Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning-Reference-Cited by-同舟云学术

Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning

Published:2022-06-23 Issue:26 Volume:61 Page:9218-9233
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Nkulikiyinka Paula¹^ORCID,Wagland Stuart T.¹^ORCID,Manovic Vasilije¹^ORCID,Clough Peter T.¹^ORCID

Affiliation:

1. Energy and Power Theme, School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, U.K.

Funder

Engineering and Physical Sciences Research Council

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c00971

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5. Experimental study of the application of a NiO/NiAl 2 O 4 catalyst and a CaO-based synthetic sorbent on the Sorption Enhanced Reforming process

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