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"thermoelectric material evaluation"×
Technical Assessments×

Geothermal Thermoelectric Generation (G-TEG) with Integrated Temperature Driven Membrane Distillation and Novel Manganese Oxide for Lithium Extraction Final Report

Mar 01, 2018
1.99 MB
Publicly accessible
This project aims to develop an innovative Geothermal ThermoElectric Generation (G-TEG) system specially designed to both generate electricity and extract high-value lithium (Li) from low-temperature geothermal brines. The process combined five modular technologies including silic...
Authors
Renew, J. et al Southern Research Institute
geothermalenergygteglithium extractionlithium sorbencythermoelectric powerdevelopmentfabricationintegrated membrane distillationmethodstechnical assessmenteconomic assessmenttechnoeconomictechno-economicpower generationmanganese oxide sorbentmn-oxidemno2

Microhole drilling technology utilizing a golden section search algorithm

Jun 23, 2021
812.35 MB
Publicly accessible
A fundamental issue in microhole drilling is that delivering high weight-on-bit (WOB), high torque rotational horsepower to a conventional drill bit does not scale down to the hole sizes necessary to realize the envisioned cost savings An optimization algorithm called a golden sec...
Authors
Su, J. et al Sandia National Laboratories
geothermalpercussive hammerweight-on-bitoptimizationmicroholesslimholesdrillingexplorationmonitoringtechnical assessmentfeasibilitywobgssgolden section searchalgorithmblue canyon domenew mexico tech energetic materials research and testing centersocorronew mexico

Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments

Feb 07, 2023
Size unavailable
Curated
Previous moderate and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable mode...
Authors
Mordensky, S. et al United States Geological Survey
geothermalenergypfahydrothermalenergy assessmentresource assessmentretrospectivemachine learninggeosciencewestern usdata-drivenbias reductionfavorabilitymappingegslow tempprocessed dataresourcecharacterization
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