Design optimization of a methane/steam reforming reactor using an evolutionary algorithm

The aim of the presented research is the multi-objective optimization of the temperature distribution inside a methane/steam reforming reactor. The optimization is conducted using the macro-patterning concept, which divides the catalyst into separate segments and alters their density. The segments’ composition is chosen as the optimization parameter, as the beneficial influence of macro-pattering was proved in our previous studies. The optimal catalyst distribution is determined using a genetic algorithm, which mimics the rules of natural selection. The multi-objective fitness function is computed based on the amount of the methane converted in the process, as well as the difference between the maximal and the minimum temperature value inside the reactor. The results of computations indicate that the genetic algorithm can be a useful technique to design catalyst distribution in chemical reactors. The obtained results show that temperature difference could be reduced from 53 to 26 degrees, with a 30 % decrease in the methane conversion rate. However, the amount of catalyst used in this case is 60 % lower, compared with a conventional reactor.