Predicting rare earth elements concentration in coal ashes with multi-task neural networks-Reference-Cited by-同舟云学术

Predicting rare earth elements concentration in coal ashes with multi-task neural networks

Published:2024 Issue: Volume: Page:
ISSN:2051-6347
Container-title:Materials Horizons
language:en
Short-container-title:Mater. Horiz.

Author:

Song Yu¹²^ORCID,Zhao Yifan¹,Ginella Alex¹,Gallagher Benjamin³,Sant Gaurav²⁴⁵⁶,Bauchy Mathieu¹⁴^ORCID

Affiliation:

1. Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab) 5731B Boelter Hall, Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA

2. Laboratory for the Chemistry of Construction Materials (LC2) 5731J Boelter Hall, Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA

3. Electric Power Research Institute (EPRI) 3420 Hillview Avenue, Palo Alto, CA 94304, USA

4. Institute for Carbon Management (ICM), University of California, Los Angeles, CA, USA

5. Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA

6. California Nanosystems Institute, University of California, Los Angeles, CA, USA

Abstract

Our multi-task neural network approach simultaneously predicts the concentration of all types of rare earth elements (REEs) in coal ashes, with an improved accuracy and robustness as compared to conventional single-task neural networks.

Funder

U.S. Department of Transportation

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrical and Electronic Engineering,Process Chemistry and Technology,Mechanics of Materials,General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2024/MH/D3MH01491F

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