Bayesian migration of Gaussian process regression for rapid process modeling and optimization
Yan, W, Hu, S, Yang, Y, Gao, F and Chen, T (2011) Bayesian migration of Gaussian process regression for rapid process modeling and optimization Chemical Engineering Journal, 166 (3). pp. 1095-1103.
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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.
|Divisions :||Faculty of Engineering and Physical Sciences > Chemical and Process Engineering|
|Date :||1 February 2011|
|Identification Number :||https://doi.org/10.1016/j.cej.2010.11.097|
|Depositing User :||Symplectic Elements|
|Date Deposited :||28 Sep 2011 11:58|
|Last Modified :||23 Sep 2013 18:44|
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