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Dynamic optimization of bioreactors using probabilistic tendency models and Bayesian active learning

Publication Date
DOI: 10.1016/b978-0-444-53711-9.50157-7
  • Bayesian Inference
  • Bioprocesses
  • Model-Based Experimental Design
  • Modeling For Optimization
  • Uncertainty
  • Design
  • Logic


Abstract First-principles models of fermentation processes typically have built-in errors in the form of structural mismatch and parametric uncertainty. A model-based optimization approach for run-to-run improvement under uncertainty of fed-batch bioreactors by integrating probabilistic tendency models with Bayesian inference is proposed. Probabilistic models grounded on first principles are used in the design of dynamic experiments to bias data gathering towards the subspace of most promising operating conditions. Results obtained in the fed-batch fermentation of penicillin G are presented.

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