STARx Hottpad for smoldering treatment of waste oil sludge: Proof of concept and sensitivity to key design parameters
Bibliographic record
Abstract
Growing stockpiles of waste oil sludge (WOS) are an outstanding problem worldwide. Self-sustaining Treatment for Active Remediation applied ex situ (STARx) is a treatment technology based on smoldering combustion. Pilot-scale experiments for the STARx Hottpad prove this new concept for the mobile treatment of WOS mixed intentionally with sand or contaminated soil. The experiments also allowed for the calibration and validation of a smoldering propagation numerical model. The model was used to systematically explore the sensitivity of Hottpad performance to system design, operational parameters, and environmental factors. Pilot-scale (~1.5 m width) simulations investigated sensitivity to injected air flux, WOS saturation, heterogeneity of intrinsic permeability, and heterogeneity of WOS saturation. Results reveal that Hottpad design is predicted to be successful for WOS treatment across a wide range of scenarios. The operator can control the rate of WOS destruction and extent of treatment by increasing the air flux injected into the bed. The potential for smoldering channeling to develop was demonstrated for the first time. Under certain conditions, such as WOS saturations of 80%, high heterogeneity of WOS saturations, or moderate to high heterogeneity of soil permeability, smoldering channeling was predicted to accelerate to the point that remedial performance was degraded. Field-scale simulations (~10 m width) predicted successful treatment, with WOS destruction rates an order of magnitude higher than the pilot-scale and treatment times increasing only linearly with bed height. This work is a key step toward the design and effective operation of field STARx Hottpad systems for eliminating WOS.
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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 | 0.005 | 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".