Deep learning for kinetics parameters identification: A novel approach for multi-variate optimization-Reference-Cited by-同舟云学术

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Deep learning for kinetics parameters identification: A novel approach for multi-variate optimization

Published:2024-06 Issue: Volume:489 Page:151149
ISSN:1385-8947
Container-title:Chemical Engineering Journal
language:en
Short-container-title:Chemical Engineering Journal

Author:

Raponi Antonello^ORCID,Marchisio Daniele^ORCID

Funder

Politecnico di Torino

Horizon 2020

Publisher

Elsevier BV

Reference44 articles.

1. Methodology of metal criticality determination;Graedel;Environ. Sci. Technol.,2012

2. Criticality of metals and metalloids;Graedel;Proc. Natl. Acad. Sci.,2015

3. Magnesium hydroxide dehydroxylation/carbonation reaction processes: Implications for carbon dioxide mineral sequestration;Béarat;J. Am. Ceram. Soc.,2002

4. Structure and properties of polypropylene composites filled with magnesium hydroxide;Chen;J. Appl. Polym. Sci.,2006

5. Flame retardant synergism of rubber and Mg(OH)2 in EVA composites;Gui;Polymer,2007

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