Numerical Modeling of Steam Flushing for Removal of Dense Nonaqueous Phase Liquids
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».