Analysis of dry reforming of methane under different fluidization regimes using a multiphase particle-in-cell approach

Abstract

In the present study, the dry reforming of methane (DRM) has been simulated in fluidized-bed reactors using the multiphase particle-in-cell model. The model was meticulously built to investigate the effect of a wide range of superficial gas velocities covering particulate, aggregative, and lean-phase flow regimes on bed hydrodynamics, conversion, and yields of product gases. Constant values for catalyst loading, CH4:CO2:N2 ratio (1:1:1.3), and catalyst and gas properties were maintained in all simulations. The simulation results obtained are in good agreement with the experimental data reported in the literature. The results show that under different gas velocities, conversion is relatively indiscernible in the particulate regime. In contrast, for the inhomogeneous phases, the turbulent fluidized bed had the best reactor performance with high CH4 and CO2 conversion rates, good CO + H2 productivity, and high CO/H2 molar ratio. This is due to the vigorous turbulent flow and relatively high gas-solid contact. Due to gas bypassing and backmixing triggered by bubbling, the bubbling fluidized bed generally had the worst performance and below that of the fast-fluidized bed. The present study demonstrates that the performance of DRM reactions in fluidized-bed reactors is strongly related to the hydrodynamics. Moreover, it shows the significance of gas velocity on DRM conversion, yield, and overall reactor performance.

Optimization of Yield and Conversion Rates in Methane Dry Reforming Using Artificial Neural Networks and the Multiobjective Genetic Algorithm

“…The study of Alotaibi et al involved investigating influential key operating parameters and conditions of the DRM process hosted in different reactor settings (Figure ) by utilizing the MP-PIC approach, simulating its performance, and generating an ample amount of validated data. , Contrary to conducting expensive sensitivity analysis experiments with time- and resource-intensive requirements, a thoroughly validated CFD was the preferred option, as stipulated in the Alotaibi et al results. While CFD is a crucial tool for comprehending complex multiphase behavior, machine learning (ML) takes advantage of the additional efficiency in computational time, bridging complex relationships among the parameters and enabling multiobjective optimization analysis.…”

“…In this paper, the data set provided by Alotaibi et al , will be used to build a suitable ML algorithm to predict the DRM performance considering the underlying parametric studies, and based on a specific criterion, the algorithm will determine the optimum operating conditions by employing a predetermined optimization strategy. Both data sets were generated by Barracuda software version 21.0 software…”

Dry Reforming of CH4 Using a Microreactor

“…An effective way to avoid carbon deposition could therefore be to use a reactor that allows for suitable heat management. Among other solutions, e.g., fluidized beds [19], microreactors provide an interesting alternative because their small dimensions allow for high surface-to-volume ratios, which can enhance heat and mass transfer. Several studies have been carried out in small channels, mostly focusing on steam reforming [20][21][22][23].…”