A multiobjective optimization of a catalyst distribution in a methane/steam reforming reactor using a genetic algorithm

The presented research focuses on an optimization design of a catalyst distribution inside a small-scale methane/steam reforming reactor. A genetic algorithm was used for the multiobjective optimization, which included the search for an optimum of methane conversion rate and a minimum of the difference between highest and lowest temperatures in the reactor. For the sake of computational time, the maximal number of the segment with different catalyst densities was set to be thirty in this study. During the entire optimization process, every part of the reactor could be filled, either with a catalyst material or non-catalytic metallic foam. In both cases, the porosity and pore size was also specified. The impact of the porosity and pore size on the active reaction surface and permeability was incorporated using graph theory and three-dimensional digital material representation. Calculations start with the generation of a random set of possible reactors, each with a different catalyst distribution. The algorithm calls reforming simulation over each of the reactors, and after obtaining concentration and temperature fields, the algorithms calculated fitness function. The properties of the best reactors are combined to generate a new population of solutions. The procedure is repeated, and after meeting the coverage criteria, the optimal catalyst distribution was proposed. The paper is summarized with the optimal catalyst distribution for the given size and working conditions of the system.