Physical and Chemical Processes for Removing Suspended Solids and Phosphorus from Liquid Swine Manure
Bibliographic record
Abstract
A physical/chemical treatment train, that included 24-hour preliminary settling followed by coagulation/flocculation and sedimentation, was tested at a laboratory bench scale to treat liquid swine manure for the removal of total suspended solids (TSS) and total phosphorus (TP). Preliminary (i.e., natural) settling time had an effect on TSS removal within only the first 24 hours. TSS removal efficiency reached 75% (TSS concentration was reduced from 5,800 to 1,450 mg 1(-1)) after 24 hours of preliminary settling. Also, as a result of the 24-hour preliminary settling, TP concentration was reduced from 533 to 318 mg 1(-1), thus leading to a TP removal efficiency of 40%. When compared to ferric chloride, alum was more effective in reducing both TSS and TP. At a 95% confidence interval, alum dose, coagulation Gt (coagulation velocity gradient * rapid-mixing time), and flocculation Gt (flocculation velocity gradient * slow-mixing time) were not significant for TSS removal while alum dose was the only significant factor for TP removal. For the 24-hour settled liquid manure that had a TP concentration in the range of 362 to 401 mg l(-1)and as alum dose increased up to 1,600 mg 1(-1), TP removal efficiency increased up to 70%. Then, the rate of increase in TP removal efficiency per increase in alum dose started to decrease and TP removal efficiency reached about 93% at an alum dose of 3,000 mg 1(-1). Sequential alum dosing improved the TSS removal efficiency while it had no effect on TP removal efficiency. The mass ratio of removed TSS/applied alum increased from about 0.38, during a one-step dosing of alum at a concentration of 1,600 mg l(-1), to about 0.58 during a two-step dosing of alum at a concentration of 1,600 mg l(-1) (i.e., 800 mg l(-1) followed by another 800 mg l(-1)).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".