Predicting Hydrogen Production from Steam Methane Reforming Powered by Induction Heating: An Application of a Hybrid Bayesian Neural Network

Steam methane reforming (SMR) powered by induction heating offers a promising route for low CO2-emission hydrogen production, but predictive modelling remains challenging because the available experimental data are limited and heterogeneous. This study proposes a hybrid Bayesian neural network (H-BNN) to predict the mass of hydrogen (MoH) from literature-derived SMR data using operating variables including temperature, flow rate, power input, time-on-stream, and interval duration. Feedforward neural network (FNN) and classical Bayesian neural network (BNN) models were also developed as benchmarks, and all three architectures were evaluated with ReLU, Tanh, and GELU activation functions. To address data scarcity, only the training split was augmented at scales of k=2, 5, and 10, while the validation and test sets were kept unchanged. The H-BNN combines deterministic feature extraction with Bayesian uncertainty-aware prediction, enabling a balance between accuracy and uncertainty representation. Across the validation-selected models, test performance reached R2∼0.9894 to 0.9969, with mean absolute errors of 0.0126 g to 0.0217 g. The strongest advantage appeared at k = 2, where the H-BNN outperformed the benchmark models. Overall, the proposed H-BNN is a promising framework for hydrogen prediction under data-scarce conditions, although its predictive intervals remain informative rather than fully calibrated.