Removal of nutrients from hydroponic greenhouse effluent by alkali precipitation and algae cultivation method
Bibliographic record
Abstract
Abstract BACKGROUND: Hydroponic greenhouse effluent has high concentrations of total phosphorus (30–100 mg PO4‐P L−1) and nitrates (200–300 mg NO3‐N L−1). Current technologies for effluent treatment have limitations of performance and high maintenance costs. The goals of this study were to investigate strategies which combine alkali treatment and microalgae cultivation for removal of nutrients from hydroponic greenhouse effluent. RESULTS: Treatment with strong alkali was found to effectively remove 97% of total phophorous especially in the form of phosphate, without affecting the nitrate ion concentration in the greenhouse effluent. After alkali treatment, marine algae Dunaliella salina (UTEX 1644) cultivation on treated hydroponic effluent (pH 7.5) showed > 80% decrease in nitrate content in the effluent within 4 days of cultivation. In the same period, the carotene content of the micro‐algal system was in the range 0.5 ± 0.02 µg mg−1 (dry cell weight) which was 1.5 times higher than in the control. CONCLUSION: This study demonstrated that combination of a conventional alkali precipitation method with a microalgae treatment system is a highly efficient approach for the removal of excess nutrients from hydroponic greenhouse effluent in a short treatment time. The microalgae can provide a source of value in the form of carotene. © 2012 Society of Chemical Industry
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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) | 0.000 | 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".