Addition of microbial consortium to the rice straw biomethanization: effect on specific methanogenic activity, kinetic and bacterial community
Bibliographic record
Abstract
The biomethanization of lignocellulosic wastes remains an inefficient and complex process due to lignin structures that hinder the hydrolysis step, therefore, some treatments are required. This work describes the addition of an enriched microbial consortium in the biomethanization of rice straw. The experiment was carried out in lab batch reactors following two strategies: (i) pretreatment of rice straw for 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 was described using three models. As a result, the microbial consortium molecular characterization showed 58 different bacterial species, predominantly the Lactobacillaceae family (45.7%), and the Clostridiaceae family (19.1%), which were responsible for the positive effect obtained by bioaugmentation with methane yield increases of 16% (290 LNCH4/kgVS) respect to the control. All kinetic models applied fitted the experimental data for cumulative methane production, although the modified Hill model showed the best fit. Bioaugmentation strategies demonstrate their effectiveness in lignocellulosic biodegradation, but the novelty of this research lies in the application of an enriched microbial consortium obtained by the authors through soil isolation techniques, which are very inexpensive and affordable for developing countries.
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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) | 0.000 | 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".