Effective treatment of high phosphorus pharmaceutical wastewater by chemical precipitation
Bibliographic record
Abstract
Abstract The conditions for the treatment of high phosphorus pharmaceutical wastewater using chemical precipitation were determined to achieve high efficiency on the removal of total phosphorus (TP). The key factors affecting the TP removal efficiencies of four metal salts (FeCl3, AlCl3, MgCl2, and CaCl2), such as molar ratio, initial wastewater pH, and reaction time, were investigated. The optimal metal mole ratios of FeCl3, AlCl3P, MgCl2, and CaCl2 for the TP removal were 1.8:1, 1.8:1, 1.6:1, and 1.6:1 in the initial pH range, respectively. The TP removal efficiencies obtained by FeCl3, AlCl3, MgCl2, and CaCl2 were 99.8 %, 98.3 %, 96.9 %, and 99.2 % at the optimal mole ratio and pH conditions with 30 min reaction time, respectively. FeCl3 and AlCl3 had better removal performance on COD, wherein the removal rates of COD by FeCl3 and AlCl3 were 38.6 % and 27.6 %, respectively. FeCl3 and CaCl2 were selected for the combined process of removing TP and overcoming the aforementioned problem. The final effluent quality was better than the one only used on one of the four metal salts, in which the removal rates of TP and COD were as high as 99.8 % and 46.9 %, respectively. The combined process with FeCl3 and CaCl2 was an effective method for the pretreatment of high phosphorus pharmaceutical wastewater to reduce the processing cost and to decrease the chemical oxygen demand (COD) and TP.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".