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Cited article:

Multi-output machine learning models for kinetic data evaluation : A Fischer–Tropsch synthesis case study

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Unravelling the influence of catalyst properties on light olefin production via Fischer–Tropsch synthesis: A descriptor space investigation using Single-Event MicroKinetics

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Chemical Engineering Journal 419 129633 (2021)
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Cobalt-catalyzed nitrile hydrogenation: Insights into the reaction mechanism and product selectivity from DFT analysis

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Building a Microkinetic Model from First Principles for Higher Amine Synthesis on Pd Catalyst

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Industrial & Engineering Chemistry Research 58 (41) 19022 (2019)
https://doi.org/10.1021/acs.iecr.9b03577

Discerning complex reaction networks using automated generators

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https://doi.org/10.1002/aic.16663

A Review of Kinetic Modeling Methodologies for Complex Processes

Luís P. de Oliveira, Damien Hudebine, Denis Guillaume, Jan J. Verstraete and J.F. Joly
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https://doi.org/10.2516/ogst/2016011

Hydrocarbon selectivity models for iron-based Fischer–Tropsch catalyst

Tomasz Olewski, Branislav Todic, Lech Nowicki, Nikola Nikacevic and Dragomir B. Bukur
Chemical Engineering Research and Design 95 1 (2015)
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Selectivity of the Fischer–Tropsch process: deviations from single alpha product distribution explained by gradients in process conditions

Katrina D. Kruit, David Vervloet, Freek Kapteijn and J. Ruud van Ommen
Catalysis Science & Technology 3 (9) 2210 (2013)
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Development of a General Modelling Methodology for Vacuum Residue Hydroconversion

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https://doi.org/10.2516/ogst/2013135