Numerical Modeling of Steam Flushing for Removal of Dense Nonaqueous Phase Liquids
Bibliographic record
Abstract
In situ thermal remediation technologies (ISTR) such as hot water and steam flushing were initially developed in the petroleum industry for enhanced oil recovery. These developments were followed by the advancement of electrical resistance heating (ERH) and thermal conductive heating (TCH). Over the last three decades, a variety of field scale projects and laboratory experiments have been performed using the various types of in situ thermal treatment technologies for non-aqueous phase liquid (NAPL) contaminants and a number of sophisticated numerical models have been developed for simulating thermal remediation. In the first part of this study the fundamental mechanisms associated with thermal remediation are examined and specific factors for field scale implementation of each thermal technology are discussed in the context of published case studies. The most appropriate conditions for successful application of each of these methods for removal of NAPLs from the subsurface and advantages and limitations of each method for ISTR technologies are reviewed. In the second part of this study, a series of previously performed two-dimensional laboratory experiments of steam flushing for removal of dense nonaqueous phase liquids (DNAPL), including perchloroethylene (PCE), a mixture of PCE and nonane, and a mixture of monochlorobenzene (MCB) and dichlorodiphenyltrichloroethane (DDT) perched on a barrier layer were simulated with a nonisothermal multiphase, multicomponent model (CompSim) to both test the model’s ability to simulate DNAPL removal by steam flushing and to examine the effects of steam flushing on the fate and transport, remobilization, and vertical redistribution of DNAPL from layered media. The model simulations matched experimental observations well and experimental results and simulations demonstrated the challenge of avoiding downward DNAPL mobilization in the application of steam flushing for removal of DNAPL perched on barrier layers, primarily due to the desaturation of the barrier layer that occurs during steam flushing. In additional numerical simulations, the effectiveness of hot air and hot air combined with steam at different mass ratios (1.6 kg, 2.5 kg, 4 kg, and 7 kg of hot air to 1 kg of steam) was compared to steam flushing for the removal of the PCE and nonane mixture. Flushing with a combined hot air and steam approach using a 1.6:1 mass ratio resulted in the complete removal of DNAPL in the shortest time. Despite the lower peak soil temperature observed in hot air combined with steam flushing (82.5°C with a 7:1 hot air to steam mass ratio) compared to steam flushing (125°C), the continuous removal of NAPLs in the early stages and limited downward migration and redistribution of DNAPL resulted in earlier and more rapid DNAPL removal during the combined hot air and steam flushing process. For the DDT-MCB mixture, modeling studies were conducted to investigate the impact of groundwater flow rate, preheating with hot water injection prior to steam flushing, and the permeability of the barrier layer on DNAPL redistribution and removal with steam flushing. A hypothetical field scale steam flushing scenario for removal of DNAPL mixture perched on barrier layer was also numerically simulated. The model predictions confirmed the observations made during the laboratory experiments with respect to the limited effectiveness of steam flushing for removal of SVOC from DNAPL mixtures of VOCs and SVOCs. The results illustrated conditions under which vertical remobilization of DNAPL, particularly the semi-volatile compounds, would be limited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".