Machine learning based interpretation of microkinetic data: a Fischer–Tropsch synthesis case study-Reference-Cited by-同舟云学术

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

Published:2022 Issue:1 Volume:7 Page:101-110
ISSN:2058-9883
Container-title:Reaction Chemistry & Engineering
language:en
Short-container-title:React. Chem. Eng.

Author:

Chakkingal Anoop¹²,Janssens Pieter¹,Poissonnier Jeroen¹^ORCID,Barrios Alan J.¹²,Virginie Mirella²,Khodakov Andrei Y.²^ORCID,Thybaut Joris W.¹^ORCID

Affiliation:

1. Laboratory for Chemical Technology (LCT), Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium

2. CNRS, Centrale Lille, Univ. Lille, ENSCL, Univ. Artois, UMR 8181 – UCCS – Unité de Catalyse et Chimie du Solide, F-59000 Lille, France

Abstract

A systematic approach for analysing kinetic data and identifying hidden trends using interpretation techniques in data science with the ANN.

Funder

European Regional Development Fund

Publisher

Royal Society of Chemistry (RSC)

Subject

Fluid Flow and Transfer Processes,Process Chemistry and Technology,Chemical Engineering (miscellaneous),Chemistry (miscellaneous),Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2022/RE/D1RE00351H

Reference48 articles.

1. Artificial intelligence and automation in computer aided synthesis planning

2. Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening

3. Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes

4. A machine learning approach to improve ignition properties of high-ash Indian coals by solvent extraction and coal blending

5. Prediction of cell voltage and current efficiency in a lab scale chlor-alkali membrane cell based on support vector machines

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