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
Publication type: Journal Article
Publication date: 2022-10-01
Journal:
Applied Catalysis B: Environmental
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 22.1
ISSN: 09263373, 18733883
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.
Top-30
Citations by journals
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4
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ChemPlusChem
2 publications, 3.08%
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Nature Communications
2 publications, 3.08%
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Environmental Science & Technology
2 publications, 3.08%
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1 publication, 1.54%
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1 publication, 1.54%
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1 publication, 1.54%
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Biochar
1 publication, 1.54%
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1 publication, 1.54%
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ACS Omega
1 publication, 1.54%
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1 publication, 1.54%
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Engineering
1 publication, 1.54%
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Green Chemistry
1 publication, 1.54%
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Environmental Pollution
1 publication, 1.54%
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Coordination Chemistry Reviews
1 publication, 1.54%
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Environmental Research
1 publication, 1.54%
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1 publication, 1.54%
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1 publication, 1.54%
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1 publication, 1.54%
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Nanoscale
1 publication, 1.54%
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Russian Chemical Reviews
1 publication, 1.54%
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1
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4
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Citations by publishers
5
10
15
20
25
30
35
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Elsevier
34 publications, 52.31%
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Royal Society of Chemistry (RSC)
9 publications, 13.85%
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American Chemical Society (ACS)
7 publications, 10.77%
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Wiley
5 publications, 7.69%
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Springer Nature
4 publications, 6.15%
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Multidisciplinary Digital Publishing Institute (MDPI)
2 publications, 3.08%
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IEEE
1 publication, 1.54%
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Tianjin Daxue/Tianjin University
1 publication, 1.54%
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Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii
1 publication, 1.54%
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5
10
15
20
25
30
35
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- We do not take into account publications without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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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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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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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}
}
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