Reactor bed temperature control method and system

Abstract

The present application relates to the field of monitoring and control technology, and provides a reactor bed temperature control system. Operational data is collected from the reactor through the data interface of the sensor network. A feature subset representing the current working properties of the reactor is extracted from the operational data through data analysis, and a target variable is determined based on the feature subset. Key task parameters are extracted from the current task information of the reactor. The feature subset, target variable, and key task parameters are input into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment. The temperature control strategy is sent to the controller through the hardware interface, and the flow and temperature of the cooling medium and the power of the heating element are controlled by the controller. Precise temperature control is achieved, which not only improves the accuracy and efficiency of temperature control, but also enhances the robustness and adaptability of the control system, providing a strong guarantee for the stable operation of the reactor.

Description

Technical Field

[0001] The present application relates to the field of monitoring and control technology, and in particular to a reactor bed temperature control system and method. Background Art

[0002] Controlling the reactor bed temperature is a critical aspect of reactor operation. In chemical reactions, temperature is a crucial factor influencing reaction rate, selectivity, and conversion. In fixed-bed reactors, where the catalyst bed is stationary and the reactants react through it, bed temperature control is particularly important. Fixed-bed reactors are widely used in the chemical industry, such as in catalytic reforming and natural gas conversion in the oil refining industry, as well as in ammonia synthesis, sulfur trioxide synthesis, and methanol synthesis in the chemical industry. These processes typically involve gas-solid phase catalytic reactions, where the activity, selectivity, and stability of the catalyst have a direct impact on the reaction outcomes. Therefore, precise control of the bed temperature is essential to maintain optimal catalyst performance.

[0003] In practical applications, reactor bed temperature control is often based on real-time data from a single reactor bed. However, due to the large size and high heat capacity of the bed, as well as the significant hysteresis associated with temperature changes, adjusting operating conditions to alter the temperature can require significant time to see the effects. This increases the difficulty and uncertainty of temperature control, especially for different mission requirements. Summary of the Invention

[0004] The present application provides a reactor bed temperature control system and method, which can, at least to a certain extent, solve the problem of low accuracy and efficiency in the reactor bed temperature control process under different task requirements.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0006] According to one aspect of the present application, a method for controlling the bed temperature of a reactor is provided, comprising:

[0007] In the present application, based on the aforementioned scheme, the data interface of the sensor network is used to collect operation data from the reactor and obtain the current task information of the reactor; a feature subset representing the current working properties of the reactor is extracted from the operation data through data analysis, and the target variable to be controlled is determined from the preset variables based on the feature subset; key task parameters are extracted from the current task information of the reactor; the key task parameters include reaction time, temperature range and pressure upper limit; the feature subset, the target variable and the key task parameters are input into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment; the temperature control strategy is sent to the controller through the hardware interface, and the flow and temperature of the cooling medium and the power of the heating element are controlled by the controller.

[0008] In the present application, based on the aforementioned scheme, a feature subset characterizing the current working properties of the reactor is extracted from the operating data through data analysis, and the target variable to be regulated is determined from the preset variables based on the feature subset, including: extracting a feature subset from the operating data through data analysis, the feature subset being used to characterize the current working properties of the reactor; determining the linear relationship between the features in the feature subset and the preset variables according to the preset variables of the reactor; and determining the target variable to be regulated from the preset variables according to the linear relationship.

[0009] In the present application, based on the aforementioned scheme, the feature subset is extracted from the operation data through data analysis, including: selecting features from the operation data according to the association parameters between the operation data and the preset labels to form a first subset; constructing a linear regression model, and recursively removing non-essential features from the first subset until the number of features in the set reaches a preset number of features, thereby generating a second subset; constructing an interaction network between the features of the second subset, and determining the feature subset based on the influence between the features in the interaction network.

[0010] In the present application, based on the above-mentioned scheme, the key task parameters are extracted from the current task information of the reactor, including: predicting the reaction time corresponding to this task based on the reactant concentration, estimated pressure and activation energy in the task information; predicting the temperature range corresponding to this task based on the reaction heat, reactant concentration and estimated pressure in the task information; predicting the pressure upper limit corresponding to this task based on the reaction volume, reaction heat, reactant concentration and estimated pressure in the task information.

[0011] In the present application, based on the aforementioned scheme, the feature subset, the target variable and the key task parameters are input into a pre-trained temperature control model to generate the temperature control strategy corresponding to the current moment, and it also includes: determining the state space and spatial action according to the equipment parameters of the reactor; determining the reward function according to the performance indicators of the reactor, which is used to evaluate the temperature control strategy through the reward function; generating a reinforcement learning algorithm according to the gradient strategy, training based on the state space, the spatial action and the reward function, and generating the temperature control model.

[0012] In the present application, based on the above-mentioned solution, it also includes: real-time tracking and recording of the predictive performance of the temperature control model through a monitoring tool, and immediately triggering an adaptive adjustment mechanism once a decline in model performance or an abnormal situation is found.

[0013] In the present application, based on the above scheme, the operating data include temperature, pressure, reactant concentration and product generation rate.

[0014] According to one aspect of the present application, a reactor bed temperature control system is provided, comprising:

[0015] an acquisition unit, configured to collect operation data from the reactor through a data interface of the sensor network and acquire current task information of the reactor;

[0016] an extraction unit, configured to extract a feature subset representing the current working properties of the reactor from the operating data through data analysis, and determine a target variable to be regulated from preset variables based on the feature subset;

[0017] A task unit is used to extract key task parameters from the current task information of the reactor; the key task parameters include reaction time, temperature range and pressure upper limit;

[0018] A training unit, configured to input the feature subset, the target variable, and the key task parameters into a pre-trained temperature control model to generate a temperature control strategy corresponding to a current moment;

[0019] The control unit is used to send the temperature control strategy to the controller through the hardware interface, and control the flow and temperature of the cooling medium and the power of the heating element through the controller.

[0020] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the reactor bed temperature control system as described in the above embodiments is implemented.

[0021] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the reactor bed temperature control system as described in the above embodiments.

[0022] According to one aspect of the present application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to implement the reactor bed temperature control system provided in the various optional implementations described above.

[0023] In the technical solution of the present application, operating data is collected from the reactor through the data interface of the sensor network, and the current task information of the reactor is obtained; a feature subset representing the current working properties of the reactor is extracted from the operating data through data analysis, and the target variable to be controlled is determined from the preset variables based on the feature subset; key task parameters are extracted from the current task information of the reactor; the key task parameters include reaction time, temperature range and pressure upper limit; the feature subset, the target variable and the key task parameters are input into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment; the temperature control strategy is sent to the controller through the hardware interface, and the controller controls the flow and temperature of the cooling medium and the power of the heating element. The reactor bed temperature control method accurately extracts the features of the reactor operating data and combines the key task parameters generated by the task information to perform model training and temperature control. The key task parameters are adaptively combined based on the actual operating state of the reactor bed to achieve precise temperature control, which not only improves the accuracy and efficiency of temperature control, but also enhances the robustness and adaptability of the control system, providing a strong guarantee for the stable operation of the reactor.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0026] Figure 1The flowchart of the reactor bed temperature control method in one embodiment of the present application is schematically shown.

[0027] Figure 2 The flowchart for determining target variables in one embodiment of the present application is schematically shown.

[0028] Figure 3 The figure schematically shows a reactor bed temperature control system in one embodiment of the present application.

[0029] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0031] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0034] The implementation details of the technical solution of this application are described in detail below:

[0035] Figure 1 FIG. 1 is a flow chart showing a method for controlling the temperature of a reactor bed according to an embodiment of the present application. Figure 1As shown, the reactor bed temperature control method includes at least steps S110 to S150, which are described in detail as follows:

[0036] In step S110 , operation data is collected from the reactor through a data interface of the sensor network, and current task information of the reactor is obtained.

[0037] In one embodiment of the present application, a sensor network data interface automatically collects real-time operational data from the reactor, including temperature, pressure, and reactant concentrations. Simultaneously, information about the reactor's current task, such as the reaction recipe, target product, and expected yield, is acquired to provide fundamental data support for subsequent data analysis and temperature control strategy development.

[0038] The sensor network's data interface collects operational data, ensuring real-time and accurate data. Acquiring information about the reactor's current mission provides the foundation for subsequent temperature control strategies. This enables comprehensive monitoring of the reactor's operating status, providing a reliable data source for subsequent data analysis and strategy development.

[0039] In step S120, a feature subset representing the current working properties of the reactor is extracted from the operating data through data analysis, and a target variable to be regulated is determined from preset variables based on the feature subset.

[0040] In one embodiment of the present application, the operating data collected from the reactor is deeply processed using a built-in data analysis module, and the key features that can accurately characterize the current working state of the reactor are identified and extracted through an algorithm to form a feature subset. These features may cover the temperature fluctuations, pressure stability, changing trends of reactant concentrations, and dynamics of product formation rate during the reaction process. Subsequently, the system further analyzes the feature subset based on pre-set rules or machine learning models to determine the key factors affecting the reactor temperature control, i.e., target variables, such as cooling rate adjustment requirements, reactant ratio optimization direction, etc., so as to provide a decision basis for the subsequent temperature control strategy formulation.

[0041] like Figure 2 As shown, in one embodiment of the present application, a feature subset representing the current working properties of the reactor is extracted from the operating data through data analysis, and a target variable to be regulated is determined from the preset variables based on the feature subset, including:

[0042] S210, extracting a feature subset from the operation data through data analysis, wherein the feature subset is used to characterize the current working properties of the reactor;

[0043] S220, determining a linear relationship between the features in the feature subset and the preset variables according to the preset variables of the reactor;

[0044] S230: Determine a target variable to be regulated from the preset variables according to the linear relationship.

[0045] Specifically, in one embodiment of the present application, extracting a feature subset from the operation data through data analysis in S210 includes:

[0046] Selecting features from the operation data according to association parameters between the operation data and preset tags to form a first subset;

[0047] Constructing a linear regression model, recursively removing unnecessary features from the first subset until the number of features in the set reaches a preset number of features, and generating a second subset;

[0048] An interaction network is constructed between the features of the second subset, and a feature subset is determined based on the influence between the features in the interaction network.

[0049] In one embodiment of the present application, the correlation parameters between features and labels in the operation data can be calculated using methods such as the Pearson correlation coefficient, the Spearman rank correlation coefficient, or the mutual information, and the features with the highest correlation parameters are selected to form the first subset.

[0050] Specifically, for the application scenario of this scheme based on the reactor bed, the associated parameters are calculated for:

[0051]

[0052] in, 、 Respectively represent the feature value and label value corresponding to the i-th feature, 、 denote the mean of the feature value and label value respectively, represents the weight of feature i, i represents the feature identifier, and n represents the number of samples.

[0053] After that, a linear regression model is constructed, and non-essential features are determined and removed from the first subset in a recursive manner until the predetermined number of features is reached or the model performance is no longer significantly improved. In the process of determining non-essential features, the regression coefficients corresponding to the features in the first subset are determined based on the eigenvalues ​​in the first subset, the regression coefficients corresponding to the adjacent eigenvalues, and the preset regularization parameters. for:

[0054]

[0055] in, represents the eigenvalue of the i-th node, is the dependent variable corresponding to the i-th feature, represents the regression coefficient corresponding to the i-th feature, represents the preset regularization parameter, represents the regression coefficient corresponding to the jth feature adjacent to i.

[0056] In this embodiment, the calculated regression parameter is used to measure the necessity of each feature in the first subset. The regression parameter is then compared with a set threshold. Features with regression parameters greater than or equal to the set threshold are identified as non-essential features. Non-essential features are deleted until the set reaches a preset number of features, generating a second subset.

[0057] After generating the second subset, not only are the least important features removed, but the interaction effects of the features are also considered. Therefore, in this embodiment, an interaction network is constructed between the features in the second subset. The impact of removing features on the overall network structure is evaluated. The feature subset is determined based on the influence of each feature in the interaction network, thereby more accurately selecting the feature subset.

[0058] The above process optimizes the feature subset through correlation parameter calculation, linear regression model recursion, and interactive network construction. This improves the quality of the feature subset, making the temperature control strategy more dependent on key features and reducing unnecessary interference.

[0059] After extracting a feature subset, the linear relationship between that feature and a predefined variable (e.g., reactor temperature, pressure, reaction rate, etc.) is determined. A linear fit can be performed near each data point, and then the results of all local models are combined to predict the global trend, resulting in a linear relationship between each feature in the feature subset and the predefined variable.

[0060] After determining the linear relationship between the feature subset and the pre-set variables, the strength of the linear relationship is determined, and variables with strong correlation with the feature subset are extracted from the pre-set variables as target variables. Specifically, target variables include those that have a significant impact on reactor performance or product quality and can be adjusted through control strategies, such as cooling rate and reactant ratio.

[0061] Through the detailed elaboration and execution of the above steps, not only can a subset of features representing the reactor's current operating properties be extracted from the operating data, but the linear relationship between these features and pre-set variables can also be accurately determined, ultimately leading to the extraction of target variables that have a significant impact on reactor performance or product quality. This feature extraction and target variable determination simplifies the complexity of the temperature control problem, making the control strategy more precise and efficient.

[0062] In step S130, key task parameters are extracted from the current task information of the reactor; the key task parameters include reaction time, temperature range and upper pressure limit.

[0063] In one embodiment of the present application, after receiving the reactor's current task information, the system analyzes the details contained therein, such as the type and ratio of reactants and the specifications of the target product. Based on this detailed information, the system then predicts and calculates the key task parameters required for the reaction. These parameters include the expected duration of the reaction (i.e., reaction time), the temperature fluctuation range allowed during the reaction (i.e., temperature range), and the maximum pressure limit set to ensure safe reaction operation (i.e., upper pressure limit). This step enables a comprehensive understanding of the specific requirements of the reactor's current task, providing the necessary constraints and goal guidance for the subsequent formulation of a temperature control strategy.

[0064] In one embodiment of the present application, key mission parameters are extracted from the current mission information of the reactor, including:

[0065] Predict the reaction time for this task based on the reactant concentration, estimated pressure, and activation energy in the task information;

[0066] Predicting the temperature range corresponding to the task based on the reaction heat, reactant concentration, and estimated pressure in the task information;

[0067] The upper pressure limit corresponding to the task is predicted based on the reaction volume, reaction heat, reactant concentration and estimated pressure in the task information.

[0068] In one embodiment of the present application, rules can be preset to refine the key task parameters initially extracted from the task information. For example, if the task description mentions "high temperature reaction", the rule base can refine it to a specific temperature range, such as "200°C to 400°C".

[0069] Furthermore, based on the historical working data of the reactor, this embodiment creatively proposes to determine the reaction time, temperature range, upper pressure limit and other data of this task based on the task information as key task parameters.

[0070] Specifically, based on the reactant concentration, estimated pressure and activation energy in the task information, the reaction time corresponding to this task is predicted. for:

[0071]

[0072] Where C represents the reactant concentration, P represents the estimated pressure, E represents the activation energy, and R represents the reaction rate constant calculated based on historical data. 、 、 as well as is a constant obtained by fitting the experimental data.

[0073] Based on the reaction heat, reactant concentration, and estimated pressure in the task information, the minimum and maximum values ​​in the temperature range corresponding to this task are predicted to be:

[0074]

[0075]

[0076] in,[ ] represents the temperature range, ΔH represents the heat of reaction, and the remaining symbols have the same meanings as above. This formula determines the temperature range using a quadratic equation related to temperature, taking into account the effects of reaction heat and pressure on temperature, making the calculated temperature range more accurate.

[0077] Predict the upper pressure limit for this task based on the reaction volume, reaction heat, reactant concentration, and estimated pressure in the task information. for:

[0078]

[0079] Where V represents the reaction volume, and k represents a constant related to gas properties calculated based on historical data. This formula accounts for the effects of reaction rate, concentration, temperature, and volume on pressure, making the calculated upper pressure limit more accurate.

[0080] The above process predicts key parameters such as reaction time, temperature range, and pressure limit from the mission information. This provides more specific and accurate constraints for the formulation of the temperature control strategy, ensuring its effectiveness and safety.

[0081] In step S140 , the feature subset, the target variable, and the key task parameters are input into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment.

[0082] In one embodiment of the present application, the extracted feature subset, the determined target variable, and the parsed key task parameters are used as input and passed to a temperature control model that has been pre-trained with a large amount of historical data and expert knowledge. This model integrates complex algorithmic logic, which can quickly and accurately analyze the input data, comprehensively consider the current working state, target requirements, and operating restrictions of the reactor, and then intelligently generate the optimal temperature control strategy for the current moment. This strategy can include flow adjustment instructions for the cooling medium, power setting recommendations for the heating element, etc., to ensure that the reactor can operate in a safe and efficient state while meeting the specific requirements of the production task.

[0083] In one embodiment of the present application, before inputting the feature subset, the target variable, and the key task parameter into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment, the method further includes:

[0084] Determining a state space and a spatial action according to equipment parameters of the reactor;

[0085] determining a reward function according to the performance indicator of the reactor, and evaluating the temperature control strategy through the reward function;

[0086] A reinforcement learning algorithm is generated according to a gradient strategy, and training is performed based on the state space, the spatial action, and the reward function to generate the temperature control model.

[0087] In one embodiment of the present application, a state space is defined based on the actual conditions of the reactor. In this embodiment, the state space includes key variables that reflect the reactor state, such as the current temperature, reactant concentration, and pressure. An action space is defined, which is the set of actions that the reinforcement learning model can perform. In a temperature control task, the action space may include control instructions such as the heating rate and cooling rate.

[0088] A reward function is designed based on the reactor's performance metrics, such as product purity, reaction rate, and energy consumption. In this example, the reward function is used to evaluate the performance of each temperature control strategy and serves as the optimization objective of the reinforcement learning model. In determining the reward function, the reactor's characteristics and control requirements are fully considered to ensure that the model learns an effective temperature control strategy.

[0089] Based on the task characteristics and data size, select an appropriate reinforcement learning algorithm, such as policy gradient descent. Implement the reinforcement learning algorithm, which includes key steps such as state representation, action selection, reward calculation, and policy update. During training, continuously experiment and adjust the temperature control strategy to maximize the reward function. Find the optimal strategy through numerous iterations and experiments. Train the reinforcement learning model using the preprocessed data. During training, the model learns how to select the optimal action based on the current state to maximize the cumulative reward. Validate the trained model to evaluate its performance on unseen data.

[0090] The trained reinforcement learning model is deployed into the reactor's control system to achieve automated temperature control. During actual operation, new data is continuously collected for further optimization and updating of the model.

[0091] The above process, through a reinforcement learning model with the goal of maximizing a reward function, optimizes reactor performance indicators such as product purity and reaction rate. This allows the model to learn a more precise temperature control strategy, improving the reactor's control accuracy and stability. Furthermore, the reinforcement learning model automatically adjusts the control strategy based on the actual conditions and changes in the reactor, enhancing the system's adaptability and robustness. By optimizing the temperature control strategy, the reinforcement learning model helps reduce the reactor's energy consumption, enabling the temperature control model to adaptively adjust the control strategy to maximize performance indicators and improve energy efficiency.

[0092] In one embodiment of the present application, it also includes: tracking and recording the predicted performance of the temperature control model in real time through a monitoring tool, and immediately triggering the adaptive adjustment mechanism once the model performance deteriorates or an abnormal situation is found.

[0093] In one embodiment of the present application, appropriate monitoring tools are selected based on the characteristics and requirements of the temperature control model. These tools should have functions such as real-time data acquisition, anomaly detection, and performance analysis. Model performance indicators to be monitored, such as prediction accuracy, mean square error, and mean absolute error, are configured in the monitoring tool. Thresholds and alarm rules for anomaly detection are also set. The monitoring tool is connected to the data source of the temperature control model to ensure real-time access to the model's predicted and actual data.

[0094] The monitoring tool collects the temperature control model's predicted and actual data, as well as related performance indicators, either periodically or in real time. It preprocesses and analyzes the collected data to calculate model performance indicators, such as prediction accuracy. The analysis results are recorded in the monitoring system's database for subsequent analysis and query.

[0095] The monitoring tool determines whether the model is experiencing performance degradation or anomalies based on pre-set anomaly detection thresholds and rules. Once an anomaly is detected, the monitoring tool immediately triggers an alarm and notifies relevant personnel via email, text message, or system notification. Exception information, including the time, type, and scope of impact, is recorded in the monitoring system's logs. Based on the cause of the anomaly, adaptive adjustment strategies are developed, such as retraining the model, adjusting model parameters, and optimizing data preprocessing.

[0096] Optionally, the developed adjustment strategy can be input into a monitoring tool, which can then automatically or manually execute the adjustment. For example, if performance degradation is determined to be caused by model parameter drift, the monitoring tool can automatically adjust the model parameters; if the problem is data quality, the data preprocessing process needs to be optimized.

[0097] This solution uses real-time monitoring tools to track and record the temperature control model's predictive performance. Once an anomaly is detected, an adaptive adjustment mechanism is immediately triggered, enabling continuous optimization and improvement of the model. This real-time monitoring and adaptive adjustment approach is more efficient and accurate than traditional manual monitoring and adjustment.

[0098] In step S150, the temperature control strategy is sent to a controller via a hardware interface, and the controller controls the flow rate and temperature of the cooling medium and the power of the heating element.

[0099] In one embodiment of the present application, the generated temperature control strategy is sent to the controller in the form of a digital signal via a hardware interface (such as a serial communication interface, an Ethernet interface, etc.). This hardware interface ensures efficient and accurate data transmission between the computer and the controller.

[0100] After receiving the temperature control strategy from the computer system, the controller parses it. This parsing process involves identifying the command type (such as adjusting the cooling medium flow rate or setting the heating element power), extracting specific parameter values ​​(such as flow rate, temperature setpoint, and power level), and preparing to convert these parameter values ​​into corresponding control signals. Based on the parsed parameter values, the controller's internal actuators (such as solenoid valves, proportional valves, temperature sensors, and power regulators) precisely adjust the cooling medium flow rate and temperature, while also setting the heating element power. These actuators, based on the controller's instructions, adjust the reactor's heat exchange conditions in real time to achieve the desired temperature control strategy.

[0101] Through rapid data transmission between the computer and controller, and the controller's instant analysis and execution of temperature control strategies, the entire system enables real-time response and adjustment to the reactor temperature. This helps ensure stable reactor operation under complex and changing operating conditions. The temperature control strategy is formulated based on the reactor's current operating status, target requirements, and operational constraints, enabling precise temperature control. This improves the control system's automation level, reduces operator workload, and enhances system reliability and stability.

[0102] In the technical solution of the present application, operating data is collected from the reactor through the data interface of the sensor network, and the current task information of the reactor is obtained; a feature subset representing the current working properties of the reactor is extracted from the operating data through data analysis, and the target variable to be controlled is determined from the preset variables based on the feature subset; key task parameters are extracted from the current task information of the reactor; the key task parameters include reaction time, temperature range and pressure upper limit; the feature subset, the target variable and the key task parameters are input into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment; the temperature control strategy is sent to the controller through the hardware interface, and the controller controls the flow and temperature of the cooling medium and the power of the heating element. The reactor bed temperature control method accurately extracts the features of the reactor operating data and combines the key task parameters generated by the task information to perform model training and temperature control. The key task parameters are adaptively combined based on the actual operating state of the reactor bed to achieve precise temperature control, which not only improves the accuracy and efficiency of temperature control, but also enhances the robustness and adaptability of the control system, providing a strong guarantee for the stable operation of the reactor.

[0103] The following describes an apparatus embodiment of the present application, which can be used to implement the reactor bed temperature control method described in the aforementioned embodiments of the present application. It is understood that the apparatus can be a computer program (including program code) running on a computer device, such as application software; the apparatus can be used to perform the corresponding steps of the method provided in the embodiments of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the aforementioned embodiment of the reactor bed temperature control method of the present application.

[0104] Figure 3 A block diagram of a reactor bed temperature control system according to one embodiment of the present application is shown.

[0105] Reference Figure 3 As shown, a reactor bed temperature control system according to one embodiment of the present application includes:

[0106] an acquisition unit 310 for collecting operation data from the reactor through a data interface of the sensor network and acquiring current task information of the reactor;

[0107] An extraction unit 320 is configured to extract a feature subset representing the current working properties of the reactor from the operating data through data analysis, and determine a target variable to be regulated from preset variables based on the feature subset;

[0108] The task unit 330 is used to extract key task parameters from the current task information of the reactor; the key task parameters include reaction time, temperature range and pressure upper limit;

[0109] A training unit 340 is configured to input the feature subset, the target variable, and the key task parameters into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment;

[0110] The control unit 350 is used to send the temperature control strategy to the controller through the hardware interface, and control the flow and temperature of the cooling medium and the power of the heating element through the controller.

[0111] In the present application, based on the aforementioned scheme, a feature subset characterizing the current working properties of the reactor is extracted from the operating data through data analysis, and the target variable to be regulated is determined from the preset variables based on the feature subset, including: extracting a feature subset from the operating data through data analysis, the feature subset being used to characterize the current working properties of the reactor; determining the linear relationship between the features in the feature subset and the preset variables according to the preset variables of the reactor; and determining the target variable to be regulated from the preset variables according to the linear relationship.

[0112] In the present application, based on the aforementioned scheme, the feature subset is extracted from the operation data through data analysis, including: selecting features from the operation data according to the association parameters between the operation data and the preset labels to form a first subset; constructing a linear regression model, and recursively removing non-essential features from the first subset until the number of features in the set reaches a preset number of features, thereby generating a second subset; constructing an interaction network between the features of the second subset, and determining the feature subset based on the influence between the features in the interaction network.

[0113] In the present application, based on the above-mentioned scheme, the key task parameters are extracted from the current task information of the reactor, including: predicting the reaction time corresponding to this task based on the reactant concentration, estimated pressure and activation energy in the task information; predicting the temperature range corresponding to this task based on the reaction heat, reactant concentration and estimated pressure in the task information; predicting the pressure upper limit corresponding to this task based on the reaction volume, reaction heat, reactant concentration and estimated pressure in the task information.

[0114] In the present application, based on the aforementioned scheme, the feature subset, the target variable and the key task parameters are input into a pre-trained temperature control model to generate the temperature control strategy corresponding to the current moment, and it also includes: determining the state space and spatial action according to the equipment parameters of the reactor; determining the reward function according to the performance indicators of the reactor, which is used to evaluate the temperature control strategy through the reward function; generating a reinforcement learning algorithm according to the gradient strategy, training based on the state space, the spatial action and the reward function, and generating the temperature control model.

[0115] In the present application, based on the above-mentioned solution, it also includes: real-time tracking and recording of the predictive performance of the temperature control model through a monitoring tool, and immediately triggering an adaptive adjustment mechanism once a decline in model performance or an abnormal situation is found.

[0116] In the present application, based on the above scheme, the operating data include temperature, pressure, reactant concentration and product generation rate.

[0117] In the technical solution of the present application, operating data is collected from the reactor through the data interface of the sensor network, and the current task information of the reactor is obtained; a feature subset representing the current working properties of the reactor is extracted from the operating data through data analysis, and the target variable to be controlled is determined from the preset variables based on the feature subset; key task parameters are extracted from the current task information of the reactor; the key task parameters include reaction time, temperature range and pressure upper limit; the feature subset, the target variable and the key task parameters are input into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment; the temperature control strategy is sent to the controller through the hardware interface, and the controller controls the flow and temperature of the cooling medium and the power of the heating element. The reactor bed temperature control method accurately extracts the features of the reactor operating data and combines the key task parameters generated by the task information to perform model training and temperature control. The key task parameters are adaptively combined based on the actual operating state of the reactor bed to achieve precise temperature control, which not only improves the accuracy and efficiency of temperature control, but also enhances the robustness and adaptability of the control system, providing a strong guarantee for the stable operation of the reactor.

[0118] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0119] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0120] In this embodiment, the computer system includes a central processing unit (CPU) 401, which can execute various appropriate actions and processes based on programs stored in a read-only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403, such as executing the reactor bed temperature control system described in the above embodiments. RAM 403 also stores various programs and data required for system operation. CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output interface 405 is also connected to bus 404.

[0121] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, and the like; an output section 407 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 408 including devices such as a hard disk; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read from the removable media can be installed in the storage section 408 as needed.

[0122] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409 and / or installed from a removable medium 411. When the computer program is executed by the central processing unit 401, the various functions defined in the system of the present application are performed.

[0123] It should be noted that the computer-readable medium described in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0126] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0127] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not incorporated into the electronic device. The computer-readable medium carries one or more programs, and when executed by the electronic device, the electronic device implements the reactor bed temperature control system described in the above embodiments.

[0128] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0129] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0130] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0131] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for controlling the bed temperature of a reactor, characterized in that: include: Collecting operation data from the reactor through a data interface of the sensor network and obtaining current task information of the reactor; Extracting a feature subset representing the current working properties of the reactor from the operating data through data analysis, and determining a target variable to be regulated from preset variables based on the feature subset; Extracting key task parameters from the current task information of the reactor; the key task parameters include reaction time, temperature range and pressure upper limit; Inputting the feature subset, the target variable, and the key task parameters into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment; The temperature control strategy is sent to a controller via a hardware interface, and the controller controls the flow and temperature of the cooling medium and the power of the heating element; Among them, key task parameters are extracted from the current task information of the reactor, including: Predict the reaction time corresponding to this task based on the reactant concentration, estimated pressure and activation energy in the task information for: Where C represents the reactant concentration, P represents the estimated pressure, E represents the activation energy, and R represents the reaction rate constant calculated based on historical data. 、 、 as well as is the constant obtained by fitting the experimental data; Based on the reaction heat, reactant concentration, and estimated pressure in the task information, the minimum and maximum values ​​in the temperature range corresponding to this task are predicted to be: in,[ ] represents the temperature range, ΔH represents the reaction heat, and the other symbols have the same meanings as above; The upper pressure limit corresponding to the task is predicted based on the reaction volume, reaction heat, reactant concentration and estimated pressure in the task information.

2. The reactor bed temperature control method according to claim 1, characterized in that: Extracting a feature subset representing the current working properties of the reactor from the operating data through data analysis, and determining a target variable to be regulated from preset variables based on the feature subset, including: extracting a feature subset from the operation data through data analysis, wherein the feature subset is used to characterize the current working properties of the reactor; According to the preset variables of the reactor, determining the linear relationship between the features in the feature subset and the preset variables; According to the linear relationship, the target variable to be regulated is determined from the preset variables.

3. The reactor bed temperature control method according to claim 2, characterized in that: Extracting a feature subset from the operational data through data analysis includes: Selecting features from the operation data according to association parameters between the operation data and preset tags to form a first subset; Constructing a linear regression model, recursively removing unnecessary features from the first subset until the number of features in the set reaches a preset number of features, and generating a second subset; An interaction network is constructed between the features of the second subset, and a feature subset is determined based on the influence between the features in the interaction network.

4. The reactor bed temperature control method according to claim 1, characterized in that: Before inputting the feature subset, the target variable, and the key task parameters into a pre-trained temperature control model to generate a temperature control strategy corresponding to the current moment, the method further includes: Determining a state space and a spatial action according to equipment parameters of the reactor; determining a reward function according to the performance indicator of the reactor, and evaluating the temperature control strategy through the reward function; A reinforcement learning algorithm is generated according to a gradient strategy, and training is performed based on the state space, the spatial action, and the reward function to generate the temperature control model.

5. The reactor bed temperature control method according to claim 4, characterized in that: Also includes: The predicted performance of the temperature control model is tracked and recorded in real time by a monitoring tool. Once a degradation or abnormality in the model performance is detected, an adaptive adjustment mechanism is triggered.

6. The reactor bed temperature control method according to claim 1, characterized in that: The operating data include temperature, pressure, reactant concentrations, and product formation rates.

7. A reactor bed temperature control system, characterized in that: include: an acquisition unit, configured to collect operation data from the reactor through a data interface of the sensor network and acquire current task information of the reactor; an extraction unit, configured to extract a feature subset representing the current working properties of the reactor from the operating data through data analysis, and determine a target variable to be regulated from preset variables based on the feature subset; A task unit is used to extract key task parameters from the current task information of the reactor; the key task parameters include reaction time, temperature range and pressure upper limit; A training unit, configured to input the feature subset, the target variable, and the key task parameters into a pre-trained temperature control model to generate a temperature control strategy corresponding to a current moment; A control unit, configured to send the temperature control strategy to a controller via a hardware interface, and control the flow and temperature of the cooling medium and the power of the heating element via the controller; Among them, key task parameters are extracted from the current task information of the reactor, including: Predict the reaction time corresponding to this task based on the reactant concentration, estimated pressure and activation energy in the task information for: Where C represents the reactant concentration, P represents the estimated pressure, E represents the activation energy, and R represents the reaction rate constant calculated based on historical data. 、 、 as well as is the constant obtained by fitting the experimental data; Based on the reaction heat, reactant concentration, and estimated pressure in the task information, the minimum and maximum values ​​in the temperature range corresponding to this task are predicted to be: in,[ ] represents the temperature range, ΔH represents the reaction heat, and the other symbols have the same meanings as above; The upper pressure limit corresponding to the task is predicted based on the reaction volume, reaction heat, reactant concentration and estimated pressure in the task information.

8. The reactor bed temperature control system according to claim 7, characterized in that: Extracting a feature subset representing the current working properties of the reactor from the operating data through data analysis, and determining a target variable to be regulated from preset variables based on the feature subset, including: extracting a feature subset from the operation data through data analysis, wherein the feature subset is used to characterize the current working properties of the reactor; According to the preset variables of the reactor, determining the linear relationship between the features in the feature subset and the preset variables; According to the linear relationship, the target variable to be regulated is determined from the preset variables.

9. The reactor bed temperature control system according to claim 8, characterized in that: Extracting a feature subset from the operational data through data analysis includes: Selecting features from the operation data according to association parameters between the operation data and preset tags to form a first subset; Constructing a linear regression model, recursively removing unnecessary features from the first subset until the number of features in the set reaches a preset number of features, and generating a second subset; An interaction network is constructed between the features of the second subset, and a feature subset is determined based on the influence between the features in the interaction network.

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