Extraction of the Sugary Juice from Sweet Pearl Millet and Sweet Sorghum Using a Hydraulic Press and a Four-Roller Press
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<abstract> <b><sc>Abstract.</sc></b> 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<sup>-1</sup> could be achieved using the hydraulic press to extract the juice from sweet sorghum without leaves.
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.
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 | 0.000 | 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) | 0.000 | 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 it