Optimizing membrane reactor structures for enhanced hydrogen yield in CH4 tri-reforming: Insights from sensitivity analysis and machine learning approaches

Abstract

This study explores the optimization of membrane reactor configurations to enhance hydrogen production through CH4 tri-reforming. The investigation employs ceramic membranes for oxygen, vapor, and carbon dioxide distribution within the reactor bed. A differential evolution algorithm is utilized alongside cuckoo search algorithm (CSA) and support vector regression (SVR) to determine optimal values for O2/CH4, H2O/CH4, and CO2/CH4 ratios, membrane thickness, and shell pressure, with hydrogen yield as the objective function. Results demonstrate that the oxygen membrane reactor achieves the highest hydrogen yield, reaching 2.02 and 1.75 for direct methanol synthesis and Fischer–Tropsch processes, respectively, representing a 7.98 % and 10.03 % increase compared to the conventional tri-reforming reactor. Furthermore, CSA and SVR emerge as invaluable tools, facilitating robust optimization and predictive modeling. The CSA efficiently navigates complex solution spaces to identify optimal parameters, while SVR accurately models relationships between input variables and hydrogen yield. Incorporating these methodologies enhances the effectiveness of membrane reactor design and synthesis gas production. This study contributes to advancements in clean energy technologies by providing insights into efficient hydrogen production methods using membrane reactors.