University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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.

wyan11-cej-web.pdf - Accepted version Manuscript
Available under License : See the attached licence file.

Download (284kB)
[img] Text (licence)

Download (1kB)


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
Authors :
Yan, W
Hu, S
Yang, Y
Gao, F
Chen, T
Date : 1 February 2011
DOI : 10.1016/j.cej.2010.11.097
Depositing User : Symplectic Elements
Date Deposited : 28 Sep 2011 11:58
Last Modified : 31 Oct 2017 14:08

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800