Energy analysis in fluidized-bed drying of large wet particles
Bibliographic record
Abstract
Energy analysis of a fluidized-bed drying system is undertaken to optimize the fluidized-bed drying conditions for large wet particles (Group D) using energy models. Three critical factors; the inlet air temperature, the fluidization velocity, and the initial moisture contents of the material (e.g., wheat) are studied to determine their effects on the overall energy efficiency to optimize the fluidized bed drying process. In order to verify the model, different experimental data sets for wheat material taken from the literature are used. The results show that the energy efficiencies of the fluidized-bed dryer decrease with increasing drying time and become the lowest at the end of the drying process. It is observed that the inlet air temperature has an important effect on energy efficiency for the material where the diffusion coefficient depends on both the temperature and the moisture content of the particle. Furthermore, the energy efficiencies showed higher values for particles with high initial moisture content while the effect of gas velocity varied depending on the material properties. A good agreement is achieved between the model predictions and the available experimental results. Copyright © 2002 John Wiley & Sons, Ltd.
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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) | 0.000 | 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".