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排序方式: 共有440条查询结果,搜索用时 15 毫秒
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Anne Pinton Julia Boubnova François Becmeur Pierre Kuhn Marie-Victoire Senat Julien Stirnemann Marianne Capelle Jonathan Rosenblatt Jérôme Massardier Pascal Vaast Gwenaelle Le Bouar Amélie Desrumaux Laure Connant Laetitia Begue Benoit Parmentier Franck Perrotin Alain Diguet Guillaume Benoist Charles Muszynski Aurélien Scalabre Norbert Winer Jean-Luc Michel Florence Casagrandre-Magne Jean-Marie Jouannic Denis Gallot Perrine Coste Mazeau Emmanuel Sapin Alexis Maatouk Anne-Hélène Saliou Loïc Sentilhes Florence Biquard Nicolas Mottet Romain Favre Alexandra Benachi Nicolas Sananès 《黑龙江环境通报》2020,40(8):949-957
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Richard Martel Pierre J. Glinas Jacques E. Desnoyers 《Journal of contaminant hydrology》1998,29(4):226
Phase diagrams were used for the formulation of alcohol–surfactant–solvent and to identify the DNAPL (Dense Non Aqueous Phase Liquid) extraction zones. Four potential extraction zones of Mercier DNAPL, a mixture of heavy aliphatics, aromatics and chlorinated hydrocarbons, were identified but only one microemulsion zone showed satisfactory DNAPL recovery in sand columns. More than 90 sand column experiments were performed and demonstrate that: (1) neither surfactant in water, alcohol–surfactant solutions, nor pure solvent can effectively recover Mercier DNAPL and that only alcohol–surfactant–solvent solutions are efficient; (2) adding salts to alcohol–surfactant or to alcohol–surfactant–solvent solutions does not have a beneficial effect on DNAPL recovery; (3) washing solution formulations are site specific and must be modified if the surface properties of the solids (mineralogy) change locally, or if the interfacial behavior of liquids (type of oil) changes; (4) high solvent concentrations in washing solutions increase DNAPL extraction but also increase their cost and decrease their density dramatically; (5) maximum DNAPL recovery is observed with alcohol–surfactant–solvent formulations which correspond to the maximum solubilization in Zone C of the phase diagram; (6) replacing part of surfactant SAS by the alcohol n-butanol increases washing solution efficiency and decreases the density and the cost of solutions; (7) replacing part of n-butanol by the nonionic surfactant HOES decreases DNAPL recovery and increases the cost of solutions; (8) toluene is a better solvent than D-limonene because it increases DNAPL recovery and decreases the cost of solutions; (9) optimal alcohol–surfactant–solvent solutions contain a mixture of solvents in a mass ratio of toluene to D-limonene of one or two. Injection of 1.5 pore volumes of the optimal washing solution of n-butanol–SAS–toluene–D-limonene in water can recover up to 95% of Mercier DNAPL in sand columns. In the first pore volume of the washing solution recovered in the sand column effluent, the DNAPL is in a water-in-oil microemulsion lighter than the excess aqueous phase (Winsor Type II system), which indicates that part of the DNAPL was mobilized. In the next pore volumes, DNAPL is dissolved in a oil-in-water microemulsion phase and is mobilized in an excess oil phase lighter than the microemulsion (Winsor Type I system). The main drawback of this oil extraction process is the high concentration of ingredients necessary for DNAPL dissolution, which makes the process expensive. Because mobilization of oil seems to occur at the washing solution front, an injection strategy must be developed if there is no impermeable limit at the aquifer base. DNAPL recovery in the field could be less than observed in sand columns because of a smaller sweep efficiency related to field sand heterogeneities. The role of each component in the extraction processes in sand column as well as the Winsor system type have to be better defined for modeling purposes. Injection strategies must be developed to recover ingredients of the washing solution that can remain in the soil at the end of the washing process. ©1997 Elsevier Science B.V. 相似文献
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Pierre H. Lemoine Eric G. Reichard Irwin Remson 《Journal of the American Water Resources Association》1986,22(3):417-423
ABSTRACT: By extending the concept of response matrix to consider “active” and “passive” effects, an efficient response matrix method is developed for coupling a groundwater simulator and a regional agricultural management model The method eliminates the need to store all of the recovery information from preceding time periods. Active effects are those which occur during the actual application of a pumping or recharge stress while passive effects represent the recovery of water levels from an initial departure from steady-state conditions at the beginning of a time step. Derivation of the required matrices and a numerical example are presented for the Salinas Valley groundwater basin in California. 相似文献