Bibliographic record
Abstract
This chapter starts from the state of the art on particle mixing in spouted beds, as presented in the classical book by Mathur and Epstein. In subsequent years, segregation has been considered in fundamental studies aimed at describing real systems of various bed compositions. Gross solids mixing behavior The mixing properties of spouted beds result from interaction among the spout, fountain, and annulus. In a continuously operated unit, the positioning of the solids inlet port with respect to the discharge opening is of fundamental importance to prevent bypassing. Dead zones could arise from problematic solids circulation – for example, because of an incorrect base design. To prevent segregation, the simplest conceivable operating condition corresponds to a mono-sized particulate material and a single unit in which each particle undergoes many cycles before being discharged. In such cases and for continuous operation, the internal circulation far exceeds the net in-and-out flow of solids in all cases studied. The very different particle residence times in the spout (progressively loaded with solids along its height), in the fountain (where the particles have both axial and radial velocity components), and in the annulus (where particles travel downward in nearly plug flow) generate nearly well-mixed overall solids residence time distributions (RTDs). Stimulus-response techniques have been applied to determine the RTD of particles. A typical downstream normalized tracer concentration at the discharge, called the F curve , generated in response to an upstream step input of colored particles, is given in Figure 8.1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".