Hydrogen production and thermal transport processes in heat integrated microchannel reactors using chemical kinetics and computational fluid dynamics

In order to improve the thermal and chemical efficiency of such reactors, efforts have been directed to improve the uniformity of heat distribution in the tubes within the reactor to secure high chemical conversion of fuel into hydrogen and maintain catalyst bed temperature within certain limits in order to avoid premature catalyst aging while minimizing the amount of energy used to produce each unit of hydrogen containing gas. Calculations are performed using chemical kinetics and computational fluid dynamics to investigate the hydrogen production and thermal transport processes in a heat integrated microchannel reactor by steam-methanol reforming. The change of thermal energy in the reactor is fully described in order to analyze the influences of fluid velocities and solid properties on the thermal behavior of the reactor. Computational fluid dynamics is a branch of fluid dynamics. Computational fluid dynamics uses numerical methods and algorithms to solve and analyze problems that involve fluid flows. For example, computers may be used to perform the calculations required to simulate the interaction of fluids and gasses with a surface defined by boundary conditions. The result of these calculations may be data for a number of points in an area of the surface. For example, the area of the surface may be a two-dimensional planar area of the surface. Data generated for the number of points may include values for various properties related to fluid flow over the surface. For example, such properties may include temperature, pressure, heat flux, velocity, and other properties. Computational fluid dynamics data is generated by computer simulation of a flow of fluid over the surface. Computational fluid dynamics data is calculated for many points in areas defined on the surface. Computational fluid dynamics data is available for use in heat transfer analysis. The analysis effort concentrates on the use of advanced viscous computational fluid dynamics methods for the analyses. Numerical solution convergence is an important factor in the quality and accuracy of the computational fluid dynamics results obtained. The solution process starts with the assumption of a uniform flow field in every grid cell in the flow field, and through an iterative process in which each cell communicates with every other cell in the flow field, the flow finally converges to a solution which is accepted as the correct solution for that flow around that configuration. This process can take several thousand iterations, the number depending on the size and type of grid and the flow characteristics. The results are also dependent on the density of the grid as well as the general quality of the grid. The larger grids are used because they provided better definition and resolution of the critical separated flow regions which are important for drag analysis. These grids sizes are not optimized but are refined to reduce the uncertainty in the results and accept the longer run time as a penalty. Comparisons are performed between the predicted results and the data obtained by measurements. The predicted results agree with the data obtained by measurements.