Journal
CHEMICAL ENGINEERING SCIENCE
Volume 134, Issue -, Pages 23-35Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2015.04.045
Keywords
Process modeling; Model migration; Bayesian parameter estimation; Markov chain Monte Carlo; Sequential design; Injection molding process
Categories
Funding
- National Natural Science Foundation of China [61227005]
- Ministry of Science and Technology, Taiwan [MOST 103-2622-E-007-025-]
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We consider a Bayesian inference approach to enhance model migration, building on concepts laid out in an earlier paper (Lu and Gao, 2008a). Previous studies have been limited to a least-squares solution and have failed to take prior knowledge into consideration, possibly tending to cause overfitting and inaccurate estimations. We present a framework for Bayesian migration that can naturally incorporate and use prior information. The approach involves imposing normal-inverse-gamma priors over the migration parameter and exploring the resulting posterior distributions using a Markov chain Monte Carlo method. In addition, we provide a batch sequential design framework for iterative implementation of model migration, which thus avoids an exhaustive treatment of a predetermined number of design points. The effectiveness of these proposed methods is demonstrated using two examples: a numerical study and an injection molding process. (C) 2015 Elsevier Ltd. All rights reserved.
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