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• W2133160798 abstract "Data-based empirical models, though widely used in process optimization, are restricted to a specific process being modeled. Model migration has been proved to be an effective technique to adapt a base model from a old process to a new but similar process. This paper proposes to apply the flexible Gaussian process regression (GPR) for empirical modeling, and develops a Bayesian method for migrating the GPR model. The migration is conducted by a functional scale-bias correction of the base model, as opposed to the restrictive parametric scale-bias approach. Furthermore, an iterative approach that jointly accomplishes model migration and process optimization is presented. This is in contrast to the conventional “two-step” method whereby an accurate model is developed prior to model-based optimization. A rigorous statistical measure, the expected improvement, is adopted for optimization in the presence of prediction uncertainty. The proposed methodology has been applied to the optimization of a simulated chemical process, and a real catalytic reaction for the epoxidation of trans-stilbene." @default.
• W2133160798 created "2016-06-24" @default.
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• W2133160798 date "2011-02-01" @default.
• W2133160798 modified "2023-10-18" @default.
• W2133160798 title "Bayesian migration of Gaussian process regression for rapid process modeling and optimization" @default.
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• W2133160798 doi "https://doi.org/10.1016/j.cej.2010.11.097" @default.
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