Dynamic optimization of bioreactors using probabilistic tendency models and Bayesian active learning
| Autor: | Ernesto Martínez, Mariano Daniel Cristaldi, Ricardo José Antonio Grau |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
MODELING FOR OPTIMIZATION
Engineering Active learning (machine learning) General Chemical Engineering Bayesian probability INGENIERÍAS Y TECNOLOGÍAS Machine learning computer.software_genre Bayesian inference FED-BATCH FERMENTATION SENSITIVITY ANALYSIS Parametric statistics Data collection EXPERIMENTAL DESIGN Ingeniería de Procesos Químicos business.industry Probabilistic-based design optimization Probabilistic logic RUN-TO-RUN OPTIMIZATION Computer Science Applications Ingeniería Química BAYESIAN INFERENCE Artificial intelligence business computer Subspace topology |
| Zdroj: | Computers & Chemical Engineering. 49:37-49 |
| ISSN: | 0098-1354 |
| Popis: | 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. Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Grau, Ricardo José Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect