Effects of Particle Size and Shape on Solids Holdups Distributions Modelling in a LSCFB Reactor using Abductive Network
Bibliographic record
Abstract
The Abductive Network (AN), a group method data handling (GMDH) algorithm‐based self‐organizing model, was applied to study the solids holdup distribution of a liquid‐solid circulating fluidized bed (LSCFB) system. At first, the AN model was trained with experimental data sets obtained from a pilot scale LSCFB operated with spherical glass beads and irregularly shaped lava rock particles as solid phases, and water as liquid phase. In the model training the effects of various auxiliary and primary liquid velocities and superficial solids velocities on radial phase distribution at different axial positions were considered. The developed AN model was employed to predict the solids holdups at various locations of the LSCFB riser under different operating conditions. The competency of the developed AN model was confirmed by comparing the model‐predicted and experimental solids holdups of the LSCFB system. Both the experimental and model‐predicted outputs showed that under different superficial liquid velocities the solids holdup was higher for the glass beads than the lava rocks. The solids holdup decreased with increasing liquid velocity at all axial locations. The radial non‐uniformity distributions of solids holdup in the central region decreased toward the wall. The higher drag force acting on the spherical‐shaped particles was likely responsible for this variation of the solids holdups.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".