Integrating the Bottom Ash Residue from Biomass Power Generation into Anaerobic Digestion To Improve Biogas Production from Lignocellulosic Biomass
Bibliographic record
Abstract
The bottom ash residue derived from biomass power generation was characterized by alkalinity and contained about 35 mineral elements, which offered a possibility to improve anaerobic digestion of lignocellulose biomass via providing mineral nutrients for microorganisms and alkali conditions for pretreatment. Technically, bottom ash was mixed with distilled water to prepare the extracting solution for this investigation. To check the function as a nutrient provider of bottom ash, the increased bottom ash loading from 0.37 to 2.96% was employed to achieve the extracting solutions with different concentrations of mineral elements. Rice straw was mixed with these extracting solutions for 7 days and then employed for the anaerobic digestion in batch. Results indicated that 314.38 mL/g of total solid biogas was achieved by 0.37% bottom ash, which was 21.2 and 15.8% higher than that of the blank and 0.12% NaOH (equivalent alkalinity of 0.37% bottom ash), respectively, proving that bottom ash substantially functioned as a nutrient provider with the resultant improvement. However, the decreased biogas production at higher loadings suggested potential inhibitions. Besides, to check the potential function of alkali pretreatment using bottom ash, the soaking duration of rice straw in the extracting solution (the calculated bottom ash loading of 0.37%) was prolonged from 0 to 7 days to achieve the different intensities, and the results indicated that the lignin and hemicellulose removal was promoted to 20.1 and 25.3% after 7 days of pretreatment, respectively, and the corresponding biogas production and biomethane yield were increased by 63.1 and 28.3%, respectively, proving that the bottom ash can work as a base provider for alkali-pretreating rice straw to facilitate anaerobic digestion.
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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) | 0.000 | 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".