Bridging equilibrium and kinetics prediction with a data-weighted neural network model of methane steam reforming

Hydrogen’s role is growing as an energy carrier, increasing the need for efficient production, with methane steam reforming being the most widely used technique. This process is crucial for applications like fuel cells, where hydrogen is converted into electricity, pushing for reactor miniaturization and optimized process control through numerical simulations. Existing models typically address either kinetic or equilibrium regimes, limiting their applicability. Here we show a surrogate model capable of unifying both regimes. A deep neural network trained on a comprehensive dataset that includes experimental data from kinetic and equilibrium experiments, interpolated data, and theoretical data derived from mathematical models for each regime. Data augmentation and assigning appropriate weights to each data type enhanced training. After evaluating Bayesian Optimization, the optimal model demonstrated high predictive accuracy for the composition of the post-reaction mixture under varying operating parameters, indicated by strong Pearson correlation coefficients of 0.963, a mean squared error of 0.000840, and a determination coefficient of 0.921. The network’s ability to provide continuous derivatives of its predictions makes it particularly useful for process modeling and optimization. The results confirm the surrogate model’s robustness for simulating methane steam reforming in kinetic and equilibrium regimes, making it a valuable tool for design and process optimization. • A surrogate model links kinetic and equilibrium regimes in methane steam reforming. • The network trained on three types of datasets with assigned weights of importance. • The model predicts mixture composition accurately under varying conditions.