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Comparing in silico flowsheet optimization strategies in biopharmaceutical downstream processes.

Daphne Keulen1, Myrto Apostolidi1, Geoffroy Geldhof2

  • 1Department of Biotechnology, Delft University of Technology, Delft, The Netherlands.

Biotechnology Progress
|October 19, 2024
PubMed
Summary

This study compares biopharmaceutical downstream process optimization strategies. Decomposition methods with mechanistic models are most time-efficient for complex flowsheets.

Keywords:
artificial neural networkschromatographyfiltrationmechanistic modelingsuperstructure‐based optimization

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Area of Science:

  • Biopharmaceutical Manufacturing
  • Chemical Engineering
  • Process Optimization

Background:

  • Designing biopharmaceutical downstream processes involves selecting unit operations and optimizing conditions.
  • Complex flowsheets require strategic approaches for efficient optimization.

Purpose of the Study:

  • To compare simultaneous, top-to-bottom, and superstructure decomposition strategies for flowsheet optimization.
  • To evaluate these strategies using chromatographic Mechanistic Models (MMs) and Artificial Neural Networks (ANNs).

Main Methods:

  • Optimization of 39 flowsheets, including chromatography and buffer exchange.
  • Comparative analysis of three distinct optimization strategies.
  • Evaluation using both Mechanistic Models and Artificial Neural Networks.

Main Results:

  • All strategies converged on orthogonal structures as optimal.
  • Mechanistic Models and Artificial Neural Networks yielded consistent performance values.
  • The decomposition method with Mechanistic Models demonstrated superior time-efficiency on multi-core systems.

Conclusions:

  • The choice of optimization strategy significantly impacts flowsheet design outcomes.
  • Decomposition strategies, particularly with Mechanistic Models, offer time-efficiency for complex bioprocess optimization.
  • Both Mechanistic Models and Artificial Neural Networks are viable for evaluating optimization strategies.