Hydrothermal processing for plastic waste valorisation: Technical pathways, environmental performance, and prospects for commercialisation
Bibliographic record
Abstract
Hydrothermal processing (HTP) is a promising solution to the global plastic waste crisis, integrating waste reduction, hazard mitigation, and resource recovery to advance the circular economy. Operating under high-temperature and high-pressure aqueous conditions, HTP transforms plastics into value-added products such as hydrochar, bio-oil, organic acids, and syngas. Different types of HTP technologies provide great potential for plastic waste valorisation, with several companies working to commercialise HTP plants fully; however, despite many advancements, HTP faces significant barriers to large-scale adoption, including high energy demand, complex system design, and economic feasibility concerns. This study highlights the environmental benefits of HTP, including reduced reliance on virgin resources and enhanced waste valorisation. Transparent communication among researchers, industry stakeholders, policy makers, and the public, supported by pilot demonstrations and cost-efficient strategies, is essential for broader societal acceptance of HTP systems. Enhancing energy efficiency through water recycling streams and process optimisation may enhance the economic costs associated with HTP systems. Furthermore, government support via subsidies and carbon credits will improve HTP’s viability as a large-scale plastic waste solution, bridging the gap between laboratory success and commercial adoption. • HTP offers cleaner plastic waste solutions compared to current methods. • HTP produces fuels and chemicals that support the circular economy. • HTC and HTL yield up to 72 % hydrochar, 80 % bio-oil from plastics. • HTD and HTG can degrade 95–98 % of plastics under ideal conditions. • High costs and poor standardisation hinder HTP scale-up globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".