Liquid and solids phase backmixing in a bubble and slurry bubble column using a virtual tracer response methodology based on the trajectory data of the radioactive particle tracking (<scp>RPT</scp>) technique
Bibliographic record
Abstract
Abstract Virtual tracer response methodology developed based on the trajectory data of the computer aided radioactive particle tracking (CARPT) technique was demonstrated. The demonstrated virtual tracer technique has advantages of non‐invasiveness, near perfect injection/sampling, and flexibility in choosing the sampling/injection boundaries in a specific spatial pattern. With the developed virtual tracer technique, liquid and solids backmixing were investigated at the conditions mimicking Fischer‐Tropsch synthesis. Experiments were conducted at different pressure, solids loading, and superficial gas velocity. The axial dispersion model (ADM) and recirculation and cross flow dispersion (RCFD) models were used to model the liquid mixing. Transient sedimentation dispersion model (SDM) was used to model the solids mixing. It was found that the measured axial dispersion coefficient (Dl) in the ADM model increases with increase in the pressure. The increase of dispersion coefficient was explained with the experimental values of mean axial diffusivity and mean recirculation velocity. Axial dispersion coefficients (Dz,uDz,d) in the RCFD model (compartment model) were apparently lower than the Dl, due to the decoupling of global recirculation from the dispersion coefficients in the RCFD. Further, it was found that the dispersion coefficients in the RCFD model follow the trend of the axial eddy diffusivity with change in the operating conditions revealing the dominance of the turbulence in the upflow and downflow compartments. From the solids backmixing study, axial dispersion coefficient (Ds) of solids was found to increase with increase in the solids loading, pressure, and superficial gas velocity at the studied conditions.
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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 | 0.000 |
| 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 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".