Hydrothermal Liquefaction of Sludge for Biocrude Production and Synergistic Utilization of Byproducts in Anaerobic Digestion
Bibliographic record
Abstract
Hydrothermal liquefaction (HTL) is a promising thermochemical process for converting sludge into biocrude. However, the effective utilization of other HTL byproducts, such as hydrochar (HC) and an aqueous phase (HTL-AP), remains a significant challenge. This study investigates the synergistic utilization of HTL-AP and HC in anaerobic digestion (AD) for effective biogas production. HTL experiments were conducted at three temperatures (280 °C, 330 °C, and 370 °C). The higher HTL temperatures increased the biocrude yield while reducing HC and AP production. The methane production in AD was influenced by the HTL-AP composition and the presence of HC, with the highest cumulative methane production (327 ± 1.01 mL/g VS) observed for AP produced at 280 °C with HC addition. The addition of HC improved methane production by 25% at 280 °C, 29% at 330 °C, and 19% at 370 °C compared to reactors without HC. HC amendment increased the relative abundance of Firmicutes, Clostridium, and Methanobacterium, supporting improved microbial syntrophic interactions, particularly at 330 °C. The results further suggested that HC might also facilitate direct interspecies electron transfer (DIET), enhancing methane production by promoting electron exchange between syntrophic bacteria and methanogens. These findings highlight the potential of co-utilizing HTL byproducts in AD as a sustainable strategy for sludge management and bioenergy recovery.
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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 | 0.001 |
| 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".