Upcycling Canola: Closed‐Loop Water Retting System for Sustainable Fiber Production from Waste Canola Stalks
Bibliographic record
Abstract
Abstract A closed‐loop water retting system is developed and fabricated in this study to process discarded canola stalks into fibers. The effects of retting parameters are studied using Latin Hypercube statistical design, modeled using Altair HyperStudyTM, and subjected to a multi‐objective optimization. The retting time is reduced from a range of ≈168–1080 h for the conventional water retting system to 60 h for the developed closed‐loop system. The fiber yield increased from ≈0.84% to 11.26%, the crystallinity index (CI) increased from ≈55.6% to 67.3%, and linear density decreased from ≈73.6 to 51.7 Tex with the increase in retting time, temperature, and water flow rate. However, the overall trends are complicated due to the heterogeneity in the structures and properties of the starting plant materials. The optimal retting parameters are 60 h‐time, 60 °C‐temperature, and 150 mL min −1 ‐water flow rate. Under these conditions, canola fibers exhibited ≈11.26% yield, ≈67.32% crystallinity index, and ≈56.24 Tex linear density. Canola fibers exhibited a multifiber structure surface (mean fiber diameter ≈957.8 µm) and non‐cellulosic component dominant cross‐section due to their higher pectic polysaccharides content (≈32.5–41.8%). The canola fiber production accounts for ≈169.42 kg CO 2 e/tonne, which is significantly lower than the emissions associated with equivalent flax fiber production (≈403.15 kg).
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".