A thermal-mass-pollution coupling simulation method and system for an in-situ thermal desorption process

Abstract

The application discloses a heat-mass-pollution coupling simulation method and system for an in-situ thermal desorption process, and comprises the following steps: constructing a three-dimensional geometric model of in-situ thermal desorption, discretizing a calculation domain to determine an optimal number of grid units; setting simulation basic parameters and constructing multi-physical field coupling control equations; adopting boundary layer grid encryption processing on a heat source area, adopting relatively sparse grid processing on a constant temperature area far away from the heat source, updating a simulation model, solving all the multi-physical field coupling control equations, and outputting numerical values; comparing the obtained data with physical experiment results, modifying existing deviations, and forming an optimized simulation model; extracting intrinsic property parameters of multiple pollutants from the output simulation model and migration ability indexes obtained through simulation, establishing a multiple linear regression model, and realizing quantitative prediction of migration rules of different property pollutants.

Description

Technical Field

[0001] This invention belongs to the field of soil remediation simulation technology, specifically relating to a thermal-mass-pollution coupling simulation method and system for in-situ thermal desorption processes, applicable to the simulation and optimization of in-situ thermal desorption remediation processes for hydrophobic organic polluted soils. Background Technology

[0002] In-situ thermal desorption is an effective technology for remediating organically contaminated soil. It involves heating to desorb and volatilize pollutants, which are then collected and processed via an extraction system. This process involves the coupling of multiple physicochemical fields, including complex heat conduction, convection, phase change, fluid flow, and the chemical reactions and migration of pollutants. High-fidelity numerical simulations of this process are crucial for optimizing process parameters, improving remediation efficiency, and reducing costs.

[0003] There are some existing simulation methods for soil thermal desorption or related processes, but they all have certain limitations.

[0004] For example, CN118607277A discloses an optimization method for soil thermal desorption processes. This method optimizes radio frequency heating parameters with the goal of effective energy utilization efficiency by establishing and correcting a physical model that couples electromagnetic, flow, and temperature fields. However, this method focuses on energy optimization of the heating process itself and does not dynamically couple the pollutant migration process as the core simulation object. The pollutants are only used as a preset desorption temperature threshold condition; therefore, it cannot predict the spatiotemporal distribution and migration patterns of pollutants in the soil.

[0005] CN119623034A discloses a numerical simulation method for underground soil thermal desorption treatment in complex sites. Its core lies in employing dynamic adaptive grid technology to improve the computational efficiency and accuracy of temperature field simulation. This scheme focuses on improving the numerical calculation method (grid technology) to enhance the solution performance of a single temperature field, but it completely neglects the coupled simulation with the pollutant migration field, and therefore also lacks the ability to predict pollutant behavior.

[0006] In summary, existing technical solutions either fail to achieve deep coupling of the thermal-mass-pollution multiphysics fields, or fail to establish differentiated simulation mechanisms for various pollutants with different physicochemical properties (such as significant differences in hydrophobicity and water solubility). This results in insufficient predictive ability for the migration patterns of pollutants during in-situ thermal desorption. In particular, for extremely hydrophobic (logKOW) and extremely low water solubility pollutants such as n-dodecane, the simulation bias is large because the migration mechanism of the non-aqueous phase liquid (NAPL) that may be formed is ignored.

[0007] Therefore, there is an urgent need for a high-precision numerical simulation method that can achieve deep coupling of multiple processes of heat, mass and pollutant migration, and accurately simulate and predict the complex migration behavior of hydrophobic organic pollutants with different physical properties during in-situ thermal desorption. Summary of the Invention

[0008] The present invention aims to overcome the shortcomings of the prior art by providing a thermal-mass-contamination coupling simulation method and system for in-situ thermal desorption processes.

[0009] The first aspect of this invention is to provide a thermal-mass-contamination coupling simulation method for in-situ thermal desorption processes, comprising: Step 1: Construct an in-situ thermal desorption three-dimensional geometric model, discretize the computational domain using a free tetrahedral mesh and verify mesh independence to determine the optimal number of mesh elements; Step 2: Set the basic simulation parameters based on experimental measurements, calibrations, and actual experimental data; the basic simulation parameters include soil physical parameters, pollutant parameters, and initial and boundary conditions; Step 3: Based on the simulation parameters established in Step 2, construct multiphysics coupled control equations, including the mass conservation equations for liquid and gas two-phase flow, energy control equations, the dynamic equations for pollutant controlled thermal desorption, the temperature dependence model of pollutant water solubility, and the control equations for multiphase migration and diffusion. Among them, the pollutant control thermal desorption kinetic equation uses exponential decay to capture the characteristic that the pollutant desorption rate increases exponentially due to the increase in temperature; the pollutant water solubility temperature dependence model introduces the van't Hoff equation to describe the change of pollutant water solubility with temperature; the control equation of multiphase migration and diffusion adopts the convection-dispersion equation to describe the process of convection and dispersion of pollutants after desorption and dissolution / gasification in soil pores with the water phase / gas phase. Step 4: For the three-dimensional geometric model constructed in Step 1, a boundary layer mesh is used to refine the heat source region, and a relatively sparse mesh is used to handle the isothermal region far from the heat source. After verifying the independence of time steps, an updated simulation model is obtained. Based on the updated simulation model, the parameters set in Step 2 are substituted into all the multiphysics field coupling control equations in Step 3 to solve, and the temperature field, water content field, gas phase pressure field and pollutant concentration distribution data are output. Step 5: Compare and verify the simulation results with the physical experiment results using the data obtained in Step 4. To correct the deviations in the heating and phase change-dominant stages, introduce non-Darcy flow effect and dynamic pore scale process. To correct the simulation deviations in the presence of pollutants, supplement with oil phase formation and multiphase migration mechanism corrections to form an optimized simulation model. Step Six: Based on the optimized simulation model formed in Step Five, extract the intrinsic physical property parameters of various pollutants and the migration capacity index obtained from the simulation, establish a multiple linear regression model, and realize the quantitative prediction of the migration law of pollutants with different physical properties.

[0010] Furthermore, the boundary conditions in step two are as follows: the extraction port in the fluid field is set as a pressure Dirichlet boundary, and the other boundaries are zero flux; each boundary in the heat field is set as a heat flux condition, and the surface temperature of the heated well is set according to the experimentally measured curve.

[0011] Furthermore, the mass conservation equations for the liquid and gas two-phase flows in step three are as follows: , ; in, Evaporation amount , a w The activity of water is calculated using the following formula: ψ represents soil water potential, and the calculation formula is: ; in, This is the molar volume of water, and its value is 1.8 × 10⁻⁶. -5 m 3 ·mol -1 ; T Thermodynamic temperature R The ideal gas constant is 8.314 J·mol⁻¹. -1 ·K -1 ; a , b and γ These are all soil characteristic parameters, and their values ​​are mainly related to soil type. Soil porosity. The saturated concentration of water vapor is calculated using the following formula: ; in, For capillary pressure, the calculation formula is: ; For air intake values, 500 Pa is used for sandy soil. Effective saturation; Energy control equation: ; Effective volumetric specific heat capacity according to Calculate the effective thermal conductivity. k eff Calculated according to the experimental fitting formula; ( ); ( ); In the formula, T c The temperature of the soil in Celsius. S w This refers to the soil's water saturation level. k 0 and a 1 to a 10 All of these are empirical coefficients related to soil type and were calibrated through steady-state heat conduction experiments. The kinetic equation for controlled thermal desorption of pollutants is: ; ; in, T Thermodynamic temperature t Heating time, and / They represent the residual rate and the rate of change of the pollutant in the soil at time t, respectively. / This represents the rate of change of thermodynamic temperature over time. n It is the order of the exponential decay dynamics model; The apparent activation energy represents the desorption process. R Let be the ideal gas constant. A It refers to the pre-factor. β For correction factors; The temperature dependence and phase partitioning model of pollutant water solubility are as follows: ; in, x This represents the mole fraction of the pollutant in the water. T Thermodynamic temperature M and N These are all equation parameters related to the properties of pollutants; The governing equations for multiphase migration and diffusion are:

[0012] Among them, subscript i and j These represent the types of fluids and contaminants, respectively. c ij and D ij Pollutants j In fluid i Concentration and diffusion coefficient inu i Let i be the flow velocity of fluid i. W This represents the rate at which pollutants desorb from soil particles and enter the fluid.

[0013] Furthermore, the intrinsic physical properties in step six include boiling point and octanol-water partition coefficient.

[0014] A second aspect of the present invention provides a thermal-mass-contamination coupling simulation system for an in-situ thermal desorption process, used to implement the above method. The system includes: a three-dimensional model construction module, a parameter setting module, a physical field coupling control equation construction module, a solution calculation module, a verification and optimization module, and a law analysis module. The three-dimensional model building module is used to construct an in-situ thermal desorption three-dimensional geometric model. It uses a free tetrahedral mesh to discretize the computational domain and performs mesh independence verification to determine the optimal number of mesh elements. The parameter setting module is used to set the basic parameters of the simulation based on experimental measurements, experimental calibration, and actual experimental data. The module for constructing physical field coupling control equations is used to construct multi-physics field coupling control equations based on the parameters determined by the parameter setting module. The solution calculation module is used to refine the three-dimensional geometric model constructed by the three-dimensional model construction module, using a boundary layer mesh to process the heat source region and a relatively sparse mesh to process the isothermal region far from the heat source. After verifying the independence of time steps, an updated simulation model is obtained. Based on the updated simulation model, the parameters set by the parameter setting module are substituted into all the multiphysics coupling control equations of the physical field coupling control equation construction module to solve the model, and output the temperature field, water content field, gas phase pressure field and pollutant concentration distribution data. The verification and optimization module is used to compare the data obtained by the solution and calculation module with the physical experiment results, and to correct any existing deviations to form an optimized simulation model. The pattern analysis module is used to extract intrinsic physical property parameters of various pollutants and migration capacity indicators obtained from simulation based on the optimized simulation model formed by the verification and optimization module, establish a multiple linear regression model, and realize quantitative prediction of the migration patterns of pollutants with different physical properties.

[0015] The beneficial effects of this invention are as follows: It achieves deep full coupling of multiple physical fields of heat, mass, and pollution. By introducing exponential decay desorption kinetics and phase distribution laws, it accurately reconstructs the release and transport mechanism of pollutants during thermal desorption. The innovative introduction of non-Darcy flow effect and NAPL oil phase formation mechanism effectively eliminates the systematic bias of traditional models in the phase change-dominant stage and in the simulation of extremely low water-soluble pollutants such as n-dodecane. Attached Figure Description

[0016] Figure 1 This is a flowchart of the thermal-mass-contamination coupling simulation method for the in-situ thermal desorption process described in this invention; Figure 2a This is a comparison chart of the simulated soil temperature and the actual soil temperature at the first temperature measurement point in step five. Figure 2b This is a comparison chart of the simulated soil temperature and the actual soil temperature at the second temperature measurement point in step five. Figure 2c This is a comparison chart of the simulated soil temperature and the actual soil temperature at the third temperature measurement point in step five. Figure 3 Figures (a) and (b) show the simulated and measured residual distributions of nitrobenzene in the sandbox after the in-situ thermal desorption experiment, respectively. Figure 4 Figures (a) and (b) show the simulated and measured residual distributions of naphthalene in the sandbox after the in-situ thermal desorption experiment, respectively. Figure 5 Figures (a) and (b) show the simulated and measured residual distributions of phenanthrene in the sandbox after the in-situ thermal desorption experiment, respectively. Detailed Implementation

[0017] To make the objectives, technical solutions, beneficial effects, and significant advancements of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings provided in the examples of the present invention. Obviously, all the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description of this application, unless otherwise expressly specified and limited, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance; the term "multiple" refers to two or more; unless otherwise specified or explained, the terms "connected," "fixed," etc., should be interpreted broadly. For example, "connected" can be a fixed connection, a detachable connection, an integral connection, or an electrical connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0019] like Figure 1 As shown, a thermal-mass-pollution coupling simulation method for in-situ thermal desorption processes is presented. This embodiment takes the in-situ thermal desorption process of five typical hydrophobic organic pollutants—nitrobenzene, naphthalene, n-dodecane, 1-nitronaphthalene, and phenanthrene—in homogeneous sandy soil as an example. The simulation method specifically includes the following steps: Step 1: Construct a three-dimensional geometric model of in-situ thermal desorption.

[0020] Based on the actual in-situ thermal desorption experimental device or engineering drawings, a three-dimensional sandbox model is constructed using three-dimensional modeling software. The three-dimensional sandbox includes the spatial location and geometric dimensions of a heating well, an insulation layer, a contaminated soil area, an uncontaminated soil area, an extraction port, and several temperature measurement points. The extraction port is located on one side of the sandbox, and the heating well is located in the central area of ​​the sandbox. The dimensions and spatial location of each structure are consistent with the actual conditions to ensure the authenticity of the model.

[0021] Subsequently, the computational domain was discretized using a free tetrahedral mesh, and mesh independence was verified. The optimal number of mesh elements was determined to be 93,769 to ensure a balance between computational accuracy and efficiency. The mesh independence verification process was as follows: pre-simulations were performed sequentially using 50,000, 70,000, 93,769, 120,000, and 150,000 mesh elements. The temperature simulation results of nodes 10 cm around the heating well were compared under different mesh numbers. When the mesh number increased from 93,769 to 120,000, the temperature difference was less than 0.5℃, meeting the accuracy requirements, and the increase in computation time was less than 10%. Therefore, 93,769 was determined to be the optimal number of mesh elements.

[0022] Step 2: Set the basic simulation parameters, which include soil physical parameters, pollutant parameters, and initial and boundary conditions.

[0023] Soil physical parameters: set porosity of 0.461, inherent permeability of 1.73×10⁻¹² m³. 2 1450kg bulk density m -3 Particle density 2690kg m -3 The inlet capillary pressure is 1000 Pa, the pore size distribution coefficient is 0.5, the residual water saturation is 0.09, the critical water saturation is 0.91, and the solid-phase heat capacity is 850 J. kg -1 K -1 The latent heat of vaporization of water is 2256.4 kJ. kg -1 Evaporation rate coefficient: 0.001 s -1 .

[0024] Physicochemical parameters of pollutants: The initial concentrations of nitrobenzene, naphthalene, n-dodecane, 1-nitronaphthalene, and phenanthrene were set to 1093.43, 1556.15, 1736.62, 674.47, and 784.22 mg / kg, respectively; the desorption correction coefficient, pre-exponential factor, apparent activation energy, desorption model order, and diffusion coefficient in gas / water were configured according to the experimental settings.

[0025] Initial and boundary conditions: Initial conditions: The initial soil temperature was 290.15 K, the initial moisture content was 12%, and the initial concentration of pollutants in the polluted area was consistent with the actual experiment.

[0026] Boundary conditions: In the fluid field, the extraction port is set as a pressure Dirichlet boundary, and the other boundaries are zero flux; in the heat field, each boundary is set as a heat flux condition, and the surface temperature of the heated well is set according to the experimental measured curve. The gas phase at the extraction port is -50 Pa during the pressure heating period and 0 Pa after the heating is stopped.

[0027] Step 3: Based on the parameters set in Step 2, construct a multiphysics field coupled control equation system, including the mass conservation equation for liquid and gas two-phase flow, the energy control equation, the kinetic equation for pollutant controlled thermal desorption, the water-soluble temperature-dependent model, and the control equation for multiphase migration and diffusion.

[0028] S31: Based on the laws of conservation of mass, energy, and Darcy's form of momentum conservation, the mass conservation equations for liquid and gas two-phase flow are as follows: , ; in, Evaporation amount , a w The activity of water is calculated using the following formula: ψ represents soil water potential, and the calculation formula is: ; in, This is the molar volume of water, and its value is 1.8 × 10⁻⁶. -5 m 3 ·mol -1 ; T Thermodynamic temperature R The ideal gas constant is 8.314 J·mol⁻¹. -1 ·K -1 ; a , b and γ These are all soil characteristic parameters, and their values ​​are mainly related to soil type. Soil porosity. The saturated concentration of water vapor is calculated using the following formula: ; in, For capillary pressure, the calculation formula is: ; For air intake values, 500 Pa is used for sandy soil. This represents the effective saturation level.

[0029] water saturation S w The relative permeability of the liquid and gas phases is calculated using the corresponding correlation formula.

[0030] Water saturation S w It can be determined by liquid phase volume fraction θ w and soil porosity The calculation yielded: .

[0031] In the formula, ω Soil moisture content ρ b This refers to the bulk density of the soil.

[0032] Relative permeability of liquid and gas phases k rw and k rg All are water saturation S w The function can be calculated using the following formula: ( ); ( ); ( ); ( ); In the formula, S wr For residual water saturation, when S w ≤ S wr At that time, the liquid phase motion driven by capillary pressure stops; S cr For the critical water saturation, when S w ≥ S cr At that time, gas phase motion ceases.

[0033] S32: The energy control equations are established according to the following equations: ; Effective volumetric specific heat capacity according to Calculate the effective thermal conductivity. k eff Calculated according to the experimental fitting formula; ( ); ( ); In the formula, T c The temperature of the soil in Celsius. S w This refers to the soil's water saturation level. k 0 and a 1 to a 10 These are all empirical coefficients related to soil type. For sandy soil, It is 1.2. It is 0.1. It is 0.5. It is 0.05. It is 0.01. It is 0.3. It is -0.1. It is 0.005. It is 0.0001. The coefficient is 0.00001, and the above coefficient can be calibrated through steady-state heat conduction experiments.

[0034] S33: Dynamic equation for controlled thermal desorption of pollutants: The process of pollutants desorbing from the surface of soil particles into the porous media is modeled as a temperature-driven exponential decay process, expressed as: ; ; in, T Thermodynamic temperature t Heating time, and / They represent the residual rate and the rate of change of the pollutant in the soil at time t, respectively. / This represents the rate of change of thermodynamic temperature over time. n This is the order of the exponential decay kinetic model, with nitrobenzene taking 1.2, naphthalene taking 1.5, n-dodecane taking 2.0, 1-nitronaphthalene taking 1.8, and phenanthrene taking 1.6; The apparent activation energy represents the desorption process. RLet be the ideal gas constant. A It refers to the pre-factor. β This is the correction factor.

[0035] The above equations can accurately capture the exponential growth of pollutant desorption rates due to temperature increases, solving the problem that traditional first-order kinetic models cannot describe complex heterogeneous desorption behavior.

[0036] S34: Temperature Dependence and Phase Partition Model of Pollutant Water Solubility Since the solubility of pollutants varies significantly at different temperatures, directly affecting their distribution ratio in the liquid and gas phases, and thus their migration ability, the van't Hoff equation is introduced to describe the change in the water solubility of pollutants with temperature. The expression is as follows: ; in, x This represents the mole fraction of the pollutant in the water. T Thermodynamic temperature M and N These are all equation parameters related to the properties of the pollutants. Specifically, for nitrobenzene: M=-4500, N=12; naphthalene: M=-5200, N=13; n-dodecane: M=-6800, N=15; 1-nitronaphthalene: M=-5800, N=14; phenanthrene: M=-5500, N=13.5.

[0037] During the simulation, the solubility of pollutants at the current temperature is calculated in real time using the above-mentioned expression for the temperature dependence of water solubility.

[0038] S35: The convection-dispersion equation (ADE) is used to describe the process of convection and dispersion of pollutants after desorption and dissolution / vaporization in soil pores with the help of fluids.

[0039] Among them, subscript i and j These represent the types of fluids and contaminants, respectively. c ij and D ij Pollutants j In fluid i Concentration and diffusion coefficient in u i Let be the velocity of fluid i, which is obtained by solving the two-phase flow equation in S31. W For source and sink terms, representing the rate at which pollutants desorb from soil particles into the fluid, calculated from the thermal desorption kinetic equation of S33.

[0040] Step 4: Model Discretization and Solution. For the three-dimensional geometric model constructed in Step 1, a boundary layer mesh is used to refine the heat source region, and a relatively sparse mesh is used to handle the isothermal region far from the heat source. After verifying the independence of time steps, an updated simulation model is obtained and solved, outputting temperature field, water content field, gas phase pressure field, and pollutant concentration distribution data.

[0041] S41: For the three-dimensional geometric model constructed in step one, a boundary layer mesh is used to refine the heat source region, while a relatively sparse mesh is used in the isothermal region far from the heat source.

[0042] Considering the severe temperature and pressure gradients around the heating well and near the extraction port during in-situ thermal desorption, a boundary layer mesh is used to refine the mesh in these areas. The boundary layer thickness is 5 mm, and a total of 5 mesh layers are used to accurately capture the physical field changes near the wall. In the isothermal region far from the heat source, a relatively sparse mesh is used to save computational resources.

[0043] S42: Verify the mesh independence of the mesh size obtained in S41. The verification process is the same as that in step one, and will not be repeated here. This determines that the mesh configuration containing 93,769 domain elements, 9,524 boundary elements, and 882 edge elements is the updated simulation model. At the same time, after verifying the time step independence, the optimal time step setting is determined.

[0044] S43: Substitute the parameters set in step two into all the multiphysics coupling control equations in step three, and solve them using multiphysics simulation software. Set the relative tolerance to 0.001 to ensure the accuracy of iterative convergence. The simulation results will then be output, including the spatiotemporal distributions of the temperature field, water content field, and gas phase pressure field, as well as the concentration distributions of five pollutants and the mass flow rate data at the extraction port.

[0045] Step 5: Compare and verify the simulation results output in Step 4 with the physical experiment results, and optimize based on the verification results.

[0046] S51: Select three temperature measurement points, namely the first temperature measurement point, the second temperature measurement point and the third temperature measurement point; the first temperature measurement point is located at the midpoint of the line connecting the heating well and the extraction port, the second temperature measurement point is 15cm away from the heating well, and the third temperature measurement point is 10cm away from the extraction port. All three points are located at the middle height of the sand box. Comparing the simulated and measured temperatures in step four, the simulated and actual soil temperatures at the three temperature measurement points are as follows: Figure 2a , Figure 2b and Figure 2c As shown. Calculate the mean relative error (MRE) and coefficient of determination (R²) between the simulated and measured values. 2The measured values ​​were 11.0℃, 19.2℃, and 16.6℃, respectively, with the absolute errors during the cooling phase all within ±2℃. The maximum absolute errors for the first, second, and third temperature measurement points were 3.23%, 3.14%, and 7.34%, respectively. The R0 values ​​for the first, second, and third temperature measurement points were also measured. 2 The values ​​were 0.989, 0.977, and 0.981, respectively, and the NRMSE values ​​for the first, second, and third temperature measurement points were 2.49%, 3.75%, and 3.49%, respectively.

[0047] Analysis shows that although the model exhibits extremely high accuracy during the cooling stage (error within ±2℃), some monitoring points show significant absolute errors (up to 19.2℃) during the heating and phase transition-dominated stages (80~100℃).

[0048] S52: To address the deviations during the aforementioned heating phase, non-Darcy flow and dynamic pore-scale processes are introduced for correction. Specifically, the non-Darcy flow correction uses the Forchheimer equation instead of Darcy's law; the dynamic pore-scale process correction considers the evolution of pore structure caused by temperature changes.

[0049] S53: The simulated residual concentrations of five typical pollutants were compared with the measured values. For the four pollutants, nitrobenzene, naphthalene, 1-nitronaphthalene and phenanthrene, the simulation results showed a high degree of agreement. The mean relative error (MRE) was controlled within a reasonable range of 27% to 38%, and the coefficient of determination was ≥ 0.86, which proved the effectiveness of the thermal desorption kinetic model. Figure 3 Figures (a) and (b) show the simulated and measured residual distributions of nitrobenzene in the sandbox after the in-situ thermal desorption experiment, respectively. Figure 4 Figures (a) and (b) show the simulated and measured residual distributions of naphthalene in the sandbox after the in-situ thermal desorption experiment, respectively. Figure 5 Figures (a) and (b) show the simulated and measured residual distributions of phenanthrene in the sandbox after the in-situ thermal desorption experiment, respectively.

[0050] The uncorrected simulated MRE for n-dodecane reached 63.78%, showing a significant positive bias. This is because the traditional model does not consider its behavior of condensing into an oil phase in the cooling region.

[0051] To address the aforementioned deviations, corrections are made to the oil phase formation and multiphase migration mechanisms. Specifically, this includes introducing a NAPL phase saturation equation. When the local pollutant concentration exceeds the solubility at the current temperature, the NAPL phase is formed, and its migration is driven by capillary forces. The three-phase (water, gas, and NAPL) flow is coupled through an extended relative permeability model.

[0052] After the above modifications, an optimized simulation model is formed.

[0053] Step Six: Based on the optimized simulation model developed in Step Five, extract the intrinsic physical property parameters of the five pollutants and the migration capacity indicators obtained from the simulation, and establish a multiple linear regression model to achieve quantitative prediction of the migration patterns of pollutants with different physical properties. The intrinsic physical property parameters include boiling point and octanol-water partition coefficient.

[0054] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style of the specification is merely for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in the embodiments can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A thermal-mass-contamination coupling simulation method for in-situ thermal desorption processes, characterized in that, include: Step 1: Construct an in-situ thermal desorption three-dimensional geometric model, discretize the computational domain using a free tetrahedral mesh and verify mesh independence to determine the optimal number of mesh elements; Step 2: Set the basic simulation parameters based on experimental measurements, calibrations, and actual experimental data; the basic simulation parameters include soil physical parameters, pollutant parameters, and initial and boundary conditions; Step 3: Based on the simulation parameters established in Step 2, construct multiphysics coupled control equations, including the mass conservation equations for liquid and gas two-phase flow, energy control equations, the dynamic equations for pollutant controlled thermal desorption, the temperature dependence model of pollutant water solubility, and the control equations for multiphase migration and diffusion. Among them, the pollutant control thermal desorption kinetic equation uses exponential decay to capture the characteristic that the pollutant desorption rate increases exponentially due to the increase in temperature; the pollutant water solubility temperature dependence model introduces the van der Hoff equation to describe the change law of pollutant water solubility with temperature; the control equation of multiphase migration and diffusion adopts the convection-dispersion equation to describe the process of convection and dispersion of pollutants after desorption and dissolution / gasification in soil pores with water / gas phase. Step 4: For the three-dimensional geometric model constructed in Step 1, a boundary layer mesh is used to refine the heat source region, and a relatively sparse mesh is used to handle the isothermal region far from the heat source. After verifying the independence of time steps, an updated simulation model is obtained. Based on the updated simulation model, the parameters set in Step 2 are substituted into all the multiphysics field coupling control equations in Step 3 to solve the problem, and the temperature field, water content field, gas phase pressure field and pollutant concentration distribution data are output. Step 5: Compare and verify the numerical values ​​output in Step 4 with the physical experimental results. To correct the deviations in the heating and phase change-dominant stages, introduce non-Darcy flow effect and dynamic pore scale process. To correct the simulation deviations in the presence of pollutants, supplement with oil phase formation and multiphase migration mechanism corrections to form an optimized simulation model. Step Six: Based on the optimized simulation model formed in Step Five, extract the intrinsic physical property parameters of various pollutants and the migration capacity index obtained from the simulation, establish a multiple linear regression model, and realize the quantitative prediction of the migration law of pollutants with different physical properties.

2. The thermal-mass-contamination coupling simulation method for in-situ thermal desorption process according to claim 1, characterized in that, The boundary conditions in step two are as follows: the extraction port in the fluid field is set as a pressure Dirichlet boundary, and the other boundaries are zero flux; the boundaries in the heat field are set as heat flux conditions, and the surface temperature of the heated well is set according to the experimentally measured curve.

3. The thermal-mass-contamination coupling simulation method for in-situ thermal desorption process according to claim 1, characterized in that, The mass conservation equations for the liquid and gas two-phase flows in step three are as follows: ， ； in, Evaporation amount , a w The activity of water is calculated using the following formula: ψ represents soil water potential, and the calculation formula is: ； in, This is the molar volume of water, and its value is 1.8 × 10⁻⁶. -5 m 3 ·mol -1 ; T Thermodynamic temperature R The ideal gas constant is 8.314 J·mol⁻¹. -1 ·K -1 ; a , b and γ These are all soil characteristic parameters, and their values ​​are mainly related to soil type. Soil porosity; The saturated concentration of water vapor is calculated using the following formula: ； in, For capillary pressure, the calculation formula is: ; For air intake values, 500 Pa is used for sandy soil. Effective saturation; Energy control equation: ； Effective volumetric specific heat capacity according to Calculate the effective thermal conductivity. k eff Calculated according to the experimental fitting formula; ( ); ( )； In the formula, T c The temperature of the soil in Celsius. S w This refers to the soil's water saturation level. k 0 and a 1 to a 10 All of these are empirical coefficients related to soil type and were calibrated through steady-state heat conduction experiments. The kinetic equation for controlled thermal desorption of pollutants is: ； ； in, T Thermodynamic temperature t Heating time, and / They represent the residual rate and the rate of change of the pollutant in the soil at time t, respectively. / This represents the rate of change of thermodynamic temperature over time. n It is the order of the exponential decay dynamics model; The apparent activation energy represents the desorption process. R Let be the ideal gas constant. A It refers to the pre-factor. β For correction factors; The temperature dependence and phase partitioning model of pollutant water solubility is as follows: ； in, x This represents the mole fraction of the pollutant in the water. T Thermodynamic temperature M and N These are all equation parameters related to the properties of pollutants; The governing equations for multiphase migration and diffusion are: Among them, subscript i and j These represent the types of fluids and contaminants, respectively. c ij and D ij Pollutants j In fluid i Concentration and diffusion coefficient in u i Let i be the flow velocity of fluid i. W This represents the rate at which pollutants desorb from soil particles and enter the fluid.

4. The thermal-mass-contamination coupling simulation method for in-situ thermal desorption process according to claim 1, characterized in that, The intrinsic physical properties in step six include boiling point and octanol-water partition coefficient.

5. A thermal-mass-contamination coupling simulation system for an in-situ thermal desorption process, used to implement the method according to any one of claims 1-4; the system comprising: The system includes a 3D model building module, a parameter setting module, a physical field coupling control equation construction module, a solution calculation module, a verification and optimization module, and a pattern analysis module. The three-dimensional model building module is used to construct an in-situ thermal desorption three-dimensional geometric model. It uses a free tetrahedral mesh to discretize the computational domain and performs mesh independence verification to determine the optimal number of mesh elements. The parameter setting module is used to set the basic parameters of the simulation based on experimental measurements, experimental calibration, and actual experimental data. The module for constructing physical field coupling control equations is used to construct multi-physics field coupling control equations based on the parameters determined by the parameter setting module. The solution calculation module is used to refine the three-dimensional geometric model constructed by the three-dimensional model construction module, using a boundary layer mesh to process the heat source region and a relatively sparse mesh to process the isothermal region far from the heat source. After verifying the independence of time steps, an updated simulation model is obtained. Based on the updated simulation model, the parameters set by the parameter setting module are substituted into all the multiphysics coupling control equations of the physical field coupling control equation construction module to solve the model, and output the temperature field, water content field, gas phase pressure field and pollutant concentration distribution data. The verification and optimization module is used to compare the numerical values ​​output by the solution and calculation module with the physical experiment results, and to correct any existing deviations to form an optimized simulation model. The pattern analysis module is used to extract intrinsic physical property parameters of various pollutants and migration capacity indicators obtained from simulation based on the optimized simulation model formed by the verification and optimization module, establish a multiple linear regression model, and realize quantitative prediction of the migration patterns of pollutants with different physical properties.

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