Thermal Desorption Treatment of Contaminated Soils in a Novel Batch Thermal Reactor
Bibliographic record
Abstract
Low-temperature thermal desorption, in which thermal energy is used to vaporize and physically separate volatile and semivolatile organic contaminants from soil, is among the most promising and economic ex situ soil remediation alternatives. Experiments were performed using a bench-scale thermal desorber, the batch thermal reactor, which was developed as a prototype to commercial desorbers. A treatability study using four representative samples of industrial contaminated soil was followed by a fundamental study of the thermal desorption process using three controlled samples prepared by mixing a soil with binary mixtures of selected polynuclear aromatic hydrocarbons. For the industrial samples, the effect of desorber residence time, temperature, and several pretreatments on contaminant removal was investigated. Three of the five samples were successfully treated to the legislated soil remediation limits. Using the prepared samples, the effects of sample porosity, contaminant molar mass, desorber residence time, and temperature on thermal desorption were investigated. The experimental results were fitted to an exponential desorption equation, and the desorption rate curves were generated to provide a basis for scale-up.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".