A Bayesian data modelling framework for chemical processes using adaptive sequential design with Gaussian process regression. (10th August 2022)

• Record Type: Journal Article
• Authors: Fleming, Liam; Emerson, Joseph; Stitt, Hugh; Zhang, Jie; Coleman, Shirley
• Other Names: Reis Marco S. guestEditor.; Haselgruber Nikolaus guestEditor.
• Is Part Of: Applied stochastic models in business and industry. Volume 38:Number 5(2022)
• Page Start: 787
• Page End: 805
• Publication Date: 2022-08-10
• Subjects: non‐linearity -- orthogonal design -- prediction -- process improvement -- simulation; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2709
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract: Accurate simulators are relied upon in the process industry for plant design and operation. Typical simulators, based on mechanistic models, require considerable resources: skilled engineers, computational time, and proprietary data. This article explores the complexities of developing a statistical modelling framework for chemical processes, focusing on inherent non‐linearity in phenomena and the difficulty of obtaining data. A Bayesian approach to modelling is forwarded in this article, utilising Bayesian sequential design to maximise information gain for each experiment. Gaussian process regression is used to provide a highly flexible model class to capture non‐linearities in the process data. A non‐linear process simulator, modelled in Aspen Plus is used as a surrogate for a real chemical process, to test the capabilities of the framework.