Electric-field enhanced fluidized beds: A low-energy bubble control method
Bibliographic record
Abstract
Reducing the size of gas bubbles can significantly improve the performance of gas-solid fluidized beds one of the most common reactor types in the chemical industry applied for such diverse systems as gasoline and plastics production to foods processing. However, a control of bubbles in these reactors is difficult to realize without measures that either use a lot of energy or deteriorate the fluidization behavior. In this thesis the application of low-energy electric fields to fluidized beds is decribed. This method is capable of reducing the average bubble size by as much as 80%, while maintaining the free movement of particles so essential to fluidization. The power consumption in such a system, ideally consisting of non-conductive, dielectric particles in dry gas, is as low as 50 W/m3. The smaller bubbles result in better gas-solid mass transfer, which can increase both the conversion and the selectivity for chemical conversions, or the efficiency for physical processes such as drying or coating. In addition, the build-up of electric charge may be lower because the whirling motion of particles around bubbles is reduced. The system is investigated both experimentally and through modelling, on the scale of inter-particle forces, bubble behavior, and reactor performance. A significant increase of the conversion is demonstrated using proof-of-principle ozone decomposition experiments in a 3-D bench scale reactor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".