Low- and high-temperature thermal hydrolysis pretreatment for anaerobic digestion of sludge: Process evaluation and fate of emerging pollutants
Bibliographic record
Abstract
The thermal hydrolysis process is a cutting-edge, widely used technology for the pretreatment of sludge before anaerobic digestion. Previous review articles addressed the mechanisms, optimization, and advantages of thermal hydrolysis for sludge management; however, a holistic comparison between low-temperature and high-temperature thermal hydrolysis processes is still lacking. The review aims to comprehensively examine the impacts of both low-temperature and high-temperature thermal hydrolysis processes on sludge solubilization, subsequent anaerobic digestion process performance and produced biosolids quality. Further, current thermal hydrolysis-related challenges, such as inhibitory compounds and residual ammonia, and their possible solutions are discussed. Moreover, both types of thermal hydrolysis processes are critically evaluated from technical, energetic, and economic standpoints. This review sheds light on the impact of thermal hydrolysis pretreatment on the fate of various emerging pollutants in sludge, a topic overlooked in most previous review articles. Finally, this review highlights the research gaps worthy of coverage in future studies. By compiling and critically analyzing previous studies, this review aims to function as a comprehensive handbook for researchers looking to optimize the selection of the most suitable thermal hydrolysis process based on specific sludge conditions. Enhancing the efficiency of thermal hydrolysis in sludge anaerobic digestion holds the potential for improved sludge management, increased bioenergy recovery, and the generation of high-quality biosolids to address environmental and public health concerns associated with emerging pollutants. Consequently, this review aims to contribute to achieving multiple UN Sustainable Development Goals, including #7 Affordable and Clean Energy, #11 Sustainable Cities and Communities, and #13 Climate Action.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".