Phosphorus Removal and Recovery from Hog Lagoon Supernatant Using a Gravity-Settled Batch Reactor and Increased pH
Bibliographic record
Abstract
In Canada, the majority of swine waste is stored in open lagoons and land applied according to crop nitrogen requirements. This practice over-applies phosphorus (P) two- to five-fold, leading to potential runoff or leaching loss of P to the environment. Removing P from liquid hog waste is an important step in correcting the N:P ratio of manure applied to cropland. Reclaiming this P as struvite (MgNH4PO4) would ease environmental concerns of eutrophication, provide a saleable fertilizer, and help address the limited global supply of phosphate. Most struvite reactors are capital and operationally intensive, making the process too expensive to be implemented by the average farmer. In addition, high calcium levels found in some supernatants compete with magnesium (Mg), and struvite formation is impaired. The objective of our study is to test the effective P removal and settled solids quality from using a low energy, low capital, pilot-scale gravity-settled batch reactor. Lagoon supernatant with total phosphate (TP) concentration from 58 to 216 mg L-1 and pH of 6.7 was adjusted to pH 8.5 and allowed to settle with no addition of magnesium chloride. Results show that TP was reduced by 76.5%, total suspended solids were reduced by half, soluble P was reduced by approximately 90% (94.5 to 10.3 mg L-1), and particulate mineral forms of P were reduced by 69% (83 to 26 mg L-1), perhaps by flocculation. The dried solids had a TP concentration of 5.6%, total nitrogen of 6.3%, and potassium of 3.8%. Struvite content in the solids was estimated at 30%, and calcium phosphates comprised the remainder of the mineral P compounds. Half of the dried solids were estimated to be organic. The presence of calcium did not impede good P removal, perhaps due to long settling times (24 h). These results indicate that nutrient-rich supernatants with high suspended solids are good targets for P recovery, but product purity is a challenge.
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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 | 0.000 | 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 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".