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Application of machine-learning techniques to reproduce a microkinetic model for the methanol synthesis

Lacerda de Oliveira Campos, Bruno ORCID iD icon 1; Ferreira da Costa Junior, Esly; Delgado, Karla Herrera ORCID iD icon 1; Oliveira Souza da Costa, Andréa; Pitter, Stephan ORCID iD icon 1; Sauer, Jörg ORCID iD icon 1
1 Institut für Katalyseforschung und -technologie (IKFT), Karlsruher Institut für Technologie (KIT)

Abstract:

Introduction
The conversion of sustainable COx (e.g. from biomass or captured CO2) and green H2 to methanol is a key reaction to integrate renewable energy with the chemical industry and the mobility sector, as methanol is a building block for the production of several valuable fuels (e.g. gasoline, diesel, jet fuel) and chemicals (e.g. formaldehyde, dimethyl ether) [1].
In order to better understand the methanol synthesis and allow possibilities for optimization, we previously developed a microkinetic model (MM) with extensive experimental validation [2], based on ab initio density functional theory calculations [3-4]. This type of model is useful to gain insights into the chemistry behind the process, and can simulate complex reaction networks that are not be fully described by a formal kinetic model. However, the complexity and high computational effort requirements of a MM may hinder its use in real applications, such as reactor and plant optimization, especially if the kinetic model is incorporated into detailed reactor models (i.e., computational fluid dynamics) [5].
Machine learning techniques could extract the information of the MM and pass on to the reactor model, drastically reducing the required computational effort [5]. ... mehr


Zugehörige Institution(en) am KIT Institut für Katalyseforschung und -technologie (IKFT)
Publikationstyp Vortrag
Publikationsdatum 19.06.2023
Sprache Englisch
Identifikator KITopen-ID: 1000160630
HGF-Programm 38.03.02 (POF IV, LK 01) Power-based Fuels and Chemicals
Veranstaltung 28th North American Meeting of the Catalysis Society (NAM 2023), Providence, RI, USA, 18.06.2023 – 23.06.2023
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