Continuous, self-sustaining smouldering destruction of simulated faeces
Bibliographic record
Abstract
A new approach for the rapid destruction of human waste using smouldering combustion is presented. Recently, self-sustaining smouldering combustion was shown to destroy the organic component of simulated human solid waste and dog faeces resulting in the sanitization of all pathogens using a batch process (Yermán et al., 2015). Here, a continuous smouldering process is demonstrated for the first time, allowing for a much smaller reactor size and much less energy input per mass of waste treated. The self-sustained smouldering of simulated human faeces mixed with sand is evaluated over long periods (more than 16 h) based on a single ignition. The key process of intermittent self-sustained smouldering, in which the reaction is terminated and restarted by only turning the air off and on, is demonstrated. Experiments examine the influence of two key operator controls: airflow rate and set elevation of the quasi-steady-state smouldering front in a 37 cm high reactor. Quasi-steady-state fuel destruction rates from 93 g/h to 12 g/h were achieved by varying the superficial flow velocity from 7.4 cm/s to 0.11 cm/s, the latter with a velocity approximately an order of magnitude lower than possible for a self-sustaining reaction in an equivalent batch system. Excess energy of up to 140 J/g of sand was recovered from the clean sand produced in each cycle, which could be used to further increase the energy efficiency of this novel waste treatment system.
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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 | 0.000 | 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 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".