Production and performance of bio-based mineral fertilizers from agricultural waste using ammonia (stripping-)scrubbing technology
Bibliographic record
Abstract
Development and optimization of nutrient recovery technologies for agricultural waste is on the rise. The full scale adoption of these technologies is however hindered by complex legal aspects that result from lack of science-based knowledge on characterization and fertilizer performance of recovered end-products. Ammonium sulfate (AS) and ammonium nitrate (AN), end-products of (stripping-)scrubbing technology, are currently listed by the European Commission as high priority products with the potential of replacing synthetic N fertilizers. The legal acceptance of AS and AN will be highly dependent on critical mass of scientific evidence. This study describes four different (stripping-)scrubbing pathways to recover ammonia with an aim to (i) assess product characteristics of ammonium nitrate (AN) and ammonium sulfate (AS) produced from different installations, (ii) evaluate fertilizer performance of recovered end-products in greenhouse (Lactuca sativa L.) and full field (Zea mays L.) scale settings and (iii) compare the observed performances with other published studies. Results have indicated that the recovered products might have a different legal status, as either mineral N fertilizer or yet as animal manure, depending on the used (stripping-)scrubbing process pathway. Nevertheless, no significant differences in respect to product characterization and fertilizer performance of AN and AS have been identified in this study as compared to the conventional use of synthetic N fertilizers. This indicates that recovered AS and AN are valuable N sources and therefore might be used as N fertilizers in crop cultivation.
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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 | 0.000 | 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) | 0.000 | 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 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".