Modelling and optimization of simultaneous styrene and hydrogen production in an industrial hydrogen‐permselective membrane reactor
Bibliographic record
Abstract
Coupling reaction and separation in a membrane reactor improves process efficiency and reduces purification cost in the next stages. In this work, the performance of the hydrogen–permselective membrane reactors to produce styrene and hydrogen through ethylbenzene dehydrogenation is studied at steady state condition. In the proposed configuration, the Pd/Ag membrane tubes have been placed in the adiabatic reactors to remove hydrogen from the reaction zone. Then, the membrane reactors are modelled heterogeneously based on the mass and energy conservation laws considering a detailed thermal and catalytic kinetic model. To prove the accuracy of the considered model and assumptions, the simulation results of the conventional process are compared with the plant data. In addition, the genetic algorithm as a powerful method in the global optimization is applied to maximize the styrene production. The temperature of feed and sweep gas streams are attainable decision variables due to severe effect of temperature on the equilibrium and kinetic constant. This configuration has enhanced styrene production rate about 9.98 % compared to the industrial adiabatic reactor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".