Modelling and gas–solid mixing characterization in the jiggled bed reactor ( <scp>JBR</scp> )
Bibliographic record
Abstract
Abstract The jiggled bed reactor (JBR) is a new multiphase laboratory‐scale microreactor consisting of a sealed container attached to a piston that is rapidly moved up and down by a pneumatically powered actuator. Particles and fluids in the container are mixed by this up and down motion instead of mechanical agitators or a fluidizing gas. This alternating motion provides intense mixing of all phases (gas, liquid, or solid) and intense contact between phases. Small rods inside the solids bed are heated by induction, allowing for excellent control of bed temperature and heating rate. The JBR is inexpensive and easy to operate, and it has been applied to catalytic gasification of bio‐oil, biomass pyrolysis, activated carbon production, high‐pressure oil hydrogenation, and hydrocarbons adsorption. Experiments demonstrated that solids mixing depends on the reactor platform maximum accelerations during both up and down strokes. A minimum acceleration, 55 m 2 /s for the tested JBR, was required to achieve good solids mixing. A physical model was developed to predict the reactor platform motion and its maximum acceleration. It requires a few preliminary experiments (around 10) to obtain its four empirical parameters. The model can then determine how to adjust the actuator compressed air pressure or modify the equipment to eliminate performance bottlenecks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".