Global reaction neural networks with embedded stoichiometry and thermodynamics for learning kinetics from reactor data

Kircher, Tim; Döppel, Felix A.; Votsmeier, Martin

doi:10.1016/j.cej.2024.149863

Chemical Engineering Journal

2024

DOI: 10.1016/j.cej.2024.149863

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Global reaction neural networks with embedded stoichiometry and thermodynamics for learning kinetics from reactor data

Tim Kircher,

Felix A. Döppel,

Martin Votsmeier

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2024

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Cited by 2 publications

References 69 publications

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Trendbericht: Technische Chemie 2024

Bremer,

Ränger,

Friedland

2024

Nachr Chem

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Die Zirkularität von Stoffströmen, die Nachhaltigkeit, vor allem die Nutzung biogener Rohstoffe, sowie die Ressourcen‐ und Energiesicherheit sind seit Jahren wichtige Ziele. Ob in der Reaktions‐ oder der Trenntechnik, alle Bereiche der technischen Chemie profitieren von grundlegenden Stützpfeilern. Mit den darin enthaltenen methodischen Ansätzen lassen sich maßgeschneiderte Lösungen auf allen Größenskalen erstellen, Szenarien für die Energiewende entwerfen oder mit virtuellen Visualisierungen Vorstellungen von der Zukunft bekommen.

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Trendbericht: Technische Chemie 2024

Bremer,

Ränger,

Friedland

2024

Nachr Chem

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Robust mechanism discovery with atom conserving chemical reaction neural networks

Döppel,

Votsmeier

2024

Proceedings of the Combustion Institute

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Global reaction neural networks with embedded stoichiometry and thermodynamics for learning kinetics from reactor data

Cited by 2 publications

References 69 publications

Trendbericht: Technische Chemie 2024

Trendbericht: Technische Chemie 2024

Robust mechanism discovery with atom conserving chemical reaction neural networks

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