Electrochemical Denitrification of Synthetic Aqueous Solution and Actual Contaminated Well Water: RSM Modeling, Kinetic Study, Monte Carlo Optimization, and Sensitivity Analysis
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".