Effects of municipal sludge composition on hydrothermal liquefaction products: Optimizing energy recovery via combination with anaerobic digestion
Bibliographic record
Abstract
This study evaluated the performance of an optimized hydrothermal liquefaction (HTL) process for municipal sludge with downstream anaerobic digestion (AD) for aqueous by-product valorization. Mixed sludge with various primary and secondary sludge ratios and digested sludge from mesophilic and thermophilic AD from plants under seasonal and operational fluctuations was tested. Results showed shifts in product yield, biocrude and hydrochar composition, and energy recovery (ER), with higher secondary sludge leading to increased hydrochar heavy metals and phosphorus. However, biocrude maintained consistent C (72–75 %), H (9–10 %), and N (4–5 %) contents, higher heating value (35–37 MJ/kg), dry-ash-free yield (48 ± 3 %), and ER (69 ± 1 %), demonstrating adaptability of the optimized HTL condition for varying feedstock. Biocrude ER was driven by sludge composition, ranking lipids > proteins > carbohydrates. Mesophilic AD effectively treated HTL aqueous, achieving the highest overall ER (78–82 %), energy return on investment (10.8–11.1), and net energy yield (15.7–16.2 MJ/kg, dry basis) in HTL-AD system, outperforming AD and AD-HTL-AD configurations for sludge treatment. These findings demonstrate the robustness of optimized HTL condition across diverse sludge feedstocks and highlight the potential of HTL-AD integration to enhance ER and resource sustainability in wastewater treatment practice. • Municipal sludge composition significantly affects HTL product yields and contents • Fluctuations in mixed sludge have minimal impacts on biocrude energy recovery • Sludge macromolecules contribute to biocrude by lipids > proteins > carbohydrates • More secondary sludge in feedstock leads to more metals and phosphorus in hydrochar • HTL-mesophilic anaerobic digestion maximizes energy 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".