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Lookup NU author(s): 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.
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
Online publication date: 10/08/2022
Acceptance date: 25/07/2022
Date deposited: 27/09/2022
ISSN (electronic): 1526-4025
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