LearnCK: mass conserving neural network reduction of chemistry and species of microkinetic models

Kasiraju, Sashank; Vlachos, Dionisios G.

doi:10.1039/D3RE00279A

Title: LearnCK: mass conserving neural network reduction of chemistry and species of microkinetic models

Journal Article · 20 December 2023 · Reaction Chemistry & Engineering

DOI: https://doi.org/10.1039/D3RE00279A · OSTI ID:2403376

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RAPID Manufacturing Institute and Delaware Energy Institute, University of Delaware, 221 Academy St., Newark, Delaware 19716, USA
RAPID Manufacturing Institute and Delaware Energy Institute, University of Delaware, 221 Academy St., Newark, Delaware 19716, USA, Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St., Newark, Delaware 19716, USA

A Python-TensorFlow tool that learns chemical kinetics (LearnCK) for systematic and automatic reduction of microkinetic models using artificial neural networks (NNs).

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Research Organization:: American Institute of Chemical Engineers (AIChE), New York, NY (United States); Univ. of Maryland, College Park, MD (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0007888; FE0031877

OSTI ID:: 2403376

Journal Information:: Reaction Chemistry & Engineering, Journal Name: Reaction Chemistry & Engineering Journal Issue: 1 Vol. 9; ISSN RCEEBW; ISSN 2058-9883

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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