Intensification of biogas production from rice straw using anaerobic digestion based on pre‐treatment with ultrasound
Bibliographic record
Abstract
Abstract The current work illustrates a novel method of pre‐treating rice straw using ultrasound (US) as well as using ultrasound coupled with anaerobic digestion (AD) to intensify biogas production. The primary objectives were to evaluate the effectiveness of ultrasound in increasing the utilization of rice straw and to optimize the conditions for maximum biogas yield. Important parameters such as ultrasonic power (0.2–1 W/mL), duty cycle (20%–80%), and substrate loading (2%–10% w/v) were varied to understand their effects during pre‐treatment. The results showed that the maximum increase in soluble chemical oxygen demand (sCOD), with a final value of 13,500 mg/L (an increase of 64.63%), was achieved under optimum conditions of ultrasonic power of 0.4 W/mL, a duty cycle of 50%, and a substrate loading of 6% w/v. Additionally, the study evaluated the effect of low‐intensity US exposure during AD with pre‐treated rice straw at varying irradiation times (10–30 min) and duty cycles (20%–60%). The optimal conditions of ultrasonic time of 20 min and a duty cycle of 50% resulted in nearly four times higher biogas generation compared to untreated samples. The current research successfully demonstrates the efficient use of US in the feedstock pre‐treatment and also in AD process, leading to significant intensification in biogas production within a shorter time frame.
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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) | 0.001 | 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".