Iron-containing clay and hematite iron ore in slurry-phase anaerobic digestion of chicken manure
Bibliographic record
Abstract
It is shown in this review that addition of clay minerals and hematite iron ore can significantly enhance anaerobic digestion of chicken manure. Liquid-phase anaerobic digestion of chicken manure consumes a lot of fresh water and energy to keep waste as a suspension. Meanwhile, anaerobic digestion of chicken manure in clay slurry without stirring could minimize energy and water consumption because the initial acceptable content of organic solids can be increased. For example, this content can be increased from 5% (w v<sup>-1</sup>) in suspension of chicken manure for liquidphase anaerobic digestion up to 15% (w v<sup>-1</sup>) in the slurry of chicken manure for slurry-phase anaerobic digestion than can save up to 13.3 L of water per kilogram of dry organic solids. The slurry-phase anaerobic digestion of nitrogen-, sulphur-, and fat-containing organic wastes can be enhanced using microbial reduction of Fe(Ⅲ) in clay or in hematite iron ore. This is due to adsorption or precipitation of such inhibitors of microbial acidogenesis and methanogenesis as ammonium, sulphide, long-chain fatty acids, humic and fulvic acids with clay or ferrous ions. For example, maximum concentration of ammonium decreased from 11.4 g L<sup>-1</sup> during liquid-phase anaerobic digestion to 1.4 g L<sup>-1</sup> during slurry-phase process due to adsorption of ammonium ions on clay. Addition of iron-containing clay to slurry-phase anaerobic reactor removed dissolved sulphide totally due to its precipitation with ferrous ions that are produced by bioreduction of Fe(Ⅲ) in clay. Slurry-phase anaerobic digestion enhanced with bioreduction of Fe(Ⅲ) minerals is also more effective process in terms of environmental safety than widely used liquid-phase anaerobic digestion because of an absence of water supply and wastewater effluent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".