CREC Optical-Fibre Sensors for Hydrodynamic Studies in Gas−Solid Fluidized Beds
Bibliographic record
Abstract
Optical probes can be employed in dense and dilute fluidized beds. Their application is useful to determine particle volume fraction, bubble velocity, bubble size, and solid segregation in dense-phase fluidized-bed reactors, as well as particle-cluster velocity, size, and shape, in downer/riser units. The CREC-UWO team has developed a unique and miniaturized CREC Optiprobes System (CREC-GS-OPS) equipped with a GRIN (graded refraction index) lens. The GRIN lens creates a small volume of high light irradiation by focusing a laser a few millimetres away from the front of the probe tip. This design minimizes sensor intrusiveness and, as a result, provides trustworthy measurements of hydrodynamic parameters. Through the application of the CREC-GS-OPS, advances have been achieved, leading to (a) the development of a “Y-back” unit with graphite ferrules that protects the optiprobes from fibre-optic stresses and prevents the loss of sensor calibration and (b) the establishment of statistically-based data analysis. It is envisioned that through the introduction of a few design changes, the CREC Optiprobes will be made suitable for high-temperature applications. This will allow the measurement of catalyst flow recirculation (among other measurements), in industrial-scale fluidized-bed catalytic cracking units involving fluidized riser crackers and catalyst regenerators.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".