Impact of Ozone Pre-treatment on Aerobic and Anaerobic Biodegradability of Aqueous Phase from Hydrothermal Liquefaction of Municipal Sludge
Bibliographic record
Abstract
Hydrothermal liquefaction (HTL) of municipal sludge is a promising alternative to anaerobic digestion due to resource recovery into biocrude oil (refined to transportation fuel), nutrient-rich hydrochar, smaller footprint, enhanced micropollutant destruction and substantially reduced solids for final disposal. For integration of HTL to wastewater treatment plants (WWTPs), its highest volume aqueous product (HTLaq) requires treatment on-site. However, HTLaq contains soluble refractory organics inhibitory to downstream biological processes. In this study, the impact of ozone (O₃) pre-treatment on characteristics and subsequent aerobic and anaerobic biodegradability of HTLaq were studied. HTLaq was obtained at 350°C, 15 min from sludge cake. Within a pre-treatment dose range of 0.03-0.18 g (dissolved) O3/g chemical oxygen demand (COD) of HTLaq, the highest dose of 0.18 g O₃/g COD achieved a 43% COD removal and 90% aerobic biodegradability improvement compared to control (no pre-treatment) at 20°C. For anaerobic biodegradability, the optimum pre-treatment dose was 0.14 g O₃/g COD with 98% and 89% improvements in specific methane yields from HTLaq under mesophilic (35°C) and thermophilic (55°C) temperatures, compared to controls. Removal of inhibitory N-heterocyclics and phenolics from HTLaq via ozonation was the main reason for enhanced biodegradation and biogas recovery, improving overall carbon recovery from municipal sludge.
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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".