Maximization of Hydrogen Production via Methane Steam Reforming in a Wavy Microreactor by Optimization of Catalyst Coating: A Combined Computational and Data Analytics Approach

Abstract

This study introduces an advanced methodology for optimizing catalytic coatings on microreactor walls used in the steam reforming of methane. By integrating computational fluid dynamics, data analytics, and multiobjective optimization, this approach significantly intensifies the process, reduces catalyst usage, and improves the economic and environmental aspects of hydrogen production. The challenge of identifying ideal catalytic coatings is addressed by employing surrogate functions created by extensive data sets from computational fluid dynamics and machine learning. These surrogate functions are rigorously validated, achieving 99.9% accuracy for both the total H₂ production rate and the H₂ production rate per coated surface area. The optimal catalyst coating demonstrates a 65.8% increase in the H₂ production rate per coated surface area, yet a 9% increase in total entropy generation compared to a fully coated channel. These findings underscore significant opportunities to enhance the cost-effectiveness and sustainability of future microreactors through the optimization of discrete catalytic coatings.