Modelling and simulation of a photocatalytic reactor at high <scp>TiO<sub>2</sub></scp> concentrations
Bibliographic record
Abstract
Abstract This work presents a model of an annular batch slurry photocatalytic reactor using titanium dioxide at high concentrations and pH close to 7. The model considers the effects of the agglomeration of catalyst particles that take place when the reactor operates close to neutral pH. It also includes the adsorption/desorption equilibrium of the contaminant on the catalyst during 1 h in darkness, followed by the photochemical reaction under UV irradiation. The reactor was modelled by a system of differential equations solved using the Micro‐Cap 12 program. This powerful software package numerically solves the differential equations as an equivalent network, providing a convenient graphical interface for model programming and plotting of results. The model was validated with measurements at several catalyst concentrations from 1–2.5 g/L. Aeroxide P25, supplied by Evonik, was used as catalyst, with UV irradiation at a wavelength of 254 nm. Orange II, a widely used azo dye, was chosen as the model contaminant. The model results were in satisfactory agreement with the experimental data. Simulations were carried out with different initial conditions, to analyze the influence of the system variables on the performance of the reactor. In this way, the optimal design criteria at high catalyst concentrations were found. The mathematical model is simple and robust and can be applied in a straightforward way to the design and scaling of photocatalytic reactors. It is also particularly attractive for educational purposes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".