Experimental methods in chemical engineering: Optical fibre probes in multiphase systems
Bibliographic record
Abstract
Abstract As much as 75% of the raw materials in the chemical industry and 50% of consumer products are in the form of powders or granular solids. Gasification, pyrolysis, coating, granulation, drying, and mixing are examples of processes in which particles contact fluids. Researchers examine the hydrodynamics of these fluid–solid systems with pressure signals, acoustics, tomography, radioactive particle tracking, optical fibre measurements, and spectroscopy. Among these techniques, fibre optic probes are simple, inexpensive, and sensitive (spatial resolution of 100 μm) and have sampling frequencies of Hz. Optical probes measure local hydrodynamic properties, including particle velocity, solids fraction, and voids, which are difficult to measure in heterogeneous systems like spouted beds, risers, and turbulent fluidized beds. Light from a fibre optic bundle illuminates a specific volume, and fibres from the same or separate bundle return the reflected or transmitted photons to a detector (visible, near‐infrared spectroscopy, or Raman). Sample MATLAB codes included herein together with sample experimental data demonstrate how to process raw signals for gas/solids/and bubble holdup, particle and bubble velocity, bubble size, and solids flux.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".