Design, Modeling, and Analysis of a New Dehulling Process for Canola
Why this work is in the frame
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Bibliographic record
Abstract
Canola (rapeseed) is one of the most valuable and prolific crops of Canada due to its high content of protein and oil. In 2022, Canada produced 18.2 million metric tonnes of canola seed, and the Canola Council of Canada has targeted 26.0 million metric tonnes for 2025. \nCanola has been typically used for oil extraction due to its high oil content; however, canola can be processed to produce high-protein meals suitable for livestock feed and food applications. High-quality canola meal production requires removal of fibre-rich seed hull that typically contributes little nutrient value. Dehulling of canola seed, before oil extraction, increases the protein content and decreases fibre content in the meal, up to 50.0% and down to 10.0%, respectively. Often seed dehulling is preceded by seed preconditioning to loosen the fibrous seed coat (hull) from the embryo. The preconditioning methods that exchange heat and moisture in the seed create internal stresses on endosperm tissues that weaken bonds between the hull and the embryo. Due to the complexity in dehulling canola seed, several methods have been explored by the canola processing industry and the academia. Unfortunately, a thorough review of the pertinent literature yielded no mention of an efficient technology for dehulling canola that could be scaled to industrial mass production. Therefore, the objective of this work was to develop a process to remove the canola seed hull with minimal damage to the embryo.\nThis dissertation provides a review of current canola dehulling technology using the FCBPSS (function-context-behavior-principle-state-structure) framework to create an ontology on dehulling methods, which could aid in the design of a more suitable and specific canola dehulling method. To determine the design requirements for the dehulling process, mechanical tests were conducted to characterize canola’s behaviour under uncoupled stresses such as compression and shear, which correspond to stresses generated by common dehulling equipment used in the industry, such as mills. Additionally, hardness tests were performed with a nano-indenter machine to determine seed hull properties. The overall results from these experiments provided the information used to construct a computational model that represents the deformation of canola seed under compression stresses.\nPreconditioning of canola seed involving thermal treatments were also investigated to determine and measure their ability to disconnect the hull from the embryo without the application of external forces. Assessment of seeds after tempering was conducted using micro-computer tomography imaging. The results showed that the embryos of seed tempered with a fluidized bed drying method shrank producing a gap in between the hull and the embryo. \nFinally, with the valuable information obtained from the experiments conducted to compare the effects of different mechanical loading modes and preconditioning methods on canola seed, a new dehulling process was proposed. Such a process was developed by following the Axiomatic Design Theory (ADT), which is presented step-by-step. Dehulling tests conducted with the new proposed method showed promising results, such as producing whole embryos with minimal damage and achieving a dehulling effectivity higher than other proposed methods found in the literature reviewed. Moreover, the dehulling process could be easily adapted and considered for industrial applications since it uses technology and methods already used in the canola processing industry.
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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 | 0.001 | 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) | 0.000 | 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