Helium separation method, device, storage medium and electronic equipment

Abstract

The present application belongs to the technical field of helium separation, and provides a helium separation method, device, storage medium and electronic equipment, the method comprising: verifying a response surface model equation at a non-experimental matrix point corresponding to an experimental matrix to obtain a verification result; determining whether to trigger correction of the response surface model based on the verification result; and in the case of determining not to trigger correction of the response surface model based on the verification result and meeting a constraint condition, performing optimization and solving by using a tabu search algorithm based on the response surface model equation to obtain an optimal combination of pressure, temperature and flow rate. Through the separation method, the optimal combination of pressure, temperature and flow rate can be obtained, and the optimal combination can maximize the separation efficiency of helium. In this way, the separation efficiency of helium is greatly improved.

Description

Technical Field

[0001] This application belongs to the field of helium separation technology, and particularly relates to a helium separation method, apparatus, storage medium and electronic device. Background Technology

[0002] Helium, a rare and non-renewable resource, has wide applications in high-tech fields such as aerospace, medicine, electronics, and cryogenic superconductivity due to its unique physicochemical properties. Helium's low boiling point, non-flammability, and high thermal conductivity make it irreplaceable in these fields. However, the abundance of helium in the Earth's atmosphere is extremely low (approximately 0.00052%), making direct extraction from the atmosphere insufficient to meet industrial demands. Therefore, commercial helium extraction primarily relies on separation from natural gas.

[0003] During natural gas extraction, helium often escapes from the formation along with the natural gas and dissolves in the drilling mud. Drilling mud is a complex fluid system mainly used for cooling and lubricating the drill bit, carrying cuttings, and balancing formation pressure. Because helium has a low concentration in natural gas and is easily soluble in drilling mud, efficiently separating helium from it has become a technical challenge.

[0004] Currently, there are two main methods for separating helium from drilling mud: the positive pressure method and the negative pressure method.

[0005] Positive pressure method

[0006] The positive pressure method requires pressurizing the drilling mud to transfer helium dissolved in it from the liquid phase to the gas phase. This process is typically carried out in a high-pressure vessel, where pressurization and heating are used to increase the rate of helium escape. However, the application of the positive pressure method has some significant drawbacks:

[0007] High energy consumption: The pressurization process requires a large amount of energy, resulting in high energy consumption throughout the separation process and increased operating costs.

[0008] Safety hazards: Because it needs to be operated in a high-pressure environment, there is a risk of explosion and leakage, resulting in low safety and increasing the safety requirements for equipment and operators.

[0009] The equipment is complex: it requires special high-pressure vessels and heating devices, and the equipment is complex and has high maintenance costs.

[0010] negative pressure method

[0011] The negative pressure method works by reducing pressure, causing helium dissolved in the mud to escape. This method is typically carried out under low pressure, separating helium from the mud by reducing the system pressure. Compared to the positive pressure method, the negative pressure method has the following advantages:

[0012] Lower energy consumption: Since high-pressure equipment is not required, the negative pressure method has relatively low energy consumption, reducing operating costs.

[0013] High safety: Operating in a low-pressure environment offers relatively high safety, reducing the risks associated with high-pressure operations.

[0014] However, existing negative pressure techniques also have some problems in practical applications:

[0015] Low separation efficiency: Existing negative pressure equipment still needs to improve its separation efficiency and cannot efficiently extract high-purity helium from mud.

[0016] High cost: Although energy consumption is low, the low separation efficiency leads to a large processing volume and low equipment utilization, resulting in a relatively high overall cost.

[0017] Positive pressure and negative pressure methods each have their advantages and disadvantages. While the positive pressure method has higher separation efficiency, its high energy consumption and safety concerns limit its practical application. On the other hand, although the negative pressure method has lower energy consumption and higher safety, its separation efficiency and cost issues limit its widespread use.

[0018] Therefore, how to provide a separation method that can greatly improve the efficiency of helium separation is a technical problem to be solved. Summary of the Invention

[0019] Therefore, it is necessary to address the shortcomings of low helium separation efficiency in existing technologies by providing a helium separation method and apparatus, a computer-readable storage medium, and an electronic device.

[0020] In a first aspect, embodiments of the present invention provide a method for separating helium gas, the method comprising:

[0021] Within a set range of multiple parameters, an experimental matrix is ​​generated through a central composite design to determine the effect of all parameter combinations on helium separation efficiency based on the experimental matrix. The experimental matrix includes: axis point, center point, and star point. The multiple parameters include at least: pressure, temperature, and flow rate.

[0022] The response surface model is fitted based on a first preset algorithm that can minimize the sum of squared residuals and the data in the experimental dataset obtained from the experimental matrix to obtain the response surface model. The equation corresponding to the response surface model is the response surface model equation, which has multiple optimal fitting coefficients.

[0023] The response surface model equation is verified at the non-experimental matrix points corresponding to the experimental matrix, and the verification results are obtained.

[0024] Based on the verification results, determine whether to trigger a correction to the response surface model;

[0025] Based on the verification results, if it is determined that the modification of the response surface model is not triggered and the constraints are met, the tabu search algorithm is used to optimize the solution based on the response surface model equation to obtain the optimal combination of pressure, temperature and flow rate, which can maximize the separation efficiency of helium.

[0026] Optionally, the optimization solution using the tabu search algorithm to obtain the optimal combination of pressure, temperature, and flow rate includes:

[0027] Generate neighborhood solutions based on the current measured values;

[0028] Multiple disturbance parameters are randomly configured within a preset range, including at least: pressure disturbance parameters, temperature disturbance parameters, and flow velocity disturbance parameters;

[0029] Set multiple conditions for iterative search, including: tabu rule definition, tabu object, and amnesty criterion;

[0030] Based on the aforementioned multiple conditions, a global and iterative search for the optimal solution is performed within the domain solution space of the preset range.

[0031] The optimal solution among the non-taboo solutions is taken as the current solution;

[0032] When the current conditions meet the preset termination condition, the iteration ends, and the optimal combination of pressure, temperature, and flow rate is obtained.

[0033] Optionally, the preset termination condition includes: the current iteration number has reached the maximum iteration number; or,

[0034] The improvement of the objective function in the current multiple iterations is less than the preset value.

[0035] Optionally, the tabu rule definition includes at least: the tabu list length;

[0036] The forbidden objects include at least: the hash value of the parameter combination;

[0037] The amnesty criterion includes at least the following: if the objective function of the taboo solution is better than that of the optimal solution, then the taboo is broken and the solution is adopted.

[0038] Optionally, the response surface model equation is validated at non-experimental matrix points corresponding to the experimental matrix, including:

[0039] At the non-experimental matrix points corresponding to the experimental matrix, multiple sets of verification experiments are conducted under preset conditions. The relative error between the predicted first helium separation efficiency and the measured second helium separation efficiency is calculated to obtain the relative error. The preset conditions include: preset temperature conditions, preset pressure conditions, and preset flow rate conditions.

[0040] The response surface model equation is verified based on the relative error.

[0041] Optionally, determining whether to trigger a correction to the response surface model based on the verification result includes:

[0042] If the relative error in the verification result exceeds the preset relative error range, it is determined that the response surface model should be corrected to obtain a corrected response surface model; otherwise, the correction process for the response surface model is ignored.

[0043] Optionally, after determining the trigger to modify the response surface model, the method further includes:

[0044] The response surface model can be corrected by supplementing preset experiments within the error region where the relative error is greater than the preset relative error range to increase experimental data; or...

[0045] The response surface model is refitted by introducing higher-order cross terms to correct the response surface model.

[0046] Optionally, after obtaining the response surface model, the method further includes:

[0047] Based on the response surface equation corresponding to the response surface model, a model prediction and control framework under constraints is constructed. The constraints include the range of pressure gradient stability and the range of temperature change rate under constraints.

[0048] Optionally, after obtaining the response surface model, the method further includes:

[0049] The second preset algorithm is used to quantify the contribution of the multiple parameters to the helium separation efficiency, and the corresponding quantification results are obtained.

[0050] Optionally, before fitting the response surface model based on a first preset algorithm capable of minimizing the sum of squared residuals and data from the experimental dataset obtained from the experimental matrix, the method further includes:

[0051] Obtain different combinations of parameters set for the experimental matrix;

[0052] Multiple sets of experiments were conducted based on different parameter combinations until each set of experiments reached a stable state, thus obtaining the experimental dataset.

[0053] Optionally, before generating the experimental matrix through the central composite design, the method further includes: establishing multiple models based on a single parameter, the multiple models including a pressure model, a temperature model, and a flow rate model.

[0054] Optionally, the establishment of multiple models based on a single parameter includes:

[0055] With pressure as the sole parameter, the pressure model is constructed to calculate the pressure setpoint for each stage of the tower using an exponential distribution formula; or...

[0056] With temperature as the single parameter, the temperature model is constructed, which is used to calculate the temperature distribution of each stage of the tower based on the pressure gradient; or...

[0057] With the flow rate as the single parameter, the flow rate model is constructed, which is used to dynamically adjust the flow rate according to the change in helium concentration.

[0058] Optionally, after establishing multiple models based on a single parameter, the method further includes:

[0059] Under constant temperature and flow rate conditions, the current pressure is gradually changed, and the helium separation efficiency at each pressure point is recorded; or,

[0060] Under constant pressure and flow rate, the current temperature is gradually changed, and the helium separation efficiency at each temperature point is recorded; or,

[0061] Under fixed pressure and temperature conditions, the current flow rate is gradually changed, and the helium separation efficiency at each flow rate point is recorded during the process of gradually changing the current flow velocity.

[0062] Secondly, embodiments of the present invention provide a helium separation device, the device comprising:

[0063] The generation module is used to generate an experimental matrix through a central composite design within a set range of multiple parameters, so as to determine the influence of all parameter combinations on helium separation efficiency based on the experimental matrix. The experimental matrix includes: axis point, center point and star point, and the multiple parameters include at least: pressure, temperature and flow rate.

[0064] The fitting module is used to fit the response surface model based on a first preset algorithm that can minimize the sum of squared residuals and the data in the experimental dataset obtained from the experimental matrix, to obtain the response surface model, wherein the equation corresponding to the response surface model is the response surface model equation, and the response surface model equation has multiple optimal fitting coefficients.

[0065] The verification module is used to verify the response surface model equation at non-experimental matrix points corresponding to the experimental matrix, and obtain the verification results.

[0066] The determination module is used to determine whether to trigger a correction of the response surface model based on the verification result;

[0067] The optimization solution module is used to optimize the solution based on the response surface model equations by using a tabu search algorithm, under the condition that the verification results determine that the modification of the response surface model is not triggered and the constraints are met, so as to obtain the optimal combination of pressure, temperature and flow rate, which can maximize the separation efficiency of helium.

[0068] Thirdly, a helium separation system is provided, the system comprising: the aforementioned helium separation device, mud pretreatment device, helium purification device, and automatic control device;

[0069] The mud pretreatment device is used to remove solid particles and emulsified oil from the mud by means of a vibrating screen, a centrifugal separator and a chemical demulsifier;

[0070] The helium purification device is used to combine an adsorption tower and a distillation tower to remove impurity gases from helium.

[0071] The automatic control device is used to control the start and stop of the mud pretreatment device, the start and stop of the helium separation device, and the start and stop of the helium purification device.

[0072] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method of the first aspect.

[0073] Fifthly, an electronic device is provided, including a memory and a processor, wherein the memory stores executable code, and the processor executes the executable code to implement the method of the first aspect.

[0074] In this embodiment of the invention, within a set range of multiple parameters, an experimental matrix is ​​generated through a central composite design to determine the impact of all parameter combinations on helium separation efficiency. The experimental matrix includes: axis points, center points, and star points. The multiple parameters include at least: pressure, temperature, and flow rate. A response surface model is fitted using a first preset algorithm that minimizes the sum of squared residuals and data from the experimental dataset obtained from the experimental matrix to obtain the response surface model. The equation corresponding to the response surface model is the response surface model equation, which has multiple optimal fitting coefficients. The response surface model equation is verified at non-experimental matrix points corresponding to the experimental matrix to obtain verification results. Based on the verification results, it is determined whether to trigger a correction to the response surface model. If, based on the verification results, it is determined that no correction to the response surface model is triggered and the constraints are met, the response surface model equation is optimized using a tabu search algorithm to obtain the optimal combination of pressure, temperature, and flow rate. This optimal combination maximizes the helium separation efficiency. The separation method provided in this invention can obtain an optimal combination of pressure, temperature and flow rate, which can maximize the separation efficiency of helium; thus, it greatly improves the separation efficiency of helium. Attached Figure Description

[0075] Exemplary embodiments of the present invention can be more fully understood by referring to the accompanying drawings. The drawings are provided to further illustrate the embodiments of the present invention and form part of the specification. They are used together with the embodiments of the present invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0076] Figure 1 A flowchart illustrating a helium separation method according to an exemplary embodiment of the present invention;

[0077] Figure 2 This is a structural diagram of a helium separation device according to a specific application scenario of the present invention;

[0078] Figure 3 This is a schematic diagram of a helium separation system provided according to an exemplary embodiment of the present invention. Detailed Implementation

[0079] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0080] It should be noted that, unless otherwise stated, the technical or scientific terms used in this invention should have the ordinary meaning as understood by one of ordinary skill in the art.

[0081] Furthermore, the terms "first" and "second," etc., are used to distinguish different objects, not to describe a specific order. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to those processes, methods, products, or devices.

[0082] This invention provides a method and apparatus for separating helium, a computer-readable medium, and an electronic device, which will be described below with reference to the accompanying drawings.

[0083] Please refer to Figure 1 It illustrates a flowchart of a helium separation method provided by some embodiments of the present invention, such as... Figure 1 As shown, the helium separation method may include the following steps:

[0084] Step S101: Within the range of multiple parameters, generate an experimental matrix through a central composite design to determine the effect of all parameter combinations on helium separation efficiency based on the experimental matrix. The experimental matrix includes: axis point, center point, and star point. The multiple parameters include at least: pressure, temperature, and flow rate.

[0085] A specific multi-parameter experimental design, with exemplary steps, is as follows:

[0086] By collecting separation efficiency under different parameter combinations, the entire parameter space is covered;

[0087] For example: parameter range constraints:

[0088] , , ;

[0089] Experimental matrix generation: A central composite design was adopted, defining a three-factor, five-level experimental matrix, including:

[0090] Axis points: Extreme values ​​of each parameter, i.e.:

[0091] 1. and ;

[0092] 2. and ;

[0093] 3. and ;

[0094] Center point: Baseline operating condition, i.e.:

[0095] 1. ;

[0096] 2. ;

[0097] 3. ;

[0098] Star point: using expansion factor The extended parameter range value is 1.68 times the design space boundary;

[0099] Calculation of total number of experimental groups: Group; Indicates the number of parameters; This indicates the number of times the center point is repeated.

[0100] In one example, before generating the experimental matrix through a central composite design, the helium separation method provided in this embodiment of the invention may further include the following steps: establishing multiple models based on a single parameter, the multiple models including a pressure model, a temperature model, and a flow rate model; wherein, the pressure model is used to calculate the pressure setpoint of each stage of the tower using an exponential distribution formula; the temperature model is used to calculate the temperature distribution of each stage of the tower based on the pressure gradient; and the flow rate model is used to dynamically adjust the flow rate according to the change in helium concentration.

[0101] It should be noted that in the helium separation method provided in the embodiments of the present invention, multi-stage negative pressure separation can be achieved by connecting multiple separation towers in series, wherein the negative pressure is provided by a vacuum pump.

[0102] In one example, multiple models are built based on a single parameter, including the following steps:

[0103] With pressure as the single parameter, a pressure model is constructed. The pressure model is used to calculate the pressure setpoint for each stage of the tower using the exponential distribution formula.

[0104] In practical applications, the pressure model is as follows:

[0105] Pressure model: Calculate the nth level pressure setpoint using the exponential distribution formula:

[0106] Formula (1);

[0107] In the formula: This represents the pressure at level n; represents the initial pressure; e represents the base of the natural logarithm; This represents the pressure attenuation coefficient, with a value ranging from 0.25 to 0.4. n represents the current level; or,

[0108] With temperature as the single parameter, a temperature model is constructed, which is used to calculate the temperature distribution of each stage of the tower based on the pressure gradient.

[0109] In practical applications, the temperature model is as follows:

[0110] Temperature model: Temperature distribution calculated based on pressure gradient.

[0111] Formula (2);

[0112] In the formula: This represents the temperature of the nth level; Indicates the initial temperature; This represents the temperature regulation coefficient, with a value ranging from 15 to 20. Represents the natural logarithm function; or,

[0113] With the flow rate as the single parameter, a flow rate model is constructed to dynamically adjust the flow rate based on changes in helium concentration.

[0114] It should be noted that the exponential distribution formula of the single-parameter pressure model is obtained through... The attenuation rate can be adjusted between 0.25 and 0.4 to adapt to tower structures with different numbers of stages (n=5-8 stages).

[0115] In practical applications, the traffic model is as follows:

[0116] Flow model: Dynamically adjust the flow rate based on changes in helium concentration.

[0117] Formula (3);

[0118] In the formula: This represents the flow rate at level n; This represents the flow rate at level n-1; This represents the flow regulation coefficient, with a value ranging from 0.2 to 0.5. This represents the helium concentration at level n-1; Indicates the target helium concentration.

[0119] In one example, after establishing multiple models based on a single parameter, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0120] Under fixed temperature and flow rate conditions, the current pressure is gradually changed, and the helium separation efficiency at each pressure point is recorded during the process of gradually changing the current pressure.

[0121] In another example, after establishing multiple models based on a single parameter, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0122] Under fixed pressure and flow rate conditions, the current temperature is gradually changed, and the helium separation efficiency at each temperature point is recorded during the process of gradually changing the current temperature.

[0123] In yet another example, after establishing multiple models based on a single parameter, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0124] Under fixed pressure and temperature conditions, the current flow rate is gradually changed, and the helium separation efficiency at each flow rate point is recorded during the process of gradually changing the current flow velocity.

[0125] In one example, before generating the experimental matrix through a central composite design, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0126] Collect correlation data related to the separation efficiency of helium, including pressure data, temperature data, and flow rate data;

[0127] In one example, collecting correlation data related to the separation efficiency of helium includes the following steps:

[0128] Pressure data is collected using a pressure sensor;

[0129] Temperature data is collected using a temperature sensor;

[0130] Flow data is collected using a mass flow meter.

[0131] In practical applications, piezoresistive pressure sensors, PT100 platinum resistance temperature sensors, and Coriolis mass flow meters are installed in each stage of the separation tower to collect pressure, temperature, and flow data in real time. The data acquisition frequency is once per second, ensuring real-time monitoring and control.

[0132] In practical applications, the installation of piezoresistive pressure sensors is as follows: each stage of the tower is equipped with a piezoresistive pressure sensor (range 0-50 kPa, accuracy ±0.1% FS), and dual redundant measuring points are set up at the gas phase outlet and liquid phase zone.

[0133] The temperature sensor is installed as follows: A distributed electric heating film (power density) is embedded in the walls of the first three stages of the tower. ), using PWM control, temperature fluctuation The latter two stages are integrated semiconductor thermoelectric modules (TECs), improving cooling efficiency. ), in conjunction with microchannel cold plates (flow rate) Achieve rapid cooling (rate) Eight sets of PT100 platinum resistance thermometers (50 mm apart) are arranged inside each stage of the tower to construct a three-dimensional temperature field.

[0134] The installation of a Coriolis mass flow meter is as follows: Based on the cross-sectional area of ​​the separation tower (typical value) Set the gas phase flow rate to Through Coriolis mass flow meter (accuracy) Real-time feedback. Electric regulating valves (CV value) are installed in the inter-tower piping. Response time The opening degree is automatically adjusted according to the helium concentration output from the pre-amplifier (detected by an online mass spectrometer).

[0135] After obtaining the collected data, the data is preprocessed in a conventional manner, which will not be described in detail here.

[0136] Step S102: Fit the response surface model based on the first preset algorithm that can minimize the sum of squared residuals and the data in the experimental dataset obtained from the experimental matrix to obtain the response surface model. The equation corresponding to the response surface model is the response surface model equation, which has multiple optimal fitting coefficients.

[0137] In a specific application scenario, the first preset algorithm is the Levenberg-Marquardt algorithm, which can minimize the sum of squared residuals.

[0138] It should be noted that the Levenberg-Marquardt algorithm can provide numerical solutions for nonlinear minimization (local minima). This algorithm can combine the advantages of the Gauss-Newton algorithm and gradient descent by modifying parameters during execution, and improve upon their shortcomings (such as the shortcomings of the Gauss-Newton algorithm: the absence of an inverse matrix; or the initial value being too far from the local minimum).

[0139] It should be noted that in the helium separation method provided in this embodiment of the invention, by modeling experimental data and fitting the response surface model using the Levenberg-Marquardt algorithm, mathematical expressions for parameters such as pressure, temperature, and flow rate are obtained. The response surface model can not only describe the helium separation efficiency under existing operating conditions, but also predict the impact of unaddressed parameter combinations on the separation efficiency.

[0140] In practical applications, the process of constructing the response surface model is as follows:

[0141] Constructing a response surface model based on the experimental dataset:

[0142] Based on the principles of fluid mechanics and thermodynamics, the equations of the response surface model are pre-defined as follows:

[0143] Formula (4);

[0144] In the formula: Theoretical separation efficiency; , , , represents the fitting coefficient; P represents pressure; T represents temperature; v represents flow rate.

[0145] The Levenberg-Marquardt algorithm is used to minimize the sum of squared residuals, where the objective function is as follows:

[0146] Formula (5);

[0147] in: This represents the separation efficiency of the i-th experimental data set; This represents the pressure on the i-th set of experimental data; This represents the temperature of the i-th set of experimental data; Let represent the flow rate of the i-th experimental data set; based on this, a, b, c, and d are fitted, with initial values ​​set to 0.

[0148] Iteration termination condition: residual change rate < 0.1% or number of iterations > 100.

[0149] Finally, the response surface equation (containing the specific values ​​of fitting coefficients a, b, c, and d) is output after the fitting technique; through fitting, the specific values ​​of the optimal fitting coefficients a, b, c, and d can be found.

[0150] In one example, before fitting the response surface model based on a first preset algorithm that minimizes the sum of squared residuals and data from the experimental dataset obtained from the experimental matrix, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0151] Obtain different combinations of parameters set for the experimental matrix;

[0152] Multiple sets of experiments were conducted based on different parameter combinations until each set of experiments reached a stable state, thus obtaining the experimental dataset.

[0153] In practical applications, parameter combinations are set according to the experimental matrix, and each experimental group is run until stable, and the results are recorded.

[0154] Input parameters: P, T, v;

[0155] Output parameters: (Measured by online mass spectrometer, accuracy ±0.5%)

[0156] Randomizing the experimental sequence eliminates environmental interference;

[0157] The final experimental dataset (20 data pairs) was obtained.

[0158] It should be noted that in the helium separation method provided in this embodiment of the invention, real-time prediction and optimized control of the helium separation process are performed based on multi-parameter experimental design and response surface model optimization, combined with sensor data (pressure, temperature, flow rate). That is, advanced experimental design methods such as Central Composite Design (CCD) are used to comprehensively cover possible combinations of operating parameters such as pressure, temperature, and flow rate, and the interaction of these parameters is systematically explored through experimental matrix generation. This provides a rich data foundation for constructing an accurate response surface model, ensuring that the response surface model can truly reflect the impact of operating conditions on the helium separation efficiency.

[0159] It should be noted that in the helium separation method provided in the embodiments of the present invention, the nonlinear term of the response surface model (such as...) is used... , It can accurately capture parameter coupling effects, and the model's coefficient of determination... To ensure the accuracy of predictions.

[0160] Step S103: Verify the response surface model equation at the non-experimental matrix points corresponding to the experimental matrix, and obtain the verification results.

[0161] In one example, the response surface model equations are validated at non-experimental matrix points corresponding to the experimental matrix, including the following steps:

[0162] At the non-experimental matrix points corresponding to the experimental matrix, multiple sets of verification experiments are conducted under preset conditions. The relative error between the predicted first helium separation efficiency and the measured second helium separation efficiency is calculated to obtain the relative error. The preset conditions include preset temperature conditions, preset pressure conditions, and preset flow rate conditions.

[0163] The equations of the response surface model are verified based on relative error.

[0164] It should be noted that the preset conditions can be configured according to the needs of different application scenarios, and no specific limitations are made here. For example, in a specific application scenario, the preset conditions could be: , , .

[0165] In a specific application scenario, the equations of the response surface model are verified. The specific verification steps are as follows:

[0166] Verification experiment: at non-experimental matrix points (e.g., , , Ten sets of verification experiments were conducted, and the prediction error was calculated.

[0167] Formula (6);

[0168] In the formula: This indicates the separation efficiency predicted by the model; This represents the separation efficiency measured in actual experiments;

[0169] The maximum relative error should be ≤5%, and the average error should be ≤2%.

[0170] In one example, after obtaining the response surface model, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0171] A model prediction and control framework under constraints is constructed based on the response surface equation corresponding to the response surface model. The constraints include the range of pressure gradient stability and the range of temperature change rate under the constraints.

[0172] It should be noted that pressure stability: pressure gradient constraints ensure that the pressure drop between each stage is gradual, and the standard deviation of measured pressure fluctuation is ≤0.3 kPa.

[0173] In practical applications, a model predictive control framework is established based on the constructed response surface model equations:

[0174] Formula (7);

[0175] Constraints: Pressure gradient stability ( ), rate of temperature change .

[0176] In one example, after obtaining the response surface model, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0177] The second preset algorithm is used to quantify the contribution of multiple parameters to the helium separation efficiency, and the corresponding quantification results are obtained.

[0178] In a specific application scenario, the second preset algorithm is the Sobol exponential method. Based on the idea of ​​model decomposition, the Sobol exponential method can analyze the sensitivity of parameters at the first, second, and higher orders, and can distinguish between the sensitivity of independent parameters and those that interact with each other.

[0179] In a specific application scenario, the Sobol exponent method is used to quantify the impact of each parameter. The degree of influence and contribution.

[0180] Formula (8);

[0181] In the formula: Represents the i-th parameter ( ); This represents the first-order sensitivity exponent of the i-th parameter; This represents the total variance of the separation efficiency; This represents the conditional expectation of the separation efficiency given i parameters.

[0182] In a specific application scenario, the model predictive control framework specifically includes:

[0183] Predictive model: Embedded with RSM (Response Surface Methodology) equations, it predicts in real time the trend of helium separation efficiency with pressure, temperature and flow rate in the future period;

[0184] Rolling optimizer: solves the optimization problem in each control cycle: adjusts the operating variables (pressure / temperature / flow rate) under constraints to make the prediction efficiency approach the target value.

[0185] Constraint processor: Dynamically monitors and enforces constraints.

[0186] Pressure gradient ≤ stability threshold - temperature change rate ≤ safety limit - flow fluctuation range;

[0187] Feedback correction module: compares the actual separation efficiency with the predicted value to obtain the comparison result; and corrects the deviation in the result by comparing the residual correction model to enhance robustness.

[0188] Reference trajectory setting module: Set the target trajectory for separation efficiency (such as step improvement or incremental optimization), and use the target trajectory as a reference trajectory to guide the controller's dynamic response.

[0189] It should be noted that response surface methodology is an optimization method that integrates experimental design and mathematical modeling. It can effectively reduce the number of experiments and examine the interactions between influencing factors.

[0190] The construction process of the above-mentioned model predictive control framework includes the following key steps:

[0191] Model transformation: Discretize the RSM equations into a state-space model;

[0192] Objective function definition: Minimize the tracking error and control increment over the next N steps;

[0193] Online optimization solution: The tabu search algorithm is used to solve the above constrained optimization problem;

[0194] Solve iteratively.

[0195] For a description of the tabu search algorithm mentioned above, please refer to the descriptions in the same or similar sections above, and they will not be repeated here.

[0196] Step S104: Determine whether to trigger the correction of the response surface model based on the verification results.

[0197] In one example, determining whether to trigger a modification to the response surface model based on the verification results includes the following steps:

[0198] If the relative error in the verification result exceeds the preset relative error range, the response surface model is determined to be corrected to obtain the corrected response surface model; otherwise, the correction process for the response surface model is ignored.

[0199] In one example, after determining that a correction to the response surface model has been triggered, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0200] By supplementing preset experiments within the error region where the relative error exceeds the preset relative error range, experimental data is increased, and the response surface model is corrected.

[0201] In a specific application scenario, the pre-set experiment can be a Design of Experiments (DOE), an experimental design method used to explore and verify the influence of factors on results. In DOE, the experiment is typically divided into multiple combinations, each controlling for one factor and measuring its impact on the outcome. This approach allows for a more comprehensive understanding of the factors' influence on the results and the determination of the optimal factor combination.

[0202] In another example, after determining the trigger for modifying the response surface model, the helium separation method provided in this embodiment of the invention may further include the following steps:

[0203] By introducing higher-order cross terms, the response surface model is refitted to correct it.

[0204] In practical applications, after modifying the response surface model, the modified response curve equation and model verification report are obtained and output.

[0205] It should be noted that in the helium separation method provided in the embodiments of the present invention, the error predicted based on the response surface model is controlled within 5% by a dynamic feedback mechanism, and high-precision modeling is maintained by continuously correcting coefficients (fitting coefficients a, b, c, d in the aforementioned formula (4)).

[0206] Step S105: Based on the verification results, if it is determined that the modification of the response surface model is not triggered and the constraint conditions are met, the tabu search algorithm is used to optimize the solution based on the response surface model equation to obtain the optimal combination of pressure, temperature and flow rate. The optimal combination can maximize the separation efficiency of helium.

[0207] It should be noted that the constraints include: the range of pressure gradient stability under the constraints and the range of temperature change rate under the constraints.

[0208] In practical applications, the constraint is: pressure gradient stability ( ), rate of temperature change .

[0209] In one example, the tabu search algorithm is used to optimize the solution and obtain the optimal combination of pressure, temperature, and flow rate, including the following steps:

[0210] Generate neighborhood solutions based on the current measured values;

[0211] Multiple disturbance parameters are randomly configured within a preset range. These multiple disturbance parameters include at least: pressure disturbance parameters, temperature disturbance parameters, and flow velocity disturbance parameters.

[0212] Set multiple conditions for iterative search, including: tabu rule definition, tabu object, and pardon criterion;

[0213] Based on multiple conditions, a global and iterative search for the optimal solution is performed within the domain solution space of a preset range.

[0214] The optimal solution among the non-taboo solutions is taken as the current solution;

[0215] The iteration ends when the current conditions meet the preset termination conditions, yielding the optimal combination of pressure, temperature, and flow rate.

[0216] In one example, the preset termination condition includes: the current iteration count has reached the maximum iteration count.

[0217] In another example, the preset termination condition includes: the improvement of the objective function in the current multiple iterations is less than a preset value.

[0218] In one example, the tabu rule definition includes at least: the tabu table length; the tabu object includes at least: the hash value of the parameter combination; the amnesty criterion includes at least: if the objective function of the tabu solution is better than the optimal solution, then the tabu is broken and adopted.

[0219] In a specific application scenario, the optimization process based on the tabu search algorithm is described as follows:

[0220] Initial solution generation: Based on the current measured values; generate neighborhood solutions, randomly perturbing parameters within ±5% range:

[0221] Formula (9);

[0222] in: These represent the disturbance parameters for pressure, temperature, and flow rate, respectively. , , These are new pressure, temperature, and flow rate parameters generated based on the disturbance parameters.

[0223] Tabu rule definition: The tabu list has a length of 10 (i.e., it records the local optima in the most recent 10 searches);

[0224] Forbidden objects: Hash values ​​of parameter combinations (e.g., concatenating (P,T,v) after discretizing it into integers);

[0225] Amnesty Criterion: If the objective function of the taboo solution is better than the optimal solution, then the taboo is broken and the solution is adopted.

[0226] Based on the above settings, perform an iterative search:

[0227] First, generate 50 neighborhood solutions and calculate the value of the objective function, i.e., the value of the model predicting the control framework.

[0228] Next, select the optimal solution from the non-taboo solutions and use it as the current solution; update the taboo table and record the current solution.

[0229] Termination condition: Reaching the maximum number of iterations or an improvement of less than 0.1% in the objective function over 10 consecutive iterations;

[0230] The optimal parameter combination is output after the iteration is complete;

[0231] Command generation: Converts the optimal combination of output parameters into equipment control signals to achieve precise regulation of pressure, temperature, and flow rate.

[0232] It should be noted that in the helium separation method provided in the embodiments of the present invention, the tabu search algorithm is used to globally optimize within the ±5% neighborhood solution space to avoid getting trapped in local optima.

[0233] In the helium separation method provided in this embodiment of the invention, a response surface model and a tabu search algorithm are employed to enable real-time self-adjustment under different operating conditions, ensuring that key parameters such as pressure, temperature, and flow rate are always maintained within ideal ranges. This dynamic adjustment mechanism allows the system to cope with environmental fluctuations and operational deviations, enhancing the stability and reliability of the entire separation process.

[0234] The helium separation method provided in this invention can achieve an optimal combination of pressure, temperature, and flow rate, which maximizes the separation efficiency of helium, thus significantly improving the separation efficiency. Furthermore, the helium separation method provided in this invention achieves comprehensive improvements in separation efficiency, stability, energy consumption, and adaptability, providing a high-precision, low-energy-consumption, and robust solution for the field of industrial gas separation.

[0235] In the above embodiments, a method for separating helium gas is provided. Correspondingly, the present invention also provides a helium gas separation device. The helium gas separation device provided in the embodiments of the present invention can implement the above-described helium gas separation method. The helium gas separation device can be implemented by software, hardware, or a combination of both. For example, the helium gas separation device may include integrated or separate functional modules or units to perform the corresponding steps in the above methods.

[0236] Please refer to Figure 2 This diagram illustrates a helium separation apparatus provided by some embodiments of the present invention. Since the apparatus embodiments are substantially similar to the method embodiments, the description is relatively simple; relevant details can be found in the description of the method embodiments. The apparatus embodiments described below are merely illustrative.

[0237] like Figure 2 As shown, the helium separation device may include:

[0238] The generation module 201 is used to generate an experimental matrix through a central composite design within a set range of multiple parameters, so as to determine the influence of all parameter combinations on helium separation efficiency based on the experimental matrix. The experimental matrix includes: axis point, center point and star point, and the multiple parameters include at least: pressure, temperature and flow rate.

[0239] The fitting module 202 is used to fit the response surface model based on the first preset algorithm that can minimize the sum of squared residuals and the data in the experimental dataset obtained from the experimental matrix, so as to obtain the response surface model. The equation corresponding to the response surface model is the response surface model equation, which has multiple optimal fitting coefficients.

[0240] The verification module 203 is used to verify the response surface model equation at non-experimental matrix points corresponding to the experimental matrix and obtain the verification results.

[0241] The determination module 204 is used to determine whether to trigger the correction of the response surface model based on the verification results;

[0242] The optimization solution module 205 is used to optimize the solution based on the response surface model equations and the tabu search algorithm, based on the verification results, to obtain the optimal combination of pressure, temperature and flow rate, which can maximize the separation efficiency of helium gas, provided that the correction of the response surface model is not triggered and the constraints are met.

[0243] In some embodiments of the present invention, the optimization solution module 205 is specifically used for:

[0244] Generate neighborhood solutions based on the current measured values;

[0245] Multiple disturbance parameters are randomly configured within a preset range. These multiple disturbance parameters include at least: pressure disturbance parameters, temperature disturbance parameters, and flow velocity disturbance parameters.

[0246] Set multiple conditions for iterative search, including: tabu rule definition, tabu object, and pardon criterion;

[0247] Based on multiple conditions, a global and iterative search for the optimal solution is performed within the domain solution space of a preset range.

[0248] The optimal solution among the non-taboo solutions is taken as the current solution;

[0249] The iteration ends when the current conditions meet the preset termination conditions, yielding the optimal combination of pressure, temperature, and flow rate.

[0250] In some embodiments of the present invention, the preset termination conditions include: the current iteration number has reached the maximum iteration number; or, the improvement value of the objective function in the current multiple iterations is less than a preset value.

[0251] In some embodiments of the present invention, the taboo rule definition includes at least: the taboo list length;

[0252] Forbidden objects include at least: the hash value of the parameter combination;

[0253] The amnesty criterion includes at least the following: if the objective function of the taboo solution is better than that of the optimal solution, then the taboo is broken and the solution is adopted.

[0254] In some embodiments of the present invention, the verification module 203 is specifically used for:

[0255] At the non-experimental matrix points corresponding to the experimental matrix, multiple sets of verification experiments are conducted under preset conditions. The relative error between the predicted first helium separation efficiency and the measured second helium separation efficiency is calculated to obtain the relative error. The preset conditions include preset temperature conditions, preset pressure conditions, and preset flow rate conditions.

[0256] The equations of the response surface model are verified based on relative error.

[0257] In some embodiments of the present invention, the determining module 204 is specifically used for:

[0258] If the relative error in the verification result exceeds the preset relative error range, the response surface model is determined to be corrected to obtain the corrected response surface model; otherwise, the correction process for the response surface model is ignored.

[0259] In some embodiments of the present invention, the helium separation device may further include:

[0260] Correction module (in) Figure 2 (Not shown in the image) is used to modify the response surface model by supplementing the experimental data in the error region where the relative error is greater than the preset relative error range after determining the trigger to modify the response surface model; or by introducing higher-order cross terms to refit the response surface model to modify the response surface model.

[0261] In some embodiments of the present invention, the helium separation device may further include:

[0262] Build modules (in) Figure 2 (not shown in the figure) is used to construct a model prediction and control framework under constraints based on the response surface equation corresponding to the response surface model after obtaining the response surface model. The constraints include the range of pressure gradient stability under constraints and the range of temperature change rate under constraints.

[0263] In some embodiments of the present invention, the helium separation device may further include:

[0264] Quantization module (in) Figure 2 (Not shown in the figure) is used to quantify the contribution of multiple parameters to helium separation efficiency using a second preset algorithm after obtaining the response surface model, and to obtain the corresponding quantification results.

[0265] In some embodiments of the present invention, the helium separation device may further include:

[0266] Get module (in) Figure 2(not shown in the figure) is used to obtain different combinations of parameters set by the experimental matrix before fitting the response surface model based on a first preset algorithm that can minimize the sum of squared residuals and data in the experimental dataset obtained from the experimental matrix;

[0267] Test module (in) Figure 2 (not shown in the image) is used to conduct multiple sets of experiments based on different parameter combinations until each set of experiments reaches a stable state, thus obtaining the experimental dataset.

[0268] In some embodiments of the present invention, the helium separation device may further include:

[0269] Model building module (in Figure 2 (Not shown in the image) is used to establish multiple models based on a single parameter before generating the experimental matrix through the central composite design. The multiple models include: a pressure model, a temperature model, and a flow rate model.

[0270] In some embodiments of the present invention, the model building module is specifically used for:

[0271] With pressure as the sole parameter, a pressure model is constructed to calculate the pressure setpoint for each stage of the tower using an exponential distribution formula; or...

[0272] With temperature as the sole parameter, a temperature model is constructed to calculate the temperature distribution of each stage of the tower based on the pressure gradient; or...

[0273] With the flow rate as the single parameter, a flow rate model is constructed to dynamically adjust the flow rate based on changes in helium concentration.

[0274] In some embodiments of the present invention, the helium separation device may further include:

[0275] Recording module (in) Figure 2 (Not shown in the image) is used to, after establishing multiple models based on a single parameter, gradually change the current pressure under fixed temperature and flow rate conditions, and record the helium separation efficiency at each pressure point during the gradual pressure change process; or, under fixed pressure and flow rate conditions, gradually change the current temperature, and record the helium separation efficiency at each temperature point during the gradual temperature change process; or, under fixed pressure and temperature conditions, gradually change the current flow rate, and record the helium separation efficiency at each flow rate point during the gradual flow rate change process.

[0276] In some embodiments of the present invention, the helium separation device provided in the present invention is based on the same inventive concept and has the same beneficial effects as the helium separation method provided in the foregoing embodiments of the present invention.

[0277] According to another embodiment, a helium separation system is also provided, such as... Figure 3 As shown, the helium separation system includes: the helium separation device, the mud pretreatment device, the helium separation device, the helium purification device, and the automatic control device.

[0278] A mud pretreatment unit is used to remove solid particles and emulsified oil from mud by means of a vibrating screen, a centrifugal separator and a chemical demulsifier;

[0279] Helium purification equipment is used to combine an adsorption tower and a distillation tower to remove impurity gases from helium.

[0280] Automatic control devices are used to control the start and stop of the mud pretreatment device, the helium separation device, and the helium purification device.

[0281] It should be noted that the above-mentioned helium separation device achieves multi-stage negative pressure separation by connecting multiple separation towers in series, wherein the negative pressure is provided by a vacuum pump.

[0282] It should be noted that the aforementioned helium separation device employs multi-stage negative pressure separation technology. By connecting multiple separation towers in series and optimizing the pressure, temperature, and gas flow rate in each tower, the separation efficiency of helium is improved. Furthermore, the helium output from the separation towers is condensed in a condenser before being supplied to the helium purification device.

[0283] By employing a multi-stage negative pressure tower system, helium is separated stage by stage under different pressure gradients, significantly improving the purity and separation efficiency of helium. Precise control of each stage ensures high efficiency during the separation process; furthermore, through precise adjustment of negative pressure, temperature, and flow rate, all parameters have been optimized in both experimental and practical applications, improving the overall efficiency and stability of the separation system.

[0284] For a description of the helium separation device in the helium separation system, please refer to [link / reference]. Figure 2 The descriptions of the same or similar parts will not be repeated here.

[0285] According to another embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed in a computer, causes the computer to perform a combination Figure 1 The method described.

[0286] According to another embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements a combination... Figure 1 The method described.

[0287] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.

[0288] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for separating helium, characterized in that, The method includes: Within a set range of multiple parameters, an experimental matrix is ​​generated through a central composite design to determine the effect of all parameter combinations on helium separation efficiency based on the experimental matrix. The experimental matrix includes: axis point, center point, and star point. The multiple parameters include at least: pressure, temperature, and flow rate. The response surface model is fitted based on a first preset algorithm that can minimize the sum of squared residuals and the data in the experimental dataset obtained from the experimental matrix to obtain the response surface model. The equation corresponding to the response surface model is the response surface model equation, which has multiple optimal fitting coefficients. The response surface model equation is verified at the non-experimental matrix points corresponding to the experimental matrix, and the verification results are obtained. Based on the verification results, determine whether to trigger a correction to the response surface model; Based on the verification results, if it is determined that the modification of the response surface model is not triggered and the constraints are met, the tabu search algorithm is used to optimize the solution based on the response surface model equation to obtain the optimal combination of pressure, temperature and flow rate. The optimal combination can maximize the separation efficiency of helium. Before generating the experimental matrix through the central composite design, the method further includes: establishing multiple models based on a single parameter, the multiple models including: a pressure model, a temperature model, and a flow rate model; The method of establishing multiple models based on a single parameter includes: With pressure as the sole parameter, the pressure model is constructed, which is used to calculate the pressure setpoint for each stage of the tower using an exponential distribution formula; or... With temperature as the sole parameter, the temperature model is constructed to calculate the temperature distribution of each stage of the tower based on the pressure gradient; or... With the flow rate as the single parameter, the flow rate model is constructed, which is used to dynamically adjust the flow rate according to the change in helium concentration.

2. The separation method according to claim 1, characterized in that, The optimization solution using the tabu search algorithm to obtain the optimal combination of pressure, temperature, and flow rate includes: Based on the current measured values, generate a neighborhood solution; Multiple disturbance parameters are randomly configured within a preset range, including at least: pressure disturbance parameters, temperature disturbance parameters, and flow velocity disturbance parameters; Set multiple conditions for iterative search, including: tabu rule definition, tabu object, and amnesty criterion; Based on the aforementioned multiple conditions, a global and iterative search for the optimal solution is performed within the domain solution space of the preset range. The optimal solution among the non-taboo solutions is taken as the current solution; When the current conditions meet the preset termination condition, the iteration ends, and the optimal combination of pressure, temperature, and flow rate is obtained.

3. The separation method according to claim 2, characterized in that, The preset termination conditions include: the current iteration count has reached the maximum iteration count; or... The improvement of the objective function in the current multiple iterations is less than the preset value.

4. The separation method according to claim 2, characterized in that, The taboo rule definition includes at least: the taboo list length; The forbidden objects include at least: the hash value of the parameter combination; The amnesty criterion includes at least the following: if the objective function of the taboo solution is better than that of the optimal solution, then the taboo is broken and the solution is adopted.

5. The separation method according to claim 1, characterized in that, The response surface model equations are validated at non-experimental matrix points corresponding to the experimental matrix, including: At the non-experimental matrix points corresponding to the experimental matrix, multiple sets of verification experiments are conducted under preset conditions. The relative error between the predicted first helium separation efficiency and the measured second helium separation efficiency is calculated to obtain the relative error. The preset conditions include: preset temperature conditions, preset pressure conditions, and preset flow rate conditions. The response surface model equation is verified based on the relative error.

6. The separation method according to claim 5, characterized in that, The step of determining whether to trigger a correction to the response surface model based on the verification result includes: If the relative error in the verification result exceeds the preset relative error range, it is determined that the response surface model should be corrected to obtain a corrected response surface model; otherwise, the correction process for the response surface model is ignored.

7. The separation method according to claim 6, characterized in that, After determining the trigger to modify the response surface model, the method further includes: The response surface model can be corrected by supplementing preset experiments within the error region where the relative error is greater than the preset relative error range to increase experimental data; or... The response surface model is refitted by introducing higher-order cross terms to correct the response surface model.

8. The separation method according to claim 1, characterized in that, After obtaining the response surface model, the method further includes: Based on the response surface equation corresponding to the response surface model, a model prediction and control framework under constraints is constructed. The constraints include the range of pressure gradient stability and the range of temperature change rate under constraints.

9. The separation method according to claim 1, characterized in that, After obtaining the response surface model, the method further includes: The second preset algorithm is used to quantify the contribution of the multiple parameters to the helium separation efficiency, and the corresponding quantification results are obtained.

10. The separation method according to claim 1, characterized in that, Before fitting the response surface model based on a first preset algorithm that minimizes the sum of squared residuals and data from the experimental dataset obtained from the experimental matrix, the method further includes: Obtain different combinations of parameters set for the experimental matrix; Multiple sets of experiments were conducted based on different parameter combinations until each set of experiments reached a stable state, thus obtaining the experimental dataset.

11. The separation method according to claim 1, characterized in that, After establishing multiple models based on a single parameter, the method further includes: Under constant temperature and flow rate conditions, the current pressure is gradually changed, and the helium separation efficiency at each pressure point is recorded; or, Under constant pressure and flow rate, the current temperature is gradually changed, and the helium separation efficiency at each temperature point is recorded; or, Under fixed pressure and temperature conditions, the current flow rate is gradually changed, and the helium separation efficiency at each flow rate point is recorded during the process of gradually changing the current flow velocity.

12. A helium separation device, characterized in that, The device includes: The generation module is used to generate an experimental matrix through a central composite design within a set range of multiple parameters, so as to determine the influence of all parameter combinations on helium separation efficiency based on the experimental matrix. The experimental matrix includes: axis point, center point and star point, and the multiple parameters include at least: pressure, temperature and flow rate. The fitting module is used to fit the response surface model based on a first preset algorithm that can minimize the sum of squared residuals and the data in the experimental dataset obtained from the experimental matrix, to obtain the response surface model, wherein the equation corresponding to the response surface model is the response surface model equation, and the response surface model equation has multiple optimal fitting coefficients. The verification module is used to verify the response surface model equation at non-experimental matrix points corresponding to the experimental matrix, and obtain the verification results. The determination module is used to determine whether to trigger a correction of the response surface model based on the verification result; The optimization solution module is used to optimize the solution based on the response surface model equations by using a tabu search algorithm, under the condition that the verification results determine that the modification of the response surface model is not triggered and the constraints are met, so as to obtain the optimal combination of pressure, temperature and flow rate, which can maximize the separation efficiency of helium. Before generating the experimental matrix through the central composite design, the method further includes: establishing multiple models based on a single parameter, wherein the multiple models include: a pressure model, a temperature model, and a flow rate model; The method of establishing multiple models based on a single parameter includes: With pressure as the sole parameter, the pressure model is constructed, which is used to calculate the pressure setpoint for each stage of the tower using an exponential distribution formula; or... With temperature as the sole parameter, the temperature model is constructed to calculate the temperature distribution of each stage of the tower based on the pressure gradient; or... With the flow rate as the single parameter, the flow rate model is constructed, which is used to dynamically adjust the flow rate according to the change in helium concentration.

13. A helium separation system, characterized in that, The system includes: the helium separation device, the mud pretreatment device, the helium purification device, and the automatic control device as described in claim 12; The mud pretreatment device is used to remove solid particles and emulsified oil from the mud by means of a vibrating screen, a centrifugal separator and a chemical demulsifier; The helium purification device is used to combine an adsorption tower and a distillation tower to remove impurity gases from helium. The automatic control device is used to control the start and stop of the mud pretreatment device, the start and stop of the helium separation device, and the start and stop of the helium purification device.

14. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1 to 11.

15. An electronic device, characterized in that, The method includes a memory and a processor, wherein the memory stores executable code, and the processor executes the executable code to implement the method according to any one of claims 1 to 11.

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