Analog simulation operation method and medium for octafluorocyclobutane purification equipment

Abstract

The invention provides an analog simulation operation method for octafluorocyclobutane purification equipment and a medium, and relates to the technical field of analog simulation, and the method comprises the steps: obtaining structural design information, and generating a three-dimensional model of the purification equipment; the method comprises the following steps: carrying out node disassembly on an octafluorocyclobutane purification process to obtain N purification process nodes; a purification equipment operation data space is constructed, and N node purification equipment operation data sets are obtained through classification identification; creating N node operation simulation mechanisms; and mapping to a three-dimensional model of the purification equipment for analogue simulation fusion, determining a digital simulation model of the purification equipment, and performing simulation operation control. The technical problem that the purification efficiency of the octafluorocyclobutane purification equipment is affected due to low process parameter optimization efficiency caused by lack of analogue simulation in the prior art is solved, full-flow optimization of the octafluorocyclobutane purification process is realized through analogue simulation, and the purification efficiency of the octafluorocyclobutane purification equipment is improved.

Description

Technical Field

[0001] This application relates to the field of simulation technology, and in particular to simulation operation methods and media for octafluorocyclobutane purification equipment. Background Technology

[0002] Octafluorocyclobutane purification equipment is used to separate impurities from octafluorocyclobutane to achieve high purity. The working principle of octafluorocyclobutane purification equipment is to gradually remove impurities from octafluorocyclobutane through a series of precisely controlled units and processes, ultimately obtaining high-purity octafluorocyclobutane. Current octafluorocyclobutane purification equipment largely relies on manual experience and simple control rules to adjust process parameters, making it difficult to fully consider the dynamic changes and complex process interactions during equipment operation, and unable to respond to changes in real time. Due to the lack of simulation, the equipment control strategy is often fixed and rigid, making it difficult to respond to sudden changes in the production process in real time, resulting in low efficiency and insufficient precision in the adjustment process, thus affecting the improvement of purification efficiency.

[0003] In summary, the existing technology suffers from a lack of simulation, which leads to low efficiency in optimizing process parameters and thus affects the purification efficiency of octafluorocyclobutane purification equipment. Summary of the Invention

[0004] The purpose of this application is to provide a simulation operation method and medium for octafluorocyclobutane purification equipment, in order to solve the technical problem in the prior art that the lack of simulation leads to low efficiency in optimizing process parameters, thereby affecting the purification efficiency of octafluorocyclobutane purification equipment.

[0005] In view of the above problems, this application provides a simulation operation method and medium for octafluorocyclobutane purification equipment.

[0006] Firstly, this application provides a simulation operation method for an octafluorocyclobutane purification device, wherein the simulation operation method includes: acquiring the structural design information of the octafluorocyclobutane purification device; performing three-dimensional modeling based on the structural design information to generate a three-dimensional model of the purification device; decomposing the octafluorocyclobutane purification process into nodes to obtain N purification process nodes; performing historical data mining based on the octafluorocyclobutane purification device to construct a purification device operation data space; classifying and labeling the purification device operation data space using the N purification process nodes to obtain N node purification device operation datasets; performing control logic analysis and operation simulation prediction on the N node purification device operation datasets to create an N node operation simulation mechanism; mapping the N node operation simulation mechanism to the three-dimensional model of the purification device for simulation fusion to determine a digital simulation model of the purification device; and performing simulation operation control based on the digital simulation model of the purification device.

[0007] Optionally, the structural design information is used to extract component parameters to obtain a set of purification equipment component parameters, which includes specifications, dimensions, material properties, and assembly requirements. Based on the set of purification equipment component parameters, three-dimensional modeling of each component is performed to obtain a set of three-dimensional models of purification equipment components. The set of three-dimensional models of purification equipment components is assembled and connected according to the spatial connection relationship of the components to obtain an assembly model of the purification equipment. Based on the model accuracy requirements, the assembly model of the purification equipment is optimized for dimensionality reduction to generate the three-dimensional model of the purification equipment.

[0008] Optionally, the key nodes of the octafluorocyclobutane purification process are extracted according to the process functional characteristics to obtain M key process nodes; based on the M key process nodes, the octafluorocyclobutane purification process is further extracted into related processes to obtain a set of M related sub-nodes; according to the simulation requirements of the purification equipment, the number of process nodes N is set, where N≤M; based on the number of process nodes N, the set of M related sub-nodes is re-planned to determine N purification process nodes.

[0009] Optionally, a node indicator evaluation system is established, and the importance of the M key process nodes is evaluated according to the node indicator evaluation system to obtain M node importance factors; based on the M node importance factors, the allocation ratio of the number of M nodes is determined; the number of process nodes N is allocated according to the allocation ratio of the number of M nodes to determine the number of processes associated with the M nodes; based on the number of processes associated with the M nodes, the set of M associated sub-nodes is re-planned to obtain the N purification process nodes.

[0010] Optionally, the control logic of the N node purification equipment operation dataset is parsed sequentially to obtain the N node equipment control logic; based on the N purification process nodes, N node equipment operation prediction tasks are constructed; and based on the N node equipment control logic, the N node purification equipment operation dataset is simulated and predicted according to the N node equipment operation prediction tasks to obtain the N node operation simulation mechanism.

[0011] Optionally, the operating variables are extracted sequentially from the operating dataset of the N-node purification equipment to obtain N-node input variable sets and N-node output variable sets; based on the node output variables in the N-node output variable sets, the N-node input variable sets are respectively associated and mapped to obtain N-node associated variable sets; the fitting relationship of the N-node associated variable sets is analyzed to obtain N-node variable fitting relationship sets, and the N-node variable fitting relationship sets are used as the control logic of the N-node equipment.

[0012] Optionally, the N node device operation prediction tasks and the N node purification device operation datasets are correlated to determine the N node task operation datasets; and the N node task operation datasets are simulated and predicted based on the N node device control logic to obtain the N node operation simulation mechanism.

[0013] Optionally, the simulation mechanism logic of the N nodes is mapped to the three-dimensional model of the purification equipment for simulation fusion to obtain a basic equipment digital simulation model; the basic equipment digital simulation model is then optimized by full process simulation to determine the purification equipment digital simulation model.

[0014] Optionally, based on the digital simulation model of the purification equipment, the preset purification equipment control parameters are simulated for process operation and product performance evaluation to obtain octafluorocyclobutane purification performance parameters; the preset purification equipment control parameters are then used for iterative simulation optimization and purification equipment control.

[0015] In a second aspect, a computer-readable storage medium storing a computer program that, when executed, implements the steps of the simulation operation method for an octafluorocyclobutane purification apparatus as described in any one of the first aspects above.

[0016] One or more technical solutions provided in this application have at least the following beneficial effects: By acquiring the structural design information of the octafluorocyclobutane purification equipment, a 3D model of the purification equipment is generated based on the structural design information. The octafluorocyclobutane purification process is decomposed into N purification process nodes. Historical data mining is performed on the octafluorocyclobutane purification equipment to construct a purification equipment operation data space. The purification equipment operation data space is classified and labeled using the N purification process nodes to obtain N node purification equipment operation datasets. Control logic analysis and operation simulation prediction are performed on the N node purification equipment operation datasets to create N node operation simulation mechanisms. The N node operation simulation mechanisms are mapped to the 3D model of the purification equipment for simulation fusion to determine the digital simulation model of the purification equipment. Simulation operation control is performed based on the digital simulation model of the purification equipment. In other words, based on the structural composition of the octafluorocyclobutane purification equipment and its three-dimensional model, the octafluorocyclobutane purification process is broken down into multiple nodes. Historical operating data of the octafluorocyclobutane purification equipment is obtained, and an N-node operation simulation mechanism is constructed. This mechanism is mapped to the three-dimensional model of the purification equipment for simulation fusion, thus determining the digital simulation model of the purification equipment and performing simulated operation control. This achieves full-process optimization of the octafluorocyclobutane purification process and improves the purification efficiency of the octafluorocyclobutane purification equipment.

[0017] 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

[0018] 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.

[0019] Figure 1 This is a schematic diagram of the simulation operation method for the octafluorocyclobutane purification equipment used in this application; Figure 2 This is a flowchart illustrating the control logic of the node equipment obtained in the simulation operation method of the octafluorocyclobutane purification equipment used in this application. Detailed Implementation

[0020] This application addresses the technical problem in existing technologies where the lack of simulation leads to low efficiency in optimizing process parameters, thus affecting the purification efficiency of octafluorocyclobutane purification equipment. By providing a simulation operation method and medium for octafluorocyclobutane purification equipment, this application solves the problem of low purification efficiency due to insufficient simulation in existing technologies. Based on the structural composition of the octafluorocyclobutane purification equipment and its three-dimensional model, the octafluorocyclobutane purification process is broken down into multiple nodes. Historical operating data of the octafluorocyclobutane purification equipment is obtained, and a simulation mechanism for N nodes is constructed. This mechanism is mapped to the three-dimensional model of the purification equipment for simulation fusion, determining the digital simulation model of the purification equipment. Simulation operation control is then implemented, achieving full-process optimization of the octafluorocyclobutane purification process and improving the purification efficiency of the octafluorocyclobutane purification equipment.

[0021] 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.

[0022] Example 1, please refer to the appendix. Figure 1 This application provides a simulation operation method for an octafluorocyclobutane purification device, wherein the simulation operation method for the octafluorocyclobutane purification device specifically includes the following steps: S100: Obtain the structural design information of the octafluorocyclobutane purification equipment, perform three-dimensional modeling based on the structural design information, and generate a three-dimensional model of the purification equipment.

[0023] Furthermore, this application S100 includes: S110: Extract component parameters from the structural design information to obtain a set of purification equipment component parameters, which includes specifications, dimensions, material properties, and assembly requirements; S120: Perform 3D modeling of each component based on the set of purification equipment component parameters to obtain a set of 3D purification equipment component models; S130: Assemble and connect the set of 3D purification equipment component models according to the spatial connection relationship of the components to obtain an assembly model of the purification equipment; S140: Perform dimensionality reduction optimization on the assembly model of the purification equipment based on the model accuracy requirements to generate a 3D model of the purification equipment.

[0024] Specifically, structural design information for the octafluorocyclobutane purification equipment is collected from design drawings and technical documents, including the specific dimensions, shapes, and assembly methods of each component (such as distillation columns, condensers, and reboilers). Key parameters for each part of the octafluorocyclobutane purification equipment are extracted from the structural design information, including dimensions (such as length, width, and height), material properties (such as the type of material used and its physical properties: steel, aluminum, copper, etc.), and assembly requirements (such as tolerances between parts, installation methods, and connection methods). The purification equipment component parameter set includes the technical parameters (such as dimensions, material properties, and assembly requirements) of each part of the octafluorocyclobutane purification equipment, helping to understand how the parts work in conjunction with other components.

[0025] Based on the parameter set of the purification equipment parts, each part is modeled in 3D using computer-aided design software, generating a set of 3D model files. Using the extracted part parameters in the computer-aided design software, the 3D model of each part is gradually constructed by drawing its geometry, adding material properties and dimensions. After each part (such as a condenser, distillation column, valve, etc.) is 3D modeled, it is saved as an independent 3D model file, collectively forming the set of 3D model files. After completing the 3D modeling of the parts, inspection and calibration must be performed to ensure that the model meets the design requirements and actual manufacturing requirements. This includes checking the accuracy of the fit between parts, whether the dimensions meet tolerance requirements, and whether the material selection is appropriate.

[0026] The design drawings or assembly process requirements clearly define the connection relationships between various parts, resulting in spatial connection relationships. Based on these spatial connection relationships, the 3D model set of purification equipment parts is assembled and connected, ensuring the correct position, orientation, and fit of each part, resulting in an assembly model of the purification equipment. Spatial connection relationships refer to the relative positions and connection methods between various parts in three-dimensional space, including how different parts are combined using methods such as bolts, welding, and snap-fits. Typically, the 3D model of each part in the purification equipment parts 3D model set is loaded into computer-aided design software. Based on the connection methods between parts, appropriate fit constraints, such as alignment, fixing, and rotation operations, are applied in the computer-aided design software. Through dragging and rotating operations, the positions of the parts are adjusted to conform to the actual spatial layout and design requirements.

[0027] For example, a distillation column has a first feed inlet, a second feed inlet, a first discharge outlet, a second discharge outlet, and an impurity discharge outlet; a condenser has an inlet, a first outlet, and a second outlet, and the inlet of the condenser is connected to the first discharge outlet of the distillation column through an exhaust pipe; the first outlet of the condenser is connected to the second feed inlet of the distillation column through a reflux pipe.

[0028] During assembly, in addition to putting the parts together, it's crucial to check for any physical interference or connection problems that might arise. If issues are found, the size, position, or connection method of the parts needs adjustment to ensure smooth assembly. Based on the requirements of simulation and actual use, the model accuracy requirements are determined, including the precision of the equipment's 3D model, such as dimensional accuracy, shape detail, and material physical properties. The purification equipment assembly model is then optimized for dimensionality reduction, simplifying the 3D model to decrease the resource consumption required for computation and simulation.

[0029] A complete 3D model may contain excessive detail and unnecessary complexity, increasing computational load, simulation time, and system load. Dimensionality reduction optimization maintains necessary accuracy and reduces unnecessary computational burden by reducing the number of faces in the model and simplifying complex geometries. Dimensionality reduction optimization includes simplifying geometry, reducing the number of faces in the model, and merging small parts. Simplifying geometry involves removing redundant details and simplifying complex geometric structures, such as simplifying parts with excessive details (e.g., small holes, tiny scratches) to basic geometric shapes. Reducing the number of faces in the model reduces the computational burden by simplifying parts with a large number of faces to fewer faces. Merging small parts reduces the number of components in the model by merging multiple small parts into a single part, thereby reducing computational complexity.

[0030] The dimensionality-reduced and optimized 3D model will become the final digital representation of the purification equipment, i.e., the 3D model of the purification equipment. Through precise 3D modeling and assembly simulation, the accuracy of equipment design and manufacturing will be improved, assembly errors will be reduced, and the operating efficiency of each component will be optimized.

[0031] S200: The octafluorocyclobutane purification process is broken down into N purification process nodes.

[0032] Furthermore, this application S200 includes: S210: Extract key nodes from the octafluorocyclobutane purification process according to its functional characteristics to obtain M key process nodes; S220: Extract related processes from the octafluorocyclobutane purification process based on the M key process nodes to obtain a set of M related sub-nodes; S230: Set the number of process nodes N according to the simulation requirements of the purification equipment, where N≤M; S240: Re-plan the set of M related sub-nodes based on the number of process nodes N to determine N purification process nodes.

[0033] Furthermore, this application also includes the following steps: S241: Establish a node indicator evaluation system, and evaluate the importance of the M key process nodes according to the node indicator evaluation system to obtain M node importance factors; S242: Determine the allocation ratio of the number of M nodes according to the M node importance factors; S243: Allocate the number N of process nodes according to the allocation ratio of the number of M nodes to determine the number of processes associated with the M nodes; S244: Based on the number of processes associated with the M nodes, perform node replanning on the set of M associated sub-nodes to obtain the N purification process nodes.

[0034] Specifically, the entire octafluorocyclobutane purification process is broken down into a series of key process nodes, resulting in multiple purification process nodes. Each node represents an important operational step or decision point, such as the feed to the distillation column or the cooling of the condenser. The process functional characteristics are the basic operating principles, objectives, and functions of each step in the octafluorocyclobutane purification process, including the basic functions and parameter control (such as temperature, pressure, and flow rate) of process steps such as distillation, condensation, and vacuum. Based on the process functional characteristics of the octafluorocyclobutane purification process, each process node and its target function are analyzed, extracting M key process nodes. These are the most important operational steps in the entire purification process, and each node directly affects the purity of the final product and the process efficiency. For example, it is assumed that the distillation and condensation processes are key nodes in the octafluorocyclobutane purification process because they directly affect product quality.

[0035] The octafluorocyclobutane purification process is analyzed using M key process nodes. This involves identifying other steps or operations related to the key node operations, resulting in a set of M related sub-nodes. These sub-nodes are not all executed independently, but they are closely related to the execution of the key nodes, ensuring the smoothness and efficiency of the entire process. For example, related process sub-nodes in the distillation process include feed control, discharge control, and impurity removal control; related sub-nodes in the condensation process include coolant flow control, condenser temperature monitoring, and condenser pressure control. The set of M related sub-nodes includes all process nodes associated with the M key process nodes.

[0036] Obtain the simulation requirements for the purification equipment, i.e., the requirements for simulating the octafluorocyclobutane purification equipment, including the characteristics and objectives of its operation. Determine the number of process nodes N from the simulation requirements, i.e., the number of selected process nodes. N must be less than or equal to the number of all critical process nodes M, i.e., N≤M. Reorganize the set of M related sub-nodes based on the number of process nodes N, i.e., restructure the M related sub-nodes to describe the entire process as completely as possible within the constraint of N nodes.

[0037] Based on the impact of each process node on the final product quality, its contribution to equipment efficiency, its complexity, and its dependence on the overall process flow, a node index evaluation system is established to measure and evaluate the role and importance of each process node in the purification equipment. The node index evaluation system can include the node's impact on product purity, its impact on equipment stability and efficiency, and its dependence on other process nodes.

[0038] A node index evaluation system is used to assess the importance of M key process nodes, resulting in an importance factor for each node. The importance factor is typically a numerical value reflecting the node's impact on the overall process flow. Based on these M node importance factors, resources are allocated to each node in the simulation, thus determining the allocation ratio of the M nodes. In other words, the allocation ratio of the M key process nodes in the overall simulation model is determined based on their importance factors. Nodes with higher importance receive a higher allocation ratio.

[0039] Based on the allocation ratio of the M nodes, the number of process nodes N is allocated to determine the number of processes associated with the M nodes. In other words, for each of the M nodes, the number of associated processes that each node needs to handle is determined by the allocated quantity and ratio. Some nodes may involve multiple sub-process steps (e.g., a distillation process may have multiple control parameters such as temperature and pressure), while other nodes may only be associated with a few sub-processes.

[0040] Based on the number of processes associated with the M nodes, the set of M associated sub-nodes is replanned. The specific steps involved in each node are adjusted according to the number of processes associated with each node, ensuring that each node can run efficiently during simulation and avoiding computational redundancy. The resulting N purification process nodes represent the most critical process steps in the entire octafluorocyclobutane purification process, and after optimization, they can be accurately and quickly simulated and optimized during simulation.

[0041] For example, the importance of M key process nodes is assessed using a node indicator evaluation system, including the node's impact on the overall process, node complexity, node operational stability, and node resource consumption. Some of the scores obtained are shown in Table 1. Table 1. Key Process Node Scoring Table node Impact on rating Complexity score Stability rating Resource consumption rating Overall score Distillation 9 8 7 7 31 Condensation 6 7 7 5 25 Blowing 5 3 6 2 16 The importance factor of each node is calculated based on the total score, which is the node allocation ratio: distillation is 31 / (31+25+16)=0.43, condensation is 25 / (31+25+16)=0.35, and purging is 16 / (31+25+16)=0.22. Assuming the number of process nodes N is 10, the number of process nodes allocated according to the node allocation ratio is: distillation nodes are 0.43*10=4 nodes; condensation nodes are 0.35*10=4 nodes; and purging nodes are 0.22*10=2 nodes. Based on the number of processes associated with each node, the set of M related sub-nodes is replanned to obtain the final purification process nodes. A reasonable number and allocation method of nodes ensures that the most important links in simulation and actual operation are optimized, reducing unnecessary process nodes and sub-nodes, and lowering the burden of computation and analysis.

[0042] S300: Based on the octafluorocyclobutane purification equipment, historical data mining is performed to construct a purification equipment operation data space. The purification equipment operation data space is classified and labeled using the N purification process nodes to obtain N node purification equipment operation datasets.

[0043] Specifically, historical data mining was performed on the octafluorocyclobutane purification equipment. Historical data was extracted from past equipment operation data, including operating status, process parameters, and sensor data such as temperature, pressure, flow rate, material concentration, and runtime. The purpose of historical data mining was to discover patterns and trends in equipment operation and identify key factors affecting purification efficiency. The collected historical data was then cleaned, denoised, and formatted. For example, invalid data (such as duplicates, missing values, or outliers) was removed, and data interpolation was performed to ensure accuracy and consistency. Data from different data sources was standardized to ensure consistent data units for easier subsequent analysis.

[0044] The processed data is organized into a unified space. The dimensions of this data space typically include time, process parameters (such as temperature, pressure, and flow rate), and equipment status. Each data point represents the operating status of the equipment under specific time and process conditions. The purification equipment operation data space refers to the collection containing all equipment operation-related data, including multiple process parameters such as temperature, pressure, flow rate, and concentration, as well as equipment status information.

[0045] Based on N purification process nodes, the purification equipment operation data space is classified and labeled. In other words, the data in the purification equipment operation data space is categorized according to these N purification process nodes. For example, each dataset is labeled with the corresponding process step (such as condensation, adsorption, etc.), and a specific set of data is assigned to each process step. The historical data of each node is mapped to the corresponding process operation, making the data more targeted and structured. The N-node purification equipment operation dataset is a collection of historical operation data corresponding to each node, obtained through the above classification and labeling. Each node's operation dataset contains historical data for that node under different operating conditions, such as parameter fluctuations and equipment performance information.

[0046] By constructing a structured purification equipment operation data space and dividing it into N datasets related to process nodes, it is helpful to conduct detailed data analysis and parameter adjustment for each process node to achieve the best purification effect.

[0047] S400: Perform control logic analysis and operation simulation prediction on the operating dataset of the N node purification equipment, and create an N node operation simulation mechanism.

[0048] Furthermore, this application S400 includes: S410: Sequentially parse the control logic of the N node purification equipment operation dataset to obtain the N node equipment control logic; S420: Construct N node equipment operation prediction tasks based on the N purification process nodes; S430: Perform operation simulation prediction on the N node purification equipment operation dataset according to the N node equipment control logic based on the N node equipment operation prediction tasks to obtain the N node operation simulation mechanism.

[0049] Further details are attached. Figure 2 As shown, this application also includes the following steps: S411: Extract operating variables from the N node purification device operation dataset sequentially to obtain N node input variable sets and N node output variable sets; S412: Associate and map each node output variable in the N node output variable set with the N node input variable set to obtain N node association variable sets; S413: Analyze the fitting relationship of the N node association variable sets to obtain N node variable fitting relationship sets, and use the N node variable fitting relationship sets as the control logic of the N node devices.

[0050] Furthermore, this application also includes the following steps: S431: Perform correlation analysis between the predicted operation tasks of the N node devices and the operation datasets of the N node purification devices to determine the operation datasets of the N node tasks; S432: Perform operation simulation prediction on the operation datasets of the N node tasks based on the control logic of the N node devices to obtain the operation simulation mechanism of the N nodes.

[0051] Specifically, operational variables are extracted sequentially from the operating dataset of N purification devices. This involves extracting various parameters or indicators affecting device performance, such as temperature, pressure, flow rate, and purity. This yields the input and output parameters during device operation, resulting in N sets of input variables and N sets of output variables. Node input variables are typically adjustable parameters affecting device operation, such as temperature, flow rate, and pressure; node output variables are the device's response, such as product purity and yield.

[0052] This involves mapping the output variables of each node in the set of N node output variables to the set of N node input variables. This clarifies the relationship between the input and output variables of each node, understanding how these variables interact and influence the final output. The set of N node associated variables includes the pairings between the input and output variables of all nodes.

[0053] For the input-output relationship of each node, a suitable fitting method is selected for modeling, such as linear regression or nonlinear regression. Through fitting analysis, a mathematical model for each node is derived. For example, if the input of a node is temperature and flow rate, and the output is purity, then the fitted mathematical model might be: Purity = 0.2 * Temperature + 0.1 * Flow Rate + 70. Fitting relationship analysis refers to using statistical methods to mathematically model the relationship between the input and output variables of each node, forming a mathematical formula or model to describe the relationship between these variables. In other words, fitting the data from the set of related variables of N nodes to a straight line determines the set of fitting relationships for the N node variables.

[0054] The fitted set of relationships between N node variables is used as the control logic for N node devices to adjust their operation in real time. By acquiring input variables in real time, substituting them into the fitted formula, the output variables are predicted, and the device settings are adjusted accordingly to achieve optimized control.

[0055] Based on N purification process nodes, construct corresponding N node equipment operation prediction tasks. The goal of the node equipment operation prediction tasks is usually to predict how the equipment will operate under different operating conditions and to predict its output results. For example, for the temperature control node, the prediction task could aim to predict the changes in reaction rate or purity under set flow rate and temperature conditions.

[0056] The node equipment operation prediction task is based on the input and output data of each purification process node. Using data mining, machine learning, and other methods, it predicts the future state or output of the equipment at that node. For example, predicting the output purity or flow rate of the equipment at a certain node. Following the N node equipment operation prediction task, the operation simulation prediction is performed on the N node purification equipment operation datasets through N node equipment control logics. Each node has a set of control logic to guide how input variables are adjusted, thereby affecting the output. For example, the control logic of the temperature control node might include adjusting the heater power to achieve a predetermined temperature. Simulation calculations are performed using the historical dataset (containing input and output data) of each node. Using the control logic and dataset inputs, simulations are performed using mathematical models (such as regression models, machine learning algorithms, etc.) to predict the equipment performance under given conditions.

[0057] Through correlation analysis, the device prediction tasks of each node are linked to the corresponding operational datasets, ensuring that the output data of each task can be validated and optimized using actual operational data. Each node's operational dataset contains input and output variables. Correlation analysis determines the relationship between the task's input variables (such as temperature and flow rate) and output variables (such as purity and efficiency). For example, correlation analysis can determine the relationship between flow rate and yield.

[0058] The N-node operational simulation mechanism is obtained by performing operational simulation predictions on N node task operation datasets using the control logic of N node devices. In other words, the task operation dataset of each node is input into the simulation model to simulate the device response under different input conditions. Based on the device control logic, the operating state of each node is simulated, such as how temperature adjusts and purity changes under different input flow rates. The simulation results will demonstrate the control response of each node, forming the node operational simulation mechanism, reflecting the control behavior of each node under different conditions. The specific training process of the node operational simulation mechanism is as follows: Define the target of the prediction task, such as device output, device status, and production efficiency. Simultaneously, acquire historical operational data for each process node, including relevant variables such as temperature, flow rate, pressure, and purity. Divide the historical operational data into training and testing sets, typically 80% training and 20% validation. Select a suitable machine learning algorithm for training, such as a support vector machine. Use the training set to train the model, learning the relationship between input and output, and adjusting model parameters to improve prediction accuracy. Use the testing set to evaluate the trained model, for example, by using methods such as cross-validation to evaluate the model's accuracy, such as prediction error (e.g., mean squared error, MSE) and precision. The trained model is optimized by searching for the optimal hyperparameters using a grid search.

[0059] The prediction tasks of each node are integrated. Simulation results allow for the simulation of each stage of the purification process, predicting the equipment's future performance. Multiple simulations are performed using the control logic and prediction results of each node. Each simulation provides the equipment's operational output at that node, helping to optimize the equipment control strategy. By continuously adjusting input variables (such as flow rate and temperature), the optimal operating state of the equipment is discovered. For example, when the prediction result of a certain node shows a decrease in purity, the simulation can automatically adjust the settings of other nodes to ensure the equipment achieves optimal operating results. The simulation mechanisms of each node are integrated to form an N-node operational simulation mechanism, simulating each step of the entire octafluorocyclobutane purification process and predicting the equipment's operating state.

[0060] By establishing predictive tasks and control logic for node equipment operation, the system accurately predicts the equipment's performance under different operating conditions, helping to optimize equipment control strategies. Through simulation prediction, the control decisions of each node can be optimized, ensuring that the equipment achieves its optimal operating state in actual operation and avoiding over-adjustment or ineffective operation.

[0061] S500: Map the N node operation simulation mechanism to the three-dimensional model of the purification equipment for simulation fusion, determine the digital simulation model of the purification equipment, and perform simulation operation control based on the digital simulation model of the purification equipment.

[0062] Furthermore, this application S500 includes: S510: Map the logic of the N node operation simulation mechanism to the three-dimensional model of the purification equipment for simulation fusion to obtain the basic equipment digital simulation model; S520: Perform full process simulation optimization on the basic equipment digital simulation model to determine the purification equipment digital simulation model.

[0063] Specifically, the simulation mechanisms of N nodes are mapped onto the 3D model of the purification equipment. This involves combining the simulation mechanism of each node with the 3D geometric model of the equipment to form a complete digital simulation model, enabling comprehensive simulation of the equipment's operational status in a virtual environment. In other words, the control logic and simulation mechanism of each node are associated with the corresponding components in the 3D model of the equipment. The basic equipment digital simulation model, obtained by combining the simulation mechanisms of N nodes with the 3D model of the purification equipment, can simulate the overall operation of the equipment and evaluate its performance under different conditions.

[0064] A full-process simulation optimization was performed on the digital simulation model of the basic equipment, incorporating all process steps (including temperature control, flow regulation, and reactant concentration control) into the optimization process and evaluating their performance under different operating conditions. This comprehensive process simulation optimization included adjusting model parameters, optimizing the process flow, and verifying the model's accuracy. The optimization goal was to ensure that the simulation model accurately reflects the actual operating conditions of the equipment and can predict performance under different process parameters. Through this optimization, a final digital simulation model of the purification equipment was determined for further simulation analysis and process optimization.

[0065] In simulation models, multiple parameters can influence the model's output and performance, including the physical characteristics of the equipment (such as dimensions and material properties), process conditions (such as temperature, pressure, and flow rate), and operating parameters (such as feed rate and stirring speed). Adjusting model parameters aims to make the model more closely resemble the actual operation of the equipment. Optimizing the process involves redesigning and rearranging process steps to improve overall purification efficiency, including changing the order of process steps, adjusting operating conditions, and introducing new process technologies. The goal of optimization is to find the optimal process flow to achieve the highest purification effect and the lowest cost.

[0066] After model adjustment and optimization, the accuracy of the model needs to be verified by comparing the model's predictions with actual experimental data or field operation data. If the model's predictions match the actual data, the model can be considered accurate. If significant differences exist, further adjustments to the model's parameters or structure may be necessary. Through full-process simulation optimization, a digital simulation model of the purification equipment was finally determined for further simulation analysis and process optimization.

[0067] By constructing a comprehensive digital simulation model of the purification equipment, all process nodes in actual operation can be simulated. Different process parameters and equipment configurations can be evaluated without conducting actual experiments, thereby reducing trial and error costs and improving production efficiency.

[0068] Furthermore, this application also includes the following steps: S530: Based on the digital simulation model of the purification equipment, perform process simulation operation and product performance evaluation on the preset purification equipment control parameters to obtain octafluorocyclobutane purification performance parameters; S540: Use the octafluorocyclobutane purification performance parameters to perform iterative simulation optimization and purification equipment control on the preset purification equipment control parameters.

[0069] Specifically, before conducting the simulation, the control parameters of the purification equipment are preset, such as reactor temperature, pressure, and flow rate, representing the initial operating conditions of the equipment under different working conditions. For example, it is assumed that the preset reactor temperature is 150℃ and the flow rate is 10m³ / h. 3 / h, pressure 2MPa. Preset control parameters are input into the digital simulation model for simulation operation, simulating the actual performance of the equipment under these conditions, including purification effect, energy consumption, and production efficiency. The simulation results will provide the equipment's operating status and predict its working efficiency under current conditions.

[0070] After process simulation, the purification performance of the product is evaluated, and purification performance parameters of octafluorocyclobutane, such as purity, yield, and energy efficiency, are obtained to provide feedback on the rationality of the control parameter settings. Based on the octafluorocyclobutane purification performance parameters, the preset purification equipment control parameters are iteratively simulated and optimized to gradually improve the equipment's operating efficiency and achieve better purification results. In each iteration, the simulation is run again based on the new control parameters, and new purification performance parameters are calculated. The control parameters are continued to be adjusted until the optimal process conditions are obtained. For example, assuming that after several iterations, the equipment operates at a temperature of 170℃ and a flow rate of 13m³ / h... 3 The purity reached 99% at a pressure of 2.5 MPa, with an energy consumption of 4 kWh / kg and a productivity of 220 kg / h. The optimization goal is to improve product purity, reduce energy consumption, increase productivity, or find the optimal balance among these goals.

[0071] After iterative optimization, the optimal parameter combination was selected, and the final equipment control strategy was determined accordingly to achieve the highest possible purification efficiency. Based on the optimal parameter combination, various operating parameters of the octafluorocyclobutane purification equipment (such as temperature and flow rate) were adjusted to achieve the best purification effect. By optimizing the control parameters of the purification equipment, the purification efficiency of octafluorocyclobutane is improved. Predicting and improving equipment performance before actual operation reduces trial-and-error costs and increases production efficiency.

[0072] In summary, the simulation operation method for octafluorocyclobutane purification equipment provided in this application has the following beneficial effects: By acquiring the structural design information of the octafluorocyclobutane purification equipment, a 3D model of the purification equipment is generated based on the structural design information. The octafluorocyclobutane purification process is decomposed into N purification process nodes. Historical data mining is performed on the octafluorocyclobutane purification equipment to construct a purification equipment operation data space. The purification equipment operation data space is classified and labeled using the N purification process nodes to obtain N node purification equipment operation datasets. Control logic analysis and operation simulation prediction are performed on the N node purification equipment operation datasets to create N node operation simulation mechanisms. The N node operation simulation mechanisms are mapped to the 3D model of the purification equipment for simulation fusion to determine the digital simulation model of the purification equipment. Simulation operation control is performed based on the digital simulation model of the purification equipment. In other words, based on the structural composition of the octafluorocyclobutane purification equipment and its three-dimensional model, the octafluorocyclobutane purification process is broken down into multiple nodes. Historical operating data of the octafluorocyclobutane purification equipment is obtained, and an N-node operation simulation mechanism is constructed. This mechanism is mapped to the three-dimensional model of the purification equipment for simulation fusion, thus determining the digital simulation model of the purification equipment and performing simulated operation control. This achieves full-process optimization of the octafluorocyclobutane purification process and improves the purification efficiency of the octafluorocyclobutane purification equipment.

[0073] Example 2: Based on the same inventive concept as the simulation operation method for the octafluorocyclobutane purification equipment in Example 1, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of the simulation operation method for the octafluorocyclobutane purification equipment described in any one of Examples 1.

[0074] 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.

[0075] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A simulation operation method for an octafluorocyclobutane purification device, characterized in that, include: Obtain the structural design information of the octafluorocyclobutane purification equipment, and perform three-dimensional modeling based on the structural design information to generate a three-dimensional model of the purification equipment. The purification process of octafluorocyclobutane is broken down into N purification process nodes. Historical data mining is performed on the octafluorocyclobutane purification equipment to construct a purification equipment operation data space. The N purification process nodes are used to classify and label the purification equipment operation data space to obtain N node purification equipment operation datasets. The control logic is analyzed and the operation simulation prediction is performed on the operating dataset of the N-node purification equipment to create an N-node operation simulation mechanism; The simulation mechanism of the N nodes is mapped to the three-dimensional model of the purification equipment for simulation fusion to determine the digital simulation model of the purification equipment, and the simulation operation control is performed based on the digital simulation model of the purification equipment.

2. The simulation operation method for an octafluorocyclobutane purification device as described in claim 1, characterized in that, The three-dimensional model of the purification equipment includes: The structural design information is used to extract part parameters to obtain a set of purification equipment part parameters, which includes specifications, dimensions, material properties and assembly requirements. Based on the parameter set of the purification equipment parts, three-dimensional modeling of the parts is performed to obtain a three-dimensional model set of purification equipment parts; The three-dimensional model set of purification equipment parts is assembled and connected according to the spatial connection relationship of the parts to obtain the purification equipment assembly model. Based on the model accuracy requirements, the assembly model of the purification equipment is optimized by dimensionality reduction to generate a three-dimensional model of the purification equipment.

3. The simulation operation method for an octafluorocyclobutane purification device as described in claim 1, characterized in that, The obtained N purification process nodes include: Based on the process functional characteristics, the key nodes of the octafluorocyclobutane purification process were extracted to obtain M key process nodes. Based on the M key process nodes, the octafluorocyclobutane purification process is associated with process extraction to obtain a set of M associated sub-nodes. Based on the simulation requirements of the purification equipment, the number of process nodes N is set, where N≤M; Based on the number N of process nodes, the set of M associated sub-nodes is replanned to determine N purification process nodes.

4. The simulation operation method for an octafluorocyclobutane purification device as described in claim 3, characterized in that, The determination of N purification process nodes includes: Establish a node indicator evaluation system, and evaluate the importance of the M key process nodes according to the node indicator evaluation system to obtain M node importance factors; Based on the importance factors of the M nodes, determine the allocation ratio of the number of the M nodes; The number of process nodes N is allocated according to the allocation ratio of the M nodes, and the number of processes associated with the M nodes is determined. Based on the number of processes associated with the M nodes, the set of M associated sub-nodes is replanned to obtain the N purification process nodes.

5. The simulation operation method for an octafluorocyclobutane purification device as described in claim 1, characterized in that, The mechanism for creating N nodes to run the simulation includes: The control logic of the N node purification device operation datasets is sequentially parsed to obtain the control logic of the N node devices; Based on the N purification process nodes, construct N node equipment operation prediction tasks; Based on the control logic of the N node devices, the N node purification device operation dataset is simulated and predicted to obtain the N node operation simulation mechanism.

6. The simulation operation method for an octafluorocyclobutane purification device as described in claim 5, characterized in that, The obtained control logic for N node devices includes: The running variables of the N node purification device running datasets are extracted sequentially to obtain N node input variable sets and N node output variable sets; Based on the association mapping between each node output variable in the N node output variable set and the N node input variable set, an N node association variable set is obtained; The fitting relationship of the N node-related variable sets is analyzed to obtain the N node variable fitting relationship set, and the N node variable fitting relationship set is used as the control logic of the N node devices.

7. The simulation operation method for an octafluorocyclobutane purification device as described in claim 5, characterized in that, The mechanism for obtaining N nodes to run the simulation includes: By performing correlation analysis between the predicted operation tasks of the N node devices and the operation dataset of the N node purification devices, the operation dataset of the N node tasks is determined. Based on the control logic of the N node devices, the running simulation prediction of the N node task running dataset is performed to obtain the running simulation mechanism of the N nodes.

8. The simulation operation method for an octafluorocyclobutane purification device as described in claim 1, characterized in that, The determination of the digital simulation model of the purification equipment includes: The simulation mechanism logic of the N nodes is mapped to the three-dimensional model of the purification equipment for simulation fusion to obtain the digital simulation model of the basic equipment. The digital simulation model of the basic equipment is optimized through full-process simulation to determine the digital simulation model of the purification equipment.

9. The simulation operation method for an octafluorocyclobutane purification device as described in claim 1, characterized in that, The simulation operation control based on the digital simulation model of the purification equipment includes: Based on the digital simulation model of the purification equipment, the process simulation operation and product performance evaluation were carried out using the preset purification equipment control parameters to obtain the purification performance parameters of octafluorocyclobutane. The purification performance parameters of the octafluorocyclobutane were used to iteratively simulate and optimize the control parameters of the preset purification equipment and control the purification equipment.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the steps of the simulation operation method for the octafluorocyclobutane purification equipment as described in any one of claims 1 to 9.

Images