Heating well cluster cooperative control method

Abstract

The invention discloses a heating well cluster cooperative control method, which is used for an in-situ thermal desorption repair process in soil pollution treatment, and comprises the following steps: performing group division on heating wells; performing soil heating numerical simulation on each group of heating wells, generating a temperature field distribution diagram around each group of heating wells, and obtaining a relationship between a heating mode of the heating wells and temperature response of the areas around the heating wells; constructing a temperature-related repair result evaluation system, defining an effective heating area, and taking an energy consumption ratio of the effective heating area as an optimization index; constructing a machine learning prediction model, predicting a repair index under cooperative control of the heating well cluster, and determining a cooperative control method of the heating well cluster in combination with a Bayesian optimization algorithm; and the output power of the heating well combustors is adjusted, and cooperative control over the heating well cluster is achieved. Through cooperative control of the heating well cluster, heat distribution of a site is homogenized, energy consumption is reduced, and efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution control, and more specifically to the coordinated control of a heating well cluster in an in-situ thermal desorption technology. Background Art

[0002] In situ thermal desorption is one of the thermal remediation technologies for soil contamination. It uses high-temperature steam to heat contaminated soil, thereby promoting the removal of volatile and semi-volatile organic pollutants.

[0003] Research on thermal remediation technology for contaminated soil began in the 1970s. However, up to now, the process management of remediation projects is extensive, and the energy consumption control and secondary pollution control of the remediation process are still at a low level. Most of the existing burner control technologies are single-machine control (single heating well control) with a single indicator, and lack a heating well cluster control strategy.

[0004] Existing contaminated site remediation evaluation systems are mostly based on periodic exhaust gas concentration testing. However, since exhaust gas collection and testing take time, this type of testing has a lag and cannot reflect the site remediation effect in a timely manner.

[0005] Therefore, it is an urgent problem for technicians in this field to build a temperature-related remediation result evaluation system, reduce heat waste in contaminated sites, and control energy consumption. Summary of the invention

[0006] In view of this, the present invention provides a collaborative control method for a heating well cluster, which divides the heating wells in the site into several groups according to the pipe layout, and collaboratively controls the output power of the burners in each group of heating wells to achieve uniform heat distribution in the site.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] The present invention provides a heating well cluster coordinated control method for an in-situ thermal desorption remediation process in soil pollution control, comprising the following steps:

[0009] S1. Divide the heating wells into groups based on the pipeline information of the heating wells;

[0010] S2. Performing soil heating numerical simulation on each group of heating wells to generate a temperature field distribution map around each group of heating wells, and based on the temperature field distribution map, obtaining a relationship between a heating mode of the heating wells and a temperature response of an area around the heating wells;

[0011] S3. Based on the relationship between the heating mode of the heating well and the temperature response of the area around the heating well, a temperature-related repair result evaluation system is constructed; in the temperature-related repair result evaluation system, an effective heating area is defined, and the energy consumption ratio of the effective heating area is used as an optimization index;

[0012] S4. By using the temperature-related repair result evaluation system, a machine learning prediction model is constructed to predict the repair index under the coordinated control of the heating well cluster, and a coordinated control method of the heating well cluster is determined in combination with the Bayesian optimization algorithm;

[0013] S5. Based on the measured soil temperature and the current control state, the difference between the predicted index and the given index is calculated, and the output power of the heating well burner in the collaborative control method of the heating well cluster is adjusted to realize the collaborative control of the heating well cluster.

[0014] Furthermore, the pipeline information in step S1 includes the number, length, diameter, material, connection method and relative position of each heating well of the pipeline.

[0015] Furthermore, in step S2, the soil heating numerical simulation is performed on each group of heating wells, specifically including:

[0016] Acquiring basic data, wherein the basic data includes layout information of the heating well group, physical and chemical properties of the soil, and parameters of the heating well;

[0017] Use the modeling tools in CAD software or numerical simulation software to establish a geometric model based on the basic data, simulate the process of soil heating by the heating well group, and simulate the temperature field distribution diagram.

[0018] Furthermore, the temperature-related repair result evaluation system in step S3 includes: temperature achievement standard, heating uniformity, energy consumption index, time efficiency, temperature control accuracy and environmental safety factors.

[0019] Furthermore, the definition of the effective heating area in step S3 is: the area where the temperature continuously exceeds the target temperature of the site within the time T; the energy consumption ratio formula of the effective heating area is:

[0020]

[0021] Where: C is the total energy consumed in time T, in joules or kilowatt-hours;

[0022] V is the volume of the effective heating area in cubic meters.

[0023] Furthermore, the construction of the machine learning prediction model in step S4 is based on the basic information and the simulated temperature field distribution map to predict relevant indicators.

[0024] Furthermore, in step S5, the output power of the heating well burner is adjusted, specifically by adjusting the natural gas flow rate and the excess air coefficient.

[0025] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for collaborative control of a heating well cluster, which generates a temperature field distribution map by numerically simulating soil heating in the heating wells, and constructs a temperature-related remediation result evaluation system; and predicts the remediation indicators under the collaborative control of the heating well cluster by constructing a machine learning prediction model, thereby realizing the collaborative control of the heating well cluster; in order to solve the problem that single-machine control in the soil thermal desorption site cannot optimize the site temperature distribution, the collaborative control of the heating well cluster is realized, energy consumption is reduced, and efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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.

[0027] Figure 1 A flow chart of the collaborative control method for a heating well cluster provided in the present invention.

[0028] Figure 2 This is a pipeline connection diagram for the heating well cluster provided by the present invention.

[0029] Figure 3 This is a diagram for constructing the temperature-related repair result evaluation system provided by the present invention.

[0030] Figure 4 This is a schematic diagram of the single-machine control method for a heating well provided by the present invention. DETAILED DESCRIPTION

[0031] 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.

[0032] The embodiment of the present invention discloses a heating well cluster collaborative control method, referring to Figure 1-2 As shown, the in-situ thermal desorption remediation process used in soil pollution control includes the following steps:

[0033] S1. Divide the heating wells into groups based on the pipeline information of the heating wells;

[0034] S2. Performing soil heating numerical simulation on each group of heating wells to generate a temperature field distribution map around each group of heating wells, and based on the temperature field distribution map, obtaining a relationship between a heating mode of the heating wells and a temperature response of an area around the heating wells;

[0035] S3. Based on the relationship between the heating mode of the heating well and the temperature response of the area around the heating well, a temperature-related repair result evaluation system is constructed; in the temperature-related repair result evaluation system, an effective heating area is defined, and the energy consumption ratio of the effective heating area is used as an optimization index;

[0036] S4. By using the temperature-related repair result evaluation system, a machine learning prediction model is constructed to predict the repair index under the coordinated control of the heating well cluster, and a coordinated control method of the heating well cluster is determined in combination with the Bayesian optimization algorithm;

[0037] S5. Based on the measured soil temperature and the current control state, the difference between the predicted index and the given index is calculated, and the output power of the heating well burner in the collaborative control method of the heating well cluster is adjusted to realize the collaborative control of the heating well cluster.

[0038] In this embodiment, the in-situ thermal desorption remediation technology is used to treat the contaminated soil. The burners and heating wells are divided into multiple groups according to the pipeline information. Each group of heating wells is controlled. Based on the soil heating numerical simulation model, the temperature field distribution map is simulated, and the direct relationship between the heating mode of the heating well and the temperature response of the area around the heating well is derived.

[0039] Since the temperature can be monitored in real time, a temperature-related remediation result evaluation system is constructed, and the energy consumption ratio of the effective heating area is used as an optimization indicator to conduct a quantitative analysis of the optimization indicator. A machine learning model is constructed to predict the remediation indicator, and the difference between the predicted indicator and the given indicator is calculated, so as to control a group of heating wells at the same time, reduce heat waste in the contaminated site, and control energy consumption.

[0040] Wherein, the pipeline information in step S1 includes the number, length, diameter, material, connection method and relative position of each heating well of the pipeline;

[0041] Number of pipes: This refers to the total number of pipes used to connect the various heater wells at the site; this helps determine the network structure between the heater wells and how they are divided into groups.

[0042] Pipeline length: The length of each pipeline affects the efficiency of heat transfer and the distribution of the thermal field.

[0043] Pipeline diameter: The diameter of the pipeline determines the heat and fluid transmission capacity, affecting the distribution of heat and the control effect of the heating well.

[0044] Pipe material: The material of the pipe (such as steel, plastic, composite material, etc.) will affect its heat resistance, corrosion resistance and heat conductivity.

[0045] Pipeline connection method: describes how the pipeline connects the heating wells (such as straight connection, branch connection, ring connection, etc.), which has a direct impact on the heat transfer path and the overall coordination of the heating wells.

[0046] Relative position of each heating well: The specific location and spatial layout of each heating well in the site; this information helps understand the diffusion of heat in the soil and optimize the grouping and coordinated control strategy of the heating wells.

[0047] Through comprehensive understanding and integration of this pipeline information, the heating wells can be grouped more accurately, thereby improving the coordinated control effect of the heating well cluster and achieving more efficient soil pollution remediation.

[0048] The principles for grouping heating wells mainly include:

[0049] Geographically close: Heating wells that are geographically close are grouped together to facilitate unified management and control;

[0050] Similar heat transfer conditions of pipelines: Considering factors such as the length, diameter, and material of the pipelines, heating wells with similar heat transfer conditions are grouped into the same group;

[0051] Convenient pipeline connection: Ensure that the heating wells in the same group can be conveniently connected through pipelines to facilitate heat transfer and regulation;

[0052] For example, a contaminated site of 10,000 square meters can be initially divided into five areas, each area containing several heating wells, forming five heating well groups; each group of heating wells is interconnected by pipes to form an independent heating unit.

[0053] In one embodiment, in step S2, soil heating numerical simulation is performed on each group of heating wells, and the specific process is as follows:

[0054] First, basic data is collected; including the layout information of the heating well group, the physical and chemical properties of the soil and the parameters of the heating equipment.

[0055] in;

[0056] Layout information of the heating well group: including the number, location, depth, spacing, etc. of the heating wells.

[0057] Physical and chemical properties of soil: such as thermal conductivity, specific heat capacity, density, moisture content, etc.; these data can be obtained through laboratory testing or by consulting relevant literature.

[0058] Parameters of heating equipment: such as heating power, heating method (electric heating, thermal fluid circulation, etc.), specifications and layout of heating elements, etc.

[0059] Secondly, build the geometric model;

[0060] Use the modeling tools in CAD software or numerical simulation software to build a geometric model based on the actual layout of the heating well group. The model should include the detailed structure of the soil area, the heating well and its internal components. Based on Fourier's law of heat conduction and the principle of energy conservation, establish the heat transfer equation in the soil; including comprehensive consideration of multiple heat transfer modes such as heat conduction, heat convection and heat radiation.

[0061] The initial conditions of the model are set according to the initial soil temperature distribution.

[0062] The boundary conditions of the model are set based on heat exchange between the surface and the atmosphere, adiabatic or isothermal conditions at the bottom boundary of the soil, etc.

[0063] Use a suitable programming language to write code to implement the above mathematical model and numerical method, run the simulation program on the computer, input the necessary parameters and data, and obtain simulation results such as soil temperature distribution; obtain the relationship between the heating mode of the heating well and the temperature response of the area around the heating well.

[0064] In one embodiment, the temperature-related remediation result evaluation system involved in step S3 is an important component of the soil in-situ thermal desorption technology, which mainly focuses on evaluating and monitoring the temperature changes of the soil during the heating process and judging the remediation effect. This evaluation system is crucial to ensure that the contaminated soil is effectively treated.

[0065] Reference Figure 3 As shown in the figure, in the related technology, the existing contaminated site remediation evaluation system is mostly based on periodic exhaust gas concentration detection. However, this detection has a lag (because exhaust gas collection and detection take time), and cannot reflect the site remediation effect in a timely manner. Therefore, it is necessary to propose temperature-related remediation result evaluation indicators and build a temperature-related remediation result evaluation system.

[0066] The following are the key elements and their descriptions included in the evaluation system of the present invention:

[0067] Temperature reaching standard:

[0068] Temperature achievement criteria are the core indicators in the evaluation system; one or more target temperatures are usually set based on the boiling point of the pollutants and the thermal stability of the soil.

[0069] These target temperatures need to be high enough to ensure that organic pollutants can be effectively broken down or evaporated, but not too high to avoid causing irreversible damage to the soil structure.

[0070] Heating uniformity:

[0071] Heating uniformity reflects how evenly heat is distributed in the soil; uniform heat distribution ensures that the entire contaminated area reaches an adequate temperature, thereby improving remediation efficiency.

[0072] By simulating the heating process using numerical simulation tools such as COMSOL Multiphysics, we can obtain the temperature distribution map in the soil and evaluate the heating uniformity.

[0073] Energy consumption index:

[0074] Energy consumption indicators are used to evaluate the energy utilization efficiency during the restoration process; effective energy consumption control can significantly reduce the operating costs of the project.

[0075] It is usually measured by the energy required to heat a unit volume of soil to a target temperature.

[0076] Time efficiency:

[0077] Time efficiency focuses on the time required to achieve the desired repair effect; this includes heating time, holding time, and cooling time.

[0078] Reaching target temperatures quickly and efficiently not only improves remediation efficiency, but also reduces site closure time.

[0079] Temperature control accuracy:

[0080] Temperature control accuracy refers to how accurately the heating system maintains a set temperature point; high-precision control helps avoid overheating and wasted energy.

[0081] Modern heating systems are equipped with advanced sensors and control systems, which can achieve control accuracy within ±1°C.

[0082] Environmental safety factors:

[0083] Environmental safety factors should also be considered in the evaluation system, including steam leakage and secondary pollution risks.

[0084] Monitoring equipment and safety measures are used to ensure that the heating process does not have a negative impact on the surrounding environment and personnel.

[0085] By combining temperature-related indicators and project requirements, the optimization plan can control costs while ensuring the repair effect.

[0086] The temperature-related remediation result evaluation system provides a comprehensive evaluation and monitoring method for the implementation of soil in-situ thermal desorption technology by comprehensively considering the above aspects. This not only ensures the efficient implementation of the remediation work, but also greatly improves the economic benefits and environmental safety of the project.

[0087] In step S3, the effective heating area energy consumption ratio is used as an optimization index in the temperature-related remediation result evaluation system, which refers to the ratio of the total energy consumed to achieve a certain temperature target within a specific time to the actual effective heating area volume. In short, it is an indicator to evaluate the energy consumed per unit volume of soil to reach the target temperature.

[0088] In mathematical terms, it can be expressed as:

[0089]

[0090] Where: C is the total energy consumed in time T, in joules or kilowatt-hours;

[0091] V is the volume of the effective heating area in cubic meters.

[0092] The factors affecting the effective heating area energy consumption ratio are:

[0093] Heating well layout: The layout of the heating well directly affects the distribution and penetration efficiency of heat energy, thereby affecting the energy consumption ratio. Reasonable layout can reduce heat overlap and loss and improve energy efficiency.

[0094] Soil type and moisture: Soil types and moisture levels have different thermal conductivity properties, which affects heating efficiency and energy consumption.

[0095] Operating temperature: The choice of operating temperature should be optimized based on the boiling point of the pollutants and the thermal stability of the soil. Too high or too low a temperature may lead to an unnecessary increase in energy consumption.

[0096] System control strategy: Advanced control strategies, such as heating well cluster collaborative control, can adjust operating parameters in real time and optimize energy consumption ratio.

[0097] Low energy consumption ratio means higher energy efficiency and lower operating costs, thus better economic benefits for the entire project; when evaluating the environmental protection effect of a project, low energy consumption ratio usually means less carbon emissions and environmental impact. By analyzing the energy consumption ratio, thermal desorption technology can be optimized, including improving the design of heating wells and optimizing operating parameters.

[0098] The effective heating area energy consumption ratio provides a quantitative indicator to help engineers and decision makers evaluate and compare the efficiency of different thermal desorption projects and technologies. By optimizing relevant parameters and adopting efficient control strategies, the economic and environmental benefits of soil pollution control projects can be significantly improved. The application of this indicator is of great value in promoting the advancement and practical application of environmental protection technologies.

[0099] In one embodiment, step S4 refers to Figure 3As shown, according to the input parameters of the heating model and the temperature field distribution simulated by the heating model, a corresponding machine learning prediction model is constructed to design an optimization strategy.

[0100] The basic information for model construction is the basic data of the heating well and the simulated temperature field distribution map; the predicted repair index is the repair effect under the coordinated control of the heating well cluster; and the Bayesian optimization algorithm is used to adjust the prediction model to determine the optimal heating well cluster control method to improve the repair effect and energy efficiency.

[0101] In this embodiment, the machine learning prediction model uses a regression algorithm to predict the restoration indicators and extracts features (such as the power, layout, soil properties, etc. of the heating well) based on the basic data and simulation results. These features will be used for model training.

[0102] The optimization target of the Bayesian optimization algorithm used in this embodiment is the repair index predicted by the machine learning model, such as temperature distribution, heating uniformity, etc.

[0103] The optimization process is as follows: define the optimization objective function, which in this embodiment is the difference between the repair effect predicted by the model and the actual effect; use the Bayesian optimization algorithm to adjust the model's hyperparameters (such as learning rate, number of trees, etc.) to optimize the prediction performance; and obtain the optimal heating well cluster control method through Bayesian optimization.

[0104] In step S5, the output power of the heating well burner is adjusted, and the working mode can be adjusted. Different burners can control different contents in different scenarios. In this embodiment, the in-situ thermal desorption remediation technology is used to treat contaminated soil. Most of the time, several different working modes are switched. Each working mode corresponds to fixed fan parameters, etc. By adjusting the working mode, the natural gas flow rate and the excess air coefficient are adjusted; thereby achieving the best energy efficiency and remediation effect in the remediation process.

[0105] Through the construction of machine learning prediction models and the application of Bayesian optimization algorithms, the remediation effect of the heating well cluster under coordinated control can be accurately predicted. The present invention significantly improves the efficiency and accuracy of the in-situ thermal desorption remediation process, optimizes energy use, and achieves more efficient soil pollution control.

[0106] In the prior art, the single-machine control method often controls a single burner based on a single indicator. Figure 4As shown in the figure, the operating conditions of a single burner are controlled by setting the parameters of a single burner, and there is a lack of overall control over the soil temperature. The figure lists three typical single-machine control methods for heating wells under three typical working conditions, namely, keeping the natural gas flow and excess air coefficient constant, keeping the outlet temperature and excess air coefficient constant, and keeping the outlet temperature constant. By calculating the difference between the given value and the sensor measurement value, a controller such as PID is used to achieve single-machine control.

[0107] The improved heating well cluster control method refers to Figure 2 As shown in the figure, the heating well cluster control method is an improvement on the single-machine method. First, the burners and heating wells are divided into multiple different groups according to the pipelines. Then, based on the actual soil temperature measured by the sensor and the current control status, the machine learning model is used to predict the restoration index, and the difference between the predicted index and the given index is calculated, so as to control a group of heating wells at the same time.

[0108] It solves the problem that single-machine control in soil thermal desorption sites cannot optimize the temperature distribution of the site, realizes the coordinated control of heating well clusters, reduces energy consumption and improves efficiency.

[0109] 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.

[0110] 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 heating well cluster coordinated control method for in-situ thermal desorption remediation process in soil pollution control, characterized in that: The following steps are involved: S1. Divide the heating wells into groups based on the pipeline information of the heating wells; S2. Performing soil heating numerical simulation on each group of heating wells to generate a temperature field distribution map around each group of heating wells, and based on the temperature field distribution map, obtaining a relationship between a heating mode of the heating wells and a temperature response of an area around the heating wells; S3. Based on the relationship between the heating mode of the heating well and the temperature response of the area around the heating well, a temperature-related repair result evaluation system is constructed; in the temperature-related repair result evaluation system, an effective heating area is defined, and the energy consumption ratio of the effective heating area is used as an optimization index; S4. By using the temperature-related repair result evaluation system, a machine learning prediction model is constructed to predict the repair index under the coordinated control of the heating well cluster, and a coordinated control method of the heating well cluster is determined in combination with the Bayesian optimization algorithm; S5. Based on the measured soil temperature and the current control state, the difference between the predicted index and the given index is calculated, and the output power of the heating well burner in the collaborative control method of the heating well cluster is adjusted to realize the collaborative control of the heating well cluster.

2. A heating well cluster collaborative control method according to claim 1, characterized in that: The pipeline information in step S1 includes the number, length, diameter, material, connection method and relative position of each heating well.

3. A heating well cluster collaborative control method according to claim 1, characterized in that: The step S2 performs soil heating numerical simulation on each group of heating wells, specifically including: Acquiring basic data, wherein the basic data includes layout information of the heating well group, physical and chemical properties of the soil, and parameters of the heating well; Use the modeling tools in CAD software or numerical simulation software to establish a geometric model based on the basic data, simulate the process of soil heating by the heating well group, and simulate the temperature field distribution diagram.

4. A heating well cluster coordinated control method according to claim 1, characterized in that: The temperature-related repair result evaluation system in step S3 includes: temperature achievement standard, heating uniformity, energy consumption index, time efficiency, temperature control accuracy and environmental safety factors.

5. A heating well cluster coordinated control method according to claim 1, characterized in that: The definition of the effective heating area in step S3 is: the area where the temperature continues to exceed the site target temperature within the time T; the energy consumption ratio formula of the effective heating area is: Where: C is the total energy consumed in time T, in joules or kilowatt-hours; V is the volume of the effective heating area in cubic meters.

6. A heating well cluster coordinated control method according to claim 1, characterized in that: The construction of the machine learning prediction model in step S4 is based on the basic information and the simulated temperature field distribution map to predict relevant indicators.

7. A heating well cluster coordinated control method as described in claims 1-6, characterized in that: In step S5, the output power of the heating well burner is adjusted, specifically, the working mode is adjusted to correspond to different natural gas flow rates and excess air coefficients.

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