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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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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.

Item Type: Article
Divisions: Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Depositing User: Symplectic Elements
Date Deposited: 28 Sep 2011 11:58
Last Modified: 23 Sep 2013 18:44
URI: http://epubs.surrey.ac.uk/id/eprint/7157

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