A Bayesian data modelling framework for chemical processes using adaptive sequential design with Gaussian process regression
Liam Fleming, Joseph Emerson, E. Hugh Stitt, Jie Zhang, Shirley Yvonne Coleman · Applied Stochastic Models in Business and Industry · 2022
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.