Investigating the effects of operating parameters on the performance of sorption-enhanced membrane reactor for ethanol steam reforming reaction using computational fluid dynamics method
Bibliographic record
Abstract
In this study, the performance of a sorption-enhanced membrane reactor (SEMR) was examined using a Pd-Ag membrane during ethanol steam reforming (ESR). During this study, simultaneous ESR and CO2 adsorption concept was adopted and computational fluids dynamic (CFD) method (two-dimensional model) was developed to evaluate the SEMR performance during ESR reaction. The employed CFD model for the present study provided information about the molar fractions and pressures of components to analyze driving forces under unsteady state condition. Regarding validation, the experimental data related to the membrane reactor (MR) during ESR reaction showed good agreement with modeling outcomes and the application of adsorption reaction improved MR performance. The SEMR performance was investigated after model validation, and during this step, SEMR and MR were compared. Moreover, the effects of main operating parameters, such as gas hour space velocity (GHSV), reaction pressure, and temperature, were studied to compare the SEMR and MR performance during C2H5OH conversion and hydrogen recovery. CFD modeling results showed that SEMR had better performance and increased the ethanol conversion about 20% (SEMR: 70% and MR: 59%) by temperature enhancement at low pressures compared with the conventional membrane reactor. The relative error between numerical and experimental data obtained was 3% in this study.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".