Effects of Pressure, Temperature, and Gas Velocity on Electrostatics in Gas−Solid Fluidized Beds
Bibliographic record
Abstract
The influences of operating pressure, temperature, and gas velocity on the degree of electrification in a fluidized bed of glass beads and different grades of polyethylene resin were investigated in a fluidization column of 150-mm inner diameter and 2.0-m height. Eight collision ball probes at different levels and radial positions measured the degree of electrification in the bed. Faraday cups also measured the charge density in the bed by taking samples from three different online sampling ports at different levels. The degree of electrification increased as pressure increased from 1.0 to 8.0 bar, probably due to an increase in bubble rise velocity, frequency, and volume fraction. The maximum static charges were found at approximately two-thirds of the bed height and near the axis. As the pressure increased, particle−particle and particle−wall collisions near the distributor and wall contributed heavily to static charge generation. At higher temperatures (up to 75 °C), the bed exhibited smoother fluidization. Temperature played a significant role in determining electrostatic charging. As the superficial gas velocity increased from 0.23 to 0.40 m/s, the degree of electrification increased. However, at higher gas velocities, the polarity in the freeboard region was opposite to that in the bed, indicating that fines entrained from the column carried charges, resulting in a net charge of polarity opposite to that inside 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 | 0.001 |
| Scholarly communication | 0.001 | 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".