Relation between Energy Efficiency and GHG Emissions in Drying Units Using Forest Biomass
Bibliographic record
Abstract
The impacts of climate change are inevitable and driven by increased levels of greenhouse gases (GHG) in the atmosphere, requiring mitigation and re-adaptation measures. In this context, this article critically analyzes the influence of drying technology type, forest biomass, and GHG emissions resulting from the energy required for drying agricultural crops, by presenting a case study of tobacco drying. In this study, the influence of increasing the technological level of drying unit (curing units CUs), using E. saligna and E. dunnii firewood and Pinus sp. pellets, was evaluated; considering consumption efficiency, energy efficiency, and concentration of gas emissions (CO, CO2, CXHY and NOX), as well as emission factors in tCO₂-eq. The results showed that when increasing the technological level of the CUs, there is a decrease in fuel consumption and emissions. The reduction can reach 60.28% for the amount of biomass consumed and 67.06% in emissions in tCO₂-eq; for the scenario of a production crop, using a CU with a continuous load (Chongololo) and firewood from E. dunnii. The use of pellets proved to be efficient, with the lowest consumption of biomass and emissions with more technological CUs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".