Iron and Nickel Supplementation Exerts a Significant Positive Effect on the Hydrogen and Methane Production from Organic Solid Waste in a Two-Stage Digestion
Bibliographic record
Abstract
Abstract Two-stage anaerobic digestion and trace metals (TM) supplementation are promising techniques to improve biogas production. Fe2+ and Ni2+ can improve process stability since they are part of the cofactors of enzymes and microorganisms’ growth. This work attempted to evaluate the effect of Fe2+ and Ni2+ addition on H2-rich biogas production from organic solid waste and the CH4-rich biogas production from the acidogenic effluents (AEs) enriched with TM. The TM concentrations that enhanced the hydrogen yield in the batch were 0.25 mg/L of Ni2+ and 334 mg/L of Fe2+. These concentrations were evaluated in a two-stage system. The substrate for the batch tests and fermentative reactor (first stage) was OSW. The AE generated in the first stage was the substrate to produce CH4-rich biogas in the second stage. In the first stage, the productivity achieved was 1823 ± 160 mL H2/L/day. However, TM supplementation decreased productivity by 65% since the VS removal increased. Megasphaera genus predominated in the first stage. Regarding the methanogenic reactor, the undiluted AE without TM caused the fast decay of the process. Nevertheless, the reactor operated stably after using AE enriched with TM as a substrate, and CH4 yields increased by 42%. The highest productivity achieved in the second stage was 1278 ± 42 mL CH4/L/day, operating with an organic loading rate of 2.8 gVS/L/day. The genera Proteiniphilum, Thermovirga, DMER64, Anaerovorax, and Syntrophomonas predominated in the second stage. In conclusion, AE enriched with TM can be used to recover the stability of anaerobic digesters, increasing methane production.
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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 | 0.000 |
| 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 | 0.001 | 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".