A comprehensive assessment of Integrating anaerobic digestion and hydrothermal liquefaction Processes: Harnessing energy from sewage sludge
Bibliographic record
Abstract
• Assessed energy recovery in two schemes: HTL-AD and AD-HTL in integrated systems. • Analyzed HTL operating conditions impacts’ on product yield in an integrated system. • Explored process efficiency at 250–350 °C and retention times of 30 & 60 min. • Achieved maximum energy recovery at 300 °C for 60 min with HTL-AD configuration. • Optimized operating conditions enhanced energy recovery in integrated systems. Integrating anaerobic digestion (AD) and hydrothermal liquefaction (HTL) offers a promising approach for efficient sewage sludge management and enhanced energy recovery. This study systematically evaluates the energy recovery efficiency of two sequencing configurations: HTL followed by AD and AD followed by HTL, at varying HTL operating conditions of 250, 300, and 350 °C for 30 and 60 min each. Our results demonstrate that the HTL-AD sequence yields higher energy recovery in the form of biocrude, with a significant concentration of fatty acids due to the high lipid content in primary sludge. Conversely, the AD-HTL sequence recovers more energy in the form of biomethane, attributed to the easily degradable nature of primary sludge with lower nitrogen content. Energy recovery for the HTL-AD sequence ranges from 47.2 % to 84.5 %, while the AD-HTL sequence ranges from 57.2 % to 77.3 %. The HTL-AD system recovers the highest energy at 300 °C for 60 min, whereas at other operating conditions, the AD-HTL system achieves higher energy recovery than the HTL-AD system. These findings provide valuable insights for optimizing sewage sludge valorization processes and advancing toward a sustainable circular bioeconomy. Future research should focus on long-term stability, economic feasibility, and scalability assessments of these integrated systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".