A heating well cluster coordinated control method

Abstract

The application discloses a heating well cluster cooperative control method, which is used in the in-situ thermal desorption remediation process in soil pollution treatment and comprises the following steps: grouping and dividing the heating wells; performing soil heating numerical simulation on each group of heating wells to generate a temperature field distribution diagram around each group of heating wells and obtain the relationship between the heating mode of the heating wells and the temperature response of the area around the heating wells; constructing a temperature-related remediation result evaluation system, defining an effective heating area, and taking the energy consumption ratio of the effective heating area as an optimization index; constructing a machine learning prediction model to predict the remediation index under the cooperative control of the heating well cluster, and combining a Bayesian optimization algorithm to determine the heating well cluster cooperative control method; and adjusting the output power of the heating well burner to realize the cooperative control of the heating well cluster. Through the cooperative control of the heating well cluster, the application realizes the homogenization of the site heat distribution, reduces the energy consumption, and improves the efficiency.

Description

Technical Field

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

[0002] In situ thermal desorption is a type of soil contamination thermal remediation technology that uses high-temperature steam to heat contaminated soil, thereby promoting the removal of volatile and semi-volatile organic pollutants.

[0003] Research on thermal remediation technologies for contaminated soil began in China and abroad in the 1970s. However, to date, remediation process management has been extensive, and energy consumption and secondary pollution control during the remediation process remain at a low level. Existing burner control technologies mostly rely on single-indicator, stand-alone control (single heating well control), lacking strategies for clustered heating well control.

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

[0005] Therefore, building a temperature-related remediation result evaluation system to reduce heat waste in contaminated sites and control energy consumption are issues that technical personnel in this field urgently need to solve. 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 solutions:

[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 a soil heating numerical simulation for 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 the heating pattern of the heating wells and the temperature response of the area around the heating wells;

[0011] S3. Constructing a temperature-related remediation result evaluation system based on the relationship between the heating mode of the heating well and the temperature response of the area surrounding the heating well; defining an effective heating area in the temperature-related remediation result evaluation system, and using the energy consumption ratio of the effective heating area as an optimization indicator;

[0012] S4. Using the temperature-related repair result evaluation system, a machine learning prediction model is constructed to predict the repair indicators under the coordinated control of the heating well cluster, and a Bayesian optimization algorithm is used to determine the coordinated control method of the heating well cluster;

[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 achieve 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.

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

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

[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 the heating well group heating the soil, 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 effective heating area in step S3 is defined as: 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 solution that compared with the existing technology, the present invention discloses a method for collaborative control of a heating well cluster. By performing numerical simulation of soil heating on the heating well, a temperature field distribution map is generated, and a temperature-related remediation result evaluation system is constructed. By constructing a machine learning prediction model, the remediation indicators under the collaborative control of the heating well cluster are predicted, and the collaborative control of the heating well cluster is realized. This solves the problem that single-machine control in the soil thermal desorption site cannot optimize the site temperature distribution, realizes the collaborative control of the heating well cluster, reduces energy consumption, and improves efficiency. 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0027] Figure 1 Flowchart of the collaborative control method for heating well clusters provided by 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 of the temperature-related repair result evaluation system provided by the present invention.

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

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0032] The embodiment of the present invention discloses a method for collaborative control of a heating well cluster, referring to Figure 1-2 As shown, the in-situ thermal desorption remediation process for 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 a soil heating numerical simulation for 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 the heating pattern of the heating wells and the temperature response of the 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 remediation result evaluation system is constructed; in the temperature-related remediation result evaluation system, an effective heating area is defined, and the energy consumption ratio of the effective heating area is used as an optimization indicator;

[0036] S4. Using the temperature-related repair result evaluation system, a machine learning prediction model is constructed to predict the repair indicators under the coordinated control of the heating well cluster, and a Bayesian optimization algorithm is used to determine the coordinated control method of the heating well cluster;

[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 achieve collaborative control of the heating well cluster.

[0038] In this example, in-situ thermal desorption remediation technology was used to treat contaminated soil. Burners and heating wells were divided into multiple groups based on pipeline information. Each group of heating wells was controlled. Based on a soil heating numerical simulation model, a temperature field distribution diagram was simulated, and a direct relationship between the heating pattern of the heating wells and the temperature response of the area around the heating wells was derived.

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

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

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

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

[0047] Through comprehensive understanding and integration of this pipeline information, the heating wells can be divided into groups 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 pipeline length, diameter, and material, heating wells with similar heat transfer conditions are grouped together;

[0051] Convenient pipe connection: Ensure that heating wells in the same group can be conveniently connected through pipes 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 reference to relevant literature.

[0058] Parameters of the 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 modeling tools in CAD software or numerical simulation software to create a geometric model based on the actual layout of the heating wells. The model should include detailed structures of the soil area, the heating wells, and their internal components. Based on Fourier's law of heat conduction and the principle of conservation of energy, establish a heat transfer equation in the soil, including comprehensive consideration of various heat transfer modes such as conduction, convection, and 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 a computer, input the necessary parameters and data, and obtain simulation results such as soil temperature distribution; obtain the relationship between the heating pattern of the heating well and the temperature response of the area around the heating well.

[0064] In one embodiment, the temperature-dependent remediation evaluation system involved in step S3 is a key component of in-situ soil thermal desorption technology. It primarily focuses on evaluating and monitoring soil temperature changes during the heating process, and uses this to determine the remediation effectiveness. This evaluation system is crucial to ensuring that contaminated soil is effectively treated.

[0065] Reference Figure 3 As shown in the previous article, existing contaminated site remediation evaluation systems are mostly based on periodic exhaust gas concentration monitoring. However, this type of monitoring has a lag (due to the time required for exhaust gas collection and testing), and cannot timely reflect the site remediation results. Therefore, it is necessary to propose temperature-related remediation result evaluation indicators and establish a temperature-related remediation result evaluation system.

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

[0067] Temperature reaches 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 a sufficient temperature, thereby improving remediation efficiency.

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

[0073] Energy consumption indicators:

[0074] Energy consumption indicators are used to evaluate the energy 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 restoration 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 whether the heating system can accurately maintain the set temperature point; high-precision control helps avoid overheating and energy waste.

[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 ensure the repair effect while controlling costs.

[0086] By comprehensively considering all of these factors, the temperature-dependent remediation results evaluation system provides a comprehensive assessment and monitoring tool for the implementation of in-situ thermal desorption technology. This not only ensures the efficient implementation of remediation work, but also significantly 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 indicator in the temperature-related remediation result evaluation system. It refers to the ratio of the total energy consumed to achieve a certain temperature target within a specific time period to the actual volume of the effective heating area. 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 energy consumption ratio of the effective heating area are:

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

[0094] Soil type and moisture: Soil of different types and moisture levels has different heat transfer properties, which affects heating efficiency and energy consumption.

[0095] Operating temperature: The selection 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] A low energy consumption ratio means higher energy efficiency and lower operating costs, resulting in better economic returns for the entire project. When evaluating a project's environmental impact, a low energy consumption ratio generally means less carbon emissions and environmental impact. By analyzing the energy consumption ratio, thermal desorption technology can be optimized, including improving heater well design and optimizing operating parameters.

[0098] The effective heating area energy consumption ratio provides a quantitative metric to help engineers and decision-makers evaluate and compare the efficiency of different thermal desorption projects and technologies. By optimizing relevant parameters and adopting effective control strategies, the economic and environmental benefits of soil pollution control projects can be significantly improved. The application of this metric 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 in the figure, 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 wells 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 wells) based on basic data and simulation results. These features will be used for model training.

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

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

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

[0105] By building a machine learning prediction model and applying a Bayesian optimization algorithm, the remediation effect of a cluster of heating wells under coordinated control can be accurately predicted. This 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 existing technology, the single-machine control method is often based on a single indicator to control a single burner. Figure 4As shown, the operating conditions of individual burners are controlled by setting their parameters, lacking overall control over soil temperature. The figure illustrates three typical single-unit heating well control methods under three typical operating conditions: maintaining a constant natural gas flow rate and excess air coefficient, maintaining a constant outlet temperature and excess air coefficient, and maintaining a constant outlet temperature. Single-unit control is achieved using a controller such as PID by calculating the difference between a setpoint and a sensor measurement.

[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 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 remediation 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 site temperature distribution, realizes the coordinated control of heating well clusters, reduces energy consumption and improves efficiency.

[0109] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0110] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily 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 is not limited to the embodiments shown herein but is intended to conform 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 a soil heating numerical simulation for 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 the heating pattern of the heating wells and the temperature response of the area around the heating wells; S3. Constructing a temperature-related remediation result evaluation system based on the relationship between the heating mode of the heating well and the temperature response of the area surrounding the heating well; defining an effective heating area in the temperature-related remediation result evaluation system, and using the energy consumption ratio of the effective heating area as an optimization indicator; S4. Using the temperature-related repair result evaluation system, a machine learning prediction model is constructed to predict the repair indicators under the coordinated control of the heating well cluster, and a Bayesian optimization algorithm is used to determine the coordinated control method of the heating well cluster; 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 achieve collaborative control of the heating well cluster.

2. A heating well cluster coordinated 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 coordinated control method according to claim 1, characterized in that: The step S2 performs a soil heating numerical simulation on each group of heating wells, specifically including: Acquiring basic data, the basic data including layout information of the heating well group, physical and chemical properties of the soil, and parameters of the heating wells; 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 the heating well group heating the soil, 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. The method for collaborative control of a heating well cluster according to claim 1, wherein: The effective heating area in step S3 is defined as: 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: 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 3, characterized in that: The construction of the machine learning prediction model in step S4 is based on the basic data and the simulated temperature field distribution map to predict relevant indicators.

7. A heating well cluster coordinated control method according to any one of claims 1 to 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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