Extraction of the Sugary Juice from Sweet Pearl Millet and Sweet Sorghum Using a Hydraulic Press and a Four-Roller Press
Bibliographic record
Abstract
Abstract. Sweet sorghum and sweet pearl millet have good agro-industrial potential because they can be used as energy crops, using the sugary juice contained in their stalks to produce bioethanol, while the bagasse (pressing residues) can be used as animal feed. However, the process of extracting the juice and consequently the sugar from the biomass of these crops needs to be explored. For this study, two experimental presses, a four-roller press and a hydraulic press, were designed and built at the Department of Soils and Agri-Food Engineering of Université Laval, in Quebec, Canada. With bioethanol production in mind, an experiment was carried out using the hydraulic press to investigate the effects of stalk chopping mode (fine vs. coarse) and various compressive forces, as well as a comparison between the two presses, to determine the more suitable press for extracting the sugary juice from these crops with or without leaves. The roller press gave good results with sweet sorghum, as no significant difference between the presses was found in the volume of juice extracted if the leaves were removed prior to extraction. However, the hydraulic press was more suitable than the roller press for extracting juice from sweet pearl millet. Chopping mode did not have any effect on the volume of juice extracted. Leaves should be removed prior to pressing with the roller press. When using the hydraulic press, leaf removal was necessary only with sweet sorghum. An estimated ethanol yield of 1956 L ha-1 could be achieved using the hydraulic press to extract the juice from sweet sorghum without leaves.
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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 | 0.000 |
| 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".