Developing a Hybrid Modeling Framework for Enhanced Prediction in Chemical Reaction Kinetics

Accurate modeling of chemical reaction kinetics is fundamental for the optimization and control of industrial processes. However, conventional kinetic modeling approaches often prove inadequate due to the limited understanding of the complex reaction mechanisms present. Hybrid modeling has emerged as a promising alternative, integrating a mechanistic backbone with data-driven models to enhance interpretability and predictive accuracy. This study develops and evaluates two hybrid models, constructed upon distinct mechanistic foundations─a Langmuir–Hinshelwood and a power-law backbone─augmented by an artificial neural network to predict the behavior of a reaction system encompassing the water–gas shift reaction and methanol synthesis from syngas. The proposed models achieved high predictive accuracy and robust uncertainty estimation across a wide range of reaction conditions, with the power-law based hybrid model providing new insights into kinetic behavior. Overall, this work underscores the potential of hybrid modeling as a precise framework for quantifying chemical reaction kinetics in complex systems.