Desenvolvimento de sistema de produção de biogás para uma unidade rural produtora de leite bovino utilizando esterco bovino como substrato
Bibliographic record
Abstract
Nowadays, there is an increasing amount of research into the search for new renewable energy sources. In Brazil, although the main source of electricity is hydroelectric, this is not proving to be the solution. The objective of this work is to present a biogas production chain model for a dairy in the state of Paraíba with 12 animals. The research begins with tests for the energetic evaluation of bovine manure, presenting drying and carbonization tests to define the volatile solids and fixed carbon content, following the NBR 8112 methodology. Based on this validation, the paper presents the modeling of a Canadian model biodigester for application at the study site, defining feeding parameters, volume and hydraulic retention time. In order to carry out validations, two biodigester models were built for generation at the Sustainable Energy Laboratory of the Federal University of Paraíba. Through energy analysis, a value of 91.38% fixed carbon was obtained, thus affirming bovine manure as an energy solution. After testing the biogas generated in the laboratory with a thermal camera that showed favorable temperatures for microorganisms in the methanogenesis phase, as well as the flame test, it was possible to see a high methane content in the mixture. The results show a monthly electricity generation of 969 KW, based on a monthly biogas generation of 353 m3. An economic analysis of the system shows a financial return in 26 months. The system developed is modular and can be implemented with other types of substrates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".