic dynamic optimization scheme was developed within the gPROMS modeling and optimization environment. In order to handle the dynamics at the cyclic steady state, a tailored method with a Newton-based solver was employed which has been reported in our previous studies [2,3]. A detailed comparison was made considering the productivity, purity, and recovery of the purified enantiomer. Furthermore, we explore several modified operations; for GC-SMB, the pressure gradient operation, which has been proposed for supercritical SMB chromatography [4], was investigated and compared to the standard isobaric operation. In the pressure gradient operation, the pressures in the adsorption columns can be independent, not uniform. We find the pressure gradient operation outperforms the isobaric operation significantly when the equilibrium deviates from Henry's law. For PSA, the vacuum swing adsorption (VSA) and five-step VSA involving product rinse step operations were considered additionally. The improvement by introducing these modifications was also demonstrated by our systematic optimization approach. Finally, the performances of PSA and SMB were compared systematically based on numerical optimization. 1. G. Biressi, F. Quattrini, M. Juza, M. Mazzotti, V. Schurig, and M. Morbidelli, "Gas chromatographic simulated moving bed separation of the enantiomers of the inhalation anesthetic enflurane", Chem. Engr. Sci. 55, 4537 (2000) 2. Y. Kawajiri and L. Biegler, "Optimization Strategies for Simulated Moving Bed and PowerFeed Processes", AIChE J. 52, 1343 (2006) 3. Q. Huang, A. Malekiana, and M. Eić, "Optimization of PSA process for producing enriched hydrogen from plasma reactor gas", Sep. Pur. Technol. 62, 22 (2008) 4. M. Mazzotti, G. Storti, and M. Morbidelli, "Supercritical fluid simulated moving bed chromatography", J. Chrom. A 786, 309 (1997)