The influence of inlet structural parameter on the particles residence time distribution and mixing performance in a gas solid counter flow contact cyclone reactor for <scp>MTP</scp>
Bibliographic record
Abstract
Abstract A novel gas solid counter flow contact cyclone reactor (GS‐CFCCR), aimed at enhancing reactant mixing, has been proposed for the methanol‐to‐propylene process in this paper. The gas–solid mixing reaction is accelerated by the impinging flow generated through the collision of mixtures within the inlet tubes on both sides, and the separation of products is enhanced by the strong swirling flows induced by the guide vanes. An investigation was conducted on the residence time distribution (RTD), mean residence time (MRT), and flow field characteristics in the mixing chamber under three distinct inlet structural parameters, namely the particle incidence angle ( α = 30°–150°), the effective length of accelerating tube ( L = 120–160 mm), and the insertion depth of feed tube ( l = 0–20 mm). The results indicate that the axial RTD curve follows a normal unimodal distribution. The interfacial contact area between the fluid phases reaches its maximum at the cross section of z = 30 mm. At α = 90°, homogeneous mixing is achieved with maximal mixing intensity observed across axial cross‐sections. Shallower l prevents flow separation induced by feed tube intrusion, thereby mitigating localized turbulence. L = 160 mm represents an equilibrium between stability and efficiency. Backmixing predominantly occurs along sidewalls proximal to the feed ports. It has been demonstrated through research that the GS‐CFCCR exhibits a commendable mixing efficiency with utilizing inlet parameters of α = 90°, l = 0 mm, and L = 160 mm.
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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 | 0.000 | 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) | 0.001 | 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".