Physics—Informed neural ordinary differential equations for hydrocracking kinetics modeling and system identification

Accurate kinetic modeling of hydrocracking, a process that converts heavy hydrocarbons into lighter, high-value products, is crucial for refinery optimization and energy efficiency. Traditional lumped mechanistic models often suffer from parameter identifiability issues, while purely data-driven methods ignore physical laws, limiting interpretability and extrapolation. This work presents a Physics Informed Neural Ordinary Differential Equation (PINODE) framework that integrates reaction stoichiometry, rate positivity, and Arrhenius temperature dependence directly into the learning architecture. Using synthetic datasets from two established five-lump hydrocracking models, multiple PINODE variants were benchmarked, demonstrating that progressively embedding physical constraints reduces concentration prediction errors by 40%–55% relative to an unconstrained baseline. The best-performing variants achieved test RMSE (Root Mean Squared Error) values below 0.25 and recovered activation energies within acceptable limits of literature values. Validation against semi-empirical and experimental data from Coronel-García et al. confirmed that the framework generalizes beyond synthetic conditions, reproducing measured product trends and temperature effects. Bootstrap resampling and 95% confidence intervals from Arrhenius regressions established the statistical robustness of the recovered parameters under noise and data sparsity. Overall, the PINODE framework serves as an interpretable and statistically reliable grey-box modeling tool, bridging mechanistic and data-driven approaches for kinetic modeling and refinery process optimization. • Physics-Informed Neural ODEs with embedded physics like stoichiometry and Arrhenius laws. • Demonstrates strong predictive accuracy and 40%–55% RMSE reduction vs. unconstrained NODE. • Validated against experimental data via a semi-empirical surrogate with bootstrap confidence intervals. • Recovers physically meaningful kinetic parameters with 95% uncertainty bounds.