Improving potassium recovery with new solubility product values for K-struvite
Bibliographic record
Abstract
Crystallisation of ammonium (NH4) struvite is a viable method of recovering valuable nutrients such as nitrogen and phosphorus from waste waters. Little work has been done on potassium (K) recovery since neither is it an environmental pollutant nor is society facing global potassium shortages. However, potassium is an essential plant macronutrient, worldwide imbalances in nutrient and fertiliser use exist and there is a need for a slow-release potassium fertiliser. The goal of this research was to develop a fundamental understanding of K-struvite formation as the first step in recovering potassium to allow for producing a full complement nitrogen–phosphorus–potassium slow-release fertiliser from waste waters. Specific objectives included determination of solubility product values for K-struvite at 10, 25 and 35°C. By using these values, optimal supersaturation conditions for K-struvite precipitation were modelled using aqueous equilibrium modelling software and subsequently validated by experiment. The new solubility product values confirmed that K-struvite was less soluble than previously reported. This research adds to the body of literature in nutrient recovery by furthering the understanding of K-struvite formation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".