Numerical simulation method and system for complex field soil thermal desorption treatment

Abstract

The application discloses a numerical simulation method and system for soil thermal desorption treatment in a complex field, and relates to the technical field of data simulation, and specifically comprises the following steps: constructing a three-dimensional model of the field based on the field working condition, soil structure and thermodynamic parameters; performing grid processing on the three-dimensional model of the field to generate a preliminary grid model, and dynamically adjusting the preliminary grid model based on a preset index threshold to obtain a dynamic self-adaptive grid model; and performing numerical simulation calculation on the soil thermal desorption treatment process by using the dynamic self-adaptive grid model, and saving the calculation result. The numerical simulation method based on the dynamic self-adaptive grid technology can automatically adjust the grid density, and the adaptability ensures that the area with a sharp temperature change has a higher grid resolution, thereby greatly improving the simulation calculation precision and making the simulation result closer to the temperature distribution in the actual soil thermal desorption process.

Description

Technical Field

[0001] This invention relates to the field of data simulation technology, and more specifically to a numerical simulation method and system for thermal desorption treatment of soil in complex sites. Background Technology

[0002] With the acceleration of industrialization and the advancement of urbanization, the number of organically contaminated sites is increasing. Given the harmfulness of organic pollutants to human health and the ecological environment, effective remediation work is particularly urgent.

[0003] In-situ thermal desorption technology has been widely used in recent years due to its high remediation efficiency, strong applicability, and lack of rebound effect, and has gradually become one of the important means of remediating organically contaminated soil. However, this technology also faces the problem of high energy costs, mainly because energy consumption accounts for 60% to 80% of the total application cost during thermal desorption. Therefore, how to reduce energy consumption has become a key factor restricting the promotion of this technology.

[0004] Currently, the management of most thermal desorption projects is rather extensive, typically relying on operational experience from past projects to control the heating process. However, due to differences in soil composition at application sites, this experience-based approach is difficult to adapt to different regions, thus affecting the consistency and reliability of remediation results.

[0005] In addition, existing laboratory studies are mostly focused on small-scale site models. While these models can provide some experimental data support, they have limitations in simulating changes in soil parameters in actual large-scale sites and cannot fully reveal the remediation mechanisms of large-scale sites.

[0006] To overcome the aforementioned challenges, it is necessary to develop a numerical simulation method suitable for soil thermal desorption treatment under complex conditions. This method should be able to effectively simulate large-scale contaminated sites and achieve a balance between computational accuracy and efficiency. Traditional methods, such as structured grids and static adaptive grids, have shown shortcomings in addressing this problem. The former lacks accuracy with sparse grids, while dense grids lead to excessive consumption of computational resources; the latter, due to its fixed grid structure, cannot flexibly adjust to changes in the temperature field, resulting in local grid density not matching temperature variations. Therefore, how to achieve a balance between computational accuracy and efficiency by adjusting grid density is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a numerical simulation method and system for thermal desorption treatment of soil in complex sites, overcoming the above-mentioned defects.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A numerical simulation method for underground soil thermal desorption treatment in complex sites, comprising the following steps:

[0010] A three-dimensional model of the site is constructed based on site conditions, soil structure, and thermodynamic parameters.

[0011] The site's three-dimensional model is meshed to generate a preliminary mesh model, and the preliminary mesh model is dynamically adjusted periodically based on preset index thresholds to obtain a dynamic adaptive mesh model.

[0012] The dynamic adaptive grid model was used to perform numerical simulation calculations on the soil thermal desorption process, and the calculation results were saved.

[0013] Optionally, the steps for constructing the three-dimensional model of the site are as follows:

[0014] The layout of the heating wells is obtained based on the site conditions.

[0015] A soil stratification model was constructed based on the soil structure and the thermodynamic parameters.

[0016] The site three-dimensional model is constructed based on the heating well layout and the soil stratification model.

[0017] Optionally, the steps for obtaining the dynamic adaptive mesh model are as follows:

[0018] Based on historical data, indicators related to the rate of temperature change were selected and the threshold values ​​for each indicator were determined.

[0019] The parameters of each original grid in the preliminary grid model are acquired periodically, and the index values ​​of each index in each original grid are calculated.

[0020] The size of each updated grid is determined based on the relationship between the index value and the index threshold.

[0021] The dynamic adaptive mesh model is generated based on the dimensions of each of the updated meshes.

[0022] Optionally, the indicators include temperature gradient and time derivative; the temperature gradient is the maximum value of the derivative of the temperature change with respect to coordinates in the dynamic adaptive mesh model; the time derivative is the derivative of the temperature change with respect to time.

[0023] Optionally, the indicator threshold is a multi-level threshold.

[0024] Optionally, the specific steps for generating the dynamic adaptive mesh model based on the dimensions of each of the updated meshes are as follows:

[0025] The original meshes in the preliminary mesh model are split and merged according to the size of each updated mesh to obtain the updated mesh;

[0026] The updated mesh inherits the temperature of the original mesh according to a preset temperature update rule, generating the dynamic adaptive mesh model.

[0027] Optionally, the temperature update rule is:

[0028] Step 221: Determine whether the size of any of the updated meshes is the same as the size of the corresponding original mesh. If not, proceed to step 222; if yes, proceed to step 223.

[0029] Step 222: Scale the original grid level by level according to the index threshold to obtain a scaled grid, and then jump to step 221;

[0030] Step 223: Assign a temperature value to the updated mesh based on the temperature value of the original mesh.

[0031] Optionally, after obtaining the simulation data, the simulation data is compensated using the covariance matrix.

[0032] Optionally, the steps for obtaining the covariance matrix are as follows:

[0033] Acquire sensor data of the site, and obtain the temperature distribution of the site based on the sensor data;

[0034] Obtain site parameters, and use a fitting model to obtain fitted temperature values ​​based on the site parameters;

[0035] The site parameters are input into the dynamic adaptive grid model to obtain simulated values;

[0036] The covariance matrix is ​​estimated using a Bayesian method based on the sensor data, the fitted temperature value, and the simulated value.

[0037] A numerical simulation system for thermal desorption treatment of underground soil in complex sites includes:

[0038] The model building module is used to build a three-dimensional model of the site based on site conditions, soil structure, and thermodynamic parameters.

[0039] The dynamic adjustment module is used to perform meshing processing on the site 3D model to generate a preliminary mesh model, and to dynamically adjust the preliminary mesh model at timed intervals based on preset index thresholds to obtain a dynamic adaptive mesh model.

[0040] The numerical simulation module is used to perform numerical simulation calculations on the soil thermal desorption process using the dynamic adaptive grid model and save the calculation results.

[0041] As can be seen from the above technical solution, the present invention discloses a numerical simulation method and system for thermal desorption treatment of underground soil in complex sites, which has the following advantages compared with the prior art:

[0042] 1. Improved computational accuracy: This invention employs a numerical simulation method based on dynamic adaptive grid technology, which can automatically adjust the grid density according to changes in local soil thermodynamic parameters. This adaptability ensures higher grid resolution in areas with drastic temperature changes, thereby significantly improving the accuracy of the simulation calculation and making the simulation results closer to the actual temperature distribution during soil thermal desorption.

[0043] 2. Optimized Energy Utilization: This invention, through precise simulation of the soil thermal desorption process, provides a scientific basis for designing heating strategies and helps formulate more rational heating plans. This not only avoids unnecessary energy waste but also ensures that pollutants are fully pyrolyzed and volatilized, thereby improving energy utilization efficiency.

[0044] 3. Enhanced Applicability: The dynamic adaptive mesh technology of this invention enables the simulation method to be applied to various complex soil environments, including mixed layers of different soil types, porous media, and scenarios with non-uniform heat conduction conditions. This greatly expands the application scope of the technology, allowing it to address more diverse remediation needs for contaminated sites.

[0045] 4. Simplified project management: The numerical simulation system provided by this invention can provide project managers with an intuitive visualization tool. Through the simulation results, managers can monitor the status of the thermal desorption process in real time, identify and solve problems in a timely manner, simplify project management and decision-making processes, and improve work efficiency. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the method flow provided by the present invention;

[0048] Figure 2(a) is a schematic diagram of the triangular soil heating well layout in the typical thermal desorption process provided by the present invention; Figure 2(b) is a schematic diagram of the quadrilateral soil heating well layout in the typical thermal desorption process provided by the present invention; Figure 2(c) is a schematic diagram of the hexagonal soil heating well layout in the typical thermal desorption process provided by the present invention.

[0049] Figure 3 A schematic diagram illustrating the process of obtaining the updated mesh size provided by the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, 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.

[0051] This invention discloses a numerical simulation method for underground soil thermal desorption treatment in complex sites, such as... Figure 1 As shown, the specific steps are as follows:

[0052] Step 1: Construct a three-dimensional model of the site based on site conditions, soil structure, and thermodynamic parameters;

[0053] Step 2: Mesh the 3D site model to generate a preliminary mesh model, and dynamically adjust the preliminary mesh model at regular intervals based on preset index thresholds to obtain a dynamic adaptive mesh model.

[0054] Step 3: Perform numerical simulation calculations on the soil thermal desorption process using a dynamic adaptive grid model, and save the calculation results.

[0055] In one embodiment, the steps for constructing the three-dimensional model of the site are as follows:

[0056] Step 11: Obtain the layout of heating wells based on site conditions;

[0057] Step 12: Construct a soil stratification model based on soil structure and thermodynamic parameters;

[0058] Step 13: Construct a three-dimensional model of the site based on the layout of the heating wells and the soil stratification model.

[0059] In one embodiment, the 3D model of a complex site needs to fully consider differences in heating well layout, soil stratification information, and local thermodynamic parameter differences. Therefore, a heating well model needs to be constructed based on the site conditions. Considering that the layout of soil heating wells in typical process environments is determined according to standard shapes (triangles, quadrilaterals, hexagons), the heating well layout can be specified for constructing the 3D model in specific applications. The heating well layout is as follows: Figures 2(a)-2(c) As shown, the red dots represent heating wells and the green dots represent extraction wells. Since the extraction wells are mostly located at cold points (the points with the lowest temperature in the entire site) and the radius of the extraction wells is very small, the distance between them is negligible compared to that between the heating wells. Therefore, the temperature changes introduced by the extraction wells can be ignored when building the model.

[0060] Without considering the extraction process, the modeling of extraction wells can be omitted in the 3D model of the site. However, considering that various fillers and insulation layers will be laid on the actual site to reduce heat loss, the soil needs to be modeled in layers in the actual model, and different thermodynamic parameters should be set for each layer.

[0061] In one embodiment, the steps for obtaining the dynamic adaptive mesh model are as follows:

[0062] Step 21: Filter indicators related to temperature change rate based on historical data and determine the threshold values ​​for each indicator;

[0063] Step 22: Periodically acquire the parameters of each original grid in the preliminary grid model, and calculate the index values ​​of each index in each original grid;

[0064] Step 23: Determine the size of each update grid based on the relationship between the index value and the index threshold;

[0065] Step 24: Generate a dynamic adaptive mesh model based on the dimensions of each updated mesh.

[0066] In one embodiment, the metrics include temperature gradient and time derivative; the temperature gradient is the maximum value of the derivative of the temperature change with respect to coordinates in the dynamic adaptive mesh model; the time derivative is the derivative of the temperature change with respect to time.

[0067] In one embodiment, the index threshold is a multi-level threshold.

[0068] Furthermore, the local mesh density is determined using indices related to the rate of temperature change. Two indices are proposed here: temperature gradient T. tg and time derivative T td The former is the maximum value of the derivative of the temperature change ΔT with respect to the coordinates, max(ΔT / Δx, ΔT / Δy, ΔT / Δz), and the latter is the derivative of the temperature change ΔT with respect to time, ΔT / Δt. Thresholds [T] are designed for each of these two indices. tg1 T tg2 T tg3 ], and [T td1 T td2 T td3 At fixed time intervals, for each grid point i, calculate its temperature gradient T. tg，i and time derivative T td，i Then according to Figure 3 The successive comparison method shown determines the local mesh size as M, with the standard mesh size as 1, thereby splitting and merging the mesh.

[0069] In one embodiment, the dynamic adaptive mesh model generation process is as follows:

[0070] Step 241: Based on the size of each updated mesh, split and merge the original meshes in the preliminary mesh model to obtain the updated meshes;

[0071] Step 242: Update the mesh. Based on the preset temperature update rules, the temperature of the original mesh is inherited to generate a dynamic adaptive mesh model.

[0072] In one embodiment, the temperature update rule is as follows:

[0073] Step 2421: Determine whether the size of any updated mesh is the same as the size of the corresponding original mesh. If not, proceed to step 2422; if yes, proceed to step 2423.

[0074] Step 2422: Scale the original grid level by level according to the index threshold to obtain the scaled grid, and then jump to step 2421.

[0075] Step 2423: Assign the temperature values ​​of the original mesh to the updated mesh.

[0076] Furthermore, the mesh is split and merged based on the local mesh size M, while also considering the inheritance of mesh point temperatures. If two mesh points have the same size, they are directly inherited; if two mesh points have different sizes, inheritance is performed according to the actual sizes of the two mesh points. The following inheritance rules apply:

[0077] Inheritance Rule 1: If the original grid point is smaller than the updated grid point, increase the size of the original grid point by one level and check if it is the same as the updated grid point. If they are the same, take the average temperature of all the original grid points within the size range of the updated grid point as the updated grid point temperature. If they are different, increase the size of the original grid point by one level again, and so on.

[0078] Inheritance Rule 2: If the original grid point is larger than the updated grid point, then reduce the size of the original grid point by one level and check if it is the same as the updated grid point. If they are the same, then directly assign the temperature of the original grid point to all updated grid points in the corresponding area; if they are different, then reduce the size of the original grid point by one level again, and so on.

[0079] In one embodiment, after obtaining the simulation data, the simulation data is compensated using the covariance matrix.

[0080] In one embodiment, the steps for obtaining the covariance matrix are as follows:

[0081] Acquire sensor data from the site and determine the temperature distribution of the site based on the sensor data;

[0082] Obtain site parameters, and use a fitting model to obtain the fitted temperature value based on the site parameters;

[0083] The site parameters are input into a dynamic adaptive grid model to obtain simulated values;

[0084] The covariance matrix is ​​estimated using Bayesian methods based on sensor data, fitted temperature values, and simulated values.

[0085] Furthermore, considering the potential discrepancy between the temperature field calculated by the simulation model and the data measured by the temperature sensor, error compensation is necessary. This embodiment employs neural network prediction to compensate for the calculation errors, and the specific process is as follows:

[0086] (1) Obtain monitoring data from the existing temperature sensors (thermocouples distributed throughout the site) and use the three-dimensional kriging interpolation method to obtain the temperature distribution of the entire site.

[0087] (2) A fitting model is constructed using machine learning methods. The inputs are soil parameters, distance between the point and the center of the heating well, depth, etc., and the output is the temperature at the current point. A fitting curve is constructed.

[0088] (3) Apart from the actual measurement points, the fitting results of the neural network model are used as the actual values, and the output results of the simulation model are used as the predicted values. The covariance matrix is ​​estimated using the Bayesian method and then compensated.

[0089] For the actual measurement points, the covariance matrix estimated in (3) is used for error compensation, and the relative error distribution between the compensated result and the actual result is calculated to evaluate the quality of the error compensation result.

[0090] This embodiment also discloses a numerical simulation system for thermal desorption treatment of soil in complex sites, including:

[0091] The model building module is used to build a three-dimensional model of the site based on site conditions, soil structure, and thermodynamic parameters.

[0092] The dynamic adjustment module is used to perform meshing processing on the site's 3D model to generate a preliminary mesh model, and to dynamically adjust the preliminary mesh model at regular intervals based on preset index thresholds to obtain a dynamic adaptive mesh model.

[0093] The numerical simulation module is used to perform numerical simulation calculations on the soil thermal desorption process using a dynamic adaptive grid model and save the calculation results.

[0094] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0095] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A numerical simulation method for thermal desorption treatment of underground soil in complex sites, characterized in that, The specific steps are as follows: A three-dimensional model of the site is constructed based on site conditions, soil structure, and thermodynamic parameters. The steps for constructing the three-dimensional model of the site are as follows: The layout of the heating wells is obtained based on the site conditions. A soil stratification model was constructed based on the soil structure and the thermodynamic parameters. Construct the three-dimensional model of the site based on the layout of the heating wells and the soil stratification model; The site's three-dimensional model is meshed to generate a preliminary mesh model, and the preliminary mesh model is dynamically adjusted periodically based on preset index thresholds to obtain a dynamic adaptive mesh model. The steps for obtaining the dynamic adaptive mesh model are as follows: Based on historical data, indicators related to the rate of temperature change were selected and the threshold values ​​for each indicator were determined. The parameters of each original grid in the preliminary grid model are acquired periodically, and the index values ​​of each index in each original grid are calculated. The size of each updated grid is determined based on the relationship between the index value and the index threshold. The dynamic adaptive mesh model is generated based on the dimensions of each of the updated meshes; The indicators include temperature gradient and time derivative; The temperature gradient is the maximum value of the derivative of the temperature change with respect to the coordinates in the dynamic adaptive mesh model; the time derivative is the derivative of the temperature change with respect to time. The threshold values ​​for the indicators are multi-level thresholds; The specific steps for generating the dynamic adaptive mesh model based on the dimensions of each updated mesh are as follows: The original meshes in the preliminary mesh model are split and merged according to the size of each updated mesh to obtain the updated mesh; The updated mesh inherits the temperature of the original mesh according to a preset temperature update rule, generating the dynamic adaptive mesh model; The temperature update rule is as follows: Step 221: Determine whether the size of any of the updated meshes is the same as the size of the corresponding original mesh. If not, proceed to step 222; if yes, proceed to step 223. Step 222: Scale the original grid level by level according to the index threshold to obtain a scaled grid, and then jump to step 221; Step 223: Assign a temperature value to the updated mesh based on the temperature value of the original mesh; The dynamic adaptive grid model was used to perform numerical simulation calculations on the soil thermal desorption process, and the calculation results were saved.

2. The numerical simulation method for underground soil thermal desorption treatment in complex sites according to claim 1, characterized in that, After obtaining the simulation data, the covariance matrix is ​​used to compensate for the simulation data.

3. The numerical simulation method for underground soil thermal desorption treatment in complex sites according to claim 2, characterized in that, The steps for obtaining the covariance matrix are as follows: Acquire sensor data of the site, and obtain the temperature distribution of the site based on the sensor data; Obtain site parameters, and use a fitting model to obtain fitted temperature values ​​based on the site parameters; The site parameters are input into the dynamic adaptive grid model to obtain simulated values; The covariance matrix is ​​estimated using a Bayesian method based on the sensor data, the fitted temperature value, and the simulated value.

4. A numerical simulation system for thermal desorption treatment of soil in complex sites, applied to the numerical simulation method for thermal desorption treatment of soil in complex sites as described in any one of claims 1-3, characterized in that, include: The model building module is used to build a three-dimensional model of the site based on site conditions, soil structure, and thermodynamic parameters. The dynamic adjustment module is used to perform meshing processing on the site 3D model to generate a preliminary mesh model, and to dynamically adjust the preliminary mesh model at timed intervals based on preset index thresholds to obtain a dynamic adaptive mesh model. The numerical simulation module is used to perform numerical simulation calculations on the soil thermal desorption process using the dynamic adaptive grid model and save the calculation results.

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