Brazilian potential for CCS for negative balance emission of CO2 from biomass energy
Bibliographic record
Abstract
In this work is assessed the Brazilian potential for Carbon Capture and Geological Storage (CCS) through CO2 capture from biomass sources with focus on bioethanol production facilities. In the present document their geographic distribution is associated with localization of the sedimentary basins as well as the potential geologic reservoirs for CCS is presented, thus providing concrete basis to define and optimize longer term goals, consistent with Brazil’s volunteer commitment to help mitigate the effects of global climate change. It was found that USA, England, Canada, Australia, Germany, France, Netherlands and Japan not only are quite active in the research and technologic development, but also have strong relationships between them, owning several joint products. Historic data point out an increase in ethanol annual production in the last years, being produced mainly by Sao Paulo (61% of the domestic production). The CO2 emissions were estimated for each Brazilian state based on the ethanol production and the CO2 emissions due to the fermentation process. There were 26,959,209 m3 of total ethanol produced, corresponding to about 11.2 billion m3 of CO2 emissions at 20 °C and 1 atm, and to about 29 million m3 of CO2 in reservoir conditions. The CCS scenarios were built considering porosity in the range from 18% to 24%, using the average of the Brazilian basins for oil production. The Paraná Basin should receive over twenty million m3 of CO2, encompassing eight Brazilian states, which requires from 110 to 147 million m3 of rock. Other Basins, such as Ceará, Marajó or Maranhão, Pelotas, Potiguar, Recôncavo or SEAL, and Tacutu require from 12 to 10,861 thousand m3 of rock, having each one a specific requirement. In all scenarios, the rock volumes are smaller than the real Basins volume, thus a very favorable negative balance can be achieved for bioethanol.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".