Electrochemical Denitrification of Synthetic Aqueous Solution and Actual Contaminated Well Water: RSM Modeling, Kinetic Study, Monte Carlo Optimization, and Sensitivity Analysis
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Bibliographic record
Abstract
The process of electrochemical denitrification is applied with the aim of converting nitrate () to N 2 gas by reducing nitrate and oxidizing by‐products such as ammonia (). In this study, Ti/RuO 2 and graphite were used as anode and cathode electrodes, respectively, to treat synthetic aqueous solutions containing different concentrations of nitrate ions. Nitrate initial concentration (2.75–55 mg NO 3 ‐N/lit), voltage (2.5–30 V), pH (3–13), electrode distance (ED = 0.5–3.5 cm), and reaction time (10–180 min) were the main studied operating parameters for the electrochemical denitrification (ECD) reactor. The experiments were designed using the central composite design (CCD) method. The experimental results were modeled with the response surface methodology (RSM) technique. Scanning electron microscope (SEM), X‐ray diffraction analyzer (XRD), and Fourier transform infrared spectroscopy (FTIR) characterized electrodes were performed before and after all experiments. Optimization and sensitivity analysis was performed using the Monte Carlo simulation (MSC) approach. The energy consumption and current efficiency were calculated for the ECD reactor. Kinetic models of zero, first, and second order were evaluated, and the second‐order model was selected as the best kinetic model. Also, the effect of adding monovalent, divalent salts, and organic compounds to the process was evaluated. Finally, three nitrate‐contaminated water wells were selected near agricultural lands as real samples and investigated the performance of the ECD process on the samples. The performance of the ECD reactor for the real samples showed some decrease compared to the synthetic samples.
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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 | 0.000 | 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 | 0.000 |
| 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 it