Comparison of the effect of grounding the column wall in gas-solid fluidized beds on electrostatic charge generation
Bibliographic record
Abstract
In gas-solid fluidized beds as particles are fluidized, they continuously come into contact with other particles, as well as the fluidization column wall. This generates electrostatic charges by means of triboelectrification and frictional charging, leading to particle agglomeration, reactor wall fouling, and eventually process downtime and large financial losses. Grounding the fluidization column has been considered as a means of helping electrostatic charge dissipation within fluidized beds; however, in industrial applications despite the process vessels being grounded, the electrostatic problem still persists. This work focused on the effect of fluidization column grounding on particle wall fouling. Experiments were conducted in an atmospheric system consist of a 0.1 m in diameter carbon steel fluidization column. The mass and charge-to-mass ratio (q/m) of the particles that remained adhered to the column wall upon the completion of one hour fluidization period were measured in an electrically isolated and grounded columns to quantitatively determine the amount of reactor wall fouling. Polyethylene particles with different particle size ranges (300– 1000 μm) were fluidized with extra dry air at 1.5 times their respective minimum fluidization velocity (u mf ). Results obtained in the grounded fluidization column were not significantly different from those in the isolated column for all particle size ranges tested where the particles mass collected and q/m and were found to be generally similar.
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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 | 0.000 |
| 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".