Membrane Filtration Coupled with Chemical Precipitation to Treat Recirculating Aquaculture System Effluents
Bibliographic record
Abstract
Effluents from recirculating aquaculture systems (RAS) contain high concentrations of nitrogen and phosphorous wastes and thus often require proper treatment to prevent potential detrimental impacts on receiving water bodies. The purpose of this study was to evaluate the feasibility of membrane filtration coupled with chemical precipitation as a pretreatment step with emphasis on phosphorus removal from RAS effluents. Chemical precipitation tests were conducted by adding magnesium chloride and alum at different chemical concentrations and pH values, respectively. Crossflow, flat-sheet membrane filtration modules were used to examine the effects of transmembrane pressure and crossflow velocity in terms of solid/liquid separation efficiency and permeate flux decline. The results showed that membrane filtration can effectively separate the phosphorus precipitates after chemical precipitation. The total phosphorus in the treated effluent was reduced to less than 0.05 mg L(-1) with a removal efficiency of more than 90%. However, much lower removal efficiencies were obtained for total organic carbon (TOC), total nitrogen, and turbidity. It was concluded that membrane filtration coupled with chemical precipitation can become an effective, compact treatment technology to meet the stringent regulatory requirements for RAS effluent discharge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".