Simulating the Spatial Extent of Thermally Enhanced Reaction Zones for Low Temperature Thermal Treatment
Bibliographic record
Abstract
Abstract Low temperature thermal treatment (LTTT) is a technology that can enhance aqueous‐phase degradation reactions for organic constituents in groundwater. Understanding heat transfer in groundwater is important for the design of LTTT applications. In this study, the effect of permeability heterogeneity on temperature distributions during and after the application of heat was investigated by numerical modeling. An enhanced reaction zone was determined for the hydrolysis of 1,1,1‐trichloroethane (1,1,1‐TCA) using an average half‐life considering the temperature history during and after heating. For hydrolysis reactions, the average half‐life could be reduced substantially by reaching a high temperature for a short period of time because their reaction rates increase exponentially with increased temperatures. Results showed that the enhanced reaction zone was shifted downstream of the heater well zone at high groundwater velocities. This suggests that heaters should be shifted upstream of the target treatment zone to fully utilize the applied heat. In addition, permeability heterogeneity leads to greater macroscopic dispersion at higher velocities. This resulted in higher spreading of heat and faster heat dissipation in the simulations with a heterogeneous permeability condition compared with a homogenous permeability condition. As a result, the enhanced reaction zone was smaller in simulations with higher levels of permeability heterogeneity at a mean velocity of 0.3 m/day.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".