Simultaneous Extraction of Clean Coal and Rare Earth Elements From Coal Tailings Using Alkali-Acid Leaching Process
Why this work is in the frame
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Bibliographic record
Abstract
With the supply restriction from traditional rare earth deposits, alternative sources of rare earth elements (REEs) such as coal are being studied. The United States National Energy Technology Laboratory has identified US coal deposits as a potential source of rare earth elements. Several techniques such as physical separation, flotation, ion-exchange, agglomeration, and leaching are being evaluated for the successful exploitation of these elements from coal and its by-products. A previous study published in the Geoscience BC 2018 mineral report on the characterization of REE in the British Columbian coal samples have shown that a major portion of the rare earth in the run of mine coal reports to the middling and tailing streams. Hence, this study is focused on the extraction of the rare earth from coal tailings. Several studies have shown the use of an alkali-acid leaching process to successfully demineralize various high ash coals to produce a clean coal concentrate since the ash-bearing components such as clay and quartz were removed from the coal during this process. In this study, the alkali-acid leach process was adopted to chemically clean coal tailings as well as to extract rare earth elements. Different process parameters such as sodium hydroxide (NaOH) concentration, temperature, and time were studied. Results showed that it is possible to extract more than 85% of REE with this process and simultaneously produce clean coal from coal tailing.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it