Addition of microbial consortium to the rice straw biomethanization: effect on specific methanogenic activity, kinetic and bacterial community
Bibliographic record
Abstract
Abstract The biomethanization of lignocellulosic residues is still an inefficient and complex process due to the lignin structures that hinder the hydrolysis step. Therefore, one of the strategies has been the application of biological treatments using cellulolytic microorganisms. The objective of this work was to evaluate a microbial consortium obtained from the technology of effective microorganisms and enriched with microorganisms isolated from different agricultural soils, for bioaugmentation and/or pretreatment strategies during the biomethanization of rice straw. A laboratory-scale experiment was carried out in batch reactors, using anaerobic sludge from swine manure as inoculum, following two strategies: i) pretreatment of rice straw during 48 h using the enriched microbial consortium (dilution 1:100), and ii) addition of this enriched microbial consortium (dilution 1:100) directly to the anaerobic reactors (bioaugmentation). The kinetic behavior of the digestion process was described through three models. As a result, the molecular characterization of the enriched microbial consortia showed 58 different bacterial species responsible for the positive effect obtained in bioaugmented and pretreated reactors. The abundance of anaerobic species and the different metabolic pathways supported the higher methane yields (290 LNCH4/kgVS), even after 30 days of digestion, influenced by the addition of enriched microorganism consortia. All the kinetic models applied in this study fitted well with the experimental cumulative methane yield data, although the modified Hill model showed the best fit in all cases. The methane yield obtained from the pretreatment and bioaugmentation strategies demonstrates that these biological methods are efficient in the degradation of lignocellulosic biomass.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".