Novel Ozonation Technique to Delignify Wheat Straw for Biofuel Production
Bibliographic record
Abstract
Production of ethanol from lignocellulosic biomass is a promising alternative source of energy because this world is in need of a low cost, renewable and sustainable green energy source. Canada is among the top ten producers of wheat in the world with a capability of producing around 37.52 million tons of wheat straw per year. Hence, wheat straw could be a potential source of energy. Ozone was supposed to break down physical and chemical structure of lignin and hence make cellulose present in wheat straw more accessible for a conversion reaction. However, it was observed in our experimental work that without pretreatment of wheat straw, ozone did not produce the expected effects in the reaction with wheat straw. In spite of conducting the experiments under vigorous conditions only 13 % of lignin removal was achieved. The proposed technique, which is a strategic use of water and ozone, has overcome the problem of slow reaction of ozone with wheat straw. Through this technique, around 90% acid insoluble lignin (AIL) removal was achieved by controlling parameters such as reaction time, flow rate of ozone-oxygen feed stream, ozone concentration in ozone-oxygen feed stream, water contents and particle size. The maximum AIL (90%) was obtained at flow rates of 4L/min, 2L/min and 1L/min at reaction times 90, 120 and 180 minutes, respectively, with 2% wt ozone concentration in ozone-oxygen stream. Other factors supported maximum lignin removal were water contents at 3 times of the dry weight of wheat straw and at 0.5 mm particle size of wheat straw. It was also noted that excessive ozonation caused repolymerization of lignin by-products. The obtained results demonstrated that ozonation of wheat straw using the proposed technique was practical, easy to manage for the removal of lignin and it may be considered as an alternative method for delignification of biomass in production of biofuel.
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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 | 0.001 |
| 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".