Coupling hydrothermal liquefaction and anaerobic digestion for waste biomass valorization: A review in context of circular economy
Bibliographic record
Abstract
In light of the substantial global production of biomass waste, effective waste management and energy recovery solutions are of paramount importance. Hydrothermal liquefaction (HTL) and anaerobic digestion (AD) have emerged as innovative techniques for converting biomass waste into valuable resources. Their integration creates a synergistic framework that mitigates inherent limitations, leading to improved efficiency, enhanced product quality, and the comprehensive utilization of biomass. This review paper investigates the integration of HTL and AD, highlighting its significance and potential benefits as well as the optimal sequencing (HTL followed by AD and AD followed by HTL). The review encompasses experimental procedures, factors influencing both sequencing options, energy recovery characterizations, final product outcomes, as well as toxicological assessments and discussions on reduction. Additionally, it delves into the transition towards a circular bioeconomy and discusses the challenges and opportunities intrinsic to these processes. The findings presented in this review offer valuable insights to shape future research in this evolving field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".