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Integrated Bayesian parameter estimation with model-based design of experiments for dynamic processes
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DOI：10.1002/aic.18418.png)
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摘要
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Integration of Bayesian parameter estimation (BPE) with model-based design of experiments (MBDoE) aims to estimate process parameters efficiently and systematically. To achieve more efficient use of resources by iteratively selecting informative experiments based on current knowledge about the parameters, an integrated method with BPE and MBDoE is proposed in this article. A built-in Markov Chain Monte Carlo is also developed to yield not only the distribution profile of the estimated parameters but also aid in the determination of stopping criteria and the estimation of noise. Two applications of dynamic batch reactor systems are presented. Comparative analysis substantiates the superior performance of this approach in terms of parameter accuracy and the efficiency of experimentation.
Keyword:
Bayesian parameter estimation
design of experiments
MCMC
期刊
IF:
4
论文数:
1.1W
被引数:
2.9W
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分享收藏CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics.png)
CHEMCATCHEM
OAAI
CHEMCATCHEMIF3.9
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