Abstract
Digitalization, artificial intelligence, and autonomous experimentation are reshaping chemical process development by enabling data-driven system identification and model-based optimization. Despite these advances, mechanistic models remain a cornerstone for predicting chemical reaction behavior and supporting optimization. However, purely mechanistic models often exhibit limited predictive accuracy when key phenomena affecting kinetics, mass and energy transfer are not fully captured. To address limitations on kinetic modelling, a hybrid modelling framework is proposed in this work that integrates a lumped power-law kinetic model with a Gaussian Process (GP) residual model to predict the reaction rate across the experimental design space while quantifying the uncertainty of the predicted rate. The hybrid model is then coupled with multi-objective Bayesian optimization (MOBO) by employing a weighted-sum approach and an upper confidence bound acquisition function to guide experimental design by simultaneously maximizing reaction rate and minimizing the associated uncertainty on prediction. The approach is tested on a case study related to catalytic methane oxidation in an autonomous microreactor system, where the proposed hybrid model performs comparably to the ground truth model, identifying the same optimal operating conditions when validated against the original experimental data, with an average prediction uncertainty of approximately 2.7%. The results highlight the potential of hybrid modelling and uncertainty-aware optimization for guiding autonomous catalytic experiments in flow systems.