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Title: Advanced Sorbent Structure Recovery of REEs, Precious Metals and Other Valuable Metals from Geothermal Waters and Its Associated Technoeconomics

Abstract

This work evaluates, develops and demonstrates flexible, scalable mineral extraction technology for geothermal brines based upon solid phase sorbent materials with a specific focus upon rare earth elements (REEs). The selected organic and inorganic sorbent materials demonstrated high performance for collection of trace REEs, precious and valuable metals beyond commercially available sorbents. This report details the organic and inorganic sorbent uptake, performance, and collection efficiency results for La, Eu, Ho, Ag, Cu and Zn, as well as the characterization of these select sorbent materials. The report also contains estimated costs from an in-depth techno-economic analysis of a scaled up separation process. The estimated financial payback period for installing this equipment varies between 3.3 to 5.7 years depending on the brine flow rate of the geothermal resource.

Authors:
; ; ; ; ; ; ;
Publication Date:
Other Number(s):
927
DOE Contract Number:  
FY15 AOP 2.5.1.6
Product Type:
Dataset
Research Org.:
DOE Geothermal Data Repository; Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Geothermal Technologies Program (EE-2C)
Subject:
15 Geothermal Energy
Keywords:
geothermal; Sorbents; Nano; rare earth elements; REEs; precious metals; mineral recovery; green mining; Inorganic sorbent removal efficiency; Organic Sorbent removal efficiency; composite thin film; Technoeconomics
OSTI Identifier:
1360763
DOI:
10.15121/1360763

Citation Formats

Addleman, Shane, Chouyyok, Wilaiwan, Palo, Daniel, Dunn, Brad M., Brann, Michelle, Billingsley, Gary, Johnson, Darren, and Nell, Kara M. Advanced Sorbent Structure Recovery of REEs, Precious Metals and Other Valuable Metals from Geothermal Waters and Its Associated Technoeconomics. United States: N. p., 2017. Web. doi:10.15121/1360763.
Addleman, Shane, Chouyyok, Wilaiwan, Palo, Daniel, Dunn, Brad M., Brann, Michelle, Billingsley, Gary, Johnson, Darren, & Nell, Kara M. Advanced Sorbent Structure Recovery of REEs, Precious Metals and Other Valuable Metals from Geothermal Waters and Its Associated Technoeconomics. United States. doi:10.15121/1360763.
Addleman, Shane, Chouyyok, Wilaiwan, Palo, Daniel, Dunn, Brad M., Brann, Michelle, Billingsley, Gary, Johnson, Darren, and Nell, Kara M. 2017. "Advanced Sorbent Structure Recovery of REEs, Precious Metals and Other Valuable Metals from Geothermal Waters and Its Associated Technoeconomics". United States. doi:10.15121/1360763. https://www.osti.gov/servlets/purl/1360763. Pub date:Thu May 25 00:00:00 EDT 2017
@article{osti_1360763,
title = {Advanced Sorbent Structure Recovery of REEs, Precious Metals and Other Valuable Metals from Geothermal Waters and Its Associated Technoeconomics},
author = {Addleman, Shane and Chouyyok, Wilaiwan and Palo, Daniel and Dunn, Brad M. and Brann, Michelle and Billingsley, Gary and Johnson, Darren and Nell, Kara M.},
abstractNote = {This work evaluates, develops and demonstrates flexible, scalable mineral extraction technology for geothermal brines based upon solid phase sorbent materials with a specific focus upon rare earth elements (REEs). The selected organic and inorganic sorbent materials demonstrated high performance for collection of trace REEs, precious and valuable metals beyond commercially available sorbents. This report details the organic and inorganic sorbent uptake, performance, and collection efficiency results for La, Eu, Ho, Ag, Cu and Zn, as well as the characterization of these select sorbent materials. The report also contains estimated costs from an in-depth techno-economic analysis of a scaled up separation process. The estimated financial payback period for installing this equipment varies between 3.3 to 5.7 years depending on the brine flow rate of the geothermal resource.},
doi = {10.15121/1360763},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {5}
}

Dataset:

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