Mathematical modeling of ozone decomposition processes in wastewater treatment: A lumped kinetic approach with initial ozone demand
Bibliographic record
Abstract
Ozone-based processes involve complex reaction networks and exhibit matrix-dependent decomposition patterns when dosed to wastewater effluents. Existing models for ozone decomposition are either too complex or not sufficiently descriptive of the regime manifested during initial ozone demand and decay. Models incorporating detailed reactions have shown limited applicability due to the high number of initial states and state variables involved. This study introduces a new mathematical model that balances accuracy and complexity in describing the main phases of ozone decomposition in secondary wastewater effluents. Specifically, a simplified model is proposed based on lumped variables, including initial ozone demand, two classes of organic matter (slow and fast reacting), radical forming (and ozone-decomposing) compounds, radical scavengers, and a hypothetical target contaminant. Results revealed that the model can predict with reasonable accuracy both the rapid initial ozone demand (occurring at very short timescales <10 s) and the slower ozone decomposition processes (occurring at longer timescales >30 s). A global sensitivity analysis was also conducted to further model refinement and simplification, which led to the elimination of three reactions connected to the generation and consumption of radicals (threshold | R X Y | ≥ 0.05). Satisfactorily low residual values (RMSE≤7·10 −6 M) were observed in all cases. Finally, the adequacy of the model was further tested against an independent set of ozone decomposition experiments obtained from the literature, confirming its suitability in describing ozone decomposition in secondary wastewater effluents with initial ozone demand.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".