Hydrothermal liquefaction of sewage sludge for circular bioeconomy: Focus on lignocellulose wastes, microplastics, and pharmaceuticals
Bibliographic record
Abstract
The rapid increase in sewage sludge (SS) generation from wastewater treatment plants (WWTPs) has become a pressing global environmental challenge. The SS contains a wide variety of pollutants, including lignocellulose from plants and paper wastes, microplastics (MPs) from plastic wastes, and pharmaceutical residues (PRs), all of which pose substantial risks to ecosystems and human health. To address these waste management issues while also meeting rising energy demands, a shift towards a circular bioeconomy is essential. Hydrothermal liquefaction (HTL) of SS (SS-HTL) presents a sustainable solution by converting waste into renewable biofuels and mitigating environmental hazards. This review addresses five key areas: (1) an in-depth analysis of current advancements in SS-HTL technology; (2) factors influencing bio-oil production; (3) transformation pathways of lignocellulose, MPs, and PRs during HTL; (4) advanced methods for upgrading SS, including chemical, mechanical, and in situ liquefaction techniques; and (5) future perspectives on enhancing SS-HTL technology. Additionally, the review evaluates the potential applications of byproducts like the aqueous (AQ) phase, solid residues (SRs), and gases. By addressing the challenges in SS-HTL research and implementation, this article aims to improve economic feasibility and expand industrial applications. It serves as a valuable resource for researchers and innovators committed to advancing waste management technologies and accelerating the transition to a sustainable circular bioeconomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 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 teacher head, 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".