Phenomenological‐based kinetics modelling of dehydrogenation of ethylbenzene to styrene over a Mg<sub>3</sub>Fe<sub>0.25</sub>Mn<sub>0.25</sub>Al<sub>0.5</sub> hydrotalcite catalyst
Bibliographic record
Abstract
Abstract This communication reports a mechanism‐based kinetics modelling for the dehydrogenation of ethylbenzene to styrene (ST) using Mg3Fe0.25Mn0.25Al0.5 catalyst. Physicochemical characterisation of the catalyst indicates that the presence of basic sites Mg2+O2− on the catalysts along with Fe3+ is responsible for the catalytic activity. The kinetics experiments are developed using a CREC Fluidised Riser Simulator. Based on the experimental observations and the possible mechanism of the various elementary steps, Langmuir–Hinshelwood type kinetics model are developed. To take into account of the possible catalyst deactivation a reactant conversion‐based deactivation function is also introduced into the model. Parameters are estimated by fitting of the experimental data implemented in MATLAB. Results show that one site type Langmuir–Hinshelwood model appropriately describes the experimental data, with adequate statistical fitting indicators and also satisfied the thermodynamic restraints. The estimated heat of adsorptions of EB (64 kJ/mole) is comparable to the values available in the literature. The activation energy for the formation of ST (85.5 kJ/mole) found to be significantly lower than that of the cracking product benzene (136.6 kJ/mole). These results are highly desirable in order to achieve high selectivity of the desired product ST. © 2012 Canadian Society for Chemical Engineering
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".