Abstract
Sustainable and efficient processes require optimal design and operating conditions. The determination of optimal process routes, however, is a challenging task. Either the models and underlying chemical reaction rate equations are not able to describe the process in a wide ranges of reaction conditions and thus limit the optimization space, or the models are too complex and numerically challenging to be used in dynamic optimization. To address this problem, in this contribution, a reduction technique for chemical reaction networks is proposed. It focuses on the sensitivity of the reaction kinetic model with respect to the removal of selected reaction steps and evaluates their significance for the prediction of the overall system behavior. The method is demonstrated for a C1 microkinetic model describing methane conversion to syngas on Rh/Al2O3 as catalyst. The original and the reduced microkinetic model show excellent qualitative and quantitative agreement. Subsequently, the reduced kinetic model is used for the optimization of a methane reformer to produce a hydrogen rich gas mixture as feed for polymer electrolyte membrane (PEM) fuel cell applications.
Highlights
As economical and ecological aspects play a significant role in the design of chemical production processes, model-based optimization is crucial for the rational derivation of the best process route and optimal equipment for efficient and sustainable production [1,2].Reliable process optimization requires quantitative and sufficiently accurate information about the underlying physical and chemical phenomena
Microkinetic models for methane catalytic partial oxidation [7], reforming [8,9,10,11], oxidative coupling [12], and CO/H2 oxidation [13] are available, and models that are able to deal with multiple chemical regimes for methane conversion at the same time [14,15,16,17]
We identify a subset of the full microkinetic reaction network which contains the most significant elementary steps
Summary
As economical and ecological aspects play a significant role in the design of chemical production processes, model-based optimization is crucial for the rational derivation of the best process route and optimal equipment for efficient and sustainable production [1,2]. The reaction rates often cover many orders of magnitude, which results in bad model scaling and ill-posed numerical problems For this reason, comparative simulation studies [18,19,20,21,22], but only few rigorous optimizations are found in literature to improve the design of catalysts, reactors and processes. Progressive species reduction with parametrization of the reaction rates uses a global error function to gradually reduce the number of species in the reaction model with element flux analysis. The authors were even able to determine analytical solutions This method is difficult to use for complex chemical reaction networks, as the determination of the reaction route graphs is very difficult. We apply the Elementary Process Functions (EPF) methodology proposed by our group [1,2]
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