(177e) Towards Optimal and Sustainable Operation of Separation Processes: The Computational Approach

Authors: 
Papathanasiou, M. M., Imperial College London
Mantalaris, A., Imperial College London
Pistikopoulos, E. N., Texas A&M Energy Institute, Texas A&M University
Separation processes (such as Simulating Moving Bed, Pressure Swing Adsorption Systems and chromatographic processes) are widely applied in the chemical/biochemical industry for the separation and/or purification of various components [1-3]. Commonly, chromatographic separations are assessed by their product recovery rate, as well as the purity of the end product, with the latter being of vital importance. Consequently it is essential to guarantee that the process runs under optimal operating conditions, achieving high recovery rates of products that meet the desired specifications. However, downstream processing is coupled with high capital and operational costs that are significantly increased as a function of the feed stream produced by the upstream [4]. Current industrial needs suggest the development of sustainable processes, characterized by lower investment and/or operation costs. To answer to global demand and competition arising from novel solutions such as single-use technologies [5, 6], separation processes and particularly preparative chromatography, requires significant advancements.

Along this line, in this work we focus on the development of advanced control strategies for a semi-continuous, chromatographic separation process [7] aiming to increase both product purity and yield. In this framework we examine a highly nonlinear, periodic system and we design novel control strategies to ensure that optimal operation is maintained. The controllers are designed and tested following our recently presented PAROC framework that comprises four steps: (i) development of a high-fidelity process model, (ii) approximation of the complex, process model, (iii) design of a multi-parametric controller and (iv) â??closed-loopâ??, in-silico validation of the controller against the process model. The controllers are tested in-silico against the high-fidelity process model and demonstrate an overall satisfactory performance, tracking the predefined setpoints and providing input profiles of reduced utility consumption. In addition, the proposed control scheme accounts for variable feeding composition, thus enabling the downstream processing to operate independently from the mixture composition resulting from the upstream.

Acknowledgements

The authors would like to thank Mr R. Oberdieck and Miss A. Quiroga-Campano for their contribution in the assessment of the controller validation. The authors would also like to thank Mr. Fabian Steinebach & Prof. M. Morbidelli from ETH Zurich, as well as Dr. Thomas Mueller-Spaeth from ChomaCon AG for their valuable input on the understanding of the MCSGP process. Financial support from the European Commission (OPTICO/G.A. No.280813) & Texas A&M University are also gratefully acknowledged.

References

1. Subramani, H.J., K. Hidajat, and A.K. Ray, Optimization of Simulated Moving Bed and Varicol Processes for Glucoseâ??Fructose Separation. Chemical Engineering Research and Design, 2003. 81(5): p. 549-567.

2. Khajuria, H. and E.N. Pistikopoulos, Dynamic modeling and explicit/multi-parametric MPC control of pressure swing adsorption systems. Journal of Process Control, 2011. 21(1): p. 151-163.

3. Müller-Späth, T., et al., Chromatographic separation of three monoclonal antibody variants using multicolumn countercurrent solvent gradient purification (MCSGP). Biotechnology and Bioengineering, 2008. 100(6): p. 1166-1177.

4. Gronemeyer, P., R. Ditz, and J. Strube, Trends in Upstream and Downstream Process Development for Antibody Manufacturing. Bioengineering, 2014. 1(4): p. 188-212.

5. Shukla, A.A. and U. Gottschalk, Single-use disposable technologies for biopharmaceutical manufacturing. Trends in Biotechnology, 2013. 31(3): p. 147-154.

6. Xenopoulos, A., A new, integrated, continuous purification process template for monoclonal antibodies: Process modeling and cost of goods studies. J Biotechnol, 2015. 213(0): p. 42-53.

7. Krättli, M., F. Steinebach, and M. Morbidelli, Online control of the twin-column countercurrent solvent gradient process for biochromatography. Journal of Chromatography A, 2013. 1293: p. 51-59.