Optimization of wheat debranning using laboratory equipment for ethanol production
Bibliographic record
Abstract
Abstract: Ethanol production from starchy cereal grains is increasing rapidly due to increasing demand for alternative fuels. In Canada, wheat is the primary feedstock in ethanol plants. To improve the productivity of the ethanol plants in terms of product quality and yield, debranning of wheat grains may be employed. Debranning is advantageous in two ways. Firstly, bran removal increases the starch content of the feedstock, improving the fermentation efficiency of the ethanol plants. Secondly, bran, a valuable co-product can be used as an animal feed ingredient. In this study, experiments to optimize the debranning process were carried out using two kinds of abrasive equipment, the Satake and the TADD (tangential abrasive dehulling device) mills. Wheat samples (30 and 200 g) were debranned in the Satake mill at 1 215, 1 412, and 1 515 r/min rotational speeds, 30, 36, and 40 grit sizes, and 30, 60, and 90 s retention times, and in the TADD mill at 900 r/min rotational speed, 30, 36, 50, and 80 grit sizes, and 120, 180, 240, and 300 s retention times. In addition to debranning efficiency, the starch separation efficiencies of the two mills were calculated in different debranning conditions. In the Satake mill, the 30 g and 200 g sample size, 1 412 r/min and 1 515 r/min rotational speeds, all grit sizes, and 60 s of retention time demonstrated the highest debranning efficiency. Correspondingly, optimal results in the TADD mill were obtained with 200 g sample size, 900 r/min rotational speed, 50 and 80 grit sizes, and 180 s and 240 s retention times. However, based on the experimental results, Satake mill provided better debranning values compared to the TADD mill. The starch separation efficiency values supported these results. Keywords: wheat debranning, ethanol production, Satake mill, tangential abrasive dehulling device, grit size, retention time, rotational speed, starch separation efficiency DOI: 10.3965/j.ijabe.20140706.008 Citation: George E, Rentsen B, Tabil L G, Meda V. Optimization of wheat debranning using laboratory equipment for ethanol production. Int J Agric & Biol Eng, 2014; 7(6): 54－66.
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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 | 0.000 | 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 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".