Combined Precipitation/Flocculation Method for Nutrient Recovery from Greenhouse Wastewater
Bibliographic record
Abstract
Phosphorus (P) pollution from greenhouse wastewater is currently a major issue. A treatment method that can efficiently remove P concentrations ([P]) that fluctuate between greenhouse systems and throughout the year is required. An ideal method would also recover nutrients in a reuseable form. A combined precipitation/flocculation process incorporating addition of lime and a biodegradable flocculant (guar gum, cationic starch, or chitosan) was investigated for providing optimized P removal and recovery. Effectiveness of this process was evaluated in simulated wastewater of low and high alkalinity, as well as real greenhouse wastewater. Precipitation via lime addition reduced total P to below 1 mg·L −1 in low-alkalinity simulated wastewater, but high alkalinity slightly inhibited separation. This inhibition was overcome by flocculation via guar gum or cationic starch addition, which improved separation efficiency and reduced separation time, although chitosan was ineffective as a flocculant. The precipitation/flocculation method was found to be effective for treating real greenhouse wastewater, although effectiveness varied with variation in wastewater composition. Recovered precipitate contained 57.4 g·kg −1 P as well as high levels of Ca, Mg, K, Fe, and Zn. This study demonstrates a P separation process incorporating lime and biodegradable flocculants could provide a means of reducing P in greenhouse wastewater below a 1 mg·L −1 regulatory limit in a settling time of less than 30 minutes, while simultaneously recovering P and other nutrients in a form that could be reused as fertilizer. An evaluation of viability of full-scale application of this technology is now warranted.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".