Optimal design of a thermally coupled fluidised bed heat exchanger reactor for hydrogen production and octane improvement in the catalytic naphtha reformers
Bibliographic record
Abstract
Abstract The present study combines simultaneously the definition of fluidisation and process intensification (thermally coupled heat exchanger reactor) concept and determines the optimum operational conditions in both sides of the reactor, using Differential Evolution (DE) optimisation approach. The exothermic hydrogenation of nitrobenzene to aniline takes place in a set of tubular reactors which is placed inside the naphtha reactors and thermally handle the endothermic reaction of reforming. A single objective function consists of four terms including aromatic mole fraction of the reformate and hydrogen production from each reactor in the endothermic side as well as the total molar flow rate of aniline and nitrobenzene conversion in the exothermic side is defined. Seven decision variables such as inlet temperature of exothermic and endothermic sides, exothermic molar flow rates for the first and the second reactors and the number of tubes are considered during the optimisation procedure. Temperature constraints have been considered in both sides during the optimisation in order to reduce the possibility of rapid catalyst deactivation by sintering. Results show approximately 464.4 and 598.9 kg/h increase in aromatic and aniline production rates in optimised thermally coupled fluidised bed naphtha reactor (OTCFBNR) compared with non‐optimised case (TCFBNR), respectively. Such a theoretical study is necessary prior to designing new pilot plants and revamping industrial units. © 2011 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".