1. Model-based design and experimental validation of simulated moving bed reactor for production of glycol ether ester
- Author
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Jungmin Oh, Alfred K. Schultz, Timothy C. Frank, Andreas S. Bommarius, Balamurali Sreedhar, Gaurav Agrawal, Megan E. Donaldson, Yoshiaki Kawajiri, and Shan Tie
- Subjects
Work (thermodynamics) ,Chromatography ,Optimization problem ,business.industry ,General Chemical Engineering ,Propylene glycol methyl ether acetate ,02 engineering and technology ,General Chemistry ,010402 general chemistry ,021001 nanoscience & nanotechnology ,01 natural sciences ,Industrial and Manufacturing Engineering ,Continuous production ,0104 chemical sciences ,Solvent ,chemistry.chemical_compound ,Column chromatography ,chemistry ,Scientific method ,Environmental Chemistry ,Simulated moving bed ,0210 nano-technology ,Process engineering ,business - Abstract
This work proposes a practical and systematic model-based approach to identify the optimal operating conditions for a simulated moving bed reactor (SMBR). The SMBR operation is applied to an industrial case study for the continuous production of a solvent, propylene glycol methyl ether acetate (DOWANOL™ PMA), which is produced 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. A multi-objective optimization problem was formulated for developing an SMBR process to maximize the production rate of PMA and the conversion of the esterification reaction simultaneously. In this study, this optimization problem is solved using the epsilon-constrained method and a Pareto plot is presented. The solutions that corresponded to three different values of conversion, 70%, 80%, and 85%, are experimentally validated. The SMBR model that was developed from batch kinetic and single column chromatography experiments demonstrates reasonable agreement with the experimental results. Furthermore, the SMBR experimental data was used to correct the parameters in the model. A validation study at a higher conversion of 95% demonstrates improved predictability of the corrected parameters.
- Published
- 2016
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