A Bayesian data modelling framework for chemical processes using adaptive sequential design with Gaussian process regression

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.