Uptake of rare earth elements from solution by metal oxides

Koeppenkastrop, Dirk; Carlo, Eric Heinen De

doi:10.1021/es00046a006

Cited by 174 publications

(64 citation statements)

References 31 publications

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“…Under the conditions of the experiments, the rare-earth elements are shown to have very high affinities for the oxide and phosphate phases (& >> 1,000 mL g-'). Koeppenkastrop and De Carlo (1993) further state that modeling of sorption data derived from experiments with natural particles indicates that desorption rate constants are much smaller than adsorption rate constants.…”

mentioning

confidence: 90%

Unsaturated Zone and Saturated Zone Transport Properties (U0100)

Conca¹

2000

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mentioning

confidence: 90%

Unsaturated Zone and Saturated Zone Transport Properties (U0100)

Conca¹

2000

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“…A variety of sorbents show high affinities towards a variety of REEs with good capacities, rapid kinetics, and economics. Although many REE adsorbents have been studied [49][50][51][52][53][54][55][56][57][58][59][60][61], to date, there is no adsorbent material in practical use.…”

Section: Adsorptionmentioning

confidence: 99%

On the Extraction of Rare Earth Elements from Geothermal Brines

2017

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The availability of rare earth elements from primary resources has come into question in the last two decades. This has sparked various government and industry initiatives to examine potential rare earth element resources apart from virgin ore bodies. Geothermal fluids are potentially significant sources of valuable minerals and metals, while co-recovery with geothermal energy production would be an attractive sustainable system. In this work, we give a brief survey of data collected on rare earth element concentrations in geothermal fluids. A survey of methods and technologies for extracting rare earth elements from geothermal is discussed along with the feasibility of recovering rare earth elements from geothermal brines. Based on the findings of this study, rare earth element extraction from geothermal fluids is technically possible, but neither economically viable nor strategically significant at this time.

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“…Furthermore, in order to identify the best model, three model selection criterions-Akaike information criterion (AIC), Bayesian information criterion (BIC) (López et al, 2007), and modified model selection Koeppenkastrop and DeCarlo (1993) and Bjerklie et al (2005a)-are also used in this study.…”

Section: Stationmentioning

confidence: 99%

Controls of channel morphology and sediment concentration on flow resistance in a large sand-bed river: A case study of the lower Yellow River

Huang

2016

Geomorphology

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Accurate estimation of flow resistance is crucial for flood routing, flow discharge and velocity estimation, and engineering design. Various empirical and semiempirical flow resistance models have been developed during the past century; however, a universal flow resistance model for varying types of rivers has remained difficult to be achieved to date. In this study, hydrometric data sets from six stations in the lower Yellow River during 1958-1959 are used to calibrate three empirical flow resistance models (Eqs. (5)- (7)) and evaluate their predictability. A group of statistical measures have been used to evaluate the goodness of fit of these models, including root mean square error (RMSE), coefficient of determination (CD), the Nash coefficient (NA), mean relative error (MRE), mean symmetry error (MSE), percentage of data with a relative error ≤ 50% and 25% (P 50 , P 25 ), and percentage of data with overestimated error (POE). Three model selection criterions are also employed to assess the model predictability: Akaike information criterion (AIC), Bayesian information criterion (BIC), and a modified model selection criterion (MSC). The results show that mean flow depth (d) and water surface slope (S) can only explain a small proportion of variance in flow resistance. When channel width (w) and suspended sediment concentration (SSC) are involved, the new model (7) achieves a better performance than the previous ones. The MRE of model (7) is generally b 20%, which is apparently better than that reported by previous studies. This model is validated using the data sets from the corresponding stations during [1965][1966], and the results show larger uncertainties than the calibrating model. This probably resulted from the temporal shift of dominant controls caused by channel change resulting from varying flow regime. With the advancements of earth observation techniques, information about channel width, mean flow depth, and suspended sediment concentration can be effectively extracted from multisource satellite images. We expect that the empirical methods developed in this study can be used as an effective surrogate in estimation of flow resistance in the large sand-bed rivers like the lower Yellow River.

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Uptake of rare earth elements from solution by metal oxides

Cited by 174 publications

References 31 publications

Unsaturated Zone and Saturated Zone Transport Properties (U0100)

Unsaturated Zone and Saturated Zone Transport Properties (U0100)

On the Extraction of Rare Earth Elements from Geothermal Brines

Controls of channel morphology and sediment concentration on flow resistance in a large sand-bed river: A case study of the lower Yellow River

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