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Open AccessArticle

Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration

1
Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, 1190 Vienna, Austria
2
Novasign GmbH, 1190 Vienna, Austria
*
Author to whom correspondence should be addressed.
Processes 2020, 8(12), 1625; https://doi.org/10.3390/pr8121625
Received: 23 November 2020 / Revised: 4 December 2020 / Accepted: 7 December 2020 / Published: 9 December 2020
(This article belongs to the Section Advanced Digital and Other Processes)
Ultrafiltration is a powerful method used in virtually every pharmaceutical bioprocess. Depending on the process stage, the product-to-impurity ratio differs. The impact of impurities on the process depends on various factors. Solely mechanistic models are currently not sufficient to entirely describe these complex interactions. We have established two hybrid models for predicting the flux evolution, the protein rejection factor and two components’ concentration during crossflow ultrafiltration. The hybrid models were compared to the standard mechanistic modeling approach based on the stagnant film theory. The hybrid models accurately predicted the flux and concentration over a wide range of process parameters and product-to-impurity ratios based on a minimum set of training experiments. Incorporating both components into the modeling approach was essential to yielding precise results. The stagnant film model exhibited larger errors and no predictions regarding the impurity could be made, since it is based on the main product only. Further, the developed hybrid models exhibit excellent interpolation properties and enable both multi-step ahead flux predictions as well as time-resolved impurity forecasts, which is considered to be a critical quality attribute in many bioprocesses. Therefore, the developed hybrid models present the basis for next generation bioprocessing when implemented as soft sensors for real-time monitoring of processes. View Full-Text
Keywords: semi-parametric model; neural network; tangential flow filtration; downstream processing; advanced process monitoring semi-parametric model; neural network; tangential flow filtration; downstream processing; advanced process monitoring
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MDPI and ACS Style

Krippl, M.; Bofarull-Manzano, I.; Duerkop, M.; Dürauer, A. Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration. Processes 2020, 8, 1625. https://doi.org/10.3390/pr8121625

AMA Style

Krippl M, Bofarull-Manzano I, Duerkop M, Dürauer A. Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration. Processes. 2020; 8(12):1625. https://doi.org/10.3390/pr8121625

Chicago/Turabian Style

Krippl, Maximilian; Bofarull-Manzano, Ignasi; Duerkop, Mark; Dürauer, Astrid. 2020. "Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration" Processes 8, no. 12: 1625. https://doi.org/10.3390/pr8121625

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