Sequential‐based process modelling of VOCs photodegradation in fluidized beds
Bibliographic record
Abstract
Abstract Sequential modular simulation (SMS), as a new modelling approach, was developed to simulate the photocatalytic oxidation (PCO) of gaseous pollutants in fluidized bed reactors. In the experimental part, the PCO of gaseous acetone was studied in a fluidized bed photo‐reactor (FBPR) and the influence of operating conditions (inlet concentration, relative humidity (RH) and superficial gas velocity) on both acetone conversion and mineralization was investigated. It was found that the RH, as a key factor in PCO reactions, had a contradictory effect on the conversion and mineralization of acetone. In the modelling part, the bed was divided into several sections in which the bubble and emulsion phases were considered as a plug flow and a completely mixed flow reactor, respectively. Dynamic two‐phase model was adopted as the hydrodynamic sub‐model and the Langmuir–Hinshelwood (LH) mechanism as the kinetic sub‐model. Kinetic constants of the latter sub‐model were estimated using experimental data from the literature. A new dimensionless number (HA number) was introduced to determine the optimum number of sections, as the most important factor in model predictions. The performance of the proposed model was compared with the experimental data obtained in this study and several sets of experimental data from the literature. The results showed that the simple and easy‐to‐achieve approach, which has the capability of integrating into the industrial process simulators such as Aspen Plus© and Aspen HYSYS©, can be used to simulate the behaviour of non‐ideal FBPRs in PCO processes.
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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) | 0.001 | 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".