کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
172718 458558 2013 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Dynamic optimization of bioreactors using probabilistic tendency models and Bayesian active learning
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
پیش نمایش صفحه اول مقاله
Dynamic optimization of bioreactors using probabilistic tendency models and Bayesian active learning
چکیده انگلیسی

Due to the complexity of metabolic regulation, first-principles models of bioreactor dynamics typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A run-to-run optimization strategy which integrates imperfect models with Bayesian active learning is proposed. Parameter distributions in a probabilistic model of bioreactor performance are re-estimated using data from experiments designed for maximizing information and performance. The proposed Bayesian decision-theoretic approach resorts to probabilistic tendency models that explicitly characterize their levels of confidence. Bootstrapping of parameter distributions is used to represent parametric uncertainty as histograms. The Bajpai & Reuss bioreactor model for penicillin production validated with industrial data is used as a representative case study. Run-to-run convergence to an improved policy is fast despite significant modeling errors as long as data are used to revise iteratively posterior distributions of the most influencing model parameters.


► Model-based design of experiments for run-to-run optimization with imperfect models.
► Bayesian optimal design of experiments in modeling for optimization.
► Active learning to trade-off information gain with performance improvement in biasing data gathering.
► Fast scale-up of operating policies for innovative bioprocesses.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Chemical Engineering - Volume 49, 11 February 2013, Pages 37–49
نویسندگان
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