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 . 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 . 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  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.
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