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[Display omitted] • Oxy-fuel combustion-based blue hydrogen production process. • Integrates fossil fuel-based hydrogen production and electrolysis processes. • Proposed processes are SMR + SOEC and SMR + PEMEC. • SMR + SOEC shows highest thermal efficiency (85.2%) and exergy efficiency (80.5%) • SMR + SOEC shows lowest levelized cost of hydrogen and life cycle GHG emissions. Blue hydrogen is gaining attention as an intermediate step toward achieving eco-friendly green hydrogen production. However, the general blue hydrogen production requires an energy-intensive process for carbon capture and storage, resulting in low process efficiency. Additionally, the hydrogen production processes, steam methane reforming (SMR) and electrolysis, emits waste heat and byproduct oxygen, respectively. To solve these problems, this study proposes an oxy-fuel combustion-based blue hydrogen production process that integrates fossil fuel-based hydrogen production and electrolysis processes. The proposed processes are SMR + SOEC and SMR + PEMEC, whereas SMR, solid oxide electrolysis cell (SOEC), and proton exchange membrane electrolysis cell (PEMEC) are also examined for comparison. In the proposed processes, the oxygen produced by the electrolyzer is utilized for oxy-fuel combustion in the SMR process, and the resulting flue gas containing CO 2 and H 2 O is condensed to easily separate CO 2. Additionally, the waste heat from the SMR process is recovered to heat the feed water for the electrolyzer, thereby maximizing the process efficiency. Techno-economic, sensitivity, and greenhouse gas (GHG) analyses were conducted to evaluate the efficiency and feasibility of the proposed processes. The results show that SMR + SOEC demonstrated the highest thermal efficiency (85.2%) and exergy efficiency (80.5%), exceeding the efficiency of the SMR process (78.4% and 70.4% for thermal and exergy efficiencies, respectively). Furthermore, the SMR + SOEC process showed the lowest levelized cost of hydrogen of 6.21 USD/kgH 2. Lastly, the SMR + SOEC demonstrated the lowest life cycle GHG emissions. In conclusion, the proposed SMR + SOEC process is expected to be a suitable technology for the transition from gray to green hydrogen. [ABSTRACT FROM AUTHOR]

[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]