Optimization of ultrasonication and alkalinization as pretreatment methods for leachate co-digested with food waste toward maximum synergistic effects
Bibliographic record
Abstract
Anaerobic co-digestion (AcoD) of food waste (FW) and landfill leachate has shown promising results in enhancing the methane yield. However, leachate includes toxic and refractory compounds that may impact the decomposition process. In this research, co-digested leachate was pretreated using ultrasonication and alkalinization to manipulate its characteristics toward improved synergism with FW. Experimental optimization was conducted through biochemical methane potential (BMP) assays to identify the optimum operating conditions of the pretreatment methods. The study evaluated the synergistic effects of co-digestion with raw and pretreated leachate on enhancing the performance in terms of feedstock solubilization and methane production. The BMP test demonstrated that alkalinization and ultrasonication improved the total methane generation by 35% and 27%, respectively, yielding around 397 and 375 mL CH4 per g of volatile solids. Moreover, ultrasonication and alkalinization enhanced the synergistic effects by 28% and 36%, respectively, compared to co-digestion with untreated leachate. Optimization by response surface methodology revealed that maximum performance could be achieved with leachate sonication at 212 W for 37.5 min or augmenting 788 g NaOH per kg of volatile solids. Kinetic and statistical models were derived to simulate and assess the impacts of the pretreatment parameters on the AcoD process. The results indicated that the ultrasonication energy had a higher influence on total solubility and methane production than alkaline dosage. Additionally, energy efficiency analyses were performed to examine the overall viability of the examined management approach and found that alkalinization increased the net energy efficiency by 23%, whereas ultrasonication was inefficient within the examined laboratory conditions despite the improved performance. The findings support an integrated organic waste management system where separated FW is co-treated with landfill leachate.
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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) | 0.000 | 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".