Explainable machine-learning predictions for catalysts in CO<sub>2</sub>-assisted propane oxidative dehydrogenation-Reference-Cited by-同舟云学术

Explainable machine-learning predictions for catalysts in CO₂-assisted propane oxidative dehydrogenation

Published:2024 Issue:11 Volume:14 Page:7276-7282
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Liu Hongyu¹²^ORCID,Liu Kangyu²^ORCID,Zhu Hairuo¹^ORCID,Guo Weiqing¹,Li Yuming¹

Affiliation:

1. State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, 102249, PR China

2. National Engineering Research Center for Petroleum Refining Technology and Catalyst, Research Institute of Petroleum Progressing Co., Ltd., SINOPEC, Beijing 100083, China

Abstract

Data-driven machine learning is a valuable perspective on light alkane conversion, which can advise on catalyst development.

Funder

State Key Laboratory of Heavy Oil Processing

SINOPEC Petroleum Exploration and Production Research Institute

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RA/D4RA00406J

Reference43 articles.

1. Current status and perspectives in oxidative, non-oxidative and CO2-mediated dehydrogenation of propane and isobutane over metal oxide catalysts

2. Oxidative Dehydrogenation of Propane to Propylene with Soft Oxidants via Heterogeneous Catalysis

3. Dehydrogenation of light alkanes to mono-olefins

4. Oxidative dehydrogenation of light alkanes to olefins on metal-free catalysts

5. Carbon dioxide reduction in tandem with light-alkane dehydrogenation

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