Improving robustness of kinetic models for steam reforming based on artificial neural networks and ab initio calculations

نویسندگان

چکیده

• Kinetic modeling for steam reforming of naphtha surrogates on NiMgAl catalyst. The model discrimination was conducted based artificial neural networks. Ab initio (DFT) calculations allow further refinements and corroborations. selected kinetic satisfactorily captures catalyst activity selectivity. validated a wider set operating conditions feedstocks. Steam hydrocarbons is will be, in the short-medium run, leading technology producing hydrogen. At same time, has large carbon footprint that can be decreased by implementing better models process intensification. In this work, we have developed methodology networks to fit improve robustness (hexane heptane) using derived from hydrotalcite precursors. We analyze several strategies obtain fittest discuss each. These include hydrocarbon reforming, water gas shift methanation reactions, differ mainly type adsorption term Langmuir-Hinshelwood formalism. energies calculated ab provide insights different mechanisms surface sites. validation range experimental real feeds (methane, naphtha, diesel vegetable oil). way, versatility proposed strengths weaknesses data-driven approach selection were proven.

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ژورنال

عنوان ژورنال: Chemical Engineering Journal

سال: 2022

ISSN: ['1873-3212', '1385-8947']

DOI: https://doi.org/10.1016/j.cej.2021.133201