Effect of Gasification Temperature on Biohydrogen Derived from Waste Agro Products for Alternative Fuel Application
Bibliographic record
Abstract
Hydrogen fuel is becoming a popular choice in many energy applications because of its innovative green technology, which produces zero carbon emissions. It also offers better efficiency than fossil fuels. Current research focuses on obtaining hydrogen energy from agricultural waste using a gasification process. This process involves heating the waste at gasification temperatures 300, 400, 500, 600, and 700°C, maintaining a residence time of 60 minutes, and applying a gasification pressure of 20 bar. The effects of gasification temperature on the effectiveness of hydrogen production are examined. At a high gasification temperature of 700°C and a residence time of 60 minutes, the processed agro feedstock showed impressive results. It achieved a molar fraction of 12% carbon dioxide (CO2), 31% methane (CH4), and 55% hydrogen (H2), leading to an improved hydrogen yield of 15.2 mol/kg. Additionally, it demonstrated better hydrogen selectivity at 8.1 and a higher gasification efficiency of 61%, outperforming results from other gasification temperatures. Bio-green hydrogen is used as an alternative fuel for energy purposes.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".