Numerical simulation method and system for thermal desorption treatment of soil in complex site

Abstract

The invention discloses a numerical simulation method and system for soil thermal desorption treatment in a complex site, and relates to the technical field of data simulation, and the method specifically comprises the following steps: constructing a site three-dimensional model based on site working conditions, a soil structure and thermodynamic parameters; performing gridding processing on the site three-dimensional model to generate a preliminary grid model, and performing dynamic adjustment on the preliminary grid model regularly based on a preset index threshold to obtain a dynamic adaptive grid model; and carrying out numerical simulation calculation on the soil thermal desorption treatment process by utilizing the dynamic self-adaptive grid model, and storing a calculation result. According to the method, a numerical simulation method based on a dynamic adaptive grid technology is adopted, the grid density can be automatically adjusted, and the adaptability ensures that a higher grid resolution is achieved in an area with severe temperature change, so that the precision of simulation calculation is greatly improved, and the simulation accuracy is improved. And the simulation result is closer to the temperature distribution condition in the actual soil thermal desorption process.

Description

Technical Field

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

[0002] With the acceleration of industrialization and the advancement of urbanization, organic polluted sites are increasing day by day. In view of the harm of organic pollutants to human health and ecological environment, it is particularly urgent to carry out effective remediation work.

[0003] In-situ thermal desorption technology has been widely used in recent years due to its high remediation efficiency, strong applicability and no rebound effect, and has gradually become one of the important means of remediation of organic contaminated soil. However, this technology also faces the problem of high energy costs, mainly because the energy consumption in the thermal desorption process accounts for as much as 60% to 80% of the total application cost. Therefore, how to reduce energy consumption has become a key factor restricting the promotion of this technology.

[0004] At present, the management of most thermal desorption projects is relatively extensive, usually relying on the operating experience of past projects to control the heating process. However, due to the differences in soil composition at the application site, this empirical operation mode is difficult to adapt to different regions, thus affecting the consistency and reliability of the remediation effect.

[0005] In addition, existing laboratory research is mostly focused on small-scale site models. Although these models can provide certain experimental data support, they have limitations in simulating changes in soil parameters in actual large-scale sites, making it difficult to fully reveal the restoration mechanisms of large-scale sites.

[0006] In order to overcome the above 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 computational efficiency. Traditional methods such as structured grids and static adaptive grids have shown their shortcomings in dealing with this problem. The former is not accurate enough under sparse grids, and will lead to excessive consumption of computing resources under dense grids; the latter cannot be flexibly adjusted with changes in the temperature field due to the fixed grid structure, so that the grid density in the local area cannot match the temperature change. Therefore, how to strike a balance between computational accuracy and computational efficiency by adjusting the grid density is an urgent problem that technicians in this field need to solve. 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, which overcomes the above-mentioned defects.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] A numerical simulation method for thermal desorption of soil in complex sites, the specific steps are:

[0010] Construct a three-dimensional site model based on site conditions, soil structure and thermodynamic parameters;

[0011] Gridding the three-dimensional model of the site to generate a preliminary grid model, and dynamically adjusting the preliminary grid model based on a preset index threshold to obtain a dynamic adaptive grid model;

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

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

[0014] Obtaining a heating well layout based on the site conditions;

[0015] Constructing a soil stratification model based on the soil structure and the thermodynamic parameters;

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

[0017] Optionally, the step of acquiring the dynamic adaptive grid model is:

[0018] Filter indicators related to the temperature change rate based on historical data and determine the indicator thresholds of each indicator;

[0019] Regularly acquiring the parameters of each original grid in the preliminary grid model, and calculating the index value of each index in each original grid;

[0020] Determine the size of each update grid according to the relationship between the indicator value and the indicator threshold;

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

[0022] Optionally, the index includes a temperature gradient and a time derivative; the temperature gradient is the maximum value of the derivative of the temperature change of the dynamic adaptive grid model with respect to the coordinate; and 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 of generating the dynamic adaptive grid model based on the sizes of each of the update grids are:

[0025] Splitting and merging the original grids in the preliminary grid model according to the sizes of the updated grids to obtain the updated grids;

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

[0027] Optionally, the temperature update rule is:

[0028] Step 221, determining whether the size of any of the updated grids is the same as the corresponding original grid, if not, executing step 222; if yes, executing step 223;

[0029] Step 222, scaling the original grid step by step according to the level of the indicator threshold to obtain a scaled grid, and jump to step 221;

[0030] Step 223: assigning a temperature value to the updated grid according to the temperature value of the original grid.

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

[0032] Optionally, the step of obtaining the covariance matrix is:

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

[0034] Acquiring site parameters, and obtaining fitting temperature values ​​using a fitting model according to the site parameters;

[0035] Inputting the site parameters into the dynamic adaptive grid model to obtain simulation values;

[0036] A covariance matrix is ​​estimated using a Bayesian approach based on the sensor data, the fitted temperature values, and the simulated values.

[0037] A numerical simulation system for thermal desorption of soil in complex sites, comprising:

[0038] Model building module, used to build a three-dimensional site model based on site conditions, soil structure and thermodynamic parameters;

[0039] A dynamic adjustment module, used for performing grid processing on the three-dimensional model of the site to generate a preliminary grid model, and dynamically adjusting the preliminary grid model based on a preset index threshold to obtain a dynamic adaptive grid 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] It can be seen from the above technical solutions that the present invention discloses a numerical simulation method and system for thermal desorption treatment of soil in complex sites, which has the following beneficial effects compared with the prior art:

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

[0043] 2. Optimize energy utilization: This invention can provide a scientific basis for the design of heating strategies and help formulate more reasonable heating plans by accurately simulating the soil thermal desorption process. This can not only avoid unnecessary energy waste, but also ensure that pollutants are fully pyrolyzed and volatilized, thereby improving energy utilization efficiency.

[0044] 3. Enhanced scope of application: The dynamic adaptive grid technology of the present invention enables the simulation method to be applicable to various complex soil environments, including mixed layers of different types of soil, porous media, and scenarios under non-uniform heat conduction conditions. This greatly expands the scope of application of the technology, enabling it to cope with more diverse contaminated site remediation needs.

[0045] 4. Simplify engineering management: The numerical simulation system provided by the present invention can provide engineering managers with intuitive visualization tools. Through the simulation results, managers can monitor the status of the thermal desorption process in real time, discover and solve problems in a timely manner, simplify engineering management and decision-making processes, and improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0047] Figure 1 A schematic diagram of the method provided by the present invention;

[0048] FIG2(a) is a schematic diagram of the layout of triangular soil heating wells in a typical thermal desorption process provided by the present invention; FIG2(b) is a schematic diagram of the layout of quadrilateral soil heating wells in a typical thermal desorption process provided by the present invention; FIG2(c) is a schematic diagram of the layout of hexagonal soil heating wells in a typical thermal desorption process provided by the present invention;

[0049] Figure 3 A schematic diagram of the process of obtaining the size of an updated grid provided by the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

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

[0053] Step 2: Gridding the three-dimensional model of the site to generate a preliminary grid model, and dynamically adjusting the preliminary grid model based on a preset index threshold to obtain a dynamic adaptive grid model;

[0054] Step 3: Use the dynamic adaptive grid model to perform numerical simulation calculations on the soil thermal desorption process and save the calculation results.

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

[0056] Step 11, obtaining the heating well layout based on the 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 heating well layout and soil stratification model.

[0059] In one embodiment, the three-dimensional site model of a complex site needs to fully consider the differences in heating well layout, soil stratification information, and local thermodynamic parameter differences. Therefore, it is necessary to construct a heating well model based on the site conditions. Considering that the layout of soil heating wells in a typical process environment is determined according to standard shapes (triangles, quadrilaterals, hexagons), the heating well layout can be specified in specific applications to construct a three-dimensional model. Figure 2(a)-Figure 2(c) As shown in the figure, the red points represent heating wells and the green points represent extraction wells. Since the extraction wells are mostly located at cold spots (the points with the lowest temperature in the entire site) and the radius of the extraction wells is very small, which is negligible compared to the spacing between the heating wells, the temperature changes introduced by the extraction wells can be ignored when building the model.

[0060] Without considering the extraction process, the modeling of the extraction well can be omitted in the three-dimensional model of the site. At the same time, considering that various fillers and insulation layers will be laid on the actual site to reduce heat loss, it is necessary to model the soil in layers in the actual model and set different thermodynamic parameters for each layer.

[0061] In one embodiment, the steps of acquiring the dynamic adaptive grid model are:

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

[0063] Step 22: regularly obtain the parameters of each original grid in the preliminary grid model, and calculate the index value of each index in each original grid;

[0064] Step 23, determining the size of each updated grid according to the relationship between the indicator value and the indicator threshold;

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

[0066] In one embodiment, the index includes a temperature gradient and a time derivative; the temperature gradient is the maximum value of the derivative of the temperature change of the dynamic adaptive grid model with respect to the coordinate; and the time derivative is the derivative of the temperature change with respect to time.

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

[0068] Furthermore, the local mesh density is determined by an index related to the temperature change rate. Two indexes are proposed here: temperature gradient T tg and the time derivative T td The former is the maximum value of the derivative of the simulation model temperature change ΔT relative to the coordinates (max(ΔT / Δx, ΔT / Δy, ΔT / Δz), and the latter is the derivative of the temperature change ΔT relative to time ΔT / Δt. The thresholds [T 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 the time derivative T td，i , then follow Figure 3 The successive comparison method shown in FIG. 1 determines the local grid size as M and the standard grid size as 1, thereby splitting and merging the grids.

[0069] In one embodiment, the dynamic adaptive grid model generation process is:

[0070] Step 241, splitting and merging the original grids in the preliminary grid model according to the sizes of each updated grid to obtain an updated grid;

[0071] Step 242: The updated grid inherits the temperature of the original grid according to a preset temperature update rule to generate a dynamic adaptive grid model.

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

[0073] Step 2421, determine whether the size of any updated grid is the same as the corresponding original grid, if not, execute step 2422; if yes, execute step 2423;

[0074] Step 2422, scaling the original grid step by step according to the level of the index threshold, obtaining a scaled grid, and jumping to step 2421;

[0075] Step 2423: assign the temperature value of the original grid to the updated grid.

[0076] Furthermore, the mesh is split and merged according to the local mesh size M, and the inheritance of the mesh point temperature needs to be considered. If the sizes of two mesh points are the same, they are directly inherited; if the sizes of two mesh points are different, they are inherited according to the actual sizes of the two mesh points. The following inheritance rules are followed:

[0077] Inheritance rule 1: Assuming that the original grid point size is smaller than the updated grid point size, the original grid point size is enlarged by one level to see whether it is the same as the updated grid point size. If it is the same, the temperatures of all original grid points within the size range corresponding to the updated grid point are averaged as the updated grid point temperature; if they are different, the original grid point size is further enlarged by one level, and so on.

[0078] Inheritance rule 2: Assuming that the original grid point size is larger than the updated grid point size, reduce the original grid point size by one level to see if it is the same as the updated grid point size. If it is the same, assign the original grid point temperature directly to all updated grid points in the corresponding area; if it is different, reduce the original grid point size by one level, and so on.

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

[0080] In one embodiment, the steps of obtaining the covariance matrix are:

[0081] Acquire sensor data of the site, and obtain temperature distribution of the site based on the sensor data;

[0082] Acquire site parameters, and obtain fitting temperature values ​​using a fitting model according to the site parameters;

[0083] Input the site parameters into the dynamic adaptive grid model to obtain simulation values;

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

[0085] Furthermore, considering that there may be errors between the temperature field calculated by the simulation model and the data measured by the temperature sensor, error compensation is required. In this embodiment, a neural network prediction method is used to compensate the error of the calculation result. The specific process is as follows:

[0086] (1) Obtain the monitoring data of the original temperature sensors (thermocouples 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 input is soil parameters, the distance between the point and the center of the heating well, depth, etc. The output is the current point temperature, and a fitting curve is constructed.

[0088] (3) In addition to the actual measurement points, the neural network model fitting results are used as the actual values, the simulation model output results are used as the predicted values, and the Bayesian method is used to estimate the covariance matrix and perform compensation.

[0089] For the actual measurement points, the covariance matrix estimated in (3) is used to perform 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 soil thermal desorption treatment in complex sites, including:

[0091] Model building module, used to build a three-dimensional site model based on site conditions, soil structure and thermodynamic parameters;

[0092] A dynamic adjustment module is used to perform grid processing on the three-dimensional model of the site to generate a preliminary grid model, and dynamically adjust the preliminary grid model based on a preset index threshold to obtain a dynamic adaptive grid 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] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0095] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A numerical simulation method for thermal desorption of soil in complex sites, characterized in that: The specific steps are: Construct a three-dimensional site model based on site conditions, soil structure and thermodynamic parameters; Gridding the three-dimensional model of the site to generate a preliminary grid model, and dynamically adjusting the preliminary grid model based on a preset index threshold to obtain a dynamic adaptive grid model; The dynamic adaptive grid model is used to perform numerical simulation calculations on the soil thermal desorption process, and the calculation results are saved.

2. A numerical simulation method for thermal desorption of soil in complex sites according to claim 1, characterized in that: The steps of constructing the three-dimensional model of the site are: Obtaining a heating well layout based on the site conditions; Constructing a soil stratification model based on the soil structure and the thermodynamic parameters; A three-dimensional model of the site is constructed based on the heating well layout and the soil stratification model.

3. The numerical simulation method for thermal desorption of soil in complex sites according to claim 1, characterized in that: The steps of obtaining the dynamic adaptive grid model are: Filter indicators related to the temperature change rate based on historical data and determine the indicator thresholds of each indicator; Regularly acquiring the parameters of each original grid in the preliminary grid model, and calculating the index value of each index in each original grid; Determine the size of each update grid according to the relationship between the indicator value and the indicator threshold; The dynamic adaptive mesh model is generated based on the sizes of each of the update meshes.

4. A numerical simulation method for thermal desorption of soil in complex sites according to claim 3, characterized in that: The indicators include temperature gradient and time derivative; the temperature gradient is the maximum value of the derivative of the temperature change of the dynamic adaptive grid model relative to the coordinate; the time derivative is the derivative of the temperature change relative to time.

5. The numerical simulation method for thermal desorption of soil in complex sites according to claim 3, characterized in that: The indicator threshold is a multi-level threshold.

6. A numerical simulation method for thermal desorption of soil in complex sites according to claim 5, characterized in that: The specific steps of generating the dynamic adaptive grid model based on the sizes of each updated grid are: Splitting and merging the original grids in the preliminary grid model according to the sizes of the updated grids to obtain the updated grids; The updated grid inherits the temperature of the original grid according to a preset temperature update rule to generate the dynamic adaptive grid model.

7. According to the numerical simulation method for thermal desorption of soil in complex sites according to claim 6, the temperature update rule is: Step 221, determining whether the size of any of the updated grids is the same as the corresponding original grid, if not, executing step 222; if yes, executing step 223; Step 222, scaling the original grid step by step according to the level of the indicator threshold to obtain a scaled grid, and jump to step 221; Step 223: assigning a temperature value to the updated grid according to the temperature value of the original grid.

8. The numerical simulation method for thermal desorption of soil in complex sites according to claim 1, characterized in that: After the simulation data is obtained, the simulation data is compensated using a covariance matrix.

9. A numerical simulation method for thermal desorption of soil in complex sites according to claim 8, characterized in that: The steps for obtaining the covariance matrix are: Acquire sensor data of the site, and obtain temperature distribution of the site based on the sensor data; Acquiring site parameters, and obtaining fitting temperature values ​​using a fitting model according to the site parameters; Inputting the site parameters into the dynamic adaptive grid model to obtain simulation values; A covariance matrix is ​​estimated using a Bayesian approach based on the sensor data, the fitted temperature values, and the simulated values.

10. A numerical simulation system for thermal desorption of soil in complex sites, characterized in that: include: Model building module, used to build a three-dimensional site model based on site conditions, soil structure and thermodynamic parameters; A dynamic adjustment module, used for performing grid processing on the three-dimensional model of the site to generate a preliminary grid model, and dynamically adjusting the preliminary grid model based on a preset index threshold to obtain a dynamic adaptive grid 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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