421772 Optimal Operation of Simulated Moving Bed Reactor: Model Correction and Parameter Estimation

Tuesday, November 10, 2015: 8:30 AM
155E (Salt Palace Convention Center)
Shan Tie1, Balamurali Sreedhar2, Gaurav Agrawal1, Jungmin Oh1, Megan E. Donaldson2, Alfred Schultz2, Timothy C. Frank2, Andreas Bommarius1 and Yoshiaki Kawajiri1, (1)School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, (2)The Dow Chemical Company, Midland, MI

Simulated moving bed reactor (SMBR) offers a better economic and environmental alternative to conventional sequential integration of batch reactor and separator operations. Specifically, SMBR improves separation resolution, increases productivity, and reduces solvent consumption [1]. Yet despite these benefits, the complexity in SMBR modeling and design is extremely challenging for industrial implementation. Within the past decade, work has been done on the optimal design and operation of SMBR; however, these investigations were focused on single-objective optimization or used heuristic based algorithms [2-4].

This work demonstrates a practical and deterministic model-based approach to optimize an industrially relevant SMBR process and to identify the optimal SMBR operating parameters. The SMBR operation is applied to an industry case study for the continuous production of a solvent, propylene glycol methyl ether acetate (DOWANOLTMPMA) through an acid-catalyzed esterification reaction of 1-methoxy-2-propanol and acetic acid.

The model-based approach is demonstrated by lab-scale SMBR experiments. The model is capable of identifying the operating strategy to satisfy productivity and conversion objectives and constraints for product purity. The initial model containing kinetic and adsorption parameters was obtained from batch reaction and single-column reactive chromatography experiments [4]. From these experiments, a mathematical model to predict the performance of the SMBR is developed. With this model, a multiple objective optimization problem was formulated [5] and several optimal operations, particularly conversion targets from 70% to 95% were experimentally validated. We will show that the initial model of SMBR performance is predictive of SMBR experimental results. Moreover, this model’s accuracy can be improved when the experimental SMBR data is used to recalculate the parameter values of the SMBR model.


1.         Seidel-Morgenstern, A., L.C. Keßler, and M. Kaspereit, New Developments in Simulated Moving Bed Chromatography. Chemical Engineering & Technology, 2008. 31(6): p. 826-837.

2.         Dünnebier, G., Fricke, J., Klatt, K-U., Optimal Design and Operation of Simulated Moving Bed Chromatographic Reactors. Industrial & Engineering Chemistry Research, 2000. 39(7): p. 2290-2304.

3.         Lode, F., Francesconi, G., Mazzotti, M., Morbidelli, M., Synthesis of methylacetate in a simulated moving-bed reactor: Experiments and modeling. AIChE Journal, 2003. 49(6): p. 1516-1524.

4.         Oh, J., Agrawal, G.,  Sreedhar, B., Donaldson, M. E., Schultz, A. K., Frank, T. C., Bommarius, A. S., Kawajiri, Y., Conversion improvement for catalytic synthesis of propylene glycol methyl ether acetate by reactive chromatography: Experiments and parameter estimation. Chemical Engineering Journal, 2015. 259(0): p. 397-409.

5.         Agrawal, G., Oh, J., Sreedhar, B., Tie, S., Donaldson, M. E., Frank, T. C., Schultz, A. K., Bommarius, A. S., Kawajiri, Y., Optimization of reactive simulated moving bed systems with modulation of feed concentration for production of glycol ether ester. Journal of Chromatography A, 2014. 1360(0): p. 196-208.

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