Effect of operating conditions on beneficiation of western Canadian coals by Air Dense Medium Fluidized Bed (ADMFB)
Bibliographic record
Abstract
The dry physical coal beneficiation method, Air Dense Medium Fluidized Bed (ADMFB) system, can offer an efficient solution for ROM ash removal to improve coal quality and restrict its application issues and footprint. In this study a full-factorial (23) experiment design method was used to study the effect of main operating parameters and reveal their mutual interactions on ADMFB performance when dealing with western Canadian sub-bituminous coals (31% ash). This technique was used to avoid any misleading interpretations based on the traditional one factor at-a-time methods. The superficial air velocity, U; residence time, T; bed height, H; coal particle size and fluidization medium size were the key influencing parameters affecting the apparatus performance. System separation efficiency and product (clean coal) ash content as well as organic material recovery are selected as response functions. Statistical analysis of the results on the three selected responses determined negative effect of increase of U and T on organic material recovery while H was affecting that positively. Two interactions between U and H, and, T and H were also revealed at 95% confidence level. The U and T had positive and negative effect on product ash content while H had no significant effect. The mutual interaction of T and H was found even more effective on product ash content than the direct effect of T. U and H had positive effect on separation efficiency where T and the interaction between U and T were negatively affecting efficiency. Considering the determined effectiveness of the operating parameters; U=16.5cm/s, T=90s and H=15cm were determined to be the optimum settings. Optimization of separation process resulted in product ash content, recovery and separation efficiency of 13.2%, 67.7% and 48.1% respectively for a feed with 31.5% ash.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".