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Enregistrement W1991328943 · doi:10.2118/2003-191

Propagation of Phenol in Aquifer With Reversible Adsorption

2003· article· en· W1991328943 sur OpenAlexaboutno aff
Jiawei Tang

Notice bibliographique

RevueCanadian International Petroleum Conference · 2003
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueGroundwater flow and contamination studies
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Adelaide
Mots-clésAdsorptionPhenolAquiferEnvironmental scienceChemistryGeologyGroundwaterGeotechnical engineeringOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Abstract Previous study1–3 indicated that phenol transport from spilled bitumen into flowing aquifer was controlled by slow molecular diffusion of phenol in bitumen as the ratedetermining step. This simplified de-coupled transfer mechanism has led to a 2 dimensional planar flow analytical solution, which described the time-dependent phenol concentration in a flowing stream4. The model predicted that the produced phenol concentration monitored at an observation well would rise sharply and then decline gradually as the phenol flux emerging from the bitumen surface was decreasing with time. The simplicity makes the model valuable in estimating the size, configuration and location of the spill as well as the produced phenol concentration, the time-dependent phenol spatial distribution and the time required for phenol concentration to decline to an environmentally acceptable level by natural dispersion in the aquifer. Reversible adsorption of phenol on sand surface is studied and incorporated into the model in this paper using a chromatographic transformation technique. It is found that phenol propagation is retarded by a delay factor related to the adsorption and desorption characteristics of phenol in accordance with the chromatographic theory. The enhanced model with adsorption mechanism is well suited to other water pollution problems arising from chemical spills. Introduction Canada has an estimated 400 billion m3 of heavy oil deposit mainly located in Alberta and Saskatchewan. Heavy oil is a viscous tar-like liquid or semi-solid, it has a density of close to one and a viscosity as high as 1 million mPa.s at reservoir conditions. It has been reported that bitumen as well as conventional oil contains water soluble toxic compounds such as phenols1,5, carboxylic acids, anhydrides, ketones and other high molecular weight acidic compounds generically classified as humic acids (Blum et al. 1986). During a heavy oil spill such as the one reported by Imperial Oil at Cold Lake in 19881,7, the spilled bitumen can be in contact with the flowing water in the surface aquifer. These compounds, which are soluble both in oil and water, can leach slowly into the water contacted by the bitumen, posing a threat of groundwater contamination to the environment. To assess the environmental impact of groundwater contamination that can result from a spill, experiments1,2 were designed and conducted in the lab to study the rate of the release of phenol from bitumen into flowing fresh water. Molecular diffusion of phenol in bitumen was identified as the rate-determining step and this transfer mechanism was adequately described by ade-coupled second-order convective-dispersive equation from which the diffusion coefficients of phenol were determined1 to be 2.2×10−8 cm2/s at 4 °C in water-bitumen emulsion and 4.0 ±0.3x10−8 cm2/s at 22 °C in bitumen2 respectively. The idealized "de-couple" assumption enables the development of an unsteady-state formulation derived from Green's function and convolution theory to describe phenol distribution in a flowing aquifer in contact with bitumen as well as the phenol concentration produced from a sampling observation well4. This paper, which is a continuation of the model development work, described the results and the mathematical process to incorporate the effect of reversible adsorption into the model using a chromatographic transformation technique.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,231
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,202
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2003
Routes d'admission1
Résumé présentoui

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