Monitoring Filtration in Trickle Beds Using Electrical Capacitance Tomography
Bibliographic record
Abstract
Experiments were carried out to monitor the evolution of the deposition of fine particles in trickle-bed reactors during the flow of nonpolar hydrocarbon oil-like liquid suspensions using electrical capacitance tomography (ECT) imaging. The accuracy of the ECT rendition was validated in the pristine (i.e., deposit-free) bed state by comparing the liquid holdup measurements from ECT with the liquid holdup from residence time distribution (RTD) measurements. The pulse-flow characteristics (pulse velocity and frequency) estimated from the ECT signals were in agreement with existing literature data. For filtration experiments, the effects of the initial liquid suspension distribution, the gas and liquid superficial velocities, and single-phase flow (i.e., zero gas velocity) on the structure of the deposition in the bed were studied. ECT imaging successfully tracked the unsteady-state progression of bed plugging throughout the trickle bed. It was found that increasing the liquid or gas superficial velocity resulted in increased local deposition. The transition, due to deposition, from trickle to pulse flow was also determined from ECT. In the case of stagnant gas, a filter cake formed on top of the bed.
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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 | 0.000 | 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".