Modeling, Simulation and Optimization of Dry Reforming of Methane Process by Using Artificial Neural Network and Genetic Algorithm (original) (raw)

2019

Abstract

Dry reforming of methane is one of the main ways to produce syngas. A proper Kinetic model was employed for modeling of dry reforming reaction over Ni/Al2O3 in a fixed-bed catalytic reactor. In the simulation of the reactor, a one dimensional model is applied. After modeling and simulation, more than 100 data were obtained, these data used in Artificial Neural Network then a net was made and finally the optimization of H2/CO ratio by Genetic Algorithm was done. The flow rates which optimized by GA was used in the modeling that causes H2/CO ratio about one.

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