Fine Coal Beneficiation using an Air Dense Medium Fluidized Bed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The potential of using Air Dense Medium Fluidized Bed (ADMFB) separation for cleaning sub-bituminous coal was investigated. The effect of operating parameters such as the fluidizing air velocity and medium particle size on separation efficiency was determined with coal of various size fractions. Good separation efficiencies with raw coal in the 6 to 1 mm size fraction were achieved. Partition curves showed an E p value of 0.03 for coal in 5.6 to 3.35 mm size fraction. For the 1.00 to 0.42 mm size fraction, the separation efficiency deteriorated to an E p value of 0.10. To achieve an optimum separation efficiency, a separation medium with a narrow and distinct size fraction is needed to allow a superficial gas velocity sufficiently high to create a pseudo fluid medium bed while sufficiently low to avoid back mixing of fine coal and lifting of fine size mineral matter during fluidization. Keywords: Fine coal beneficiationAir dense medium fluidized bedSub-bituminous coal The financial support for this work from Natural Sciences and Engineering Research Council of Canada (NSERC), Edmonton Power Corporation (EPCOR), and Alberta Energy Research Institute (AERI) under the NSERC/EPCOR/AERI Industry Research Chair program is greatly appreciated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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