Biogenic potassium: sources, method of recovery, and sustainability assessment
Bibliographic record
Abstract
Abstract Nutrient management methods based on ecosystems are crucial for providing agricultural nutrient needs while reducing the environmental impact of fertilizer usage. With increasing agricultural production, the global demand for potassium is increasing, with India importing potassium from countries like Canada, USA, Israel, and Russia. Biomass-fired industries generate biomass ash as a residue so management of the resultant ash is important. Agricultural residue ashes contain abundant potassium so could potentially be used for fertilizer application. This review describes different potassium sources and recovery processes, including chemical precipitation, water extraction, solvent extraction, membrane separation, and ionic exchange. Extraction time, temperature, and solid to solvent ratio affect the recovery of potassium from biomass ash. Water extraction is the most commonly used method for potassium recovery from biomass ash. The environmental impact of potassium fertilizer recovered from biomass ash is less than that of mining source of potash. This paper discusses topics not covered in previous reviews, such as different biosources of potassium, latest recovery methods, and life cycle assessment of these methods. The gaps identified in the reports are addressed, and future research opportunities are presented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".