Sustainable resource recovery and process improvement in anaerobic digesters using hydrochar: A circular bio‐economic perspective
Bibliographic record
Abstract
Abstract Hydrothermal carbonization (HTC) is a promising technology for waste valorisation and nutrient recovery to achieve sustainability. HTC converts organic waste into hydrochar, a carbon‐rich solid with numerous surface functionalities that can be used for energy and wastewater treatment. In this review, we highlight the potential of hydrochar‐based technology for improving the performance of anaerobic digestion (AD) systems and downstream applications of nutrient‐laden hydrochar. We identify knowledge gaps in hydrochar production, performance in AD systems and nutrient recovery, including the need for larger‐scale production facilities, multielement adsorption studies, and computational modelling. Techno‐economic analysis and life cycle assessment of hydrochar applications are critical to evaluating the commercial viability of this technology. Overall, hydrochar‐based technology offers a sustainable solution for waste management and resource recovery, with potential socioeconomic benefits for developing economies. The deployment of hydrochar‐based technology will directly address key issues highlighted in the United Nations' Sustainable Development Goals such as Clean water and sanitation (SDG 6); Zero hunger (SDG 2); and Climate action (SDG 13) thereby contributing to a more sustainable future.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".