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
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it