Effect of thermal and alkaline pretreatment of giant miscanthus and Chinese fountaingrass on biogas production
Bibliographic record
Abstract
Giant miscanthus (Miscanthus × giganteus) and Chinese fountaingrass (Pennisetum alopecuroides (L.) Spreng), cultivated for landscaping and soil conservation, are potential energy crops. The study investigated the effect of combined thermal and alkaline pretreatments on biogas production of these energy crops. The pretreatment included two types of alkali (6% CaO and 6% NaOH) at 22, 70 and 100 °C. The alkaline pretreatment resulted in a greater breakdown of the hemicellulose fraction, with CaO more effective than NaOH. Pretreatment of giant miscanthus with 6% CaO at 100 °C for 24 h produced a CH4 yield (313 mL g(-1) volatile solids (VS)) that was 1.7 times that of the untreated sample (186 mL g(-1) VS). However, pretreatment of Chinese fountaingrass with 6% CaO or 6% NaOH at 70 °C for 24 h resulted in similar CH4 yields (328 and 302 mL g(-1) VS for CaO and NaOH pretreatments) as the untreated sample (311 mL g(-1) VS). Chinese fountaingrass was more easily digestible but had a low overall CH4 yield per hectare (1,831 m(3) ha(-1) y(-1)) compared to giant miscanthus (6,868 m(3) ha(-1) y(-1)). This study demonstrates the potential of thermal/alkaline pretreatment and the use of giant miscanthus and Chinese fountaingrass for biogas production.
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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) | 0.001 | 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) | 0.001 | 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".