Thermal Treatment of Trichloroethene by Electrical Resistance Heating: Visualization of Gas Production in Coarse Layers
Bibliographic record
Abstract
The effective implementation of in situ thermal treatment (ISTT) technologies requires understanding of gas production and migration in heterogenous media. However, investigations of the effects of high permeability contrast on gas formation, accumulation, and migration, as well as its potential effect on the redistribution of dense non-aqueous phase liquid (DNAPL), are relatively rare. In this study, electrical resistance heating (ERH) experiments were conducted in a thin sand-packed cell to simulate common yet not well-studied scenarios encountered during ISTT applications, such as coarse lenses surrounded by finer material. Two packing configurations were employed: 2 mm glass beads surrounded by 20/30 silica sand and 20/30 silica sand overlaying 40/50 silica sand. Each experiment contained an emplaced pool of trichloroethene (TCE) within the coarse material. If permeable material or pathways were present between the coarse lens and the upper cell boundary, the gas migrated along these pathways, and local DNAPL redistribution was limited to near the top of the pool before it vaporized. In contrast, if the coarse material was surrounded by finer material and contained a sufficient volume of DNAPL, the gas accumulated inside the coarse lens leading to DNAPL displacement from the lens. For five selected DNAPLs, this volume was estimated to be 0.1% to 0.5% of the total pore volume of the coarse material. The conceptual model developed in this study improves our understanding of this common geological scenario, demonstrating the importance of considering both lower- and higher-permeability material and their effects on multiphase flow during co-boiling, as well as the design of gas extraction systems during ISTT applications.
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 | 0.001 |
| 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".