Scaling up dry methane reforming: Integrating computational fluid dynamics and machine learning for enhanced hydrogen production in industrial-scale fluidized bed reactors

This research investigates the optimization and simulation of dry methane reforming (DMR) in fluidized and fixed bed reactors at an industrial scale. By utilizing Computational Fluid Dynamics (CFD) based on the Multi-Phase Particle-in-Cell (MP PIC) approach, we analyze the performance of these reactors under various operating conditions and ascertain the superiority of the fluidized regime at increasing hydrogen and syngas production while suppressing coke formation for an important process such as Dry Methane Reforming (DMR). The study shows that fluidized bed reactors are versatile and effective at managing varying amounts of catalyst reductions, offering a promising pathway for sustainable energy systems. Machine learning models built from the generated CFD data accurately predict DMR process results and have facilitated the analysis of the reactor performance and finding effective ways to boost hydrogen production and conversion rates while minimizing the coke-to-syngas ratio and this via the multi-objective genetic algorithm. The study not only examined the optimization of the DMR process but also supported sustainable development objectives by promoting efficient energy resource utilization and diminishing environmental impacts. The results endorse incorporating fluidized bed reactors into the energy industry, emphasizing their potential to transform hydrogen production methods and significantly contribute to achieving eco-friendly energy solutions.