(460d) Design Space Identification Via Multi-Parametric Programming
AIChE Annual Meeting
Wednesday, November 10, 2021 - 1:33pm to 1:54pm
We focus on the capture step of monoclonal antibody (mAb) manufacturing using the twin column protein A chromatography model presented by Steinebach et al. (2016). The model comprises 10 equations. It uses a two-site isotherm to describe the adsorption mechanism, lumped driving force mass transfer kinetics to describe transport in pores, and partial differential equations to describe the column mass balance. The model presented by Steinebach et al. (2016) was simulated in gPROMS® ModelBuilder. Sensitivity analysis was performed using MATLAB and the SobolGSA toolbox for the identification of the most significant variables and parameters affecting product purity, yield, and productivity for the design space identification. The results showed that column switching time, column dimensions, resin properties, feed concentration and flowrate had significant impact.
We used multi-parametric programming to map the design space of the capture process for a variation of design variables. One of the main advantages of this approach was that the design space is described by a set of affine functions which map the variables and parameters to the KPIs. This can accelerate the online system evaluation, optimisation, and control (Diangelakis et al., 2017).
Figure 1 illustrates the framework as followed in this work. First, the high fidelity model was constructed and validated. Then, design space identification via GSA and optimisation were carried out. To solve the multi-parametric programming problem the high-fidelity model was adapted to reduce computational complexity (Pistikopoulos et al., 2015). Finally, the multi-parametric approach was carried out using the reduced model. The results were assessed and compared in terms of accuracy and reliability by validation with the high-fidelity model.
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