Study of gas–solids flow in a short CFB riser by statistical and chaotic deterministic analysis of optical fibre probe signals
Bibliographic record
Abstract
Abstract In this work, a short CFB riser with a height of 2.42 m and an ID of 82 mm was operated under different dilute operating conditions to study the fluid dynamics of FCC catalyst particles (dp = 80 µm, ρp = 902 kg/m3) in air. The electrical signals from the optical fibre probe were sampled at a frequency of 1000 Hz for a period of 30 s and were obtained at different positions along the radius and height of the riser. Data were analysed using both statistical methods (time average, standard deviation and frequency distribution) and chaos methods (construction of attractors; correlation dimension, DML; and Kolmogorov entropy, KML). Some results on solids holdup deduced from the electrical signal are also presented for the developed zone of the riser. It was verified that for very dilute conditions, increasing gas velocity produces more complex and less predictable fluctuations in solids concentration, while increasing solids flux generally reduces complexity and increases predictability. However, results for the most dilute condition used shows that for the radial position where solids holdup is higher (near the wall), the increase in solids concentration does not affect the mean free path of the particles, resulting in higher values of DML than in the dilute region (core). © 2012 Canadian Society for Chemical Engineering
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".