Design of a new fountain reactor for contamination degradation using advanced oxidation processes with hybrid techniques and modeling evaluation
Bibliographic record
Abstract
Abstract Due to the water and energy crises, wastewater treatment systems that are more energy efficient and capable of large volume degradation are a priority. Photochemical decomposition methods have a significant impact on pollutant treatment. The use of these methods in conjunction with a novel designed reactor and hybridization processes can result in considerable treatment results. This research used a fountain system in a UV/H2O2 process to generate a belt-type liquid film with a low thickness and high mixing to remove methyl orange as a model pollutant. The flowrate, H2O2 concentration, temperature, and UV intensity were the parameters evaluated in this series of tests. After 90 minutes under optimum conditions, the maximum degradation of methyl orange was 99.73 percent. The efficiency of the purification process was increased to 99 percent in 75 minutes by using the optimum state of hybridization of UV/US/H2O2 processes. Two deep neural network models and a pseudo-first-order kinetic model were created to fit the experimental data. The results reveal a good fit between the experimental data and the model prediction. The discovered synergistic factor (1.168) and energy yield (2.65 g/kWh) demonstrated the high efficiency of the hybridization process and the outstanding function of the designed system, respectively.
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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 | 0.004 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".