Simultaneous phosphates and nitrates removal from waste-waters by electrochemical process: Techno-economical assessment through response surface methodology
Bibliographic record
Abstract
In this study, a new multiobjective optimization of the simultaneous removal of phosphates and nitrates by electrocoagulation was studied using the Box-Behnken design. Ten aluminium electrodes, connected in a monopolar configuration in a batch reactor, were immersed in synthetic wastewater and then in real wastewater. The optimal conditions and the effects of parameters (current intensity, electrolysis time and initial pH) on phosphate and nitrate removal, the formation of by-products, and the operating cost were assessed in the case of synthetic wastewater. This optimization allowed to eliminate 89.21 % of phosphates, 69.06 % of nitrates with an operating cost of 3.44 USD m-3 against 13.67 mg L-1 of ammonium generated. Optimal conditions applied to real domestic wastewater made it possible to remove 93 % of phosphates and 90.3 % of nitrates with an ammonium residual of 30.9 mg L-1. The addition of sodium chloride reduced the residual ammonium content to 2.95 mg L-1. Further, XRD analysis of the sludge showed poor crystal structure and the FTIR spectrum suggested that the phosphate is removed by adsorption and co-precipitation.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".