Learning hydrocracking reaction dynamics via neural ODEs: A data‐driven, gradient‐interpretable lumped modelling framework

Abstract This study applies neural ordinary differential equations (neural ODEs) to model hydrocracking kinetics, a key process for converting heavy hydrocarbons into lighter products like gasoline and diesel. Neural ODEs provide a data‐driven approach, learning reaction dynamics directly from data without requiring explicit assumptions on kinetics, addressing limitations in traditional methods. Two neural ODE models were trained on synthetic hydrocracking data representing different kinetic assumptions: one based on a 2.5‐order reaction scheme (Model A) and the other on a first‐order scheme (Model B), across varying temperatures and feedstocks. The models demonstrated high predictive accuracy when predicting within the range of training data, with RMSE values remaining below 0.5 wt.% under most conditions. However, performance declined during high‐temperature extrapolation scenarios, particularly for the higher‐order model, revealing challenges in capturing nonlinear dynamics at extreme conditions. This work also enhanced the interpretability of neural ODEs by analyzing gradients within the model, which validated alignment with known kinetic principles, uncovering critical information about reaction pathways and temperature sensitivities. This analysis demonstrated the models' ability to capture temperature‐dependent behaviour and rate stabilization, as illustrated through heat maps, which further emphasized the potential of neural ODEs for both predictive accuracy and interpretative insights in hydrocracking modelling. Additionally, the extracted gradients present an exciting avenue for future advancements, such as leveraging symbolic regression techniques to uncover governing equations.