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). 1095 - 1103. ISSN 1385-8947
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Official URL: http://dx.doi.org/10.1016/j.cej.2010.11.097
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|
|Deposited By:||Symplectic Elements|
|Deposited On:||28 Sep 2011 12:58|
|Last Modified:||25 Apr 2013 13:48|
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