Visualisation of flow pattern and measurement of liquid distribution in a random packed column using electrical resistance tomography (ERT)
Bibliographic record
Abstract
The aim of this research is to use Electrical Resistance Tomography technique (ERT) to measure factors that affect local mass transfer at various axial locations in a random packed column with diameter 0.3m and bed height 150cm filled with 2 cm plastic spheres. These factors are: liquid maldistribution, velocity profiles, and flow pattern. The system was designed to run in a trickling down-flow mode and a full liquid up-flow mode. Experiments were performed at flow rates of 3, 6, and 9 gpm (or 0.27 x 10⁻², 0.54 x 10⁻² and 0.8 x 10⁻² m³/m² s) and under normal operating conditions of 25C° and atmospheric pressure. The liquid maldistribution factors were measured via ERT technique and the conventional liquid collection method. Both measurements were conducted at various fluid flow rates at different bed heights. The results of ERT were in very good agreement with the conventional method. The standard deviation values were 17% and 21% at flow rates 3 and 6 gpm respectively. The numerical values of velocity for full liquid up flow at 3 gpm were 0.83cm/s, 1.2cm/s, 1.2 cm/s and 1.11 cm/s for different heights of 30, 60 and 90 cm respectively and the numerical values of velocity for trickle down flow at 3 gpm were 16.5 cm/s, 22.5 cm/s and 24 cm/ for different heights of 30, 60 and 90 cm respectively. The values of the liquid maldistribution factor for flow rate 3 gpm were 0.43 (30cm), 0.33(60cm) and 0.30 (90cm) and for flow rate 6 gpm were 0.33(30cm), 0.27(60cm) and 0.22 (90cm). By comparison with findings of many studies conducted on liquid distribution in packed bed column, a good agreement was observed on the relation of Mf and flow rates and bed heights.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".