Effect of Temperature on Solids Mixing in a Bubbling Fluidized Bed Reactor
Bibliographic record
Abstract
Solid mixing in fluidized bed reactors has a great impact on the transport phenomena in the reactor. Most studies, concerning solid behavior and hydrodynamic correlations of fluidized bed reactors, have been done at ambient temperature. Industrially, however, fluidized bed reactors operate at high temperatures. The lack of studies at higher temperatures is due to difficulties associated with measuring techniques under these conditions. In extrapolating hydrodynamic parameters derived at ambient temperature to higher temperatures, only the physical property changes of gas and solid phases, such as density and viscosity are taken into consideration. On a microscopic scale, however, change of temperature strongly affects the interaction between particles, which in turn has a substantial impact on the hydrodynamics of a fluidized bed.In this study and for the first time, the Radioactive Particle Tracking (RPT) is used to investigate the effect of temperature on the fluidization of silica sand particles (Geldat-B) in a bubbling fluidized bed reactor. Experiments have been carried out at different temperatures (25-400oC) and superficial gas velocities (0.17-0.75 m/s). The effect of temperature on the global mixing is studied in conjunction with the changes found in the dynamic of the ascending and descending phases. A two-phase countercurrent back-mixing model (CCBM) was used to investigate global solid mixing at different temperatures. The wake exchange coefficient, in the CCBM model, is calculated and compared with the values obtained from different correlations. For various experiments, the exchange coefficient is found to be in the range of 0.6-1.7 sec-1. The correlations can predict the trend of the wake exchange coefficient change with temperature, but they all overestimate it. The correlations developed by Hoffmann et al. (1993) and Lim et al. (1993) were found to give a better agreement with the results at high temperatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".