Kinetic Modeling of Propane Oxidative Dehydrogenation over VO<sub><i>x</i></sub>/γ-Al<sub>2</sub>O<sub>3</sub>Catalysts in the Chemical Reactor Engineering Center Riser Reactor Simulator
Bibliographic record
Abstract
This study reports kinetic modeling of propane oxidative dehydrogenation (ODH) employing a new VO x /γ-Al 2 O 3 catalyst especially designed for propane ODH with a controlled acidity. This catalyst is prepared with different vanadium loadings (5–10 wt %). Kinetic experiments are carried out under an oxygen-free atmosphere in the Chemical Reactor Engineering Center fluidized bed riser simulator at 475–550 °C and atmospheric pressure. Successive-injection propane ODH experiments (without catalyst regeneration) over partially reduced catalysts show good propane conversions (11.73%-15.11%) and promising propylene selectivity (67.65–85.89%). Regarding propylene selectivity, it increases while that for CO x decreases as the catalyst degree of reduction augments with the consecutive propane injections. This suggests that a controlled degree of catalyst reduction is needed for high propylene selectivity. Under such oxygen-free conditions, the lattice oxygen of the catalyst is consumed via the ODH reaction. On the basis of the data obtained, a kinetic model is proposed. In this model, reaction rates are related to the degree of catalyst reduction using an exponential decay function. The kinetic and decay model parameters are estimated using nonlinear regression analysis. Activation energies and Arrhenius pre-exponential constants are calculated with their respective confidence intervals. The proposed parallel-series kinetic model satisfactorily predicts the ODH reaction of propane under the selected reaction conditions.
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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) | 0.000 | 0.001 |
| 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 | 0.000 | 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".