Aku, Michael; Bawa, Solomon Gajere; Lee, Ye Seol; Galvanin, Federico; (2025) Optimization of Catalytic Methane Oxidation Using Hybrid Gaussian Process and Fisher Information-Based Experimental Design Strategies. Chemical Engineering Transactions , 119 pp. 553-558. 10.3303/CET25119093.

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Abstract

Gaussian Process (GP) modelling has gained momentum as a powerful data-driven technique for effectively modelling and optimizing complex reactor systems due to its ability to predict nonlinear behaviours and quantify prediction uncertainty. In the context of optimal experimental design, Fisher Information Matrix (FIM) is a common metric used to quantify how sensitive the response of a reaction system is with respect to its kinetic model parameters and to quantify the prediction uncertainty in mechanistic models. The focus of this research is to understand how key performance indicators (KPIs) of a reaction system are influenced by the information content of the experimental design space as quantified by FIM metrics. To achieve this, a hybrid technique that combines mechanistic modelling with data-driven GP modelling was developed, where a first GP (GP1) is used to map the relationship between the FIM and KPIs and a second GP (GP2) is used to model the relationship between operating conditions and FIM using actual lab data. The approach was tested on a case study of complete methane oxidation, showing a clear positive correlation between high values of FIM metric and high conversion of reactant – the main KPI considered in this study.

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