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Machine learning based interpretation of microkinetic data: a Fischer–Tropsch synthesis case study

Abstract : A systematic approach for analysing kinetic data and identifying hidden trends using interpretation techniques in data science with the ANN.
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https://hal-cnrs.archives-ouvertes.fr/hal-03863322
Contributor : Andrei Khodakov Connect in order to contact the contributor
Submitted on : Monday, November 21, 2022 - 1:39:27 PM
Last modification on : Saturday, November 26, 2022 - 3:52:19 AM

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Anoop Chakkingal, Pieter Janssens, Jeroen Poissonnier, Alan Barrios, Mirella Virginie, et al.. Machine learning based interpretation of microkinetic data: a Fischer–Tropsch synthesis case study. Reaction Chemistry & Engineering, 2021, 7 (1), pp.101-110. ⟨10.1039/d1re00351h⟩. ⟨hal-03863322⟩

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