Applied Catalysis B: Environmental, volume 315, pages 121530

A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation

Manu Suvarna ¹

Thaylan Pinheiro Araújo ¹

Javier Pérez‐Ramírez ¹

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Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland |

Publication type: Journal Article

Publication date: 2022-10-01

Elsevier

Journal: Applied Catalysis B: Environmental

Quartile SCImago

Quartile WOS

Impact factor: 22.1

ISSN: 09263373, 18733883

DOI: 10.1016/j.apcatb.2022.121530

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Catalysis

Process Chemistry and Technology

General Environmental Science

Abstract

Thermocatalytic CO 2 hydrogenation to methanol is an attractive defossilization technology to combat climate change while producing a valuable platform chemical and energy carrier. However, predicting the performance of catalytic systems for this process remains a challenge. Herein, we present a machine learning framework to predict catalyst performance from experimental descriptors. A database of Cu-, Pd-, In 2 O 3 -, and ZnO-ZrO 2 -based catalysts with 1425 datapoints is compiled from literature and subjected to data mining. Accurate ensemble-tree models ( R 2 > 0.85) are developed to predict the methanol space-time yield ( STY ) from 12 descriptors, where the significance of space velocity, pressure, and metal content is revealed. The model prediction and its insights are experimentally validated, with a root mean squared error of 0.11 g MeOH h −1 g cat −1 between the actual and predicted methanol STY . The framework is purely data-driven, interpretable, cross-deployable to other catalytic processes, and serves as an invaluable tool for guided experiments and optimization. • Machine learning framework for CO 2 hydrogenation to methanol devised. • Database for Cu-, Pd-, In 2 O 3 - and ZnO-ZrO 2 -based catalysts compiled. • Generalized model to predict methanol space-time yield ( STY ) developed. • Efficacy and fidelity of the model experimentally validated. • Model enhancements to aid the discovery of novel catalysts required.

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Citations by journals

	1 2 3 4
Applied Catalysis B: Environmental	Applied Catalysis B: Environmental, 4, 6.15% Applied Catalysis B: Environmental 4 publications, 6.15%
Energy	Energy, 3, 4.62% Energy 3 publications, 4.62%
International Journal of Hydrogen Energy	International Journal of Hydrogen Energy, 3, 4.62% International Journal of Hydrogen Energy 3 publications, 4.62%
Fuel	Fuel, 3, 4.62% Fuel 3 publications, 4.62%
Journal of Materials Chemistry A	Journal of Materials Chemistry A, 3, 4.62% Journal of Materials Chemistry A 3 publications, 4.62%
Science of the Total Environment	Science of the Total Environment, 3, 4.62% Science of the Total Environment 3 publications, 4.62%
ACS Catalysis	ACS Catalysis, 3, 4.62% ACS Catalysis 3 publications, 4.62%
Physical Chemistry Chemical Physics	Physical Chemistry Chemical Physics, 2, 3.08% Physical Chemistry Chemical Physics 2 publications, 3.08%
Chemical Engineering Science	Chemical Engineering Science, 2, 3.08% Chemical Engineering Science 2 publications, 3.08%
ChemPlusChem	ChemPlusChem, 2, 3.08% ChemPlusChem 2 publications, 3.08%
Nature Communications	Nature Communications, 2, 3.08% Nature Communications 2 publications, 3.08%
Environmental Science & Technology	Environmental Science & Technology, 2, 3.08% Environmental Science & Technology 2 publications, 3.08%
Advanced Energy Materials	Advanced Energy Materials, 1, 1.54% Advanced Energy Materials 1 publication, 1.54%
Energies	Energies, 1, 1.54% Energies 1 publication, 1.54%
Chemosphere	Chemosphere, 1, 1.54% Chemosphere 1 publication, 1.54%
Biochar	Biochar, 1, 1.54% Biochar 1 publication, 1.54%
Journal of Cleaner Production	Journal of Cleaner Production, 1, 1.54% Journal of Cleaner Production 1 publication, 1.54%
ACS Omega	ACS Omega, 1, 1.54% ACS Omega 1 publication, 1.54%
Separation and Purification Technology	Separation and Purification Technology, 1, 1.54% Separation and Purification Technology 1 publication, 1.54%
Engineering	Engineering, 1, 1.54% Engineering 1 publication, 1.54%
Green Chemistry	Green Chemistry, 1, 1.54% Green Chemistry 1 publication, 1.54%
Environmental Pollution	Environmental Pollution, 1, 1.54% Environmental Pollution 1 publication, 1.54%
Coordination Chemistry Reviews	Coordination Chemistry Reviews, 1, 1.54% Coordination Chemistry Reviews 1 publication, 1.54%
Environmental Research	Environmental Research, 1, 1.54% Environmental Research 1 publication, 1.54%
Transactions of Tianjin University	Transactions of Tianjin University, 1, 1.54% Transactions of Tianjin University 1 publication, 1.54%
Resources Policy	Resources Policy, 1, 1.54% Resources Policy 1 publication, 1.54%
Catalysis Science and Technology	Catalysis Science and Technology, 1, 1.54% Catalysis Science and Technology 1 publication, 1.54%
Nanoscale	Nanoscale, 1, 1.54% Nanoscale 1 publication, 1.54%
Russian Chemical Reviews	Russian Chemical Reviews, 1, 1.54% Russian Chemical Reviews 1 publication, 1.54%
	1 2 3 4

Citations by publishers

	5 10 15 20 25 30 35
Elsevier	Elsevier, 34, 52.31% Elsevier 34 publications, 52.31%
Royal Society of Chemistry (RSC)	Royal Society of Chemistry (RSC), 9, 13.85% Royal Society of Chemistry (RSC) 9 publications, 13.85%
American Chemical Society (ACS)	American Chemical Society (ACS), 7, 10.77% American Chemical Society (ACS) 7 publications, 10.77%
Wiley	Wiley, 5, 7.69% Wiley 5 publications, 7.69%
Springer Nature	Springer Nature, 4, 6.15% Springer Nature 4 publications, 6.15%
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 2, 3.08% Multidisciplinary Digital Publishing Institute (MDPI) 2 publications, 3.08%
IEEE	IEEE, 1, 1.54% IEEE 1 publication, 1.54%
Tianjin Daxue/Tianjin University	Tianjin Daxue/Tianjin University, 1, 1.54% Tianjin Daxue/Tianjin University 1 publication, 1.54%
Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii	Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii, 1, 1.54% Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii 1 publication, 1.54%
	5 10 15 20 25 30 35

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Suvarna M., Pinheiro Araújo T., Pérez‐Ramírez J. A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation // Applied Catalysis B: Environmental. 2022. Vol. 315. p. 121530.

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RIS Copy

TY - JOUR

DO - 10.1016/j.apcatb.2022.121530

UR - https://doi.org/10.1016/j.apcatb.2022.121530

TI - A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation

T2 - Applied Catalysis B: Environmental

AU - Suvarna, Manu

AU - Pinheiro Araújo, Thaylan

AU - Pérez‐Ramírez, Javier

PY - 2022

DA - 2022/10/01 00:00:00

PB - Elsevier

SP - 121530

VL - 315

SN - 0926-3373

SN - 1873-3883

ER -

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@article{2022_Suvarna,

author = {Manu Suvarna and Thaylan Pinheiro Araújo and Javier Pérez‐Ramírez},

title = {A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation},

journal = {Applied Catalysis B: Environmental},

year = {2022},

volume = {315},

publisher = {Elsevier},

month = {oct},

url = {https://doi.org/10.1016/j.apcatb.2022.121530},

pages = {121530},

doi = {10.1016/j.apcatb.2022.121530}

}

Found error?

Publisher

Elsevier

Journal

Applied Catalysis B: Environmental

Quartile SCImago

Quartile WOS

Impact factor

22.1

ISSN

09263373 (Print)
18733883 (Electronic)

Profiles

Pérez-Ramírez, Javier