Self-adaptive collaborative optimization method and system based on separation simulation control

Abstract

The invention provides a self-adaptive collaborative optimization method and system based on separation simulation control, and relates to the technical field of separation, and the method comprises the steps: extracting mixture separation demand information, and screening and determining the type of a target separator. And based on the fluid mechanics parameter set, constructing a gas-liquid separation simulation optimization channel which comprises a simulation branch and an optimization branch. And according to the type of the target separator, introducing a separation constraint condition, and performing simulation to determine a result. And synchronizing a result to an optimization branch, and generating an optimization scheme. And executing an optimization scheme, monitoring in real time, generating a dynamic adjustment instruction, and realizing intelligent gas-liquid separation. According to the invention, the problem that the product quality of industrial production is further influenced due to low separation efficiency caused by incapability of responding to the dynamic change of the mixture in real time due to lack of flexibility and adaptability in the prior art can be solved. The separation parameters are automatically adjusted according to the real-time characteristics of the mixture, the optimal separation effect is achieved, and therefore the overall separation efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of separation technology, and in particular to an adaptive collaborative optimization method and system based on separation simulation control. Background Technology

[0002] In many industrial production processes, gaseous or liquid impurities in products or intermediates can affect the quality of the final product. Gas-liquid separation can effectively remove these impurities, ensuring product purity and quality. In some industrial processes, mixtures may contain more valuable gaseous or liquid components. Through separation, these components can be recycled and reused, thereby saving resources and reducing costs.

[0003] Currently, existing gas-liquid separation technologies may have some limitations, failing to provide efficient separation solutions for all types of mixtures. These technologies may rely on fixed operating parameters, be unable to adapt to changes in the fluid properties of the mixture, or lack the ability to respond in real time to dynamic changes during the separation process.

[0004] In summary, existing technologies lack flexibility and adaptability, and cannot respond to dynamic changes in mixtures in real time, resulting in low separation efficiency and further affecting the product quality of industrial production. Summary of the Invention

[0005] The purpose of this application is to provide an adaptive collaborative optimization method and system based on separation simulation control, in order to solve the problem that the existing technology lacks flexibility and adaptability, cannot respond to the dynamic changes of the mixture in real time, resulting in low separation efficiency, which further affects the product quality of industrial production.

[0006] In view of the above problems, this application provides an adaptive cooperative optimization method and system based on separate simulation control.

[0007] In a first aspect, this application provides an adaptive collaborative optimization method based on separation simulation control. The method is implemented through an adaptive collaborative optimization system based on separation simulation control. The method includes: extracting separation requirement information based on the mixture to be separated; filtering according to the separation requirement information to determine the target separator type; constructing a gas-liquid separation simulation optimization channel by extracting the set of hydrodynamic parameters of the mixture to be separated, the gas-liquid separation simulation optimization channel including a gas-liquid separation simulation branch and a gas-liquid separation optimization branch; introducing separation constraints according to the target separator type; performing separation simulation through the gas-liquid separation simulation branch in conjunction with the set of hydrodynamic parameters to determine the gas-liquid separation simulation result; synchronizing the gas-liquid separation simulation result to the gas-liquid separation optimization branch to generate a gas-liquid separation optimization scheme; executing the gas-liquid separation optimization scheme for real-time monitoring and generating dynamic adjustment commands to perform intelligent gas-liquid separation of the mixture to be separated.

[0008] Secondly, this application also provides an adaptive collaborative optimization system based on separation simulation control, used to execute the adaptive collaborative optimization method based on separation simulation control as described in the first aspect, wherein the system includes: an information filtering module, used to extract separation requirement information based on the mixture to be separated, filter according to the separation requirement information, and determine the target separator type; an optimization channel construction module, used to construct a gas-liquid separation simulation optimization channel by extracting the set of hydrodynamic parameters of the mixture to be separated, the gas-liquid separation simulation optimization channel including a gas-liquid separation simulation branch and a gas-liquid separation optimization branch; a separation simulation module, used to introduce separation constraints according to the target separator type, combine the set of hydrodynamic parameters to perform separation simulation through the gas-liquid separation simulation branch, and determine the gas-liquid separation simulation result; a separation optimization scheme generation module, used to synchronize the gas-liquid separation simulation result to the gas-liquid separation optimization branch, and generate a gas-liquid separation optimization scheme; and an intelligent separation module, used to execute the gas-liquid separation optimization scheme for real-time monitoring, and generate dynamic adjustment commands to perform intelligent gas-liquid separation of the mixture to be separated.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: By extracting separation requirement information based on the mixture to be separated, and filtering according to the separation requirement information, the target separator type is determined. A gas-liquid separation simulation optimization channel is constructed by extracting the hydrodynamic parameter set of the mixture to be separated, including a gas-liquid separation simulation branch and a gas-liquid separation optimization branch. Separation constraints are introduced according to the target separator type, and separation simulation is performed through the gas-liquid separation simulation branch in conjunction with the hydrodynamic parameter set to determine the gas-liquid separation simulation results. The gas-liquid separation simulation results are synchronized to the gas-liquid separation optimization branch to generate a gas-liquid separation optimization scheme. The gas-liquid separation optimization scheme is executed for real-time monitoring, generating dynamic adjustment commands for intelligent gas-liquid separation of the mixture to be separated. This effectively solves the problem that existing technologies lack flexibility and adaptability, and cannot respond to dynamic changes in the mixture in real time, resulting in low separation efficiency and further affecting the product quality of industrial production. The separation parameters are automatically adjusted according to the real-time characteristics of the mixture to achieve the best separation effect, thereby improving the overall separation efficiency.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the adaptive cooperative optimization method based on separate simulation control proposed in this application. Figure 2 This is a schematic diagram of the adaptive cooperative optimization system based on separate simulation control in this application.

[0013] Explanation of reference numerals in the attached figures: Information filtering module 11, optimized channel construction module 12, separation simulation module 13, separation optimization scheme generation module 14, and intelligent separation module 15. Detailed Implementation

[0014] This application provides an adaptive collaborative optimization method and system based on separation simulation control, which solves the problem that existing technologies lack flexibility and adaptability, and cannot respond to the dynamic changes of mixtures in real time, resulting in low separation efficiency and further affecting the product quality of industrial production. The method automatically adjusts separation parameters according to the real-time characteristics of the mixture to achieve optimal separation results, thereby improving overall separation efficiency.

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0016] Example 1 Please see the appendix Figure 1This application provides an adaptive cooperative optimization method based on separate simulation control, wherein the method is applied to an adaptive cooperative optimization system based on separate simulation control, and the method specifically includes the following steps: S1: Extract separation requirement information based on the mixture to be separated, filter according to the separation requirement information, and determine the target separator type.

[0017] Specifically, data on the mixture to be separated is collected, including the physical and chemical properties of the fluid, such as density, viscosity, surface tension, solubility, component ratio, temperature, and pressure. Based on the characteristics of the mixture and the separation objectives, separation requirements are determined. For example, the required separation efficiency, separation speed, desired product purity, and acceptable energy consumption level. Based on the collected mixture characteristics and separation requirements, a suitable separator type is selected. Separators include, but are not limited to, settling tanks, centrifuges, membrane separators, absorption towers, and foam separators.

[0018] S2: By extracting the set of hydrodynamic parameters of the mixture to be separated, a gas-liquid separation simulation optimization channel is constructed, which includes a gas-liquid separation simulation branch and a gas-liquid separation optimization branch.

[0019] Specifically, the hydrodynamic parameters of the mixture to be separated are measured and analyzed. These parameters may include the density, viscosity, and surface tension of the gas, the density, viscosity, and solubility of the liquid, and the flow rate, temperature, and pressure of the mixture. Computational fluid dynamics software or other simulation tools are used to construct an optimized simulation channel for gas-liquid separation. This model will simulate the flow behavior and separation process of the mixture in the separator based on the set of hydrodynamic parameters.

[0020] S3: Based on the target separator type, introduce separation constraints, combine the fluid dynamics parameter set with the gas-liquid separation simulation branch to perform separation simulation and determine the gas-liquid separation simulation results.

[0021] Specifically, the target separator type is determined, such as a centrifuge, plate column, or packed column. Based on the operating principle and design requirements of the target separator, the constraints in the separation process are determined, including maximum allowable pressure, temperature range, flow rate limits, interphase contact time, and separation efficiency requirements. A simulation is run, inputting the set of fluid dynamic parameters and separation constraints into the model. The dynamic behavior of the mixture in the separator is simulated and calculated to predict the separation effect between the gas and liquid phases. The simulation results are evaluated, including key performance indicators such as separation efficiency, component concentration distribution, pressure drop, and contact time.

[0022] S4: Synchronize the gas-liquid separation simulation results to the gas-liquid separation optimization branch to generate a gas-liquid separation optimization scheme.

[0023] Specifically, the results of gas-liquid separation simulations are analyzed to identify areas for improvement in the separation process. These analyses cover aspects such as separation efficiency, energy consumption, operational stability, and cost-effectiveness. Based on the results, clear optimization objectives are established. These objectives include improving separation efficiency, reducing energy consumption, decreasing operating costs, and improving equipment stability. The simulation data and identified problems are synchronized to the optimization branch. Using the optimization branch, combined with the simulation results and optimization objectives, the operating parameters and / or design parameters of the separator are optimized. This includes adjusting flow rate, pressure, temperature, packing type, and equipment dimensions. Based on the results of parameter optimization, an optimized gas-liquid separation scheme is generated.

[0024] S5: Execute the gas-liquid separation optimization scheme for real-time monitoring, and generate dynamic adjustment instructions to perform intelligent gas-liquid separation of the mixture to be separated.

[0025] Specifically, based on the generated optimized gas-liquid separation scheme, the operating parameters and / or design parameters of the separator are adjusted. This includes changing the feed rate, temperature, pressure, and adding chemicals. A real-time monitoring system is installed on the separator, capable of measuring key hydrodynamic parameters and separation performance indicators such as flow rate, pressure, temperature, component concentration, and separation efficiency. The real-time monitoring system is activated to collect operating data. This data will be used to evaluate whether the actual performance of the separator meets the goals of the optimization scheme. The collected data is analyzed in real time, comparing the actual performance with the optimization target. Data analysis tools, such as statistical process control or machine learning algorithms, are used to identify any trends deviating from expected performance. If real-time data analysis shows that performance deviates from the optimization target, dynamic adjustment instructions are generated automatically. These instructions are automatic. Based on the dynamic adjustment instructions, the operating parameters of the separator are automatically adjusted to correct performance deviations. For example, if the separation efficiency decreases, the feed rate is increased or the temperature setting is adjusted.

[0026] Furthermore, step S2 of this application also includes: Set measurement environment data according to the separation requirement information; determine measurement requirement information based on the gas-liquid separation target; based on the measurement environment data, measure the mixture to be separated sequentially according to the measurement requirement information, and record multiple measurement data to generate flow rate data, pressure data, velocity data, density data, and viscosity data; add the flow rate data, pressure data, velocity data, density data, and viscosity data to the fluid dynamics parameter set.

[0027] Specifically, determine the environmental conditions for on-site measurements, such as temperature, humidity, and vibration, as these factors may affect the accuracy of the measurement results. Prepare the measuring equipment, such as flow meters, pressure sensors, velocity meters, densitometers, and viscometers, ensuring that these devices are calibrated and in normal working order. Based on the goals of gas-liquid separation, determine the parameters to be measured and the required measurement accuracy. For example, if the goal is to improve separation efficiency, precise measurement of the feed flow rate and density is required. Determine the sampling frequency and measurement time for the measurement data to ensure the representativeness and reliability of the data. According to the measurement requirements, use the prepared measuring equipment to sequentially measure the mixture to be separated. For example, use a flow meter to measure the feed flow rate, a pressure sensor to measure the feed pressure, and equipment such as a laser Doppler velocimeter or particle image velocimeter to measure velocity data. Record the data at each measurement point, including flow rate, pressure, velocity, density, and viscosity. Organize the recorded measurement data into a table or database. Add the flow rate, pressure, velocity, density, and viscosity data to a fluid dynamics parameter set. This parameter set will serve as input data for gas-liquid separation simulation and optimization.

[0028] Furthermore, step S2 of this application also includes: Based on the set of fluid dynamic parameters of the mixture to be separated, features of the mixture to be separated are extracted to generate multiple feature information of the mixture to be separated; historical experimental archives of gas-liquid separation are retrieved, and separation boundary conditions are set; based on the multiple feature information and the separation requirement information, a geometric model of the gas-liquid separator is constructed according to the separation boundary conditions; the gas-liquid separation simulation branch is constructed by combining the set of fluid dynamic parameters and the geometric model of the gas-liquid separator; optimization variables are set based on the geometric model of the gas-liquid separator; optimization is performed according to the optimization variables and the optimization algorithm to construct the gas-liquid separation optimization branch; the gas-liquid separation simulation optimization channel is constructed based on the gas-liquid separation simulation branch and the gas-liquid separation optimization branch, and the output end of the gas-liquid separation simulation branch and the input end of the gas-liquid separation optimization branch are connected for communication.

[0029] Specifically, a set of fluid dynamics parameters is used to extract features from the mixture to be separated. This includes calculating the statistical properties of the fluid, flow patterns, and component distribution. The generated feature information will be used to describe the flow and separation characteristics of the mixture, providing a basis for simulation and optimization. Historical experimental archives of gas-liquid separation are retrieved to obtain successful separation cases and relevant boundary conditions, such as operating pressure, temperature, and flow rate. These boundary conditions are set as a reference for simulation and optimization. Based on the feature information and separation requirements, a geometric model of the gas-liquid separator is constructed using CAD software or CFD processing tools. The model includes all key components of the separator, such as the inlet, outlet, and internal structure. The set of fluid dynamics parameters and the geometric model of the separator are input into the CFD software to construct the simulation branch. The boundary conditions and initial conditions of the simulation are set, the simulation is run, and the flow and separation behavior of the mixture in the separator are analyzed. Based on the simulation results and separation objectives, the variables that need to be optimized are determined, such as the size, shape, and operating parameters of the separator. Optimization branches are constructed based on optimization algorithms, such as genetic algorithms, particle swarm optimization, and simulated annealing, to perform parameter optimization. The output of the simulation branch and the input of the optimization branch are connected via communication to form a closed-loop simulation-optimization channel. The output of the simulation branch, such as separation efficiency and pressure drop, serves as the input to the optimization branch, guiding the optimization process. The output of the optimization branch, containing the optimized parameters, is fed back to the simulation branch for a new round of simulation.

[0030] Furthermore, step S3 of this application also includes: Based on the target separator type, separator performance analysis is performed to determine multiple separation performance parameters, including separation flow rate parameters, separation efficiency parameters, separation pressure drop parameters, and separation structure parameters. The separation flow rate parameters are iterated through to determine boundaries and establish separation flow rate constraints. The gas or liquid content is determined according to the separation efficiency parameters to establish separation efficiency constraints. The separation pressure drop parameters are iterated through to extract maximum values ​​and establish separation pressure drop constraints. Separation structure constraints are established based on the separation requirement information and the separation structure parameters. Finally, the separation constraint conditions are determined based on the separation flow rate constraints, separation efficiency constraints, separation pressure drop constraints, and separation structure constraints.

[0031] Specifically, performance analysis is performed for the selected target separator type, including theoretical calculations and simulation analysis. Several key separation performance parameters are determined, such as separation flow rate, separation efficiency, separation pressure drop, and separation structural parameters. The separation flow rate parameters are iterated to determine the maximum and minimum flow rate range that the separator can handle. Flow constraints are established to ensure that the flow rate remains within the effective operating range of the separator during actual operation. Based on the separation efficiency parameters, the content of gas or liquid components is determined to ensure that the separation efficiency meets process requirements. Efficiency constraints are established to ensure that the separator achieves the predetermined separation effect. The separation pressure drop parameters are iterated to extract the maximum allowable pressure drop value. Pressure drop constraints are established to prevent excessive pressure drop from causing energy waste or equipment damage. Based on the separation requirement information and the separation structural parameters, structural constraints are established to ensure that the physical structure of the separator can adapt to process requirements, such as size, material, and corrosion resistance. By combining the above flow rate, efficiency, pressure drop, and structural constraints, complete separation constraint conditions are determined.

[0032] Furthermore, step S3 of this application also includes: The geometric model of the gas-liquid separator is extracted based on the target separator type; the geometric model of the gas-liquid separator is used in conjunction with the set of fluid dynamic parameters to simulate and monitor the mixture to be separated, generating simulated separation monitoring results; the simulated separation monitoring results are evaluated based on the separation constraints, and it is determined whether the simulated separation monitoring results meet the separation constraints based on the evaluation results; if the simulated separation monitoring results do not meet the separation constraints, the geometric model of the gas-liquid separator and / or the set of fluid dynamic parameters are adjusted, and the simulation analysis is repeated until the simulated separation monitoring results meet the separation constraints, and the gas-liquid separation simulation results are output.

[0033] Specifically, based on the target separator type, a corresponding geometric model is extracted, including all important parts of the separator, such as the inlet, outlet, and internal components. Combining the fluid dynamics parameter set and the separator's geometric model, a simulated separation monitoring of the mixture to be separated is performed. The flow behavior and separation effect of the mixture within the separator are calculated, generating simulated separation monitoring results. Based on separation constraints, flow rate, efficiency, pressure drop, and structural parameters, the simulated separation monitoring results are evaluated. It is determined whether the simulation results meet the predetermined separation objectives and constraints. If the simulated separation monitoring results do not meet the separation constraints, the separator's geometric model and / or fluid dynamics parameter set need to be adjusted. Adjustments include changing the separator's size, shape, and operating parameters. After adjustment, the simulation analysis is repeated until the simulated separation monitoring results meet all separation constraints. Once the simulated separation monitoring results meet all separation constraints, the final gas-liquid separation simulation results are output.

[0034] Furthermore, step S4 of this application also includes: Based on the gas-liquid separation simulation results, N gas-liquid separation optimization schemes are randomly generated, where N is an integer greater than or equal to 2. A fitness function is introduced to evaluate the fitness of the N gas-liquid separation optimization schemes, generating N fitness values, which correspond to the N gas-liquid separation optimization schemes. Based on the N fitness values, a tournament selection process is performed on the N gas-liquid separation optimization schemes to obtain M gas-liquid separation optimization schemes, where M is an integer less than N and greater than or equal to 1. Based on the M gas-liquid separation optimization schemes, a first gas-liquid separation optimization scheme and a second gas-liquid separation optimization scheme are randomly selected as parents and cross-combined to generate a first generation gas-liquid separation optimization scheme. The fitness value of the first generation gas-liquid separation optimization scheme is calculated to see if it is greater than a preset fitness threshold. The M gas-liquid separation optimization schemes are updated based on the first generation gas-liquid separation optimization scheme. The M gas-liquid separation optimization schemes are sorted in descending order according to the fitness values, and the first-ranked gas-liquid separation optimization scheme is extracted and output.

[0035] Specifically, based on the simulation results of gas-liquid separation, N gas-liquid separation optimization schemes are randomly generated. These schemes include different combinations of operating parameters or structural design variations. N should be a sufficiently large number to ensure sufficient diversity to explore the optimization space. A fitness function is introduced to evaluate the performance of each optimization scheme, such as separation efficiency, energy consumption, and cost. The fitness function is used to evaluate the N optimization schemes, generating N fitness values, each corresponding to an optimization scheme. Based on the fitness values, a tournament selection process is performed on the N optimization schemes, selecting the M schemes with the best fitness as candidate schemes for the next iteration. M is less than N, which maintains the diversity of the population while focusing on the better-performing schemes. Two schemes are randomly selected from the M candidate schemes as parents and cross-combined to generate the first generation of gas-liquid separation optimization schemes. Cross-combination can generate new schemes by exchanging some parameters of the parents, thereby exploring new parameter spaces and calculating the fitness value of the first generation of gas-liquid separation optimization schemes. If the fitness value of the first generation is greater than a preset fitness threshold, the first generation is used to update the M candidate schemes. The M candidate solutions are sorted in descending order according to their fitness values. The gas-liquid separation optimization solution that ranks first after sorting is extracted as the output, which will be the best performing solution in the current iteration.

[0036] Furthermore, step S5 of this application also includes: The gas-liquid separation optimization scheme is implemented by weighting and monitoring the flow rate, pressure, velocity, density, and viscosity data of the mixture to be separated, and determining multiple weighted monitoring data. Multiple parameter monitoring indicators are set, and the multiple weighted monitoring data are evaluated with the multiple parameter monitoring indicators to generate multiple separation scores. Based on the multiple separation scores, anomaly triggering conditions are set. When the flow rate data and / or the pressure data and / or the velocity data and / or the density data and / or the viscosity data trigger the anomaly triggering conditions, the dynamic adjustment command is generated.

[0037] Specifically, based on the determined optimization scheme, the operating parameters and / or design parameters of the gas-liquid separator are adjusted. The actual gas-liquid separation process of the mixture to be separated begins. Data such as flow rate, pressure, velocity, density, and viscosity of the mixture are monitored in real time. Weighted monitoring data is generated by assigning weights to these data according to the importance of each parameter, providing a more accurate reflection of the actual separation process. Multiple parameter monitoring indicators are set according to process requirements and separation objectives, such as maximum allowable flow rate, pressure range, and efficiency standards. These indicators will serve as standards for evaluating the monitoring data. The weighted monitoring data is compared and evaluated with the parameter monitoring indicators to generate multiple separation scores reflecting the separator's performance in various aspects. Based on the separation scores, abnormal trigger conditions are set, such as flow rate exceeding the set upper or lower limits, or abnormal pressure fluctuations. When the monitoring data triggers these conditions, it indicates a potential problem in the separation process. Once the monitoring data triggers an abnormal trigger condition, dynamic adjustment instructions are generated. These instructions include adjusting operating parameters such as flow rate, pressure, and temperature to correct deviations in the separation process.

[0038] In summary, the adaptive cooperative optimization method based on separate simulation control provided in this application has the following technical effects: By extracting separation requirement information based on the mixture to be separated, and filtering according to the separation requirement information, the target separator type is determined. A gas-liquid separation simulation optimization channel is constructed by extracting the hydrodynamic parameter set of the mixture to be separated, including a gas-liquid separation simulation branch and a gas-liquid separation optimization branch. Separation constraints are introduced according to the target separator type, and separation simulation is performed through the gas-liquid separation simulation branch in conjunction with the hydrodynamic parameter set to determine the gas-liquid separation simulation results. The gas-liquid separation simulation results are synchronized to the gas-liquid separation optimization branch to generate a gas-liquid separation optimization scheme. The gas-liquid separation optimization scheme is executed for real-time monitoring, generating dynamic adjustment commands for intelligent gas-liquid separation of the mixture to be separated. This effectively solves the problem that existing technologies lack flexibility and adaptability, and cannot respond to dynamic changes in the mixture in real time, resulting in low separation efficiency and further affecting the product quality of industrial production. The separation parameters are automatically adjusted according to the real-time characteristics of the mixture to achieve the best separation effect, thereby improving the overall separation efficiency.

[0039] Example 2 Based on the adaptive cooperative optimization method based on separate simulation control in the foregoing embodiments, and using the same inventive concept, this application also provides an adaptive cooperative optimization system based on separate simulation control. Please refer to the appendix. Figure 2 The system includes: Information filtering module 11 is used to extract separation requirement information based on the mixture to be separated, filter according to the separation requirement information, and determine the target separator type.

[0040] The optimization channel construction module 12 is used to construct a gas-liquid separation simulation optimization channel by extracting the set of hydrodynamic parameters of the mixture to be separated. The gas-liquid separation simulation optimization channel includes a gas-liquid separation simulation branch and a gas-liquid separation optimization branch.

[0041] Separation simulation module 13 is used to introduce separation constraints according to the target separator type, combine the set of fluid dynamic parameters to perform separation simulation through the gas-liquid separation simulation branch, and determine the gas-liquid separation simulation results.

[0042] Separation optimization scheme generation module 14 is used to synchronize the gas-liquid separation simulation results to the gas-liquid separation optimization branch and generate a gas-liquid separation optimization scheme.

[0043] The intelligent separation module 15 is used to execute the gas-liquid separation optimization scheme for real-time monitoring and generate dynamic adjustment commands to perform intelligent gas-liquid separation of the mixture to be separated.

[0044] Furthermore, the optimized channel construction module 12 in the system is also used for: Set measurement environment data according to the separation requirement information; determine measurement requirement information based on the gas-liquid separation target; based on the measurement environment data, measure the mixture to be separated sequentially according to the measurement requirement information, and record multiple measurement data to generate flow rate data, pressure data, velocity data, density data, and viscosity data; add the flow rate data, pressure data, velocity data, density data, and viscosity data to the fluid dynamics parameter set.

[0045] Furthermore, the optimized channel construction module 12 in the system is also used for: Based on the set of fluid dynamic parameters of the mixture to be separated, features of the mixture to be separated are extracted to generate multiple feature information of the mixture to be separated; historical experimental archives of gas-liquid separation are retrieved, and separation boundary conditions are set; based on the multiple feature information and the separation requirement information, a geometric model of the gas-liquid separator is constructed according to the separation boundary conditions; the gas-liquid separation simulation branch is constructed by combining the set of fluid dynamic parameters and the geometric model of the gas-liquid separator; optimization variables are set based on the geometric model of the gas-liquid separator; optimization is performed according to the optimization variables and the optimization algorithm to construct the gas-liquid separation optimization branch; the gas-liquid separation simulation optimization channel is constructed based on the gas-liquid separation simulation branch and the gas-liquid separation optimization branch, and the output end of the gas-liquid separation simulation branch and the input end of the gas-liquid separation optimization branch are connected for communication.

[0046] Furthermore, the separation simulation module 13 in the system is also used for: Based on the target separator type, separator performance analysis is performed to determine multiple separation performance parameters, including separation flow rate parameters, separation efficiency parameters, separation pressure drop parameters, and separation structure parameters. The separation flow rate parameters are iterated through to determine boundaries and establish separation flow rate constraints. The gas or liquid content is determined according to the separation efficiency parameters to establish separation efficiency constraints. The separation pressure drop parameters are iterated through to extract maximum values ​​and establish separation pressure drop constraints. Separation structure constraints are established based on the separation requirement information and the separation structure parameters. Finally, the separation constraint conditions are determined based on the separation flow rate constraints, separation efficiency constraints, separation pressure drop constraints, and separation structure constraints.

[0047] Furthermore, the separation simulation module 13 in the system is also used for: The geometric model of the gas-liquid separator is extracted based on the target separator type; the geometric model of the gas-liquid separator is used in conjunction with the set of fluid dynamic parameters to simulate and monitor the mixture to be separated, generating simulated separation monitoring results; the simulated separation monitoring results are evaluated based on the separation constraints, and it is determined whether the simulated separation monitoring results meet the separation constraints based on the evaluation results; if the simulated separation monitoring results do not meet the separation constraints, the geometric model of the gas-liquid separator and / or the set of fluid dynamic parameters are adjusted, and the simulation analysis is repeated until the simulated separation monitoring results meet the separation constraints, and the gas-liquid separation simulation results are output.

[0048] Furthermore, the separation optimization scheme generation module 14 in the system is also used for: Based on the gas-liquid separation simulation results, N gas-liquid separation optimization schemes are randomly generated, where N is an integer greater than or equal to 2. A fitness function is introduced to evaluate the fitness of the N gas-liquid separation optimization schemes, generating N fitness values, which correspond to the N gas-liquid separation optimization schemes. Based on the N fitness values, a tournament selection process is performed on the N gas-liquid separation optimization schemes to obtain M gas-liquid separation optimization schemes, where M is an integer less than N and greater than or equal to 1. Based on the M gas-liquid separation optimization schemes, a first gas-liquid separation optimization scheme and a second gas-liquid separation optimization scheme are randomly selected as parents and cross-combined to generate a first generation gas-liquid separation optimization scheme. The fitness value of the first generation gas-liquid separation optimization scheme is calculated to see if it is greater than a preset fitness threshold. The M gas-liquid separation optimization schemes are updated based on the first generation gas-liquid separation optimization scheme. The M gas-liquid separation optimization schemes are sorted in descending order according to the fitness values, and the first-ranked gas-liquid separation optimization scheme is extracted and output.

[0049] Furthermore, the intelligent separation module 15 in the system is also used for: The gas-liquid separation optimization scheme is implemented by weighting and monitoring the flow rate, pressure, velocity, density, and viscosity data of the mixture to be separated, and determining multiple weighted monitoring data. Multiple parameter monitoring indicators are set, and the multiple weighted monitoring data are evaluated with the multiple parameter monitoring indicators to generate multiple separation scores. Based on the multiple separation scores, anomaly triggering conditions are set. When the flow rate data and / or the pressure data and / or the velocity data and / or the density data and / or the viscosity data trigger the anomaly triggering conditions, the dynamic adjustment command is generated.

[0050] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The adaptive cooperative optimization method and specific examples based on separate simulation control in Example 1 are also applicable to the adaptive cooperative optimization system based on separate simulation control in this embodiment. Through the foregoing detailed description of the adaptive cooperative optimization method based on separate simulation control, those skilled in the art can clearly understand the adaptive cooperative optimization system based on separate simulation control in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0051] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0052] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. An adaptive cooperative optimization method based on separate analog control, characterized in that, The method includes: Based on the separation requirement information extracted from the mixture to be separated, the target separator type is determined by filtering according to the separation requirement information; By extracting the set of hydrodynamic parameters of the mixture to be separated, a gas-liquid separation simulation optimization channel is constructed, which includes a gas-liquid separation simulation branch and a gas-liquid separation optimization branch. Based on the target separator type, separation constraints are introduced, and separation simulation is performed through the gas-liquid separation simulation branch in combination with the set of fluid dynamic parameters to determine the gas-liquid separation simulation results. The gas-liquid separation simulation results are synchronized to the gas-liquid separation optimization branch to generate an optimized gas-liquid separation scheme. The gas-liquid separation optimization scheme is executed and monitored in real time, generating dynamic adjustment commands to perform intelligent gas-liquid separation of the mixture to be separated.

2. The method as described in claim 1, characterized in that, The method for extracting the set of hydrodynamic parameters of the mixture to be separated includes: Set the measurement environment data according to the separation requirement information; Determine measurement requirements based on gas-liquid separation objectives; Based on the measurement environment data, the mixture to be separated is measured sequentially according to the measurement requirements information, and multiple measurement data are recorded to generate flow rate data, pressure data, velocity data, density data, and viscosity data. The flow rate data, pressure data, velocity data, density data, and viscosity data are added to the fluid dynamics parameter set.

3. The method as described in claim 1, characterized in that, The methods for constructing a gas-liquid separation simulation optimization channel include: Based on the set of hydrodynamic parameters of the mixture to be separated, features of the mixture to be separated are extracted to generate multiple feature information of the mixture to be separated; Retrieve historical experimental records of gas-liquid separation and set separation boundary conditions; Based on the multiple feature information and the separation requirement information, a geometric model of the gas-liquid separator is constructed according to the separation boundary conditions. The gas-liquid separation simulation branch is constructed by combining the set of fluid dynamics parameters with the geometric model of the gas-liquid separator; Optimization variables are set based on the geometric model of the gas-liquid separator; The gas-liquid separation optimization branch is constructed by combining the optimization variables with the optimization algorithm. The gas-liquid separation simulation branch and the gas-liquid separation optimization branch are used to construct the gas-liquid separation simulation optimization channel. The output end of the gas-liquid separation simulation branch and the input end of the gas-liquid separation optimization branch are connected for communication.

4. The method as described in claim 1, characterized in that, The method for separating the constraints includes: Based on the target separator type, a separator performance analysis is performed to determine multiple separation performance parameters, including separation flow rate parameters, separation efficiency parameters, separation pressure drop parameters, and separation structure parameters. The separation flow parameters are iterated through to determine the boundaries and establish separation flow constraints. The content of gas or liquid is determined according to the separation efficiency parameters, and separation efficiency constraints are established. The maximum value is extracted by iterating through the separation voltage drop parameters, and separation voltage drop constraints are defined. Based on the separation requirement information and the separation structure parameters, formulate separation structure constraints; The separation constraint conditions are determined based on the separation flow constraint, the separation efficiency constraint, the separation pressure drop constraint, and the separation structure constraint.

5. The method as described in claim 3, characterized in that, Based on the target separator type, separation constraints are introduced, and separation simulation is performed through the gas-liquid separation simulation branch using the fluid dynamics parameter set to determine the gas-liquid separation simulation results. The method includes: Extract the geometric model of the gas-liquid separator based on the target separator type; Using the geometric model of the gas-liquid separator and the set of fluid dynamic parameters, the mixture to be separated is simulated and monitored to generate simulated separation and monitoring results. The simulated separation monitoring results are evaluated based on the separation constraints, and it is determined whether the simulated separation monitoring results meet the separation constraints based on the evaluation results. If the simulated separation monitoring results do not meet the separation constraints, the geometric model and / or the set of fluid dynamic parameters of the gas-liquid separator are adjusted, and the simulation analysis is repeated until the simulated separation monitoring results meet the separation constraints, and the gas-liquid separation simulation results are output.

6. The method as described in claim 1, characterized in that, The method for synchronizing the gas-liquid separation simulation results to the gas-liquid separation optimization branch to generate a gas-liquid separation optimization scheme includes: Based on the gas-liquid separation simulation results, N gas-liquid separation optimization schemes are randomly generated, where N is an integer greater than or equal to 2; A fitness function is introduced, and the fitness of the N gas-liquid separation optimization schemes is evaluated through the fitness function to generate N fitness values. The N fitness values ​​correspond to the N gas-liquid separation optimization schemes. Based on the N fitness values, a tournament selection is performed on the N gas-liquid separation optimization schemes to obtain M gas-liquid separation optimization schemes, where M is an integer less than N and greater than or equal to 1; Based on the M gas-liquid separation optimization schemes, the first gas-liquid separation optimization scheme and the second gas-liquid separation optimization scheme are randomly selected as the parent schemes and cross-combined to generate the first generation gas-liquid separation optimization scheme. Calculate whether the fitness value of the first generation gas-liquid separation optimization scheme is greater than a preset fitness threshold, and update the M gas-liquid separation optimization schemes based on the first generation gas-liquid separation optimization scheme; The M gas-liquid separation optimization schemes are sorted in descending order according to the fitness value, and the first-ranked gas-liquid separation optimization scheme is extracted and output.

7. The method as described in claim 2, characterized in that, The method for real-time monitoring of the gas-liquid separation optimization scheme includes: The gas-liquid separation optimization scheme is implemented, and the flow rate data, pressure data, velocity data, density data, and viscosity data of the mixture to be separated are monitored in a weighted manner to determine multiple weighted monitoring data. Multiple parameter monitoring indicators are set, and the multiple weighted monitoring data are evaluated with the multiple parameter monitoring indicators to generate multiple separation scores; Based on the multiple separation scores, abnormal triggering conditions are set. When the flow data and / or the pressure data and / or the velocity data and / or the density data and / or the viscosity data trigger the abnormal triggering conditions, the dynamic adjustment command is generated.

8. An adaptive cooperative optimization system based on separate analog control, characterized in that, The system comprises: steps for implementing the method according to any one of claims 1 to 7, wherein the system includes: An information filtering module is used to extract separation requirement information based on the mixture to be separated, filter according to the separation requirement information, and determine the target separator type. An optimized channel construction module is used to construct a gas-liquid separation simulation optimization channel by extracting the set of hydrodynamic parameters of the mixture to be separated. The gas-liquid separation simulation optimization channel includes a gas-liquid separation simulation branch and a gas-liquid separation optimization branch. A separation simulation module is used to introduce separation constraints according to the target separator type, combine the set of fluid dynamic parameters to perform separation simulation through the gas-liquid separation simulation branch, and determine the gas-liquid separation simulation results. A separation optimization scheme generation module is used to synchronize the gas-liquid separation simulation results to the gas-liquid separation optimization branch and generate a gas-liquid separation optimization scheme. The intelligent separation module is used to perform real-time monitoring of the gas-liquid separation optimization scheme and generate dynamic adjustment commands to perform intelligent gas-liquid separation of the mixture to be separated.

Images