MeOH to DME in bubbling fluidized bed: Experimental and modelling
Bibliographic record
Abstract
Abstract Methanol dehydration over a ZSM‐5 containing catalyst was studied in a fluidized bed reactor. At temperatures ranging from 250 to 325°C, methanol conversion varied from 30% at a contact times of 0.14 s and approached 100% of the equilibrium conversion at a contact time starting from 10 s. Sequential and parallel reactions were negligible at low temperatures while hydrocarbon formation became appreciable at 325°C. Online gas analysis by mass spectrometry provided real‐time measurements at a frequency of 4.4 Hz that allowed for fast determination of steady‐state conditions. Gas phase residence time distribution (RTD) measurements indicated that axial dispersion was essentially negligible at short contact times with a shallow bed of catalyst. With longer residence times, the flow pattern could be approximated by six continuously stirred‐tank reactors (CSTR) in series. Both the simple 1D hydrodynamic model and a detailed multi‐zone fluidized model were used to interpret the experimental data to derive a kinetic expression for the dehydration of methanol to di‐methyl ether (DME). The expression includes the reverse reaction that is most often neglected in the literature. The reaction data were best fit with the kinetics based on the 1D model. The fluidized bed is a viable reactor type for kinetic measurements of highly exothermic reactions where hotspots and radial and axial temperature gradients are problematic in fixed beds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".