Development of a surrogate artificial neural network for microkinetic modeling: case study with methanol synthesis-Reference-Cited by-同舟云学术

Development of a surrogate artificial neural network for microkinetic modeling: case study with methanol synthesis

Published:2024 Issue:5 Volume:9 Page:1047-1060
ISSN:2058-9883
Container-title:Reaction Chemistry & Engineering
language:en
Short-container-title:React. Chem. Eng.

Author:

Lacerda de Oliveira Campos Bruno¹^ORCID,Oliveira Souza da Costa Andréa²^ORCID,Herrera Delgado Karla¹^ORCID,Pitter Stephan¹^ORCID,Sauer Jörg¹^ORCID,Ferreira da Costa Junior Esly²^ORCID

Affiliation:

1. Institute for Catalysis Research and Technology (IKFT), Karlsruhe Institute of Technologie (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

2. Chemical Engineering Department, Federal University of Minas Gerais (UFMG), Av. Presidente Antônio Carlos 6627, Pampulha, 31270-901, Belo Horizonte, MG, Brazil

Abstract

Speeding up microkinetic modeling by a surrogate artificial neural network.

Funder

Helmholtz Association

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Karlsruhe Institute of Technology

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RE/D3RE00409K

Reference31 articles.

1. Trends and Outlook of Computational Chemistry and Microkinetic Modeling for Catalytic Synthesis of Methanol and DME

2. The Active Site of Methanol Synthesis over Cu/ZnO/Al 2 O 3 Industrial Catalysts

3. Reaction Mechanisms, Kinetics, and Improved Catalysts for Ammonia Synthesis from Hierarchical High Throughput Catalyst Design

4. Experimental investigations and model-based optimization of CZZ/H-FER 20 bed compositions for the direct synthesis of DME from CO₂-rich syngas

5. Simulation of automotive NH3 oxidation catalysts based on pre-computed rate data from mechanistic surface kinetics