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[Display omitted] • On-site hydrogen refueling stations require efficient catalysts/reactors. • Intrinsic kinetics of Ru catalysts were estimated using a parametric estimation. • New multistage (two- and three-stage) reactor designs were proposed. • Multistage reactors showed less temperature drop (better reactor stability). • Three-stage reactor can improve hydrogen production and conversion. In recent years, interest in the development of a hydrogen economy has increased, and the steam methane reforming (SMR) reaction, which yields so-called "gray" hydrogen, is expected to play a key role in hydrogen refueling stations (HRSs) in the interim before "green" hydrogen process is developed. For this purpose, compact and economical designs for small- to medium-scale SMR processes for HRSs are required. Herein, we investigated the intrinsic kinetics of a Ru-based catalyst using catalyst experiments and parametric estimation. In addition, reactor modelling was performed using a kinetic model with a special focus on multistage reactor configurations. The proposed reactor design showed an increase in the reactor energy supplied rate from 6.98 to 7.55 kW compared to the base case (single-stage reactor), and the conversion increased from 86.2% to 91.2%. Further, the drastic temperature drop owing to the strongly endothermic nature of the SMR reaction was reduced, and, thus, reactor stability was improved. The proposed novel design of a multistage reactor using a Ru-based catalyst will advance the development of the hydrogen economy. [ABSTRACT FROM AUTHOR]

• DNN data-driven model for steam methane reforming is developed. • Data preprocessing techniques were applied to improve the quality of datasets. • Four hyperparameters were optimized to obtain an accurate prediction model. • Process optimization was conducted with 387,320,489 cases under five constraints. • The operating conditions were optimized to achieve a thermal efficiency of 85.6%. The production and application of hydrogen, an environmentally friendly energy source, have been attracting increasing interest of late. Although steam methane reforming (SMR) method is used to produce hydrogen, it is difficult to build a high-fidelity model because the existing equation-oriented theoretical model cannot be used to clearly understand the heat-transfer phenomenon of a complicated reforming reactor. Herein, we developed an artificial neural network (ANN)-based data-driven model using 485,710 actual operation datasets for optimizing the SMR process. Data preprocessing, including outlier removal and noise filtering, was performed to improve the data quality. A model with high accuracy (average R 2 = 0.9987) was developed, which can predict six variables, through hyperparameter tuning of a neural network model, as follows: syngas flow rate; CO, CO 2 , CH 4 , and H 2 compositions; and steam temperature. During optimization, the search spaces for nine operating variables, namely the natural gas flow rate for the feed and fuel, hydrogen flow rate for desulfurization, water flow rate and temperature, air flow rate, SMR inlet temperature and pressure, and low-temperature shift (LTS) inlet temperature, were defined and applied to the developed model for predicting the thermal efficiencies for 387,420,489 cases. Subsequently, five constraints were established to consider the feasibility of the process, and the decision variables with the highest process thermal efficiency were determined. The process operating conditions showed a thermal efficiency of 85.6%. [ABSTRACT FROM AUTHOR]