Degradation of aqueous methylene blue using an external loop airlift sonophotoreactor: Statistical analysis and optimization
Bibliographic record
Abstract
Degradation and mineralization of aqueous methylene blue (MB) are investigated in a bench scale external loop airlift sonophotoreactor. A central composite design along with response surface methodology is employed to model and optimize the sonophotolytic process. A quadratic empirical expression between responses and independent variables (pH and initial concentrations of H2O2 and MB) is derived. The efficiencies of the system for the MB degradation after 10, 15, and 30 min, and total organic carbon reduction after 150 min are considered as responses. The analysis of variance performed high values for the coefficient of determination R(2) and adjusted R(2) for all four responses. Optimum values of process variables for the maximum degradation and mineralization efficiency are pH 6.6 and initial concentrations of H2O2 and MB are 1,280 and 10.56 mg/L, respectively. With optimal operating conditions, 99.93% and 55.32% MB removal (after 10 min) and TOC reduction (after 150 min) are achieved, respectively. Artificial neural networks are also used to model the experimental data. The respirometric study is conducted to compare the biodegradability of untreated and sonophotolytically pre-treated MB solutions at different reaction times. Pre-treated solutions at 180, 240, and 300 min performed higher biodegradability compared to those of untreated MB solutions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".