INCREASED REVENUE AND COST SAVINGS BY RECOVERING VALUE FROM PLANT RECIRCULATION AND TAILINGS WASTE STREAMS
Bibliographic record
Abstract
Effective recovery of fine coal and subsequent reduction in moisture to an acceptable level is mostly dependent on the following factors: (i) favorable economics, i.e., value of the product component obtained; (ii) capability and subsequent performance of the preparation equipment; (iii) extent to which the beneficiation of the total coal can be optimized; (iv) taking advantage of opportunities to generate other marketable proucts or materials that can be used within the main operation, such as roadbuilding, reclamation, and/or landscaping; and (v) cost and acceptability of the disposal method for the barren tailings. The success of this approach will be influenced by many other factors, not the least being the proportion of fines in the raw coal and the nature of the ultimate tailings. Hence, as mining and subsequent transportation and handling has become progressively more mechanized, the proportion of fines has increased and the justification for maximized fine coal recovery has also increased. However, the conundrum associated with including more fines is the added risk of increased moisture and the accompanying need for improved and cost-effective dewatering of both ultrafine coal and tailings. This paper will describe a successful pathway toward achieving the abovementioned five outcomes. The main drivers are also explained together with the contributing components of the new treatment circuits. Several examples of installations in the U.S., Canada, Australia, and Russia will be descibed to illustrate the bespoke approach that is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".