Applied Smouldering Combustion for Supporting a Circular Economy
Bibliographic record
Abstract
Sustainable waste management requires significant increases in the proportion of waste and its components being reused, repurposed, and recycled instead of landfilled. Recent research and regulations have supported growing interest in circular economies, with a significant focus on making waste management processes more cyclic, increasing reuse, and reducing disposal. Resource recovery of nutrients and metals from municipal and industrial wastewater treatment plants and other sources of sludges may relieve the depletion of essential elements and have significant environmental and economic benefits. Thermal technologies offer strong promise for combining resource recovery with the robust destruction of hazardous compounds that must be removed from a circular economy. Applied smouldering is an emerging thermal technology that has demonstrated unique benefits in managing challenging wastes, such as high-moisture-content biomass, in a self-sustaining manner with minimal energy footprint and limited pre-processing infrastructure. Therefore, smouldering can support the inclusion of challenging wastes into circular economies. Most relevant applied smouldering studies to date have focused on municipal wastewater treatment sludge (i.e., sewage sludge). Therefore, this chapter will focus largely on the application of smouldering as a circular economy solution for sewage sludge; however, its applicability can be extended to a wide range of carbon-rich waste materials.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".