Effect of nutritional bio-stimulants (NBS) on the biological treatment of the wastewater from traditional Chinese medicine production
Bibliographic record
Abstract
Nutritional bio-stimulant (NBS) technology is an attractive method to improve the efficiency of biological treatment of wastewater by stimulating the microbial growth and increasing species diversity. In this study, a commercial NBS, which consisted of organic acids, absorbable nitrogen and phosphorus and trace elements, was applied as a nutrient supplement to replace conventional chemical fertilizer (CCF) in aerobic biological treatment of a traditional Chinese medicine (TCM) wastewater. A mill trials was carried out in a commercial scale TCM wastewater treatment system for 41 days. The process performance and active sludge characteristics were continuously monitored when the CCF was replaced with NBS gradually in the system. It was found that the chemical oxygen demand (COD) of the effluent decreased from 118 to 89 mg·L-1, well below the 100 mg·L-1 wastewater discharge limit, when the CCF was replaced with NBS completely. More importantly, the ammonia concentration of the effluent stayed constantly low in the NBS stage of the trial, indicating that the added NBS was completely utilized by the microorganisms. In contrast, the effluent ammonia concentration was gradually increasing and exceeded the limit in the CCF stage of the trial, indicating that the CCF was not fully utilized by the microbes. The improved perfromance of the aerobic wastewater treatment system was attributed to the fact that the NBS nutrients were more bio-available than the CCF to the microorganisms.
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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) | 0.001 | 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) | 0.001 | 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".