Simulation method and device for continuous diazotization process of plate-type microchannel

Abstract

The invention provides a plate type microchannel continuous diazotization process simulation method and device, and relates to the technical field of data processing, simulation modeling is performed according to process equipment data to establish an equipment model, a material transmission relation in the equipment model is configured to establish a continuous flow network, and reaction process modeling is executed. Carrying out process execution monitoring, establishing a sample data set, carrying out feature classification, configuring key sample points, correcting a reaction process modeling result by utilizing the key sample points, fitting the corrected reaction process modeling result and a continuous flow network to an equipment model, and setting an operation mode of continuous feeding of raw materials and continuous discharging of reactants; and process simulation is carried out according to the fitted equipment model, so that the problem of limitation of practical operation and practical training teaching in multiple scenes due to too high training cost of a plate type micro-channel continuous diazotization process education and practical operation training mode is solved.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a plate-type microchannel continuous diazotization process simulation method and device. Background Art

[0002] In the field of chemical engineering and fine chemicals, diazotization reaction is widely used in organic synthesis, especially in the biomedicine, new materials and other industries. Diazotization reaction is the process of primary amine reacting with nitrous acid to form diazonium salt, which is usually carried out at low temperature to prevent the decomposition of nitrous acid and diazonium salt. However, since the diazotization reaction is an exothermic reaction and the reaction activity between the reactants and the products is quite different, precise temperature control and reaction control are required. If improperly controlled, it may cause the reaction to get out of control and cause a safety accident. In addition, the production mode of the diazotization process usually relies on an intermittent batch production process, which not only has the disadvantages of inflexible process, low degree of automation, bulky equipment, and low operating efficiency, but also faces high safety risks.

[0003] At present, some companies try to use multi-stage kettle process to improve the existing production mode, but this method still has many problems, such as large equipment size, insufficient automation during operation, high requirements for operators, lack of effective real-time monitoring and fault warning mechanism, which makes it difficult to avoid safety hazards in the production process. In order to overcome the above technical challenges, the plate microchannel continuous diazotization process has gradually become a feasible alternative to solve the problems of traditional intermittent kettle reaction process. The plate microchannel reactor has a smaller reaction volume, higher heat transfer efficiency and stronger safety. Through the optimized microchannel structure, the plate microchannel process can better control the reaction process and reduce heat accumulation during the reaction, thereby improving the stability and safety of the reaction. However, although the plate-type microchannel continuous diazotization process has improved production efficiency and safety, the relevant technology currently lacks a complete training and practical operation system, especially in terms of process flow, equipment operation, safety monitoring, etc. Due to the high training costs and limited operating funds, relevant personnel can often only rely on traditional "understanding teaching" or "learning by doing" methods to learn, and the existing simulation system lacks integration with real equipment and processes, and cannot comprehensively improve the skill level of operators, which greatly limits the learners' mastery of the knowledge and ability of the plate-type microchannel continuous diazotization process.

[0004] In summary, the existing plate-type microchannel continuous diazotization process education and practical training model has the problem of too high training costs and the inability to effectively combine real equipment operation and theoretical learning, which leads to limitations in practical training teaching in multiple scenarios and makes it difficult to popularize and improve skills. Summary of the invention

[0005] The present application provides a plate-type microchannel continuous diazotization process simulation method and device, which is used to solve the technical problems of the existing plate-type microchannel continuous diazotization process education and practical training mode, such as high training costs, inability to effectively combine real equipment operation and theoretical learning, resulting in limitations in practical training teaching in multiple scenarios, and difficulty in popularizing and improving skills.

[0006] In view of the above problems, the present application provides a method and device for simulating a plate-type microchannel continuous diazotization process.

[0007] In a first aspect, the present application provides a plate-type microchannel continuous diazotization process simulation method, the method comprising:

[0008] Acquire equipment data of the plate-type microchannel continuous diazotization process, perform equipment simulation modeling according to the equipment data, and establish an equipment model, wherein the equipment model includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrument system, and a piping system; configure the material transmission relationship of the microchannel reactor, the reaction cooler, and the metering feed pump in the equipment model to establish a continuous flow network; perform reaction process modeling of the plate-type microchannel continuous diazotization process, wherein the reaction process modeling includes reaction model modeling, heat transfer modeling, and flow modeling; perform process execution monitoring on the plate-type microchannel continuous diazotization process, establish a sample data set, perform feature classification on the sample data set, and configure key sample points; use the key sample points to calibrate the reaction process modeling results, and fit the calibrated reaction process modeling results and the continuous flow network to the equipment model; set the operation mode of continuous feeding of raw materials and continuous discharge of reactants, and perform process simulation according to the fitted equipment model.

[0009] In a second aspect, the present application provides a plate-type microchannel continuous diazotization process simulation device, the device comprising:

[0010] The equipment model building unit is used to obtain the equipment data of the plate-type microchannel continuous diazotization process, perform equipment simulation modeling according to the equipment data, and establish the equipment model, wherein the equipment model includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrument system, and a pipeline system; the model configuration unit is used to configure the material transmission relationship of the microchannel reactor, the reaction cooler, and the metering feed pump in the equipment model, and establish a continuous flow network; the reaction modeling unit is used to perform reaction process modeling of the plate-type microchannel continuous diazotization process, wherein the reaction process modeling includes reaction model modeling, heat transfer modeling, and flow modeling; the execution monitoring unit is used to perform process execution monitoring of the plate-type microchannel continuous diazotization process, establish a sample data set, and configure key sample points after feature classification of the sample data set; the modeling correction unit is used to correct the reaction process modeling results using the key sample points, and fit the corrected reaction process modeling results and the continuous flow network to the equipment model; the process simulation unit is used to set the operation mode of continuous feeding of raw materials and continuous discharge of reactants, and perform process simulation according to the fitted equipment model.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] The present application provides a method for simulating a plate-type microchannel continuous diazotization process. The method obtains equipment data of the plate-type microchannel continuous diazotization process, performs equipment simulation modeling according to the equipment data, and establishes an equipment model, wherein the equipment model includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrument system, and a piping system; configures the material transmission relationship of the microchannel reactor, the reaction cooler, and the metering feed pump in the equipment model to establish a continuous flow network; performs reaction process modeling of the plate-type microchannel continuous diazotization process, wherein the reaction process modeling includes reaction modeling, heat transfer modeling, and flow modeling; performs process execution monitoring on the plate-type microchannel continuous diazotization process, establishes a sample data set, and after feature classification of the sample data set, configures key sample points. ; The reaction process modeling results are corrected using the key sample points, and the corrected reaction process modeling results and the continuous flow network are fitted to the equipment model; the operation mode of continuous feeding of raw materials and continuous discharge of reactants is set, and process simulation is performed according to the fitted equipment model, which solves the technical problems of high training costs in the existing plate microchannel continuous diazotization process education and practical training mode, and cannot effectively combine real equipment operation and theoretical learning, resulting in limitations in practical training teaching in multiple scenarios, and difficulty in popularizing and improving skills, and achieves efficient and precisely controlled continuous diazotization process simulation, greatly improves operational safety and the practicality of process education, and effectively improves the technical effect of practitioners' understanding of complex chemical processes and their ability to operate. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic flow chart of a plate-type microchannel continuous diazotization process simulation method is provided for this application.

[0014] Figure 2 A structural schematic diagram of a plate-type microchannel continuous diazotization process simulation device is provided for this application.

[0015] Explanation of the reference numerals: equipment model building unit 11 , model configuration unit 12 , reaction modeling unit 13 , execution monitoring unit 14 , modeling correction unit 15 , process simulation unit 16 . DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] A plate-type microchannel continuous diazotization process simulation method and device proposed in this application adopts the method of "hardware + software integrated configuration" to configure a simulation device system that conforms to the actual process production process, including a process system, an equipment system, an electrical instrument system, a simulation operation system, and an assessment and scoring system. Specifically, the simulation device system includes core equipment such as a plate-type microchannel reactor, a plate-type microchannel reaction cooler, a diaphragm metering feed pump, and a supporting electrical instrument system and a pipeline system. Then, based on the system configuration, theoretical and practical teaching and training in the production operation process, microchannel structure and principle, process principle, process operation control, risk hidden danger identification and control of the plate-type microchannel continuous diazotization process can be carried out. Thereby, learners have a more intuitive, in-depth and overall understanding of microchannel reactors and continuous diazotization production, easily understand and master the process technology principles and operations, and effectively solve the understanding of relevant practitioners on the microchannel continuous diazotization process technology, as well as the practical training teaching problems in multiple scenarios.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a plate-type microchannel continuous diazotization process simulation method, the method comprising:

[0019] The equipment data of the plate-type microchannel continuous diazotization process is obtained, equipment simulation modeling is performed according to the equipment data, and an equipment model is established. The equipment model includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrument system, and a pipeline system.

[0020] Configure the material transfer relationship of the microchannel reactor, reaction cooler, and metering feed pump in the equipment model to establish a continuous flow network.

[0021] The reaction process modeling of the plate-type microchannel continuous diazotization process is performed, and the reaction process modeling includes reaction modeling, heat transfer modeling, and flow modeling.

[0022] Specifically, parameter data of key equipment including microchannel reactors, reaction coolers, metering feed pumps, electrical instrument systems and pipeline systems are collected. These equipment data can be obtained through on-site monitoring, sensor reading or experimental measurement, such as process variables such as flow, temperature, and pressure. Among them, a microchannel reactor is a reactor with multiple tiny channels, usually using a plate structure, and using tiny reaction channels to enhance the heat transfer, mixing and reaction efficiency between materials and reaction media. During the reaction process, the reactants are sent to the reaction area through tiny channels. These microchannels provide efficient heat exchange and material transfer, so that the reaction temperature can be accurately controlled. Due to the high specific surface area of ​​the microchannels, the reaction speed is fast, and the reaction heat can be effectively removed to avoid reaction byproducts or safety hazards caused by high temperature. The reaction cooler is a device used to remove excess heat during a chemical reaction to ensure that the temperature in the reactor does not exceed the design temperature to maintain the stability of the reaction and improve safety. Through contact with the cooling medium, the reaction cooler can quickly reduce the temperature of the reaction system to avoid overheating and decomposition of the reactants or adverse reactions. It is usually connected to the reactor to keep the reaction process within an appropriate temperature range, which is especially important in exothermic reactions. The metering feed pump is a pump device used to accurately control the flow rate of reaction materials, usually used to transport liquid or gas raw materials into the reaction system. The metering feed pump can accurately deliver raw materials to the reactor at a predetermined flow rate to ensure that the reactants continue to enter the reaction system in the designed proportion. For microchannel reactors, accurate feed control is essential because incorrect flow may affect the reaction rate, temperature and material balance. The electrical instrument system is a system composed of various sensors, controllers, displays and alarm devices, which is used to monitor and control various parameters in the chemical reaction process, such as temperature, pressure, flow, etc. The system can monitor the operating status of equipment such as microchannel reactors, reaction coolers, metering feed pumps, etc. in real time, and adjust the operating parameters of the equipment through feedback data. The electrical instrument system can also be used for equipment fault diagnosis and alarm to ensure the safety and stability of the reaction process. The pipeline system is a material delivery system composed of pipelines, valves, joints, etc., which is used to transport and circulate reactants and products between different equipment. The pipeline system connects microchannel reactors, reaction coolers, metering feed pumps and other equipment to ensure that the flow path of the reactants is unobstructed. It is responsible for the transportation of raw materials, the recovery of products, and the flow of reaction media (such as coolants or gases). The piping system is usually designed to withstand the pressure, temperature, and corrosion requirements of the reaction process to ensure its long-term stable operation. The acquisition of equipment data is the basis of the entire simulation modeling process, ensuring that the equipment model established subsequently is consistent with the characteristics of the actual process equipment and can accurately reflect the operating status of the equipment.

[0023] After obtaining the equipment data, a virtual equipment model is constructed based on these data. The equipment model includes multiple equipment modules, each of which represents a physical component and its function. For example, the microchannel reactor module describes the geometric structure, reaction area, fluid flow path, etc. of the microchannel reactor; the reaction cooler module simulates the reaction heat removal process, which usually includes factors such as heat exchange surface, cooling medium flow, heat conduction and heat dissipation; the metering feed pump module controls the precise delivery of raw materials according to parameters such as feed flow, pump efficiency, and pressure; the electrical instrument system module describes the layout of instrument equipment such as temperature sensors, pressure sensors, flow meters, etc. and the data acquisition and processing methods; the pipeline system module simulates the flow path and resistance characteristics of raw materials and reactants in the system.

[0024] Next, configure the material transfer relationships of the microchannel reactor, reaction cooler, and metering feed pump in the equipment model. In this step, define the connections and interactions between the various devices. For example, the reaction process in the microchannel reactor depends on the delivery of reactants, while the metering feed pump controls the feed flow rate to ensure the material balance of the reaction process. The material transfer relationship describes the flow process of the raw materials from the metering feed pump to the microchannel reactor, as well as the flow path of the products after the reaction, for example, they are transported to the reaction cooler through the pipeline system for cooling. Subsequently, a continuous flow network is established based on the above material transfer relationships, which reflects the continuity of the fluid in the entire process. The so-called continuous flow network refers to the state in which the material always remains in a flowing state from input to output without interruption or batch operation. Through this network, the flow and transmission of the entire production process can be simulated to ensure the coordination between equipment.

[0025] On this basis, the reaction process modeling of the plate-type microchannel continuous diazotization process is performed. The process modeling covers many aspects. The first is reaction model modeling, which describes the rate and conversion process of chemical reactions based on theories such as reaction rate equations. The second is heat transfer modeling, which simulates the generation and transfer of heat during the reaction process through the heat conduction equation to ensure that the reaction temperature is effectively controlled. The flow modeling simulates the flow characteristics of liquids or gases, and describes the velocity distribution, pressure changes, etc. of the fluid flowing in the microchannel. The above steps can accurately simulate and estimate the material conversion, heat transfer and fluid flow in the process through modeling of the reaction process, thereby providing data support for subsequent process optimization and actual operation.

[0026] Through this series of equipment modeling and process modeling steps, a realistic virtual simulation environment can be constructed, which can reflect various process conditions that may be encountered in actual production and provide training and decision-making support for operators.

[0027] The plate-type microchannel continuous diazotization process is monitored, a sample data set is established, and key sample points are configured after feature classification of the sample data set.

[0028] Optionally, the plate-type microchannel continuous diazotization process is comprehensively monitored. This process ensures that various parameters in the process (such as temperature, pressure, flow, reaction rate, etc.) are always kept within the preset normal range through real-time data collection by sensors, instruments and control systems. In this step, the monitoring equipment monitors the operating status of equipment such as reactors, coolers, and feed pumps in real time, and continuously obtains data streams of various process parameters through the data acquisition system, which constitute the sample data set. The establishment of the sample data set is the core step of the entire process control. Through the data points continuously collected during the process execution, a database containing a variety of process variables is formed. The data set contains detailed information such as temperature changes, pressure fluctuations, flow adjustments, etc. The sample data set reflects the dynamic changes of the system at different time points and provides basic data support for subsequent analysis.

[0029] After the sample data set is feature classified, the next task is to process and analyze the data in detail. Feature classification refers to classifying each data point in the sample data set according to its characteristics (such as temperature fluctuations, pressure change amplitude, etc.) to identify different types of operating modes or potential fault warnings. Through this process, different process behavior modes can be distinguished, including normal operating conditions, minor anomalies, and serious failures. Feature classification can be performed through algorithms such as cluster analysis and principal component analysis (PCA), with the aim of identifying the inherent laws of the data based on the changing trends of process parameters.

[0030] After feature classification, key sample points are further configured. Key sample points refer to monitoring data at certain specific moments in the process, which are crucial for predicting system behavior and diagnosing faults. By identifying and screening data points in the process, the system can determine a set of sample points that are critical to the process operation status. These key sample points can be selected based on several criteria, such as the stability of the reaction process, abnormal fluctuations in temperature and pressure, etc. For example, if the abnormal fluctuation of temperature at a certain moment is associated with a significant change in the reaction rate, the data point at that moment will be marked as a key sample point. These key sample points play an important role in the entire process monitoring, and they will be used for further reaction process modeling, simulation and anomaly detection. Through the analysis of these key points, the bottlenecks and potential problems in the reaction process can be accurately understood, so as to take targeted improvement measures. For example, if the heat accumulation of a reaction process at a specific temperature causes the reaction rate to decrease, then the abnormality of this key sample point will directly affect the subsequent process optimization and equipment adjustment.

[0031] In summary, through precise process execution monitoring, data collection, feature classification and configuration of key sample points, an efficient and accurate process control and monitoring method is provided, which provides reliable data support and decision-making basis for subsequent process optimization, fault prediction and equipment maintenance.

[0032] The key sample points are used to calibrate the reaction process modeling results, and the calibrated reaction process modeling results and the continuous flow network are fitted to the device model.

[0033] Further, these key sample points are used to calibrate the reaction process modeling results. The goal of this calibration process is to ensure that the reaction process model can truly and accurately reflect the key characteristics of material changes, heat transfer, flow state, etc. in the actual reaction process, so as to improve the prediction accuracy of the model. In the calibration process, the key sample points obtained by process monitoring, that is, the monitoring data points that are representative and reflect the process behavior in the process, are first compared with the reaction process model as input data. The use of key sample points is very critical because these sample points represent the typical behavior in the reaction process. Usually, in the reaction process, the physical parameters at certain time points or working conditions will change greatly, and these changes can effectively reveal the abnormality, fluctuation or potential failure in the reaction process. Therefore, these points become the core data that must be paid attention to when calibrating the model. In the calibration step, the error or deviation between the model output and the actual monitoring value is first identified by comparing the experimental data of the sample points with the modeling results. According to the size of the error, the parameter value in the reaction model is adjusted so that the result of the model output is consistent with the actual observed process result.

[0034] Next, by correcting the reaction process model, a more accurate and stable reaction process modeling result can be obtained. The corrected reaction process modeling result is more consistent with the process behavior in actual production than the uncorrected original model, thereby improving the credibility and adaptability of the model. Once the reaction process model is corrected, the corrected reaction process modeling result is fitted with the continuous flow network. The main purpose of this step is to ensure that the dynamic changes such as material flow and heat transfer in the reaction process can be well matched with other links in the entire process flow (such as microchannel reactors, reaction coolers and other equipment). The continuous flow network refers to a network model that describes the flow of materials, energy, reactants and products. It includes the connection relationship of all related equipment and the path of material flow. The fitting process is to combine the corrected reaction process model with the flow network of the entire process flow, so that the entire system can reflect the interaction between each device and its impact on the reaction process. At this point, the corrected reaction model can be closely combined with the actual situation of the equipment and material flow in the flow network to form a complete dynamic process system model. This step is of great significance for improving the accuracy of process prediction, optimizing process parameters, improving equipment operation status and improving the overall efficiency of the system. For example, in a microchannel reactor, when the reaction rate is high, the reaction heat needs to be removed through a reaction cooler. If the cooling capacity is insufficient at a certain point in time, the reaction temperature may be too high, resulting in incomplete reaction or side reactions. At this time, through reaction process modeling and flow network fitting, this problem can be identified and the parameters of the reaction cooler can be adjusted to ensure that the entire system remains stable during the process.

[0035] In summary, through the collection and application of key sample points, combined with the correction of the reaction process model and the fitting with the continuous flow network, the prediction and control accuracy of the reaction process is optimized, providing more accurate and reliable technical support for process optimization, equipment adjustment and fault prediction.

[0036] The operation mode of continuous feeding of raw materials and continuous discharge of reactants is set, and process simulation is performed according to the fitted equipment model.

[0037] Specifically, setting the operation mode of continuous feeding of raw materials and continuous discharge of reactants is a key step to ensure smooth, efficient and stable process flow. Continuous feeding of raw materials refers to the continuous input of reactants into the reaction system through a metering feed pump. This feeding process does not rely on batch operation, but ensures that the reactants are continuously supplied according to a predetermined flow rate, ratio and time during the entire reaction process, thereby maintaining the stability of the reaction. The operation mode of continuous discharge refers to the continuous discharge or transportation of the generated reactants to the downstream links through facilities such as reaction coolers and pipeline systems during the reaction process, avoiding fluctuations or instability that may be caused by intermittent operation. The setting of this operation mode is based on the results of the equipment model and reaction process modeling that have been completed above, ensuring that the input of raw materials and the output of reaction products can be highly matched with the operating state of the entire reaction system. The setting of the operation mode means that the flow rate of raw material input, the reaction rate and the amount of product discharge need to be accurately calculated, and the flow paths of these materials need to be reasonably arranged so that the entire reaction process is always in the best working state. For example, in a microchannel reactor, if the feed flow rate is too large, the reactants may not react fully, while if the feed flow rate is too small, it may cause heat accumulation during the reaction process, affecting the reaction efficiency. Therefore, when setting the operation mode, it is necessary to perform optimization adjustment based on the fitting results of the reaction model and the flow network.

[0038] According to the fitted equipment model, the system can simulate the entire reaction process in real time and predict the process results under different operation modes. The equipment model not only takes into account the physical and chemical changes in the reaction process, but also includes the interaction with the equipment, piping system and other auxiliary equipment. Therefore, when performing process simulation, based on the fitted equipment model, key parameters such as heat transfer, fluid flow, reaction rate, etc. in the reaction process can be accurately simulated, and dynamic adjustments can be made to ensure that each link plays a full role in the process. Process simulation is carried out through a computer simulation platform based on the above-mentioned operation mode and equipment model. The simulation platform can perform real-time simulation according to the preset process parameters, judge the reaction conditions under different conditions and predict possible process problems or equipment failures. For example, in the actual operation process, if the cooling system of a certain link fails to provide sufficient cooling capacity, the process simulation can detect the problem of temperature rise in advance and prompt the adjustment of cooling parameters. In addition, simulation can also be used to simulate important indicators such as product quality, reaction efficiency and energy consumption under different operation modes to help process personnel optimize process solutions. Process simulation not only helps to verify the feasibility of the operation mode, but also can perform sensitivity analysis on different operation parameters (such as feed flow, reaction temperature, pressure, etc.) to find the most suitable process conditions. For example, simulation can be used to analyze the changing trend of reaction rates at different temperatures and determine the optimal reaction temperature range to avoid poor reaction results due to excessively high or low temperatures.

[0039] In summary, by accurately setting the operating mode of continuous feeding of raw materials and continuous discharge of reactants, and combining the fitted equipment model for process simulation, the efficiency, stability and controllability of the entire reaction process are ensured, which greatly improves the process optimization, production efficiency and safety.

[0040] Furthermore, after the sample data set is feature classified, key sample points are configured, including:

[0041] A sample point scoring channel is established, and the sample point scoring channel is used to calculate the sample point scores in the sample data set, as follows:

[0042] .

[0043] in, Characterization sample points The sample point score, Characterization sample points The process parameters, Characterize controllable process variables, Characterize the sampling time window, is the time index, Characterized in Sample point in time The process parameters, Characterized in Sample point in time The process parameters, Characterization sample points The volatility score of is the total number of sample points in the sample data set, Characterization sample points With sample points The process parameter covariance, , The sample points With sample points The standard deviation of process parameters, Different from the sample point Any sample point of Characterization sample points The spatial distance is the sensitivity weighting coefficient, is the volatility weighted coefficient, is the correlation weighting coefficient, Spatial distance weighting coefficient.

[0044] Establish key sample points based on the sample point scoring calculation results.

[0045] In a specific embodiment, a sample point scoring channel is established, and the sample point scoring channel is used to calculate the sample point scores in the sample data set, and then the key sample points are determined based on the calculation results. The specific sample point scoring formula is: ;in, Characterization sample points The sample point score, Characterization sample points The process parameters, Characterize controllable process variables, Characterize the sampling time window, is the time index, Characterized in Sample point in time The process parameters, Characterized in Sample point in time The process parameters, Characterization sample points The volatility score of is the total number of sample points in the sample data set, Characterization sample points With sample points The process parameter covariance, , The sample points With sample points The standard deviation of process parameters, Different from the sample point Any sample point of Characterization sample points The spatial distance is the sensitivity weighting coefficient, is the volatility weighted coefficient, is the correlation weighting coefficient, Spatial distance weighting coefficient.

[0046] Specifically, by comprehensively using sensitivity analysis, variance calculation and distance evaluation, in-depth analysis of process variables is carried out. ) analysis is used to measure specific process variables Responsiveness to changes in operating conditions is a measure of how well a sample point responds to process fluctuations. ; It calculates the process variable For operating parameters The partial derivative of And the average value of the amplitude of parameter fluctuations in the time series. Sensitivity analysis helps operators understand which parameters have the greatest impact on the production process so that they can prioritize and control these parameters. For example, if temperature changes have a significant impact on product quality, sensitivity analysis will reveal this, allowing operators to control reaction temperature more accurately. Among them, variance ( ) is used to evaluate specific process variables The volatility score is the degree of volatility within a certain period of time. Its time average The variance is obtained by taking the average of the squares of the deviations between the two variables. This measurement is crucial for identifying unstable factors in the process and helps operators take measures to reduce fluctuations in the production process, thereby improving product consistency and quality. Distance assessment calculates the spatial distance between process variables at different time points ( ), which reflects the similarity or difference between variables, that is, the uniform distribution of sample points k in space. ;in, represents the physical distance between sample points k and j in the microchannel; The larger the value, the more likely the sample point k is to fill in the unsampled area. This evaluation helps to identify variables that exhibit different behavior patterns in the production process and provides operators with a basis for adjusting production strategies, especially when multiple variables need to be coordinated to optimize the entire production process.

[0047] By integrating these analytical technologies into the monitoring and control of the production process, not only can the efficiency and safety of chemical production be significantly improved, but also the consistency and superiority of product quality can be ensured. This approach is particularly suitable for chemical manufacturing environments with many parameters and complex processes.

[0048] Furthermore, the reaction process modeling of the plate-type microchannel continuous diazotization process includes:

[0049] Configure the reaction rate equation and establish the main reaction model.

[0050] The side reactions of the plate-type microchannel continuous diazotization process were identified and the side reaction network was established.

[0051] Conservation constraints are established, including material conservation constraints and energy conservation constraints. The main reaction model and the side reaction network are coupled based on the conservation constraints to establish the reaction model.

[0052] Furthermore, the reaction rate equation is configured to establish the main reaction model. In this step, it is first necessary to determine the rate equation of the reaction, which is a mathematical expression that describes how the chemical reaction rate depends on the concentration of the reactants, temperature and other influencing factors. Configuring the reaction rate equation is the basis for establishing a reliable reaction model because it directly affects the calculation and prediction of the reaction rate. In the plate microchannel continuous diazotization process, the microchannel structure of the reactor is particularly critical for the precise control of fluid dynamics and heat transfer characteristics, which requires the model to accurately describe these processes. By configuring the appropriate rate equation, the established main reaction model can accurately predict the behavior of the main chemical reaction and the product generation rate.

[0053] Next, the side reactions of the plate-type microchannel continuous diazotization process were identified and a side reaction network was established. The identification of side reactions is to ensure that all chemical reactions that may affect product quality and yield can be fully controlled. In chemical processes, side reactions may often lead to undesirable products or affect the efficiency and safety of the entire reaction. Therefore, it is crucial to identify these side reactions and incorporate them into a side reaction network. This network not only contains the paths and conditions of various side reactions, but also shows their interactions with the main reaction. In this way, the reaction conditions can be better controlled and optimized, and the impact of adverse side reactions can be reduced.

[0054] After that, conservation constraints are established to couple the main reaction model and the side reaction network. Conservation constraints are the basic laws that must be followed during chemical reactions, including conservation of material and conservation of energy. Conservation of material ensures that the total amount of material before and after the reaction remains unchanged, while conservation of energy ensures that the total amount of energy during the reaction remains stable. Applying these conservation constraints in the model ensures that the physical and chemical laws of the model are not violated. Applying these conservation constraints to couple the main reaction model and the side reaction network makes the simulation of the entire chemical reaction system more realistic and accurate. The coupling process takes into account the interactions and influences of all reaction paths, so that the behavior of the entire reaction system can be fully predicted.

[0055] Through the above steps, a highly optimized and controllable chemical reaction environment can be provided for the plate-type microchannel continuous diazotization process. This not only helps to improve the quality and output of the product, but also significantly enhances the safety and economy of the process, thereby ensuring the simulation optimization effect.

[0056] Furthermore, the method further comprises:

[0057] The device model after fitting is configured to operate the feedback sensor.

[0058] The fitted equipment model is used to perform practical training, and the practical data is recorded through the operation feedback sensor.

[0059] A simulation result mapping between the actual operation data and the fitted device model is established.

[0060] A practical operation report is generated according to the mapping, and the simulation results are visualized in real time.

[0061] Exemplarily, operation feedback sensors are configured in the fitted equipment model. These sensors are arranged at key control points to monitor and record various parameters in actual operation, such as temperature, pressure, flow, etc. These sensors not only provide real-time data feedback, but also serve as the basis for practical training and process control. Practical training is carried out using the fitted equipment model, and all operation data are recorded in real time through the previously configured sensors during the training process. This training allows operators to simulate various operation scenarios in the control system to understand and master complex chemical reaction processes. The collection of practical data provides an empirical basis for subsequent data analysis and process optimization. Then, the collected practical data is mapped and analyzed with the simulation results of the fitted equipment model. The key to this step is to compare the variable changes in the actual operation with the theoretical output predicted by the model to analyze the deviation and consistency. Through this mapping, the possible sources of deviation in the operation can be understood in detail and the operation guide can be optimized. Based on these analysis results, a practical report is generated, which details the key findings and improvement suggestions in the operation process. Finally, the obtained simulation results and practical data are visualized in real time, which not only enables the operator to intuitively see the impact of each operation step, but also to receive system feedback and adjustment instructions in real time. Real-time visualization is key to improving operational transparency and responsiveness, ensuring operators can respond quickly and accurately to any changes in the process.

[0062] Through the above steps, the control accuracy of the chemical production process and the skill level of operators have been significantly improved, ensuring the efficient and safe operation of the production process. In addition, the implementation of this system is of great significance for improving process design, process optimization and emergency response capabilities.

[0063] Furthermore, the method further comprises:

[0064] Establish cooling monitoring thresholds mapped with reaction stages.

[0065] When the process simulation is performed based on the fitted equipment model, a data intercommunication channel is established, and the data intercommunication channel is used to synchronize the reaction data in the microchannel reactor to the reaction cooler.

[0066] The cooling monitoring threshold is activated according to the synchronization result, and the cooling abnormality identification of the reaction cooler is performed.

[0067] According to the cooling abnormality identification results, a simulation abnormality report is issued.

[0068] Optionally, by establishing a dynamic data communication channel between the reaction stage and the cooling system, the reaction state and the cooling demand are synchronized in real time, thereby optimizing the cooling process and responding to potential cooling anomalies in a timely manner. Specifically, establishing a cooling monitoring threshold mapped to the reaction stage mainly involves defining when and under what conditions the cooling process should be adjusted or monitored. By analyzing the heat load and product characteristics during the reaction process, a series of cooling demand parameters based on specific reaction stages are set, which will serve as the basis for the response of the monitoring system. For example, when the reactants generate a large amount of heat energy after a violent reaction stage, the response threshold of the cooling system will be set more sensitive to ensure timely removal of heat energy. Next, when performing process simulation based on the fitted equipment model, a data communication channel is established, which is responsible for seamlessly synchronizing the real-time reaction data inside the microchannel reactor, such as temperature, pressure, and reaction rate, to the reaction cooler control system. This step ensures that the cooler can adjust its operating parameters according to the actual working conditions inside the reactor, optimize cooling efficiency and reduce energy consumption. Based on data synchronization, the system will activate the cooling monitoring threshold according to the synchronization results and perform automatic identification of cooling anomalies. This process is carried out by comparing real-time data with preset cooling thresholds. Once the data is detected to be outside the normal range, the abnormality identification mechanism is triggered. This mechanism is essential to prevent equipment overheating or chemical reaction runaway due to improper cooling. Finally, simulated abnormality reporting is performed based on the cooling abnormality identification results. This step involves feeding back any identified cooling abnormality information to the operator or automatic control system, providing necessary alarms and suggesting or automatically executing corresponding adjustment measures. This ensures the safety and efficiency of the entire chemical reaction process, while reducing product quality problems or safety accidents that may occur due to cooling failure.

[0069] Through the above steps, an efficient and reliable cooling monitoring and abnormal handling solution is provided, which provides important technical support for thermal management and process safety control in microchannel chemical reaction systems and significantly improves the stability and safety of chemical production.

[0070] Furthermore, the method further comprises:

[0071] A reaction completion monitoring model is established, and the device model fitted with the reaction completion monitoring model is used to simulate reaction monitoring, and a simulation reaction monitoring result is established.

[0072] A continuous discharge pressure parameter is generated according to the simulation reaction monitoring result, and material discharge control is performed through the continuous discharge pressure parameter.

[0073] Furthermore, a reaction monitoring model is established and material emission control is adjusted based on the model results to ensure the efficiency and safety of the chemical reaction. Specifically, the reaction completion monitoring model aims to evaluate the progress and completion of the chemical reaction in real time. This model estimates the completion of the reaction by analyzing the consumption rate of the reactants, the generation rate of the products, and the reaction conditions (such as temperature, pressure, etc.). This model uses mathematical and chemical principles, combined with experimental data and theoretical calculations, to provide an accurate indication of the reaction progress. This step is the key to ensure that the entire chemical reaction proceeds along the predetermined path, and helps to adjust the operating parameters in real time and optimize the reaction efficiency. After the reaction completion monitoring model is established, the model is used to simulate the reaction monitoring of the fitted equipment model. This process includes inputting the actual operating conditions into the model, simulating the reaction process, and predicting the dynamic changes of the reaction. Through this simulation, the impact of changes in reaction conditions on the reaction progress can be evaluated, thereby achieving more efficient process control and risk management. Detailed simulation reaction monitoring results are generated through the above simulation monitoring, which describe in detail the changes in various parameters during the reaction process, such as reaction rate, temperature gradient, pressure change, etc., providing data support for further analysis and decision-making. These data are not only used for monitoring and diagnosis, but also the basis for optimizing reaction conditions and improving product quality. Finally, based on the simulation reaction monitoring results, pressure parameters for continuous material discharge are generated. These pressure parameters are calculated based on the reaction completion and the current pressure in the reaction vessel to ensure that the material discharge is safe and effective throughout the discharge process. By precisely controlling the discharge pressure, the risk of excessive or low pressure inside the reaction vessel can be avoided, the stability of the system can be maintained, and the quality and purity of the product can be ensured. Ultimately, these pressure parameters are applied to the control system to automatically adjust the operation of the discharge equipment to adapt to the reaction progress and system requirements.

[0074] Through the above steps, a comprehensive method is provided to monitor and control the plate microchannel continuous diazotization process, which significantly improves the safety, efficiency and controllability of the chemical production process through precise reaction completion monitoring and data-based material emission control.

[0075] Furthermore, the method further comprises:

[0076] A real-time operation data set of a device is obtained, and the real-time operation data set is synchronized to the fitted device model.

[0077] The equipment model is used to predict hidden dangers based on the real-time operation data set, and a hidden danger prediction result is established.

[0078] Equipment abnormality management is performed based on the hidden danger prediction results.

[0079] Specifically, the operation safety and efficiency of the equipment can be improved by collecting real-time operation data from the equipment and using the equipment model to predict hidden dangers and manage abnormalities. The step of obtaining the real-time operation data set of the equipment involves using sensors and monitoring systems to continuously collect various data about the operation status of the equipment, such as key parameters such as temperature, pressure, flow rate and chemical composition. These data are regarded as key indicators describing the current working status of the equipment and are essential for timely capturing equipment operation trends and potential problems. Next, the real-time operation data set is synchronized to the fitted equipment model. This synchronization process ensures that all collected real-time data can be fed back to the equipment model in real time. This model is accurately fitted based on historical data and operating experience, and can process real-time data through algorithms, simulate the operation of the equipment, and predict future operating trends. Then, the equipment model is used to predict hidden dangers based on the real-time operation data set. In this step, the model analyzes the synchronized data set and identifies patterns and trends that may lead to equipment performance degradation or failure. The hidden danger prediction results can reveal potential risk points and precursors to failures, providing a scientific basis for taking preventive measures. Finally, equipment abnormality management is performed based on the hidden danger prediction results, which includes formulating and implementing corresponding maintenance strategies based on the prediction results, such as adjusting operating parameters, performing regular maintenance or emergency shutdown, etc. In addition, abnormality management also includes optimizing maintenance plans and budget allocations to ensure that the equipment operates in the best condition while reducing downtime and economic losses caused by failures. This method can not only improve the operational safety and reliability of the equipment, but also significantly optimize maintenance and operating costs, and optimize the rigor of practical operations.

[0080] Through the technical solutions of the above embodiments, a plate-type microchannel continuous diazotization process simulation method provided in the present application solves the technical problems that the existing plate-type microchannel continuous diazotization process education and practical training models have high training costs, cannot effectively combine real equipment operation and theoretical learning, resulting in limitations in practical training teaching in multiple scenarios, and are difficult to popularize and improve skills. It achieves efficient and precisely controlled continuous diazotization process simulation, greatly improves operational safety and the practicality of process education, and effectively enhances the technical effect of practitioners' understanding of complex chemical processes and their ability to operate.

[0081] Embodiment 2, based on the same inventive concept as the simulation method of a plate-type microchannel continuous diazotization process in the above embodiment, Figure 2 As shown, the present application provides a plate-type microchannel continuous diazotization process simulation device, the device comprising:

[0082] The equipment model building unit 11 is used to obtain equipment data of the plate-type microchannel continuous diazotization process, perform equipment simulation modeling based on the equipment data, and establish an equipment model. The equipment model includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrument system, and a pipeline system.

[0083] The model configuration unit 12 is used to configure the material transmission relationship of the microchannel reactor, the reaction cooler, and the metering feed pump in the equipment model to establish a continuous flow network.

[0084] The reaction modeling unit 13 is used to perform reaction process modeling of the plate-type microchannel continuous diazotization process, wherein the reaction process modeling includes reaction modeling, heat transfer modeling, and flow modeling.

[0085] The execution monitoring unit 14 is used to perform process execution monitoring on the plate-type microchannel continuous diazotization process, establish a sample data set, perform feature classification on the sample data set, and configure key sample points.

[0086] The modeling correction unit 15 is used to correct the reaction process modeling results using the key sample points, and fit the corrected reaction process modeling results and the continuous flow network to the equipment model.

[0087] The process simulation unit 16 is used to set the operation mode of continuous feeding of raw materials and continuous discharge of reactants, and to perform process simulation according to the fitted equipment model.

[0088] Furthermore, the execution monitoring unit 14 is further configured to perform the following steps:

[0089] A sample point scoring channel is established, and the sample point scoring channel is used to calculate the sample point scores in the sample data set, as follows:

[0090] .

[0091] in, Characterization sample points The sample point score, Characterization sample points The process parameters, Characterize controllable process variables, Characterize the sampling time window, is the time index, Characterized in Sample point in time The process parameters, Characterized in Sample point in time The process parameters, Characterization sample points The volatility score of is the total number of sample points in the sample data set, Characterization sample points With sample points The process parameter covariance, , The sample points With sample points The standard deviation of process parameters, Different from the sample point Any sample point of Characterization sample points The spatial distance is the sensitivity weighting coefficient, is the volatility weighted coefficient, is the correlation weighting coefficient, Spatial distance weighting coefficient.

[0092] Establish key sample points based on the sample point scoring calculation results.

[0093] Furthermore, the reaction modeling unit 13 is further configured to perform the following steps:

[0094] Configure the reaction rate equation and establish the main reaction model.

[0095] The side reactions of the plate-type microchannel continuous diazotization process were identified and the side reaction network was established.

[0096] Conservation constraints are established, including material conservation constraints and energy conservation constraints. The main reaction model and the side reaction network are coupled based on the conservation constraints to establish the reaction model.

[0097] Furthermore, the device further comprises a feedback recording unit, and the feedback recording unit is used to perform the following steps:

[0098] The device model after fitting is configured to operate the feedback sensor.

[0099] The fitted equipment model is used to perform practical training, and the practical data is recorded through the operation feedback sensor.

[0100] A simulation result mapping between the actual operation data and the fitted device model is established.

[0101] A practical operation report is generated according to the mapping, and the simulation results are visualized in real time.

[0102] Furthermore, the device further comprises a cooling abnormality identification unit, and the cooling abnormality identification unit is used to perform the following steps:

[0103] Establish cooling monitoring thresholds mapped with reaction stages.

[0104] When the process simulation is performed based on the fitted equipment model, a data intercommunication channel is established, and the data intercommunication channel is used to synchronize the reaction data in the microchannel reactor to the reaction cooler.

[0105] The cooling monitoring threshold is activated according to the synchronization result, and the cooling abnormality identification of the reaction cooler is performed.

[0106] According to the cooling abnormality identification results, a simulation abnormality report is issued.

[0107] Furthermore, the device further comprises a completion monitoring unit, and the completion monitoring unit is configured to perform the following steps:

[0108] A reaction completion monitoring model is established, and the device model fitted with the reaction completion monitoring model is used to simulate reaction monitoring, and a simulation reaction monitoring result is established.

[0109] A continuous discharge pressure parameter is generated according to the simulation reaction monitoring result, and material discharge control is performed through the continuous discharge pressure parameter.

[0110] Furthermore, the device further comprises a hidden danger prediction unit, and the hidden danger prediction unit is used to perform the following steps:

[0111] A real-time operation data set of a device is obtained, and the real-time operation data set is synchronized to the fitted device model.

[0112] The equipment model is used to predict hidden dangers based on the real-time operation data set, and a hidden danger prediction result is established.

[0113] Equipment abnormality management is performed based on the hidden danger prediction results.

[0114] Through the above-mentioned detailed description of a plate-type microchannel continuous diazotization process simulation method, a person skilled in the art can clearly know a plate-type microchannel continuous diazotization process simulation device in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0115] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be 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 the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A plate-type microchannel continuous diazotization process simulation method, characterized in that: The method comprises: Acquire equipment data of a plate-type microchannel continuous diazotization process, perform equipment simulation modeling according to the equipment data, and establish an equipment model, wherein the equipment model includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrument system, and a pipeline system; Configure the material transmission relationship of the microchannel reactor, reaction cooler, and metering feed pump in the equipment model to establish a continuous flow network; Execute reaction process modeling of plate-type microchannel continuous diazotization process, wherein the reaction process modeling includes reaction modeling, heat transfer modeling, and flow modeling; Performing process execution monitoring on a plate-type microchannel continuous diazotization process, establishing a sample data set, and configuring key sample points after feature classification of the sample data set; Correcting the reaction process modeling results using the key sample points, and fitting the corrected reaction process modeling results and the continuous flow network to the device model; The operation mode of continuous feeding of raw materials and continuous discharge of reactants is set, and process simulation is performed according to the fitted equipment model.

2. A plate-type microchannel continuous diazotization process simulation method as claimed in claim 1, characterized in that: After the sample data set is feature classified, key sample points are configured, including: A sample point scoring channel is established, and the sample point scoring channel is used to calculate the sample point scores in the sample data set, as follows: ； in, Characterization sample points The sample point score, Characterization sample points The process parameters, Characterize controllable process variables, Characterize the sampling time window, is the time index, Characterized in Sample point in time The process parameters, Characterized in Sample point in time The process parameters, Characterization sample points The volatility score of is the total number of sample points in the sample data set, Characterization sample points With sample points The process parameter covariance, , The sample points With sample points The standard deviation of process parameters, Different from the sample point Any sample point of Characterization sample points The spatial distance is the sensitivity weighting coefficient, is the volatility weighted coefficient, is the correlation weighting coefficient, Spatial distance weighting coefficient; Establish key sample points based on the sample point scoring calculation results.

3. A plate-type microchannel continuous diazotization process simulation method as claimed in claim 1, characterized in that: The reaction process modeling of the plate-type microchannel continuous diazotization process includes: Configure the reaction rate equation and establish the main reaction model; Identify side reactions in the plate-type microchannel continuous diazotization process and establish a side reaction network; Conservation constraints are established, including material conservation constraints and energy conservation constraints. The main reaction model and the side reaction network are coupled based on the conservation constraints to establish the reaction model.

4. A plate-type microchannel continuous diazotization process simulation method as claimed in claim 1, characterized in that: The method further comprises: configuring an operating feedback sensor on the fitted device model; Performing practical training using the fitted device model, and recording practical data through the operation feedback sensor; Establishing a simulation result mapping between the actual operation data and the fitted device model; A practical operation report is generated according to the mapping, and the simulation results are visualized in real time.

5. A plate-type microchannel continuous diazotization process simulation method as claimed in claim 1, characterized in that: The method further comprises: Establishing cooling monitoring thresholds mapped to reaction stages; When the process simulation is performed based on the fitted equipment model, a data intercommunication channel is established, and the data intercommunication channel is used to synchronize the reaction data in the microchannel reactor to the reaction cooler; Activate cooling monitoring thresholds according to synchronization results and perform cooling anomaly identification of the reaction cooler; According to the cooling abnormality identification results, a simulation abnormality report is issued.

6. A plate-type microchannel continuous diazotization process simulation method as claimed in claim 1, characterized in that: The method further comprises: Establishing a reaction completion monitoring model, using the reaction completion monitoring model to perform simulated reaction monitoring of the device model after fitting, and establishing simulated reaction monitoring results; A continuous discharge pressure parameter is generated according to the simulation reaction monitoring result, and material discharge control is performed through the continuous discharge pressure parameter.

7. A plate-type microchannel continuous diazotization process simulation method as claimed in claim 1, characterized in that: The method further comprises: Acquire a real-time operation data set of a device, and synchronize the real-time operation data set to the fitted device model; Using the equipment model to predict hidden dangers based on the real-time operation data set, and establishing hidden danger prediction results; Equipment abnormality management is performed based on the hidden danger prediction results.

8. A plate-type microchannel continuous diazotization process simulation device, characterized in that: A plate-type microchannel continuous diazotization process simulation method for implementing any one of claims 1 to 7, the device comprising: An equipment model building unit is used to obtain equipment data of a plate-type microchannel continuous diazotization process, perform equipment simulation modeling according to the equipment data, and establish an equipment model, wherein the equipment model includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrument system, and a pipeline system; The model configuration unit is used to configure the material transmission relationship of the microchannel reactor, reaction cooler, and metering feed pump in the equipment model to establish a continuous flow network; A reaction modeling unit, used for performing reaction process modeling of a plate-type microchannel continuous diazotization process, wherein the reaction process modeling includes reaction modeling, heat transfer modeling, and flow modeling; An execution monitoring unit is used to perform process execution monitoring on the plate-type microchannel continuous diazotization process, establish a sample data set, perform feature classification on the sample data set, and configure key sample points; A modeling correction unit, used to correct the reaction process modeling results using the key sample points, and fit the corrected reaction process modeling results and the continuous flow network to the device model; The process simulation unit is used to set the operation mode of continuous feeding of raw materials and continuous discharge of reactants, and to perform process simulation according to the fitted equipment model.

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