Comparison of mathematical models of steam methane reforming process for the needs of fuel cells

Wójcik, Małgorzata; Szabłowski, Łukasz; Dybiński, Olaf

doi:10.1016/j.ijhydene.2023.08.293

Cited by 13 publications

(2 citation statements)

References 138 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process represents an indirect processing step that enables the efficient utilization of hydrogen-containing hydrocarbon fuels, such as methane, by solid oxide fuel cells. Researchers have conducted numerical analyses by modeling SOFC systems where methane undergoes an indirect reforming process, including pre-processing [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Numerical and Thermodynamic Analysis of the Effect of Operating Temperature in Methane-Fueled SOFC

Kumuk,

Atak,

Dogan

et al. 2024

Energies

View full text Add to dashboard Cite

This study examines the thermodynamic and numerical analyses of a methane-fed solid oxide fuel cell (SOFC) over a temperature range varying between 873 K and 1273 K. These analyses were conducted to investigate and compare the performance of the SOFC under various operating conditions in detail. As part of the thermodynamic analysis, important parameters such as cell voltage, power density, exergy destruction, entropy generation, thermal efficiency, and exergy efficiency were calculated. These calculations were used to conduct energy and exergy analyses of the cell. According to the findings, an increase in operating temperature led to a significant improvement in performance. At the initial conditions where the SOFC operated at a temperature of 1073 K and a current density of 9000 A/m2, it was observed that when the temperature increased by 200 K while keeping the current density constant, the power density increased by a factor of 1.90 compared to the initial state, and the thermal efficiency increased by a factor of 1.45. Under a constant current density, the voltage and power density values were 1.0081 V, 1.0543 V, 2337.13 W/m2, and 2554.72 W/m2 at operating temperatures of 1073 K and 1273 K, respectively. Under a current density of 4500 A/m2, the entropy generation in the cell was determined to be 29.48 kW/K at 973 K and 23.68 kW/K at 1173 K operating temperatures. The maximum exergy efficiency of the SOFC was calculated to be 41.67% at a working temperature of 1273 K and a current density of 1500 A/m2. This study is anticipated to be highly significant, as it examines the impact of temperature variation on exergy analysis in SOFC, validating both numerical and theoretical results, thus providing a crucial roadmap for determining optimized operating conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Numerical and Thermodynamic Analysis of the Effect of Operating Temperature in Methane-Fueled SOFC

Kumuk,

Atak,

Dogan

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…The use of hydrogen in this manner requires the miniaturization of reforming reactors compared to their industrial counterparts [9]. Consequently, there is a significant demand for process optimization, for which computer simulations are necessary [10]. Scientists employ various methodologies to investigate the subject.…”

Section: Introductionmentioning

confidence: 99%

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Pizoń,

Kimijima,

Brus

2024

Energies

View full text Add to dashboard Cite

Methane steam reforming is the foremost method for hydrogen production, and it has been studied through experiments and diverse computational models to enhance its energy efficiency. This study focuses on employing an artificial neural network as a model of the methane steam reforming process. The proposed data-driven model predicts the output mixture’s composition based on reactor operating conditions, such as the temperature, steam-to-methane ratio, nitrogen-to-methane ratio, methane flow, and nickel catalyst mass. The network, a feedforward type, underwent training with a comprehensive dataset augmentation strategy that augments the primary experimental dataset through interpolation and theoretical simulations of the process, ensuring a robust model training phase. Additionally, it introduces weights to evaluate the relative significance of different data categories (experimental, interpolated, and theoretical) within the dataset. The optimal artificial neural network architecture was determined by evaluating various configurations, with the aim of minimizing the mean squared error (0.00022) and maximizing the Pearson correlation coefficient (0.97) and Spearman correlation coefficient (1.00).

show abstract

Internal dry reforming of methane in solid oxide fuel cells

Moarrefi,

Jacob,

et al. 2024

Chemical Engineering Journal

View full text Add to dashboard Cite

Comparison of mathematical models of steam methane reforming process for the needs of fuel cells

Cited by 13 publications

References 138 publications

Numerical and Thermodynamic Analysis of the Effect of Operating Temperature in Methane-Fueled SOFC

Numerical and Thermodynamic Analysis of the Effect of Operating Temperature in Methane-Fueled SOFC

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Internal dry reforming of methane in solid oxide fuel cells

Contact Info

Product

Resources

About