Removal of nitrogen from wastewater using microalgae and microalgae–bacteria consortia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Exceeding nitrogen discharge into natural water bodies can lead to eutrophication in natural aquatic environments, as well as the decline in shellfish habitat and aquatic plant life. Currently, bacterial biological treatment process is the most common process employed in wastewater treatment plants, which requires extensive oxygen. The large demand for oxygen provided by mechanical aeration is costly and can strip out volatile compounds. Microalgae are photosynthetic micro-organisms, which can be a good source of oxygen in the wastewater treatment process. The effect of using microalgae, either solo or in consortia systems along with other micro-organisms (mainly bacteria) have been studied by researchers to improve their contaminant removal efficiency. In a consortia system, microalgae generate oxygen through photosynthesis to satisfy the oxygen requirement of bacteria. Simultaneously, they also remove contaminating nutrients throughout their growth cycle. Various factors affect the performance of the consortia systems such as lighting, pH, and species of microalgae and bacteria. Since microalgae are suspended and dispersed in the media, harvesting is crucial to achieving a high-quality effluent. This paper presents an overview on nitrogen removal from wastewater using different types of systems including microalgae solo and microalgae–bacteria consortia systems. The parameters that affect system performance as well as biomass harvesting methods are also discussed.
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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) | 0.001 | 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 it