Optimisation of an industrial wastewater decontamination plant: An environment‐oriented approach
Bibliographic record
Abstract
Abstract Over a period of 24 months of operation, we optimised a physicochemical plant for the decontamination of surface treatment industrial wastewater. This article presents the abatements obtained in the levels of chemical pollution after the various optimisations validated in the laboratory and then transposed to the industrial site. The optimisation of the plant reduced the effluent organic load, nitrite and zinc levels by about 70%, 85% and 80%, respectively. To evaluate the utility of each optimisation proposal, standard bioassays based on lettuce seed germination were carried out on the final effluent. Decreasing concentrations of pollutants in the effluent led to a reduction of its impact on seed germination. The bioassays confirmed the environmental benefits obtained from the optimisation of the treatment plant. The biological tests proposed seem to be good indicators of the contaminant concentrations present in wastewater.
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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 | 0.000 | 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 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".