Energy-Qualitative and Sustainable Impacts on Differents Soy Grain Drying Technologies
Bibliographic record
Abstract
The objective of this current paper is to evaluate, in real production scale, the management of soybean batches in the storage unit of harvested grains that are submitted to drying processes with different technologies, such an evaluation can contribute to minimizing energy and qualitative losses, and to ensuring the grain quality and sustainability of the postharvest system. The experiment was realized in full-scale production and the treatments utilized were lots moist soybean crop (SUL), RR dry soybean (SSLRR), RR2 dry soybean (SSLRR2), dried soybean in continuous dryer (SSS1) (11.0%), dried soybean silo-dryer (SSS2) (12.5%), dried soybean in silo aerator (SSS3) (14.0%). Energy losses and grain quality as a function of drying management ranged from 2.5 to 16.4% in energy, from 0.23 to 3.26% in crude protein and 0.15 to 3.05% in oil—the maximum yield of wet soybeans harvested from the crop (SUL) at 17% (w.b.). Considering the annual Brazilian soybean production, energy losses reach up to 162,282.50 m³ of firewood, approximately 2,116,963,470 kg of crude protein and 810,616,800 liters of crude oil. This would ensure lower losses and higher grain quality, including better yield of protein and crude oil, specifically reducing energy impacts by increasing the efficiency of the drying system. The current study concluded that the SSS1 drying system reduces energy-environmental impacts by 80.23%, reduces crude protein losses by 94.73%, and crude oil by 95.08%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".