The growing demand for low-carbon energy has intensified interest in hydrogen, especially via methanol steam reforming (MSR) for on-site production. However, optimizing MSR reactors—particularly those using membrane and fluidized-bed technologies—is complex due to nonlinear interactions among key parameters like temperature, pressure, gas hourly space velocity (GHSV), and feed ratio. Traditional mechanistic models, while informative, are often too computationally intensive for real-time applications. To address this, the study proposes a digital shadow framework that integrates computational fluid dynamics (CFD) with machine learning (ML) to enable fast, scalable optimization of MSR systems. CFD simulations were used to model transport and reaction phenomena in four reactor types: PBR, FBR, and their membrane-equipped versions (PBMR and FBMR). The CFD simulation results were validated against experimental data from the literature, and their outputs under varied conditions provided datasets for training various ML regressors (MLP, RFR, SVR, GBR, XGB, and KNN). The goal of this study was to evaluate and compare different reactor configurations, to identify the optimal configuration for efficient hydrogen production via MSR. The ML models served as surrogates for rapid performance prediction. Among them, KNN outperformed others, achieving R 2 ∼ 1 and MSE ∼0.002 for FBMR, and was selected for optimization using Bayesian methods. Under optimized conditions, FBMR yielded the best performance with ∼98.4 % methanol conversion and ∼96.2 % hydrogen yield due to superior mixing and hydrogen removal. PBMR followed with ∼91.7 % conversion and nearly 100 % hydrogen selectivity. FBR (∼88 %) outperformed PBR (∼79 %), highlighting fluidization's benefits. Sensitivity analysis revealed that feed ratio and pressure most influenced FBMR performance, while GHSV and stoichiometry were more critical in PBR and FBR. Overall, the study confirms the advantages of silica-MRs, particularly FBMR, for high-efficiency hydrogen production. The digital shadow provides a robust, accurate tool for optimizing reactor design and operations in clean hydrogen technologies. • Developed a digital Shadow framework combining CFD and machine learning for MSR reactors. • Compared four reactor types (PBR, FBR, PBMR, FBMR) for blue hydrogen production efficiency. • KNN model achieved near-perfect accuracy (R 2 ∼1, MSE ∼0.002) for FBMR performance prediction. • FBMR showed ∼98 % methanol conversion and ∼96 % hydrogen yield under optimized conditions.
Torabi et al. (Tue,) studied this question.