Sustainable and efficient processes require optimal design and operating conditions. The determination\nof optimal process routes, however, is a challenging task. Either the models and underlying chemical\nreaction rate equations are not able to describe the process in a wide ranges of reaction conditions\nand thus limit the optimization space, or the models are too complex and numerically challenging to\nbe used in dynamic optimization. To address this problem, in this contribution, a reduction technique\nfor chemical reaction networks is proposed. It focuses on the sensitivity of the reaction kinetic model\nwith respect to the removal of selected reaction steps and evaluates their significance for the prediction\nof the overall system behavior. The method is demonstrated for a C1 microkinetic model describing\nmethane conversion to syngas on Rh/Al2O3 as catalyst. The original and the reduced microkinetic model\nshow excellent qualitative and quantitative agreement. Subsequently, the reduced kinetic model is used\nfor the optimization of a methane reformer to produce a hydrogen rich gas mixture as feed for polymer\nelectrolyte membrane (PEM) fuel cell applications.
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