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A Bayesian Data Modelling Framework for Chemical Processes using Adaptive Sequential Design with Gaussian Process Regression

Lookup NU author(s): Dr Liam Fleming, Dr Jie ZhangORCiD, Dr Shirley ColemanORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Accurate simulators are relied upon in the process industry for plant design andoperation. Typical simulators, based on mechanistic models, require considerableresources: skilled engineers, computational time and proprietary data. This paperexplores the complexities of developing a statistical modelling framework for chemicalprocesses, focusing on inherent non-linearity in phenomena and the difficulty ofobtaining data. A Bayesian approach to modelling is forwarded in this paper, utilisingBayesian sequential design to maximise information gain for each experiment.Gaussian Process (GP) regression is used to provide a highly flexible model class tocapture non-linearities in the process data. A non-linear process simulator, modelledin Aspen Plus is used as a surrogate for a real chemical process, to test the capabilitiesof the framework.

Publication metadata

Author(s): Fleming L, Emerson J, Stitt H, Zhang J, Coleman S

Publication type: Article

Publication status: Published

Journal: Applied Stochastic Models in Business and Industry

Year: 2022

Volume: 38

Issue: 5

Pages: 787-805

Online publication date: 10/08/2022

Acceptance date: 25/07/2022

Date deposited: 27/09/2022

ISSN (electronic): 1526-4025

Publisher: Wiley


DOI: 10.1002/asmb.2709


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