A simulation method and apparatus for continuous diazotization process in plate-type microchannels

Abstract

This application provides a simulation method and apparatus for a continuous diazotization process using a plate-type microchannel, relating to the field of data processing technology. It involves establishing an equipment model based on process equipment data, configuring material transport relationships within the equipment model to create a continuous flow network, performing reaction process modeling, monitoring process execution, establishing a sample dataset for feature classification, configuring key sample points, using these key sample points to correct the reaction process modeling results, and fitting the corrected reaction process modeling results and the continuous flow network to the equipment model. It also sets up an operation mode of continuous raw material feeding and continuous reactant discharge, and performs process simulation based on the fitted equipment model. This method solves the problem of excessively high training costs and limitations in practical training across multiple scenarios associated with plate-type microchannel continuous diazotization process education and hands-on training.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a simulation method and apparatus for a continuous diazotization process in a plate-type microchannel. Background Technology

[0002] In the fields of chemical engineering and fine chemicals, diazotization is widely used in organic synthesis, playing a particularly important role in industries such as biopharmaceuticals and new materials. Diazotization is the process by which a primary amine reacts with nitrous acid to form a diazonium salt, typically carried out at low temperatures to prevent the decomposition of nitrous acid and the diazonium salt. However, because diazotization is an exothermic reaction, and the reactivity of reactants and products differs significantly, precise temperature and reaction control are essential. Improper control can lead to runaway reactions and safety accidents. Furthermore, the production mode of diazotization typically relies on batch production processes, which not only suffer from inflexibility, low automation, bulky equipment, and low operating efficiency, but also face significant safety risks.

[0003] Currently, some companies are attempting to improve their existing production models using multi-stage batch reactor processes. However, this method still faces many challenges, such as large equipment size, insufficient automation, high operator skill requirements, and a lack of effective real-time monitoring and fault warning mechanisms, making safety hazards during production difficult to avoid. To overcome these technical challenges, plate-type microchannel continuous diazotization processes are gradually becoming a viable alternative to traditional batch reactor processes. Plate-type microchannel reactors offer smaller reaction volumes, higher heat transfer efficiency, and greater safety. Through optimized microchannel structures, plate-type microchannel processes can better control the reaction process, reduce heat accumulation during the reaction, and thus improve reaction stability and safety. However, although the plate microchannel continuous diazotization process has improved production efficiency and safety, the relevant technology currently lacks a comprehensive training and hands-on system, especially in terms of process flow, equipment operation, and safety monitoring. Due to the high training costs and limited operating funds, relevant personnel often have to rely on traditional "understanding-based teaching" or "learning by doing" methods. Furthermore, existing simulation systems lack integration with real equipment and processes, failing to comprehensively improve the skill level of operators and greatly limiting the learners' mastery of plate microchannel continuous diazotization process knowledge and the development of their abilities.

[0004] In summary, the existing education and practical training models for plate-type microchannel continuous diazotization processes suffer from excessively high training costs, an inability to effectively combine real equipment operation with theoretical learning, and limitations in practical training across multiple scenarios, making it difficult to popularize and improve skills. Summary of the Invention

[0005] This application provides a simulation method and apparatus for plate-type microchannel continuous diazotization process, which addresses the technical problems of existing plate-type microchannel continuous diazotization process education and practical training models, which suffer from excessively high training costs, inability to effectively combine real equipment operation and theoretical learning, resulting in limitations in practical training in multiple scenarios and difficulty in popularizing and improving skills.

[0006] In view of the above problems, this application provides a simulation method and apparatus for continuous diazotization process in plate microchannels.

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

[0008] Acquire equipment data for the plate-type microchannel continuous diazotization process, perform equipment simulation modeling based on the equipment data, and establish an equipment model including a microchannel reactor, reaction cooler, metering feed pump, electrical instrumentation system, and piping system; configure the material transfer relationships of the microchannel reactor, reaction cooler, and metering feed pump in the equipment model, and establish a continuous flow network; perform reaction process modeling for the plate-type microchannel continuous diazotization process, including reaction modeling, heat transfer modeling, and flow modeling; monitor the process execution of the plate-type microchannel continuous diazotization process, establish a sample dataset, perform feature classification on the sample dataset, and configure key sample points; use the key sample points to correct the reaction process modeling results, and fit the corrected reaction process modeling results and continuous flow network to the equipment model; set the operation mode of continuous feed of raw materials and continuous discharge of reactants, and perform process simulation based on the fitted equipment model.

[0009] Secondly, this application provides a simulation device for a plate-type microchannel continuous diazotization process, the device comprising:

[0010] The equipment model construction unit is used to acquire equipment data for the plate microchannel continuous diazotization process, perform equipment simulation modeling based on the equipment data, and establish an equipment model including a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrumentation system, and a piping system. The model configuration unit is used to configure the material transfer relationships of the microchannel reactor, reaction cooler, and metering feed pump in the equipment model, and establish a continuous flow network. The reaction modeling unit is used to perform reaction process modeling for the plate microchannel continuous diazotization process, including reaction modeling, heat transfer modeling, and flow modeling. The execution monitoring unit is used to monitor the process execution of the plate microchannel continuous diazotization process, establish a sample dataset, perform feature classification on the sample dataset, and configure key sample points. 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 feed of raw materials and continuous discharge of reactants, and perform process simulation based on the fitted equipment model.

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

[0012] This application provides a simulation method for a continuous diazotization process using plate-type microchannels. The method involves acquiring equipment data for the continuous diazotization process using plate-type microchannels, performing equipment simulation modeling based on this data, and establishing an equipment model that includes a microchannel reactor, a reaction cooler, a metering pump, an electrical instrumentation system, and a piping system. The method also configures the material transfer relationships among the microchannel reactor, reaction cooler, and metering pump in the equipment model to establish a continuous flow network. Furthermore, the method performs reaction process modeling for the continuous diazotization process using plate-type microchannels, including reaction modeling, heat transfer modeling, and flow modeling. Finally, the method monitors the process execution, establishes a sample dataset, performs feature classification on the sample dataset, and configures key sample points. The reaction process modeling results are corrected using the key sample points, and the corrected reaction process modeling results and continuous flow network are fitted to the equipment model. A continuous feed and reactant discharge operation mode is set, and process simulation is performed based on the fitted equipment model. This solves the technical problems of existing plate-type microchannel continuous diazotization process education and practical training modes, which suffer from high training costs, inability to effectively combine real equipment operation and theoretical learning, and limitations in practical training in multiple scenarios, making it difficult to popularize and improve skills. It achieves efficient and precise control of continuous diazotization process simulation, significantly improving operational safety and the practicality of process education, and effectively enhancing practitioners' understanding and operational capabilities of complex chemical processes. Attached Figure Description

[0013] Figure 1 This application provides a schematic flowchart of a simulation method for a continuous diazotization process using a plate-type microchannel.

[0014] Figure 2 This application provides a schematic diagram of a plate-type microchannel continuous diazotization process simulation device.

[0015] Explanation of reference numerals in the attached figures: 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 Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0017] This application proposes a simulation method and apparatus for a continuous diazotization process using plate-type microchannels. It employs an integrated hardware and software configuration, configuring a simulation system that matches the actual production process. This system includes a process system, equipment system, electrical instrumentation system, simulation operation system, and assessment and scoring system. Specifically, the simulation system comprises core equipment such as a plate-type microchannel reactor, a plate-type microchannel reaction cooler, and a diaphragm metering feed pump, along with supporting electrical instrumentation and piping systems. Based on this system configuration, theoretical and practical training can be conducted on the production operation flow, microchannel structure and principles, process principles, process operation control, and risk identification and management of the continuous diazotization process using plate-type microchannels. This allows learners to gain a more intuitive, in-depth, and holistic understanding of microchannel reactors and continuous diazotization production, easily grasping the process technology principles and operations. It effectively solves the challenges of understanding microchannel continuous diazotization technology and providing practical training in various scenarios for relevant professionals.

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

[0019] Acquire equipment data for the plate-type microchannel continuous diazotization process, perform equipment simulation modeling based on the equipment data, and establish an equipment model including a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrumentation system, and a piping system.

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

[0021] The reaction process of performing continuous diazotization in plate microchannels is modeled, including reaction modeling, heat transfer modeling, and flow modeling.

[0022] Specifically, it involves collecting parameter data from key equipment including microchannel reactors, reaction coolers, metering pumps, electrical instrumentation systems, and piping systems. This equipment data can be obtained through on-site monitoring, sensor readings, or experimental measurements, such as process variables like flow rate, temperature, and pressure. A microchannel reactor is a reactor with multiple tiny channels, typically employing a plate structure. It utilizes these microchannels to enhance heat transfer, mixing, and reaction efficiency between materials and the reaction medium. During the reaction, reactants are fed into the reaction zone through these tiny channels. These microchannels provide efficient heat exchange and mass transfer, allowing for precise temperature control. Due to the high specific surface area of ​​microchannels, the reaction rate is relatively fast, and it effectively removes reaction heat, avoiding reaction byproducts or safety hazards caused by high temperatures. A reaction cooler is used to remove excess heat during a chemical reaction, ensuring that the temperature inside the reactor does not exceed the design temperature to maintain reaction stability and improve safety. Through contact with a cooling medium, the reaction cooler can rapidly reduce the temperature of the reaction system, preventing overheating that could lead to reactant decomposition or adverse reactions. It is usually connected to the reactor to maintain the reaction process within an appropriate temperature range, which is especially crucial in exothermic reactions. A metering feed pump is a pump device used to precisely control the flow rate of reactants, typically used to deliver liquid or gaseous feedstocks into the reaction system. Metering feed pumps can accurately deliver feedstocks to the reactor at a predetermined flow rate, ensuring that reactants continuously enter the reaction system in the designed proportions. For microchannel reactors, precise feed control is crucial because incorrect flow rates can affect reaction rates, temperature, and material balance. The electrical instrumentation system, composed of various sensors, controllers, displays, and alarm devices, is used to monitor and control various parameters in the chemical reaction process, such as temperature, pressure, and flow rate. This system can monitor the operating status of equipment such as the microchannel reactor, reaction cooler, and metering feed pump in real time, and adjust the operating parameters of the equipment based on feedback data. The electrical instrumentation system can also be used for equipment fault diagnosis and alarms, ensuring the safety and stability of the reaction process. The piping system, composed of pipes, valves, and joints, is a material conveying system used to transport and circulate reactants and products between different devices. The piping system connects equipment such as the microchannel reactor, reaction cooler, and metering feed pump, ensuring unobstructed flow paths for reactants. It is responsible for the transportation of raw materials, the recovery of products, and the flow of reaction media (such as coolants or gases). Piping systems are typically designed to withstand the pressure, temperature, and corrosion requirements of the reaction process to ensure long-term stable operation. Acquiring equipment data is fundamental to the entire simulation modeling process, ensuring that the subsequently built equipment model matches the characteristics of the actual process equipment and accurately reflects its operating status.

[0023] After acquiring the equipment data, a virtual equipment model is constructed based on this data. The equipment model includes multiple equipment modules, each representing a physical component and its function. For example, the microchannel reactor module describes the geometry, reaction zone, and fluid flow path of the microchannel reactor; the reaction cooler module simulates the heat removal process, typically including factors such as heat exchange surfaces, cooling medium flow, heat conduction, and heat dissipation; the metering feed pump module controls the precise delivery of raw materials based on parameters such as feed flow rate, pump efficiency, and pressure; the electrical instrumentation system module describes the layout of instrumentation equipment such as temperature sensors, pressure sensors, and flow meters, as well as the data acquisition and processing methods; and the piping system module simulates the flow paths and resistance characteristics of raw materials and reactants within the system.

[0024] Next, the material transport relationships of the microchannel reactor, reaction cooler, and metering pump in the equipment model are configured. In this step, the connections and interactions between the various devices are defined. For example, the reaction process in the microchannel reactor depends on the delivery of reactants, while the metering pump controls the feed flow rate to ensure material balance during the reaction. The material transport relationships describe the flow of raw materials from the metering pump to the microchannel reactor, as well as the flow path of the post-reaction products, for example, being transported to the reaction cooler for cooling via a piping system. Subsequently, a continuous flow network is established based on the above material transport relationships. This network reflects the continuity of fluids throughout the entire process. A continuous flow network refers to a network where materials remain in a state of constant flow from input to output without interruption or batch operation. This network allows for the simulation of flow and transport throughout the entire production process, ensuring coordination between devices.

[0025] Building upon this foundation, the reaction process of the continuous diazotization process using plate-type microchannels was modeled. This modeling encompassed several aspects. First, reaction modeling, based on theories such as reaction rate equations, described the rate and transformation process of the chemical reaction. Second, heat transfer modeling simulated the generation and transfer of heat during the reaction process using heat conduction equations, ensuring effective control of the reaction temperature. Flow modeling simulated the flow characteristics of liquids or gases, describing the velocity distribution and pressure changes of the fluid flowing within the microchannels. These steps, through modeling the reaction process, accurately simulated and predicted material transformation, heat transfer, and fluid flow in the process, thus providing data support for subsequent process optimization and practical operation.

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

[0027] The continuous diazotization process of plate microchannels is monitored during process execution, a sample dataset is established, and key sample points are configured after feature classification of the sample dataset.

[0028] Optionally, comprehensive monitoring of the plate-type microchannel continuous diazotization process is implemented. This process ensures that all parameters (such as temperature, pressure, flow rate, and reaction rate) remain within preset normal ranges through real-time data acquisition from sensors, instruments, and control systems. In this step, monitoring equipment monitors the operating status of equipment such as reactors, coolers, and feed pumps in real time, continuously acquiring data streams of various process parameters through a data acquisition system. These data constitute a sample dataset. The establishment of the sample dataset is a core step in the entire process control. By continuously collecting data points during process execution, a database containing various process variables is formed. This dataset includes detailed information on aspects such as temperature changes, pressure fluctuations, and flow rate adjustments. The sample dataset reflects the dynamic changes of the system at different time points and provides fundamental data support for subsequent analysis.

[0029] After feature classification of the sample dataset, the next task is to process and analyze this data in detail. Feature classification refers to classifying each data point in the sample dataset according to its characteristics (such as temperature fluctuations, pressure changes, etc.) to identify different types of operating modes or potential fault warnings. Through this process, different process behavior patterns can be distinguished, including normal operating conditions, minor anomalies, and serious faults. Feature classification can be performed using algorithms such as cluster analysis and principal component analysis (PCA), with the aim of identifying the inherent patterns in 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 specific moments during the process, which are crucial for predicting system behavior and diagnosing faults. By identifying and filtering data points in the process, the system can determine a set of sample points critical to the process's operating 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 an abnormal temperature fluctuation at a certain moment is correlated 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 a vital role in the entire process monitoring; they will be used for further reaction process modeling, simulation, and anomaly detection. Analyzing these key points allows for a precise understanding of bottlenecks and potential problems in the reaction process, enabling targeted improvement measures. For example, if heat accumulation in a reaction process at a specific temperature leads to a decrease in the reaction rate, then the anomaly of this key sample point will directly affect subsequent process optimization and equipment adjustments.

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

[0032] 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 device model.

[0033] Furthermore, 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 realistically and accurately reflect the key characteristics of the actual reaction process, such as material changes, heat transfer, and flow states, thereby improving the model's prediction accuracy. In the calibration process, key sample points obtained through process monitoring—that is, monitoring data points that are representative of the process and reflect its behavior—are first compared with the reaction process model as input data. The use of key sample points is crucial because these points represent typical behavior in the reaction process. Typically, physical parameters change significantly at certain time points or under certain operating conditions during the reaction process, and these changes can effectively reveal anomalies, fluctuations, or potential faults in the reaction process. Therefore, these points become the core data that must be focused on during model calibration. In the calibration steps, the error or deviation between the model output and the actual monitored values ​​is first identified by comparing the experimental data and modeling results of the sample points. Based on the magnitude of the error, the parameter values ​​in the reaction model are adjusted so that the model output is consistent with the actual observed process results.

[0034] Next, by calibrating the reaction process model, a more accurate and stable modeling result can be obtained. The calibrated modeling result better reflects actual production processes than the uncalibrated original model, thus improving the model's reliability and adaptability. Once the reaction process model is calibrated, it is fitted to a continuous flow network. The main purpose of this step is to ensure that the dynamic changes in material flow, heat transfer, etc., during the reaction process are well matched with other components in the entire process flow (such as microchannel reactors, reaction coolers, etc.). A continuous flow network is a network model describing the flow of materials, energy, reactants, and products; it includes the connections between all relevant equipment and the paths of material flow. The fitting process combines the calibrated reaction process model with the flow network of the entire process flow, enabling the entire system to reflect the interactions between various devices and their impact on the reaction process. At this point, the calibrated reaction model is closely integrated with the actual situation of equipment and material flow in the flow network, forming a complete dynamic process system model. This step is of great significance for improving process prediction accuracy, optimizing process parameters, improving equipment operating conditions, and increasing overall system efficiency. For example, in microchannel reactors, when the reaction rate is high, the removal of heat of reaction requires a reaction cooler. If the cooling capacity is insufficient at a certain point in time, the reaction temperature may become too high, leading to incomplete reaction or the generation of side reactions. In this case, by modeling the reaction process and fitting the flow network, 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, by collecting and applying key sample points, and by calibrating the reaction process model and fitting it with a continuous flow network, the prediction and control accuracy of the reaction process has been optimized, providing more accurate and reliable technical support for process optimization, equipment adjustment, and fault prediction.

[0036] The operation mode of continuous raw material feeding and continuous reactant discharge is set, and process simulation is performed based on the fitted equipment model.

[0037] Specifically, establishing a continuous feed and reactant discharge operating mode is a crucial step in ensuring a smooth, efficient, and stable process. Continuous feed refers to the continuous input of reactants into the reaction system via metering pumps. This feeding process is independent of batch operation, ensuring a continuous supply of reactants at predetermined flow rates, proportions, and durations throughout the reaction, thereby maintaining reaction stability. Continuous discharge, on the other hand, refers to the continuous discharge or transport of reactants to downstream stages via reaction coolers, piping systems, and other facilities, avoiding fluctuations or instability that may result from intermittent operation. This operating mode is based on the previously completed equipment and reaction process modeling, ensuring a high degree of matching between the input of raw materials and the output of reaction products with the overall operating state of the reaction system. Setting the operating mode requires accurately calculating the input flow rate of raw materials, the reaction rate, and the amount of product discharged, and rationally arranging these material flow paths to ensure the entire reaction process remains in optimal working condition. For example, in a microchannel reactor, excessive feed flow may lead to incomplete reaction of reactants, while insufficient feed flow may cause heat accumulation during the reaction, affecting reaction efficiency. Therefore, when setting the operating mode, it is necessary to optimize and adjust it based on the fitting results of the reaction model and the flow network.

[0038] Based on the fitted equipment model, the system can simulate the entire reaction process in real time and predict process results under different operating modes. The equipment model considers not only the physicochemical changes of the reaction process but also the interactions with equipment, piping systems, and other auxiliary equipment. Therefore, during process simulation, based on the fitted equipment model, key parameters such as heat transfer, fluid flow, and reaction rate can be accurately simulated and dynamically adjusted to ensure that each step plays its full role in the process. Process simulation is conducted through a computer simulation platform based on the aforementioned operating modes and equipment model. The simulation platform can perform real-time simulations based on preset process parameters, assess reaction conditions under different conditions, and predict potential process problems or equipment failures. For example, in actual operation, if the cooling system of a certain stage fails to provide sufficient cooling capacity, the process simulation can detect the temperature rise problem in advance and prompt adjustments to the cooling parameters. Furthermore, simulation can also be used to simulate important indicators such as product quality, reaction efficiency, and energy consumption under different operating modes, helping process engineers optimize process plans. Process simulation not only helps verify the feasibility of operating modes but also enables sensitivity analysis of different operating parameters (such as feed flow rate, reaction temperature, and pressure) to find the most suitable process conditions. For example, simulations can be used to analyze the changing trends of reaction rates at different temperatures and determine the optimal reaction temperature range, thus avoiding poor reaction results due to excessively high or low temperatures.

[0039] In summary, by precisely setting the operation mode of continuous raw material feeding and continuous reactant discharge, and combining it with the fitted equipment model for process simulation, the efficiency, stability and controllability of the entire reaction process are ensured, greatly improving the optimization degree of the process, production efficiency and safety.

[0040] Furthermore, configuring key sample points after performing feature classification on the sample dataset includes:

[0041] Establish a sample point scoring channel, and use the sample point scoring channel to calculate the sample point scores in the sample dataset, as follows:

[0042] .

[0043] in, Characterizing sample points Sample point ratings, Characterizing sample points process parameters, Characterizing controllable process variables, Characterizes the sampling time window, For time indexing, Characterized in Time sample points process parameters, Characterized in Time sample points process parameters, Characterizing sample points Volatility score, The total number of sample points in the sample dataset. Characterizing sample points With sample points The covariance of process parameters, , Sample points With sample points The standard deviation of process parameters For different sample points any sample point, Characterizing sample points Spatial distance, This is a sensitivity weighting coefficient. The volatility-weighted coefficient, The correlation weighting coefficient is used to calculate the correlation weighting coefficient. Spatial distance weighting coefficient.

[0044] Key sample points are established based on the sample point scoring results.

[0045] In one specific embodiment, a sample point scoring channel is established. This channel is used to calculate the scores of sample points in the sample dataset, and the key sample points are determined based on the calculation results. The specific sample point scoring formula is as follows: ; in, Characterizing sample points Sample point ratings, Characterizing sample points process parameters, Characterizing controllable process variables, Characterizes the sampling time window, For time indexing, Characterized in Time sample points process parameters, Characterized in Time sample points process parameters, Characterizing sample points Volatility score, The total number of sample points in the sample dataset. Characterizing sample points With sample points The covariance of process parameters, , Sample points With sample points The standard deviation of process parameters For different sample points any sample point, Characterizing sample points Spatial distance, This is a sensitivity weighting coefficient. The volatility-weighted coefficient, The correlation weighting coefficient is used to calculate the correlation weighting coefficient. Spatial distance weighting coefficient.

[0046] Specifically, a comprehensive analysis of process variables is conducted by integrating sensitivity analysis, variance calculation, and distance assessment. Sensitivity ( Analysis is used to measure specific process variables. The responsiveness to changes in operating conditions measures the ability of a sample point to respond to process fluctuations. It calculates process variables. For operating parameters partial derivatives And it is determined by the average amplitude of parameter fluctuations in the time series. Sensitivity analysis helps operators understand which parameters have the greatest impact on the production process, thus allowing them to prioritize and control these parameters. For example, if temperature changes have a significant impact on product quality, sensitivity analysis will reveal this, enabling operators to control the reaction temperature more precisely. Among these, variance ( Used to evaluate specific process variables The degree of fluctuation over a certain period of time is called volatility score. This is calculated... Its time average The variance is obtained by averaging the squares of the deviations between them. This metric is crucial for identifying instabilities in the process and helps operators take measures to reduce fluctuations in production, thereby improving product consistency and quality. Distance assessment, on the other hand, calculates the spatial distance between process variables at different time points. It is implemented by reflecting the similarity or difference between variables, that is, the degree of uniformity of the sample points k in space. ;in, This represents the physical distance between sample points k and j within the microchannel; The larger the value, the more likely the sample point k is to fill in unsampled areas. This assessment helps identify variables that exhibit different behavioral patterns during production, providing 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 techniques for monitoring and controlling production processes, not only can the efficiency and safety of chemical production be significantly improved, but the consistency and superiority of product quality can also be ensured. This method is particularly suitable for chemical manufacturing environments with numerous parameters and complex processes.

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

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

[0050] Side reactions were identified in the continuous diazotization process of plate microchannels, and a side reaction network was established.

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

[0052] Further, the reaction rate equation is configured to establish the master reaction model. In this step, the first step is to determine the reaction rate equation, which is a mathematical expression describing how the chemical reaction rate depends on reactant concentrations, temperature, and other influencing factors. Configuring the reaction rate equation is fundamental to establishing a reliable reaction model, as it directly affects the calculation and prediction of the reaction rate. In plate-type microchannel continuous diazotization processes, the precise control of fluid dynamics and heat transfer characteristics is particularly critical due to the microchannel structure of the reactor, requiring a model that accurately describes these processes. By configuring a suitable rate equation, the established master reaction model can accurately predict the behavior of the main chemical reactions and the product formation rates.

[0053] Next, a side reaction network was established for the plate-type microchannel continuous diazotization process. Identifying side reactions is crucial to ensuring comprehensive control over all chemical reactions that may affect product quality and yield. In chemical processes, side reactions often lead to undesirable products or impact the efficiency and safety of the entire reaction. Therefore, identifying these side reactions and incorporating them into a side reaction network is essential. This network not only includes the pathways and conditions of various side reactions but also reveals their interactions with the main reaction. In this way, reaction conditions can be better controlled and optimized, reducing the impact of undesirable side reactions.

[0054] Next, conservation constraints are established to couple the main reaction model and the side reaction network. Conservation constraints are fundamental laws that must be followed in chemical reactions, including mass conservation and energy conservation. Mass conservation ensures that the total amount of matter remains constant before and after the reaction, while energy conservation ensures that the total amount of energy remains stable during the reaction. Applying these conservation constraints to the model ensures that the physical and chemical laws of the model are not violated. Applying these conservation constraints to the coupling of 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 considers the interactions and influences of all reaction pathways, thus enabling a comprehensive prediction of the behavior of the entire reaction system.

[0055] The above steps provide a highly optimized and controllable chemical reaction environment for the plate-type microchannel continuous diazotization process. This not only helps improve product quality and yield but also significantly enhances process safety and economy, thereby ensuring the effectiveness of simulation optimization.

[0056] Furthermore, the method also includes:

[0057] Configure the operation feedback sensor in the fitted device model.

[0058] Practical training is conducted using the fitted device model, and practical data is recorded through the operation feedback sensor.

[0059] Establish a mapping between the actual operation data and the simulation results of the fitted device model.

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

[0061] For example, operational feedback sensors are configured in the fitted device model. These sensors are placed at key control points to monitor and record various parameters during actual operation, such as temperature, pressure, and flow rate. These sensors not only provide real-time data feedback but also form the basis for hands-on training and process control. Hands-on training is conducted using the fitted device model, with all operational data recorded in real-time via the previously configured sensors. This training allows operators to simulate various operational scenarios within the control system, thereby understanding and mastering complex chemical reaction processes. The collection of operational data provides an empirical basis for subsequent data analysis and process optimization. Furthermore, a mapping analysis is performed between the collected operational data and the simulation results of the fitted device model. This step is crucial in comparing the changes in variables during actual operation with the theoretical output predicted by the model, analyzing deviations and consistency. Through this mapping, the potential sources of deviation in operation can be understood in detail, allowing for the optimization of operational guidelines. Based on these analysis results, an operational report is generated, detailing key findings and improvement suggestions during the operation. Finally, the obtained simulation results and operational data are visualized in real-time. This not only allows operators to intuitively see the impact of each operational step but also enables them to receive immediate system feedback and adjustment instructions. Real-time visualization is key to improving operational transparency and responsiveness, ensuring that operators can respond quickly and accurately to any changes in the process.

[0062] Through the above steps, the control precision 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. Furthermore, the implementation of this system is of great significance for enhancing process design, process optimization, and emergency response capabilities.

[0063] Furthermore, the method also includes:

[0064] Establish cooling monitoring thresholds that are mapped to the reaction stage.

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

[0066] Based on the synchronization results, activate the cooling monitoring threshold and perform cooling anomaly identification for the reaction cooler.

[0067] Simulation anomaly reporting is performed based on the cooling anomaly identification results.

[0068] Optionally, by establishing a dynamic data communication channel between the reaction stage and the cooling system, real-time synchronization of reaction status and cooling requirements can be achieved, thereby optimizing the cooling process and responding promptly to potential cooling anomalies. Specifically, establishing cooling monitoring thresholds mapped to reaction stages 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 requirement parameters based on specific reaction stages are set, which will serve as the basis for monitoring the system's response. For example, when reactants generate a large amount of heat energy during a vigorous reaction stage, the cooling system's response threshold will be set more sensitively to ensure timely heat removal. Next, during process simulation based on the fitted equipment model, a data communication channel is established. This channel is responsible for seamlessly synchronizing 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 operating conditions inside the reactor, optimizing cooling efficiency and reducing energy consumption. Based on data synchronization, the system will activate the cooling monitoring threshold according to the synchronization results and automatically identify cooling anomalies. This process compares real-time data with preset cooling thresholds. Once data is detected to exceed the normal range, an anomaly detection mechanism is triggered. This mechanism is crucial for preventing equipment overheating or runaway chemical reactions caused by improper cooling. Finally, a simulation anomaly report is generated based on the cooling anomaly detection results. This step involves feeding back any identified cooling anomaly information to the operator or automatic control system, providing necessary alerts and suggesting or automatically implementing appropriate adjustments. This ensures the safety and efficiency of the entire chemical reaction process while reducing potential product quality problems or safety accidents caused by cooling failures.

[0069] The above steps provide an efficient and reliable cooling monitoring and anomaly handling solution, offering important technical support for thermal management and process safety control in microchannel chemical reaction systems, and significantly improving the stability and safety of chemical production.

[0070] Furthermore, the method also includes:

[0071] A reaction completion monitoring model is established, and the equipment model fitted by the reaction completion monitoring model is used to simulate reaction monitoring, and the simulation reaction monitoring results are established.

[0072] Continuous emission pressure parameters are generated based on the simulation reaction monitoring results, and material emission is controlled using these parameters.

[0073] Furthermore, a reaction monitoring model is established, and material emission controls are adjusted based on the model results to ensure the efficiency and safety of the chemical reaction. Specifically, the reaction completion monitoring model aims to assess the progress and degree of completion of the chemical reaction in real time. This model estimates the degree of completion by analyzing the consumption rate of reactants, the formation rate of products, and reaction conditions (such as temperature and pressure). 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 crucial to ensuring that the entire chemical reaction proceeds along the predetermined path, helping to adjust operating parameters in real time and optimize reaction efficiency. After establishing the reaction completion monitoring model, it is used to simulate and monitor the reaction on the fitted equipment model. This process involves inputting 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 assessed, thereby achieving more efficient process control and risk management. Detailed simulation reaction monitoring results are generated through the above simulation monitoring. These results describe in detail the changes in various parameters during the reaction process, such as reaction rate, temperature gradient, and pressure changes, providing data support for further analysis and decision-making. These data are not only used for monitoring and diagnosis, but also form the basis for optimizing reaction conditions and improving product quality. Finally, pressure parameters for continuous material discharge are generated based on the simulated reaction monitoring results. These pressure parameters are calculated based on the reaction completion rate and the current pressure inside the reaction vessel, ensuring that material discharge is both safe and efficient throughout the entire discharge process. Precise control of the discharge pressure avoids the risks of excessively high or low pressure inside the reaction vessel, maintains system stability, and ensures product quality and purity. Ultimately, these pressure parameters are applied to the control system, automatically adjusting the operation of the discharge equipment to adapt to the reaction progress and system requirements.

[0074] The above steps provide a comprehensive method for monitoring and controlling the continuous diazotization process using plate microchannels. Through precise monitoring of reaction completion and data-driven material discharge control, the safety, efficiency, and controllability of the chemical production process are significantly improved.

[0075] Furthermore, the method also includes:

[0076] Obtain the real-time running dataset of the device and synchronize the real-time running dataset to the fitted device model.

[0077] Using the aforementioned equipment model, hazard prediction is performed based on a real-time operational dataset, and hazard prediction results are established.

[0078] Equipment anomaly management is carried out based on the predicted potential hazards.

[0079] Specifically, by collecting real-time operational data from the equipment and utilizing the equipment model for hazard prediction and anomaly management, the operational safety and efficiency of the equipment can be improved. The step of acquiring the real-time operational dataset involves continuously collecting various data on the equipment's operating status using sensors and monitoring systems, such as key parameters like temperature, pressure, flow rate, and chemical composition. This data is considered a key indicator describing the equipment's current operating condition and is crucial for timely detection of operational trends and potential problems. Next, the real-time operational dataset is synchronized to the fitted equipment model. This synchronization process ensures that all collected real-time data is instantly fed back into the equipment model, which is accurately fitted based on historical data and operational experience. This model can process real-time data through algorithms, simulate equipment operation, and predict future operational trends. Then, the equipment model is used to perform hazard prediction based on the real-time operational dataset. In this step, the model analyzes the synchronized dataset and identifies patterns and trends that may lead to equipment performance degradation or failure. The hazard prediction results can reveal potential risk points and early signs of failure, providing a scientific basis for taking preventative measures. Finally, equipment anomaly management is implemented based on the predicted hazard results. This includes developing and implementing corresponding maintenance strategies, such as adjusting operating parameters, performing regular maintenance, or emergency shutdowns. Furthermore, anomaly management also involves optimizing maintenance plans and budget allocation to ensure equipment operates in optimal condition while minimizing downtime and economic losses due to malfunctions. This approach not only improves equipment operational safety and reliability but also significantly optimizes maintenance and operating costs, and enhances the rigor of practical operations.

[0080] Through the technical solutions of the above embodiments, the plate-type microchannel continuous diazotization process simulation method provided by this application solves the technical problems of existing plate-type microchannel continuous diazotization process education and practical training modes having excessively high training costs, failing 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. It achieves efficient and precise control of continuous diazotization process simulation, greatly improves operational safety and the practicality of process education, and effectively enhances the technical effect of practitioners' understanding and operation ability of complex chemical processes.

[0081] Example 2, based on the same inventive concept as the simulation method for a continuous diazotization process using plate-type microchannels in the foregoing examples, such as... Figure 2 As shown, this application provides a simulation device for a plate-type microchannel continuous diazotization process, the device comprising:

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

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

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

[0085] The execution monitoring unit 14 is used to monitor the process execution of the plate microchannel continuous diazotization process, establish a sample dataset, classify the sample dataset by features, 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 to fit the corrected reaction process modeling results and the continuous flow network to the device model.

[0087] The process simulation unit 16 is used to set the operation mode of continuous raw material feeding and continuous reactant discharge, and to perform process simulation based on the fitted equipment model.

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

[0089] Establish a sample point scoring channel, and use the sample point scoring channel to calculate the sample point scores in the sample dataset, as follows:

[0090] .

[0091] in, Characterizing sample points Sample point ratings, Characterizing sample points process parameters, Characterizing controllable process variables, Characterizes the sampling time window, For time indexing, Characterized in Time sample points process parameters, Characterized in Time sample points process parameters, Characterizing sample points Volatility score, The total number of sample points in the sample dataset. Characterizing sample points With sample points The covariance of process parameters, , Sample points With sample points The standard deviation of process parameters For different sample points any sample point, Characterizing sample points Spatial distance, This is a sensitivity weighting coefficient. The volatility-weighted coefficient, The correlation weighting coefficient is used to calculate the correlation weighting coefficient. Spatial distance weighting coefficient.

[0092] Key sample points are established based on the sample point scoring results.

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

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

[0095] Side reactions were identified in the continuous diazotization process of plate microchannels, and a side reaction network was established.

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

[0097] Furthermore, the device also includes a feedback recording unit, which is used to perform the following steps:

[0098] Configure the operation feedback sensor in the fitted device model.

[0099] Practical training is conducted using the fitted device model, and practical data is recorded through the operation feedback sensor.

[0100] Establish a mapping between the actual operation data and the simulation results of the fitted device model.

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

[0102] Furthermore, the device also includes a cooling anomaly detection unit, which performs the following steps:

[0103] Establish cooling monitoring thresholds that are mapped to the reaction stage.

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

[0105] Based on the synchronization results, activate the cooling monitoring threshold and perform cooling anomaly identification for the reaction cooler.

[0106] Simulation anomaly reporting is performed based on the cooling anomaly identification results.

[0107] Furthermore, the device also includes a completion monitoring unit, which performs the following steps:

[0108] A reaction completion monitoring model is established, and the equipment model fitted by the reaction completion monitoring model is used to simulate reaction monitoring, and the simulation reaction monitoring results are established.

[0109] Continuous emission pressure parameters are generated based on the simulation reaction monitoring results, and material emission is controlled using these parameters.

[0110] Furthermore, the device also includes a hazard prediction unit, which performs the following steps:

[0111] Obtain the real-time running dataset of the device and synchronize the real-time running dataset to the fitted device model.

[0112] Using the aforementioned equipment model, hazard prediction is performed based on a real-time operational dataset, and hazard prediction results are established.

[0113] Equipment anomaly management is carried out based on the predicted potential hazards.

[0114] Through the foregoing detailed description of a plate-type microchannel continuous diazotization process simulation method, those skilled in the art can clearly understand the plate-type microchannel continuous diazotization process simulation device in this embodiment. As the device disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section description.

[0115] 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.

Claims

1. A simulation method for a continuous diazotization process in a plate-type microchannel, characterized in that, The method includes: Acquire equipment data for the plate-type microchannel continuous diazotization process, perform equipment simulation modeling based on the equipment data, and establish an equipment model, which includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrumentation system, and a piping system; Configure the material transfer relationships of the microchannel reactor, reaction cooler, and metering feed pump in the equipment model, and establish a continuous flow network; Modeling of the reaction process for executing a continuous diazotization process using plate microchannels, wherein the reaction process modeling includes reaction modeling, heat transfer modeling, and flow modeling; The continuous diazotization process of plate microchannels is monitored during process execution, a sample dataset is established, and key sample points are configured after feature classification of the sample dataset. The reaction process modeling results are corrected using the key sample points, and the corrected reaction process modeling results and continuous flow network are fitted to the device model. The operation mode of continuous raw material feeding and continuous reactant discharge is set, and process simulation is performed based on the fitted equipment model. The reaction process modeling for the plate-type microchannel continuous diazotization process includes: Configure the reaction rate equation and establish the master reaction model; Side reaction identification was performed on the continuous diazotization process using plate microchannels, and a side reaction network was established. Conservation constraints are established, including material conservation constraints and energy conservation constraints. Based on these conservation constraints, the main reaction model and the side reaction network are coupled to establish the reaction model.

2. The simulation method for continuous diazotization process in plate-type microchannels as described in claim 1, characterized in that, After performing feature classification on the sample dataset, configuring key sample points includes: Establish a sample point scoring channel, and use the sample point scoring channel to calculate the sample point scores in the sample dataset, as follows: Among them, S k The sample point score representing sample point k, P k The process parameters representing sample point k, θ representing controllable process variables, T representing the sampling time window, t representing the time index, and P... k (t+1) represents the process parameters at sample point k at time t+1, P k (t) represents the process parameters at sample point k at time t, Var k The volatility score representing sample point k, where N is the total number of sample points in the sample dataset, and Cov(P) is the coefficient of variation. k ,P j ) represents the covariance of process parameters between sample point k and sample point j, σ k σ j Dist represents the standard deviation of the process parameters for sample points k and j, respectively, where j is any sample point different from sample point k. k The spatial distance of sample point k is represented by α, which is the sensitivity weighting coefficient, β, which is the volatility weighting coefficient, γ, which is the correlation weighting coefficient, and δ, which is the spatial distance weighting coefficient. Key sample points are established based on the sample point scoring results.

3. The simulation method for continuous diazotization process in plate-type microchannels as described in claim 1, characterized in that, The method further includes: Configure an operation feedback sensor in the fitted device model; The fitted device model is used for practical training, and the practical data is recorded through the operation feedback sensor. Establish a mapping between the practical data and the simulation results of the fitted device model; A practical report is generated based on the mapping, and the simulation results are visualized in real time.

4. The simulation method for continuous diazotization process in plate-type microchannels as described in claim 1, characterized in that, The method further includes: Establish cooling monitoring thresholds mapped to reaction stages; When performing process simulation based on the fitted equipment model, a data communication channel is established, which is used to synchronize the reaction data in the microchannel reactor to the reaction cooler. Based on the synchronization results, activate the cooling monitoring threshold and perform cooling anomaly identification of the reaction cooler; Simulation anomaly reporting is performed based on the cooling anomaly identification results.

5. The simulation method for continuous diazotization process in plate-type microchannels as described in claim 1, characterized in that, The method further includes: Establish a reaction completion monitoring model, use the fitted equipment model to simulate reaction monitoring, and establish the simulation reaction monitoring results; Continuous emission pressure parameters are generated based on the simulation reaction monitoring results, and material emission is controlled using these parameters.

6. The simulation method for continuous diazotization process in plate-type microchannels as described in claim 1, characterized in that, The method further includes: Obtain the real-time operating dataset of the device and synchronize the real-time operating dataset to the fitted device model; Using the aforementioned equipment model, hazard prediction based on real-time operational datasets is performed, and hazard prediction results are established. Equipment anomaly management is carried out based on the predicted potential hazards.

7. A simulation device for continuous diazotization process in plate-type microchannels, characterized in that, The apparatus for implementing a simulation method for a continuous diazotization process using a plate-type microchannel as described in any one of claims 1-6 comprises: The equipment model building unit is used to acquire equipment data for the plate-type microchannel continuous diazotization process, perform equipment simulation modeling based on the equipment data, and establish an equipment model, which includes a microchannel reactor, a reaction cooler, a metering feed pump, an electrical instrumentation system, and a piping system. The model configuration unit is used to configure the material transfer relationships of the microchannel reactor, reaction cooler, and metering feed pump in the equipment model, and to establish a continuous flow network. The reaction modeling unit is used to perform reaction process modeling for the continuous diazotization process in plate microchannels. The reaction process modeling includes reaction modeling, heat transfer modeling, and flow modeling. The execution monitoring unit is used to monitor the process execution of the plate microchannel continuous diazotization process, establish a sample dataset, classify the sample dataset by features, and configure key sample points. The modeling correction unit is used to correct the reaction process modeling results using the key sample points, and to 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 raw material feeding and continuous reactant discharge, and to perform process simulation based on the fitted equipment model. The reaction modeling unit is also used to perform the following steps: Configure the reaction rate equation and establish the master reaction model; Side reaction identification was performed on the continuous diazotization process using plate microchannels, and a side reaction network was established. Conservation constraints are established, including material conservation constraints and energy conservation constraints. Based on these conservation constraints, the main reaction model and the side reaction network are coupled to establish the reaction model.

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