A method for predicting the effective diffusion coefficient of a gas phase organic contaminant in soil

Abstract

The application discloses a method for predicting effective diffusion coefficient of gaseous organic pollutants in soil, and is characterized in that the method comprises the following steps: (1) determining that the soil in a site meets a condition; (2) pretreating the soil in the site, and then determining the BET specific surface area S of the soil matrix; and (3) based on Fick's second law, introducing a correlation formula of temperature and soil pore structure parameters, and combining a binary diffusion coefficient of the gaseous organic pollutants to predict the effective diffusion coefficient of the gaseous organic pollutants in the porous medium under different temperature conditions. eff。 Based on the experimental fact that the diffusion behavior of the gaseous organic pollutants in the porous medium conforms to Fick's second law, the application has important significance for understanding and predicting the migration of the pollutants in the environment.

Description

Technical Field

[0001] This invention belongs to the field of environmental remediation and control technology of gaseous organic pollutants, specifically relating to a method for predicting the effective diffusion coefficient of gaseous organic pollutants in soil. Background Technology

[0002] The effective diffusion coefficient D of gaseous organic pollutants eff The diffusion coefficient is a crucial parameter for assessing the transport of pollutants in the vadose zone. Especially in the tailing stages of site remediation, particularly in the removal of pollutants from low-permeability soils, diffusion mechanisms can be a key limiting factor for remediation efficiency. Furthermore, determining the gas-phase diffusion coefficient plays a vital role in areas such as chemical catalytic reactors, underground transport of food or pharmaceutical drying wastes or other hazardous wastes, and VOC diffusion in buildings. Particularly due to the widespread application of thermal treatment and global warming, there is an urgent need to strengthen research on the influence of temperature on the effective diffusion coefficient of gaseous organic pollutants in porous media.

[0003] Current research on the effective diffusion coefficient under thermally enhanced conditions mainly focuses on two aspects: experimental investigation and model calculation. Experimentally, researchers have studied the effects of different porosities and water contents at room temperature on the effective diffusion coefficient of pollutants in porous media and developed some empirical formulas for prediction. However, under thermally enhanced conditions, the coupling effect of the temperature field and the confinement effect of the porous media is significant. Current techniques generally use the Fuller formula and a model of the tortuosity of the porous media to describe the influence of temperature on pollutant diffusion behavior, which has limited correlation with the pore structure of the soil medium. This can easily lead to significant deviations in the simulation of organic vapor diffusion behavior at contaminated sites. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for predicting the effective diffusion coefficient of gaseous organic pollutants in soil.

[0005] The technical solution of the present invention is as follows:

[0006] A method for predicting the effective diffusion coefficient of gaseous organic pollutants in soil, characterized by comprising the following steps:

[0007] (1) Determine that the soil in the site meets the following conditions: 0.35 < porosity Φ < 0.60, 0.10 mm < average particle size d 50 <2.0mm, organic matter content f oc <5%, moisture content ω <10%; and the gas phase of the site is determined to meet the following conditions: the temperature T of the effective diffusion coefficient of the pollutant to be measured is 4 to 70℃, the absolute pressure is 0.8 to 1.1 atm, and the gas phase organic pollutant is benzene.

[0008] (2) The soil in the above site is pretreated to remove moisture and organic matter to obtain pretreated soil, and then the BET specific surface area S of its soil matrix is ​​determined.

[0009] (3) Based on Fick's second law, a correlation between temperature and soil pore structure parameters is introduced. Combined with the available binary diffusion coefficients of gaseous organic pollutants, the effective diffusion coefficient D of gaseous organic pollutants in porous media under different temperature conditions is calculated. eff Make predictions.

[0010] In a preferred embodiment of the present invention, the method used for the pretreatment of removing moisture in step (2) is the drying method.

[0011] More preferably, the drying method includes: taking a certain mass m1 of soil and placing it in an oven at 110-130℃ for 24-48 hours, then weighing it to obtain the dried mass m2, from which the soil moisture content can be calculated. Furthermore, by combining the apparent density of the soil, the water saturation S of the soil at the site can be calculated. w Then according to S g =1-S w Calculate the gas phase saturation.

[0012] More preferably, the method used for the pretreatment to remove organic matter in step (2) is hydrogen peroxide oxidation.

[0013] In a further preferred embodiment, the hydrogen peroxide oxidation method includes: taking the material treated by the drying method, adding distilled water to moisten it, then continuously adding 20% ​​to 30% H2O2 while stirring, until no more bubbles are generated, then heating it in a water bath to remove residual H2O2, then filtering it, washing it with distilled water, and finally air-drying it in a ventilated place.

[0014] In a preferred embodiment of the present invention, the gaseous organic pollutant is benzene, toluene, xylene, or ethylbenzene.

[0015] More preferably, the correlation is: lgA=-9.293S-197.9d50-1.921,B=16.97S+504.3d 50 +2.951, of which,

[0016] D0: The available binary diffusion coefficient of gaseous organic pollutants at temperature T0, in m³. 2 / s, T0=298.15K,

[0017] S: The BET specific surface area obtained in step (2), in m².2 / g,

[0018] T: The temperature at which the effective diffusion coefficient of the pollutant to be measured in step (1) is expressed in K.

[0019] d 50 The average particle size in step (1) is in meters.

[0020] The beneficial effects of this invention are:

[0021] 1. This invention can accurately determine the effective diffusion coefficient D of gaseous organic pollutants in porous media under different temperature conditions. eff Based on the experimental fact that the diffusion behavior of gaseous organic pollutants in porous media conforms to Fick's second law, that is, the migration of the concentration C of gaseous organic pollutants with time t follows... Therefore, this invention is of great significance for understanding and predicting the migration of pollutants in the environment.

[0022] 2. This invention, through a specific pretreatment method, distinguishes the dissolution or adsorption effects of soil moisture and organic matter on pollutant migration from those of gaseous pollutants. This avoids confusion between different pollutant migration mechanisms and can be extended to pollutant migration models involving multiple transport mechanisms. Attached Figure Description

[0023] Figure 1 The flowcharts are for Embodiments 1 to 3 of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will be further explained and described below with reference to specific embodiments and accompanying drawings.

[0025] In the following embodiments, the pretreatment method for removing moisture in step (2) is a drying method, which includes: taking a certain mass m1 of soil and placing it in an oven at 110-130℃ for 24-48 hours, then weighing it to obtain the dried mass m2, from which the soil moisture content can be calculated. Furthermore, by combining the apparent density of the soil, the water saturation S of the soil at the site can be calculated. w Then according to S g =1-S w The gas phase saturation was calculated. The pretreatment method for removing organic matter was hydrogen peroxide oxidation, which included: taking the material after the drying method, adding distilled water to moisten it, then continuously adding 20% ​​to 30% H2O2 while stirring until no more bubbles were generated, then heating it in a water bath to remove residual H2O2, then filtering it and washing it with distilled water, and finally air-drying it in a ventilated place.

[0026] Example 1

[0027] This example uses five soils with different particle sizes as examples, predicting the effective diffusion coefficient D of xylene vapor in these five soils at temperatures ranging from 4 to 70°C and absolute pressures from 0.8 to 1.1 atm. eff The accuracy of this embodiment is evaluated based on laboratory measurements. This embodiment predicts the effective diffusion coefficient D of xylene vapor in soil at different temperatures. eff The process is as follows Figure 1 As shown, it includes the following steps:

[0028] (1) Determine the soil porosity Φ and average particle size d s0 Organic matter content f oc The soil texture requirements for this embodiment are met, including the moisture content ω, as shown in Table 1 below.

[0029] Table 1 Soil texture parameters

[0030]

[0031]

[0032] (2) The pollutant type is m-xylene, which meets the requirements for pollutant properties.

[0033] (3) The experimental temperatures were 10℃, 20℃, 30℃, 40℃, 50℃, and 60℃, and the experimental pressure was 1 atm, which met the environmental conditions for pollutant migration.

[0034] (4) Pre-treatment of soil samples to remove water and organic matter.

[0035] (5) After treatment, the soil samples were subjected to N2 adsorption-desorption experiments, and their BET specific surface area S was measured. The results are shown in Table 2 below:

[0036] Table 2. BET specific surface area S of the soil after post-treatment.

[0037]

[0038] (6) Based on Fick's second law, the correlation between temperature and soil pore structure parameters is introduced. Combined with the available binary diffusion coefficient of m-xylene, the effective diffusion coefficient D of m-xylene vapor in porous media under different temperature conditions is predicted. eff The correlation is: 1gA = -9.293S - 197.9d 50 -1.921, B = 16.97S + 504.3d 50 +2.951, of which,

[0039] D0: The available binary diffusion coefficient for m-xylene vapor at 298.15 K is 6.8 × 10⁻⁶. -6 m 2 / s [1] T0 = ​​298.15K

[0040] S: The BET specific surface area S obtained in step (5), in m² 2 / g,

[0041] T: The temperature in step (3), in K.

[0042] d 50 The average particle size in step (1) is in meters.

[0043] The predicted results were compared with the experimental values, and the results are shown in Table 3. To evaluate the accuracy of the predictions in this embodiment, the relative deviation (RD) between the predicted and experimental values ​​was calculated. The formula for calculating RD is as follows:

[0044]

[0045] Table 3 Effective diffusion coefficients (D) of m-xylene vapor in different soils eff Comparison of experimental and predicted values

[0046]

[0047] As shown in the table above, the relative deviation (RD) between the 25 predicted values ​​and the experimental values ​​is within 30%, and the average relative deviation is only 11.46%. This indicates that the correlation in this embodiment has good prediction accuracy under the above soil texture and temperature conditions, indicating that the method in this embodiment is suitable for predicting (1) the site soil texture conditions meet the following: 0.35 < porosity < 0.60, 0.10 mm < average particle size < 2.0 mm, organic matter content < 5%, and moisture content < 10%; (2) the effective diffusion coefficient D of meta-xylene under the conditions of temperature 4 to 70℃ and absolute pressure 0.8 to 1.1 atm. eff .

[0048] Example 2

[0049] This example uses five soils with different particle sizes as examples, predicting the effective diffusion coefficient D of toluene vapor in these five soils at temperatures ranging from 4 to 70°C and absolute pressures ranging from 0.8 to 1.1 atm. eff The accuracy of this embodiment is evaluated based on laboratory measurements. This embodiment predicts the effective diffusion coefficient D of toluene vapor in soil at different temperatures. eff The process is as follows Figure 1 As shown, it includes the following steps:

[0050] (1) Determine the soil porosity Φ and average particle size d 50 Organic matter content f oc The soil texture requirements for this embodiment are met, including the moisture content ω, as shown in Table 4 below.

[0051] Table 4 Soil texture parameters

[0052]

[0053] (2) The pollutant type is toluene, which meets the requirements for pollutant properties.

[0054] (3) The experimental temperatures were 20℃, 30℃, 40℃, 50℃, 60℃ and 70℃, and the experimental pressure was 0.8 atm, which met the environmental conditions for pollutant migration.

[0055] (4) Pre-treatment of soil samples to remove water and organic matter.

[0056] (5) After treatment, soil samples were subjected to N2 adsorption-desorption experiments, and their BET specific surface area S was measured. The results are shown in Table 5.

[0057] Table 5. BET specific surface area S of soil after post-treatment.

[0058]

[0059] (6) Based on Fick's second law, the correlation between temperature and soil pore structure parameters is introduced. Combined with the available binary diffusion coefficient of toluene vapor, the effective diffusion coefficient D of toluene vapor in porous media under different temperature conditions is predicted. eff The correlation is: 1gA = -9.293S - 197.9d 50 --1.921, B = 16.97S + 504.3d 50 +2.951, of which,

[0060] D0: The available binary diffusion coefficient for toluene vapor at 298.15 K is 8.4 × 10⁻⁶. -6 m 2 / s [2] T0 = ​​298.15K

[0061] S: The BET specific surface area S obtained in step (5), in m² 2 / g,

[0062] T: The temperature in step (3), in K.

[0063] d 50 The average particle size in step (1) is in meters.

[0064] The predicted results were compared with the experimental values, and the results are shown in Table 6. To evaluate the accuracy of the predictions in this embodiment, the relative deviation (RD) between the predicted and experimental values ​​was calculated. The formula for calculating RD is the same as in Example 1.

[0065] Table 6 Effective diffusion coefficients (D) of toluene vapor in different soils eff Comparison of experimental and predicted values

[0066]

[0067]

[0068] As shown in the table above, the relative deviation (RD) between the 25 predicted values ​​and the experimental values ​​is within 30%, and the average relative deviation is only 17.32%. This indicates that the correlation in this embodiment has good prediction accuracy under the above soil texture and temperature conditions, indicating that the method in this embodiment is suitable for predicting (1) the site soil texture conditions meet the following: 0.35 < porosity < 0.60, 0.10 mm < average particle size < 2.0 mm, organic matter content < 5%, and moisture content < 10%; (2) the effective diffusion coefficient D of toluene under the conditions of temperature 4 to 70℃ and absolute pressure 0.8 to 1.1 atm. eff .

[0069] Example 3

[0070] This example uses five soils with different particle sizes as examples, predicting the effective diffusion coefficient D of ethylbenzene vapor in these five soils at temperatures ranging from 4 to 70°C and absolute pressures ranging from 0.8 to 1.1 atm. eff The accuracy of this embodiment is evaluated based on laboratory measurements. This embodiment predicts the effective diffusion coefficient D of ethylbenzene vapor in soil at different temperatures. eff The process is as follows Figure 1 As shown, it includes the following steps:

[0071] (1) Determine the soil porosity Φ and average particle size d s0 Organic matter content f oc The soil texture requirements for this embodiment are met, including the moisture content ω, as shown in Table 7 below.

[0072] Table 7 Soil texture parameters

[0073]

[0074] (2) The pollutant type is ethylbenzene, which meets the requirements for pollutant properties.

[0075] (3) The experimental temperatures were 4℃, 20℃, 30℃, 40℃, 50℃, and 60℃, and the experimental pressure was 1.1 atm, which met the environmental conditions for pollutant migration.

[0076] (4) Pre-treatment of soil samples to remove water and organic matter.

[0077] (5) The soil samples after treatment were subjected to N2 adsorption-desorption experiments, and their BET specific surface area S was measured. The results are shown in Table 8.

[0078] Table 8. BET specific surface area S of the soil after post-treatment.

[0079]

[0080]

[0081] (6) Based on Fick's second law, the correlation between temperature and soil pore structure parameters is introduced. Combined with the available binary diffusion coefficient of ethylbenzene vapor, the effective diffusion coefficient D of ethylbenzene vapor in porous media under different temperature conditions is predicted. eff The correlation is: 1gA = -9.293S - 197.9d 50 -1.921, B = 16.97S + 504.3d 50 +2.951, of which,

[0082] D0: The available binary diffusion coefficient for ethylbenzene vapor at 298.15 K is 7.58 × 10⁻⁶. -6 m 2 / s [3] T0 = ​​298.15K

[0083] S: The BET specific surface area S obtained in step (5), in m² 2 / g,

[0084] T: The temperature in step (3), in K.

[0085] d 50 The average particle size in step (1) is in meters.

[0086] The predicted results were compared with the experimental values, and the results are shown in Table 9. To evaluate the accuracy of the predictions in this embodiment, the relative deviation (RD) between the predicted and experimental values ​​was calculated. The formula for calculating RD is the same as in Example 1.

[0087] Table 9 Effective diffusion coefficients (D) of toluene vapor in different soils eff Comparison of experimental and predicted values

[0088]

[0089]

[0090] As shown in the table above, the relative deviation (RD) between the 25 predicted values ​​and the experimental values ​​is within 30%, and the average relative deviation is only 15.78%. This indicates that the correlation in this embodiment has good prediction accuracy under the above soil texture and temperature conditions, indicating that the method in this embodiment is suitable for predicting (1) the site soil texture conditions meet the following: 0.35 < porosity < 0.60, 0.10 mm < average particle size < 2.0 mm, organic matter content < 5%, and moisture content < 10%; (2) the effective diffusion coefficient D of ethylbenzene under the conditions of temperature of 4 to 70℃ and absolute pressure of 0.8 to 1.1 atm. eff .

[0091] The above description is merely a preferred embodiment of the present invention, and therefore should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made in accordance with the scope of the patent and the contents of the specification should still fall within the scope of the present invention.

[0092] References:

[0093] [1]Yaws C L.Handbook of transport property data: viscosity, thermalconductivity, and diffusion coefficients of liquids and gases[M].GulfPublishing, Houston, 1995.

[0094] [2] Colson JM, Li Jiasen JF. Chemical Engineering: Volume 1; Fluid Flow, Heat Transfer and Mass Transfer [M]. Translated by Ding Xuzhun, Yu Guo, Liu Bao, et al. Beijing: Chemical Industry Press, 1983: 370-375.

[0095] [3] Yaws C L. Diffusion Coefficient in Air-Organic Compounds-ScienceDirect[J]. Transport Properties of Chemicals and Hydrocarbons, 2009: 407-496.

Claims

1. A method for predicting the effective diffusion coefficient of a gas phase organic contaminant in soil, characterized by: The gaseous organic pollutant is benzene, toluene, xylene, or ethylbenzene, and the process includes the following steps: (1) Determine that the soil in the site meets the following conditions: 0.35 < porosity Ф < 0.60, 0.10 mm < average particle size <2.0mm, organic matter content < 5%, moisture content <10%; and the gas phase of the site was determined to meet the following conditions: the effective diffusion coefficient of the pollutant to be measured was 4 ~ 70 ℃, the absolute pressure was 0.8 ~ 1.1 atm, and the gas phase organic pollutant was benzene-based; (2) The soil in the above site is pretreated to remove moisture and organic matter to obtain pretreated soil, and then the BET specific surface area S of its soil matrix is ​​determined. (3) Based on Fick's second law, the correlation of temperature and soil pore structure parameters is introduced, combined with the available binary diffusion coefficient of gas-phase organic pollutants, the effective diffusion coefficient D eff was predicted; The correlation is lgA = -9.293S - 197.9d 50 -1.921, ,in, D0: binary diffusion coefficient of the gas phase organic pollutant at T0, in m2 / s 2 / s, T0= 298.15 K, S: BET specific surface area of the product of step (2) in m 2 / g, T: The temperature at which the effective diffusion coefficient of the pollutant to be measured in step (1) is expressed in K. d 50 : Average particle diameter in step (1) in m.

2. The prediction method of claim 1, wherein: The method used for the pretreatment to remove moisture in step (2) is drying.

3. The prediction method of claim 2, wherein: The drying method includes: taking a certain mass m1 of soil and placing it in an oven at 110~130℃ for 24~48 hours, then weighing it to obtain the dried mass m2, from which the soil moisture content can be calculated. Furthermore, by combining this with the apparent density of the soil, the water saturation S of the soil at the site can be calculated. w Then according to S g =1-S w Calculate the gas phase saturation.

4. The prediction method of claim 3, wherein: The method used for the pretreatment to remove organic matter in step (2) is hydrogen peroxide oxidation.

5. The prediction method of claim 4, wherein: The hydrogen peroxide oxidation method includes: taking the material treated by the drying method, adding distilled water to moisten it, then continuously adding 20%~30% H2O2 while stirring, until no more bubbles are generated, then heating it in a water bath to remove residual H2O2, then filtering it, washing it with distilled water, and finally air-drying it in a ventilated place.

Images