Fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search

Abstract

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously improved.

Description

Technical Field

[0001] This invention relates to the field of reactor temperature control technology, specifically to an intelligent temperature control method for fine chemical reactors based on adaptive search. Background Technology

[0002] Fine chemical reactors are crucial equipment for chemical reactions, heat exchange, and mass transfer processes, widely used in resin synthesis, pharmaceutical intermediates, and fine chemical auxiliaries production. The chemical reactions within these reactors are often strongly exothermic or endothermic, and temperature changes directly impact reaction rates and product quality. To ensure stable reaction operation, jackets or coils are typically installed outside the reactor to heat or cool the reaction medium and regulate the reaction temperature. Therefore, reactor temperature control technology is a key aspect of automated control in fine chemical processes. Especially in multi-stage, nonlinear, and strongly coupled chemical reaction processes, higher demands are placed on the accuracy and stability of control algorithms. Currently, temperature control in fine chemical reactors primarily employs model predictive control based on fixed parameters or traditional PID control methods.

[0003] However, in current technology, model parameters are not corrected in real time as operating conditions change, which can easily lead to temperature lag, overshoot or fluctuation, and even the risk of thermal runaway. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent temperature control method for fine chemical reactors based on adaptive search, which solves the problem of temperature regulation lag caused by the model parameters not being corrected in real time with changes in operating conditions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent temperature control of a fine chemical reactor based on adaptive search, comprising:

[0006] S1. Data acquisition: Acquire reactor operating data, including reactor internal temperature, jacket inlet and outlet temperatures, fluid flow rate, and valve opening signals.

[0007] S2. Heat balance calculation: Based on the collected data, establish the heat balance equation and calculate the heat released by the reaction and the equivalent heat transfer coefficient.

[0008] S3. Model update: The calculated heat release and heat transfer coefficient of the reaction are used to update the prediction model and generate a predicted value for the future change of the kettle temperature.

[0009] S4. Predictive control solution: Based on the output of the predictive model, execute model predictive control to obtain the control input candidates;

[0010] S5. Adaptive search: An adaptive search algorithm is introduced for control command candidates. The prediction time domain length, control time domain length, weighting coefficients and temperature set trajectory parameters are iteratively searched to generate new parameter candidates.

[0011] S6. Safety constraints: During the adaptive search process, temperature range, heating rate range, and control command range are set as safety barriers, and the new parameter candidates are filtered through the trust domain to obtain control command candidates that meet the constraints.

[0012] S7. Stage Identification: Identify the stages of the reaction process, including the heating stage, the isothermal stage, and the cooling stage, and switch the search variables and optimization objectives according to the stage.

[0013] S8. Control execution: Apply the control input, after constraint screening, to the reactor to complete the temperature control process.

[0014] Preferably, the heat balance calculation includes:

[0015] Calculate the heat exchange between the jacket and the reactor based on the jacket inlet and outlet temperatures and flow rates;

[0016] Substituting the heat exchange and the rate of change of the vessel temperature into the heat balance equation, the heat released by the reaction is obtained, and the equivalent heat transfer coefficient is estimated by the recursive least squares method.

[0017] Preferably, the model update includes:

[0018] The heat transfer coefficient and heat release term in the prediction model are corrected based on the heat release of the reaction and the equivalent heat transfer coefficient to obtain the updated prediction model.

[0019] The updated prediction model is used to calculate the future changes in reactor temperature within a given prediction time domain.

[0020] Preferably, the predictive control solution includes:

[0021] The updated prediction model is used as the control prediction model, with the reactor temperature as the controlled variable and the jacket valve opening or refrigerant inlet temperature as the control variables.

[0022] Set upper and lower limits for temperature, temperature rise rate constraint, and range of control variable values ​​within the prediction time domain.

[0023] An optimization problem is constructed based on the constraints, and the control input candidates that satisfy the constraints are obtained by solving the problem in the control time domain.

[0024] Preferably, the adaptive search includes:

[0025] An adaptive search algorithm is introduced for the candidate control commands to determine the parameters to be optimized. The parameters include the prediction time domain length, the control time domain length, the weighting coefficient, and the temperature setting trajectory parameters.

[0026] New parameter candidates are generated using an adaptive search algorithm and then input into the predictive control solution process.

[0027] The predictive control problem is resolved based on the new parameter candidates to obtain the corresponding control command candidates.

[0028] Preferably, the security constraints include:

[0029] During the adaptive search process, temperature range, temperature change rate range, and control command range are set as safety barriers.

[0030] A trust domain is established based on the safety barrier to limit the range of values ​​for candidate parameters;

[0031] The parameter candidates generated by the adaptive search are filtered, and those that exceed the safety barrier or trust domain are removed, while the control command candidates that meet the conditions are retained.

[0032] Preferably, the stage identification includes:

[0033] Obtain operational data of the reaction process, and determine whether the reaction is in the heating stage, the isothermal stage, or the cooling stage based on the operational data;

[0034] When it is determined to be in the heating stage, the temperature setting trajectory parameters and the prediction time domain are selected as search variables;

[0035] When the temperature is determined to be in a constant temperature stage, the weighting coefficient is selected as the search variable.

[0036] When the temperature is determined to be in the cooling phase, the control time domain and temperature setting trajectory parameters are selected as search variables.

[0037] Preferably, the control execution includes:

[0038] The control commands, after being filtered by safety constraints, are sent to the actuators.

[0039] The actuator includes a valve for regulating the jacket fluid or a heat exchanger for regulating the refrigerant temperature.

[0040] The control commands are applied to the reaction vessel through the actuator.

[0041] Preferably, the execution process involves monitoring the running data;

[0042] When the operating data is detected to exceed the preset range, the control parameters are switched to the safety parameter set, and control commands are generated based on the safety parameter set and applied to the reactor.

[0043] An intelligent temperature control system for fine chemical reactors based on adaptive search, the system comprising:

[0044] The data acquisition module collects operating data from the reactor, including internal temperature, jacket inlet and outlet temperatures, fluid flow rate, and valve opening signals.

[0045] The heat balance calculation module establishes a heat balance equation based on operational data and calculates the heat released by the reaction and the equivalent heat transfer coefficient.

[0046] The model update module corrects the parameters of the prediction model based on the heat balance equation by using the heat released by the reaction and the equivalent heat transfer coefficient, and generates a predicted value for the future change of the reactor temperature.

[0047] The predictive control solution module constructs an optimization problem based on the prediction results and the set temperature range, heating rate range, and valve opening range, solves the control command candidates, and passes them to the adaptive search module.

[0048] The adaptive search module performs an adaptive search on the control command candidates, determines the parameters to be optimized, generates new parameter candidates, and feeds them back to the predictive control solution module for re-solving.

[0049] The safety constraint module generates new parameter candidates, establishes safety barriers and trust domains based on temperature range, heating rate range, and control command range, filters parameter candidates, and outputs control commands that meet the constraint conditions.

[0050] The stage identification module monitors the operating data and determines whether the reaction process is in the heating stage, the isothermal stage, or the cooling stage, and transmits the stage determination result to the adaptive search module to determine the corresponding search variable.

[0051] The control and execution module sends control commands to the actuator to adjust the temperature of the jacket valve or refrigerant, thereby regulating the temperature of the reactor.

[0052] The abnormal rollback module monitors the running data during control execution. When the running data exceeds the preset range, the control parameters are switched to the safety parameter set, a new control command is generated, and it is transmitted to the control execution module.

[0053] This invention provides an intelligent temperature control method for fine chemical reactors based on adaptive search. It has the following beneficial effects:

[0054] 1. This invention uses the linkage between the heat balance calculation module and the model update module to calculate the heat release and equivalent heat transfer coefficient of the reaction in real time, and uses the recursive least squares algorithm to achieve online parameter identification, so that the thermal model can be dynamically corrected with changes in operating conditions, ensuring that the predicted model is consistent with the actual reaction process, and significantly improving the accuracy of temperature prediction and the reliability of control response.

[0055] 2. This invention introduces a parameter optimization algorithm with safety constraints into the adaptive search module. Temperature, heating rate, and control command range are used as safety barriers, and the search space is constrained by a trust domain. This mechanism achieves a balance between control performance optimization and process safety, preventing over-limit operations caused by the search, thereby realizing sustainable, safe, and intelligent control.

[0056] 3. This invention uses a stage identification module to determine in real time whether the reaction process is in the heating, isothermal, or cooling stage, and dynamically adjusts adaptive search variables and optimization targets. This design achieves self-matching between the control strategy and the process stage, making heating more stable, isothermal more precise, and cooling more controllable, thereby improving the overall adaptability of reactor temperature control and process adaptability. Attached Figure Description

[0057] Figure 1 This is a flowchart of an intelligent temperature control method for a fine chemical reactor based on adaptive search, according to the present invention.

[0058] Figure 2 This is an architecture diagram of an intelligent temperature control system for a fine chemical reactor based on adaptive search, according to the present invention. Detailed Implementation

[0059] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see the appendix Figure 1 This invention provides an intelligent temperature control method for fine chemical reactors based on adaptive search, comprising:

[0061] S1. Data acquisition: Acquire reactor operating data, including reactor internal temperature, jacket inlet and outlet temperatures, fluid flow rate, and valve opening signals.

[0062] Specifically, data acquisition is achieved by setting up conventional measuring devices such as temperature, flow rate, and valve opening on the reactor and its pipelines. Each measurement signal is collected in real time by the control system and transmitted to the subsequent calculation stage, providing the necessary input information for heat balance calculation and predictive control, and ensuring that subsequent control steps are based on an accurate and continuous operating state.

[0063] S2. Heat balance calculation: Based on the collected data, establish the heat balance equation and calculate the heat released by the reaction and the equivalent heat transfer coefficient.

[0064] Thermal balance calculations include:

[0065] Calculate the heat exchange between the jacket and the reactor based on the jacket inlet and outlet temperatures and flow rates;

[0066] Substituting the heat exchange and the rate of change of the vessel temperature into the heat balance equation, the heat released by the reaction is obtained, and the equivalent heat transfer coefficient is estimated by the recursive least squares method.

[0067] Specifically, the heat exchange between the jacket and the reactor is first calculated based on the jacket inlet and outlet temperatures and flow rates. This is based on the principle of energy conservation. By monitoring the changes in temperature and fluid flow on the jacket side, the instantaneous heat transfer intensity of the system is obtained, which is used to characterize the heat transfer level between the material and the jacket. This provides a basic input for the subsequent calculation of the heat release of the reaction, reflects the heat transfer state of the system, and enables the calculation of the reactor's thermal behavior to be based on quantifiable heat transfer information, thereby supporting the dynamic solution of the heat balance model.

[0068] Subsequently, the heat exchange and the rate of change of the vessel temperature are substituted into the heat balance equation to obtain the heat released during the reaction process. This step, by analyzing the trend of the vessel temperature signal, correlates the energy change with the heat effect of the reaction, and realizes the real-time estimation of the heat release intensity of the reaction. That is, the energy change is calculated using the heat capacity parameters of the material in the vessel and the rate of temperature change, and the heat released by the reaction is obtained by combining the heat exchange, so as to characterize the heat release dynamics of the reaction process. The heat release information of the reaction is introduced into the system thermal analysis framework to form a reaction heat load that can be updated in real time, providing a dynamic heat source basis for subsequent control algorithms.

[0069] Finally, the equivalent heat transfer coefficient is estimated online using the recursive least squares method, i.e., the heat transfer relationship is the identification model: ;

[0070] It is the heat exchange obtained in step one (calculated from measurements on the jacket side). The heat exchange area of ​​the equipment (known fixed value or nameplate parameter of the device). The temperature inside the vessel (collected by the temperature sensor inside the vessel). The average temperature of the jacket (derived from the jacket inlet temperature) With outlet temperature Calculated, for example, taking ), For the equivalent heat transfer coefficient to be estimated online, the above equation is written in the form of recursive least squares (RLS) regression. ,in , , RLS update is used: , , ,in For the covariance matrix, The forgetting factor (a constant with a value less than or equal to 1, used to weaken the influence of old data) enables the heat transfer coefficient to be continuously updated as the operating conditions change and sent to the model update stage in real time. This maintains the consistency between the thermal model and the field conditions when the heat transfer conditions change over time, thereby supporting the stable operation of subsequent predictive control and adaptive search.

[0071] S3. Model update: The calculated heat release and heat transfer coefficient of the reaction are used to update the prediction model and generate a predicted value for the future change of the kettle temperature.

[0072] Furthermore, model updates include:

[0073] The heat transfer coefficient and heat release term in the prediction model are corrected based on the heat release of the reaction and the equivalent heat transfer coefficient to obtain the updated prediction model.

[0074] The updated prediction model is used to calculate the future changes in reactor temperature within a given prediction time domain.

[0075] Specifically, during model updates, the exothermic terms and heat transfer parameters in the prediction model are first corrected based on the heat release and equivalent heat transfer coefficient obtained in the previous stage. A dynamic energy equation is then established based on the heat balance relationship within the reactor. ;

[0076] in, For material density, The specific heat capacity at constant pressure of the material. The reaction volume, The temperature inside the vessel. The average temperature of the jacket, For heat exchange area, The heat released by the reaction is calculated from the heat balance. To estimate the equivalent heat transfer coefficient in real time using the recursive least squares method, by... and The latest estimated value replaces the corresponding fixed parameters in the model, so that the prediction model automatically updates the thermodynamic and heat transfer characteristics in each control cycle, thereby realizing the adaptive correction of the model in sync with the actual operating conditions, so as to maintain the accurate expression of the thermal state of the system by the prediction model and ensure that subsequent prediction calculations and control solutions are based on real-time and effective thermal characteristic parameters.

[0077] After completing the parameter correction, the updated prediction model is used to calculate the future change value of the reactor temperature in the given prediction time domain. That is, the reactor temperature and jacket temperature at the current moment are used as initial conditions, the updated model is called to perform multi-step prediction, and the temperature prediction sequence is generated by discrete integration or numerical iteration method, and the future temperature change trend is output.

[0078] S4. Predictive control solution: Based on the output of the predictive model, execute model predictive control to obtain the control input candidates;

[0079] Furthermore, the predictive control solution includes:

[0080] The updated prediction model is used as the control prediction model, with the reactor temperature as the controlled variable and the jacket valve opening or refrigerant inlet temperature as the control variables.

[0081] Set upper and lower limits for temperature, temperature rise rate constraint, and range of control variable values ​​within the prediction time domain.

[0082] An optimization problem is constructed based on the constraints, and the control input candidates that satisfy the constraints are obtained by solving the problem in the control time domain.

[0083] Specifically, the updated prediction model of S3 is first used as the control prediction model, with the reactor temperature as the controlled variable and the jacket valve opening or refrigerant inlet temperature as the control variable. The prediction equation is established through the updated energy balance model, so that the control calculation is rolled optimization based on the latest heat conduction and heat release characteristics. By updating the model parameters in each sampling period, the calculation process of predictive control is kept consistent with the changes in operating conditions.

[0084] Subsequently, constraints are set within the prediction time domain, including upper and lower limits for the vessel temperature, temperature rise rate constraints, and range constraints for the control variables (valve opening or refrigerant temperature). This introduces boundary conditions during control optimization to ensure that the control solution does not generate commands exceeding safety or equipment allowable limits. These constraints are typically embedded in the optimization problem in the form of linear inequalities, often expressed as...

[0085]

[0086] in, To predict the reactor temperature at a given time, The change in temperature For the control variables (valve opening degree or refrigerant temperature), the subscripts min and max represent their allowable ranges, thereby explicitly introducing safety and equipment constraints into the control optimization structure to prevent control results from going out of bounds and ensure safe and stable operation.

[0087] Finally, based on the constraints, an optimization problem for model predictive control is constructed, and candidate control inputs satisfying the constraints are obtained by solving the problem in the control time domain. The optimization objective is to minimize the weighted sum of squares of temperature deviation and control variable change, and the optimization objective function is expressed as: ;

[0088] in, For performance indicators, To predict the length of the time domain, To control the length of the time domain, and These are the weighting coefficients. Set the temperature value. To control the increment of the quantity, by solving this optimization problem, the optimal control input sequence for the current control cycle can be obtained, and the first control command can be extracted as the execution value. This enables temperature optimization control under multiple constraints and multiple objectives, reducing temperature fluctuations and improving system control accuracy while ensuring safety constraints.

[0089] S5. Adaptive search: An adaptive search algorithm is introduced for control command candidates. The prediction time domain length, control time domain length, weighting coefficients and temperature set trajectory parameters are iteratively searched to generate new parameter candidates.

[0090] Furthermore, adaptive search includes:

[0091] An adaptive search algorithm is introduced for the control command candidates to determine the parameters to be optimized, including the prediction time domain length, control time domain length, weighting coefficients, and temperature setpoint trajectory parameters.

[0092] New parameter candidates are generated using an adaptive search algorithm and then input into the predictive control solution process.

[0093] The predictive control problem is resolved based on the new parameter candidates to obtain the corresponding control command candidates.

[0094] Specifically, an adaptive search algorithm is first introduced into the candidate control commands obtained from the predictive control solution to determine the control parameters to be optimized, and the prediction time domain length is... Control time domain length Weighting coefficients , The temperature setpoint trajectory parameters are used as optimization variables, and the control performance index function is the objective:

[0095] ;

[0096] in, To predict the reactor temperature at a given time, Set the temperature value. To control the change in the quantity, the algorithm continuously adjusts within the parameter space. The value of makes the objective function Minimize the parameters to obtain the optimal combination for control performance, and achieve self-learning and dynamic tuning of parameters as the operating conditions change.

[0097] Subsequently, new parameter candidates are generated based on the adaptive search algorithm. The algorithm evaluates the performance of historical search results, predicts the improvement potential of the current parameter combination, and selects new search points to improve overall performance.

[0098] Finally, the new parameter candidates are input into the predictive control solution process, the control input is recalculated and new control command candidates are generated, which can be continuously executed in a loop during operation to achieve closed-loop iteration of search, optimization and control, so that the control system can maintain optimal regulation performance under different reaction stages and operating conditions.

[0099] S6. Safety constraints: During the adaptive search process, temperature range, heating rate range, and control command range are set as safety barriers, and new parameter candidates are filtered through the trust domain to obtain control command candidates that meet the constraints.

[0100] Furthermore, safety constraints include:

[0101] During the adaptive search process, temperature range, temperature change rate range, and control command range are set as safety barriers.

[0102] A trust domain is established based on the safety barrier to limit the range of values ​​for candidate parameters;

[0103] The parameter candidates generated by the adaptive search are filtered, and those that exceed the safety barrier or trust domain are removed, while the control command candidates that meet the conditions are retained.

[0104] Specifically, when implementing S6 safety constraints, safety barriers and trust domains are set for the adaptive search process itself to prevent control commands from exceeding safe limits during parameter search. Its working principle is to limit the allowable ranges of temperature, heating rate, and control commands during the search phase, using these boundaries as algorithm selection conditions rather than just constraints for control optimization. The adaptive search generates parameter candidates... Next, it is determined whether the response of the parameter combination under the model prediction is within the set safety barrier. If it is satisfied, it will proceed to the next round of optimization; otherwise, it will be eliminated.

[0105] Building upon this, the parameter update range is further limited using the trust domain method. The trust domain is based on the currently optimal parameters. Centered on, by the following conditions: ;

[0106] Define the acceptable search radius, where The radius of the trust domain is used to constrain the search step size, preventing control fluctuations caused by rapid parameter changes. Combined with model predictions, candidate parameters are safely screened to achieve iterative optimization of adaptive search while ensuring process safety.

[0107] This extends safety constraints from the control layer to the algorithm layer, allowing parameter optimization to be performed within a controlled space. It prevents the search algorithm from causing temperature out-of-bounds errors due to misjudgment or over-adjustment, thus achieving a safe and adaptive parameter optimization process.

[0108] S7. Stage Identification: Identify the stages of the reaction process, including the heating stage, the isothermal stage, and the cooling stage, and switch the search variables and optimization objectives according to the stage.

[0109] Further, phase identification includes:

[0110] Obtain operational data of the reaction process, and determine whether the reaction is in the heating stage, the isothermal stage, or the cooling stage based on the operational data;

[0111] When it is determined to be in the heating stage, the temperature setting trajectory parameters and the prediction time domain are selected as search variables;

[0112] When the temperature is determined to be in a constant temperature stage, the weighting coefficient is selected as the search variable.

[0113] When the temperature is determined to be in the cooling phase, the control time domain and temperature setting trajectory parameters are selected as search variables.

[0114] Specifically, by acquiring operational data of the reaction process and analyzing the trend of reactor temperature changes, the system determines whether the reaction is in a heating, isothermal, or cooling phase. A continuous temperature increase is identified as a heating phase, small temperature fluctuations as an isothermal phase, and a continuous temperature decrease as a cooling phase. The system automatically switches between adaptively searched variables and optimization objectives based on the different phases. In the heating phase, it optimizes the temperature setpoint trajectory parameters and prediction time domain; in the isothermal phase, it adjusts the weighting coefficients; and in the cooling phase, it optimizes the control time domain and temperature trajectory parameters. This step aims to match the control strategy with the reaction phase, achieving adaptive adjustment of temperature control at different stages, balancing stable heating, precise isothermal control, and controllable cooling.

[0115] S8. Control execution: Apply the control input, after constraint screening, to the reactor to complete the temperature control process.

[0116] Furthermore, control execution includes:

[0117] The control commands, after being filtered by safety constraints, are sent to the actuators.

[0118] The actuators include valves for regulating the jacket fluid or heat exchangers for regulating the refrigerant temperature;

[0119] The control commands are applied to the reaction vessel through the actuator.

[0120] Specifically, control commands, filtered by safety constraints, are sent to the actuators, which then perform the actual adjustment of the reactor temperature. The control results obtained from the integrated optimization of model predictive control and adaptive search are transformed into physical action signals. By controlling the flow rate of the jacket fluid through valves or adjusting the refrigerant temperature through the heat exchange device, the heat exchange process inside the reactor is directly controlled. Furthermore, the control commands are continuously updated based on real-time feedback, forming a closed-loop regulation to maintain the reaction temperature stable within the set range. This transforms the calculated optimization results into specific execution actions, ultimately implementing the temperature regulation and ensuring that the calculation results of the control strategy are accurately reflected in the actual process, keeping the reactor temperature stable and controllable to meet process requirements.

[0121] Furthermore, the operational data is monitored during the execution process;

[0122] When the operating data is detected to be outside the preset range, the control parameters are switched to the safety parameter set, and control commands are generated based on the safety parameter set and applied to the reactor.

[0123] Specifically, the system monitors the reactor's operating data in real time to determine if the system is operating within a safe range. This is achieved by collecting signals such as reactor temperature, jacket temperature, valve opening, and fluid flow rate, and comparing them to preset safety upper and lower limits. If any signal exceeds the allowable range, an abnormal state is identified. Upon detecting an anomaly, the system immediately triggers a safety switching logic, switching the control parameters to a pre-set set of safety parameters. This set typically includes conservative prediction time domain, control time domain, and control weight values ​​to reduce system response intensity and prevent further temperature deviation from the target range. Subsequently, control commands are regenerated based on this safety parameter set and applied to the reactor, controlling the actuators to enter a safe operating mode. This ensures the control system remains stable even under sudden disturbances or abnormal operating conditions, preventing temperature overshoot, runaway, or equipment exceeding its limits.

[0124] Please see the appendix Figure 2 An intelligent temperature control system for fine chemical reactors based on adaptive search, the system comprising:

[0125] The data acquisition module collects operating data from the reactor, including internal temperature, jacket inlet and outlet temperatures, fluid flow rate, and valve opening signals.

[0126] The heat balance calculation module establishes a heat balance equation based on operational data and calculates the heat released by the reaction and the equivalent heat transfer coefficient.

[0127] The model update module corrects the parameters of the prediction model based on the heat balance equation by using the heat released by the reaction and the equivalent heat transfer coefficient, and generates a predicted value for the future change of the reactor temperature.

[0128] The predictive control solution module constructs an optimization problem based on the prediction results and the set temperature range, heating rate range, and valve opening range, solves the control command candidates, and passes them to the adaptive search module.

[0129] The adaptive search module performs an adaptive search on the control command candidates, determines the parameters to be optimized, generates new parameter candidates, and feeds them back to the predictive control solution module for re-solving.

[0130] The safety constraint module generates new parameter candidates, establishes safety barriers and trust domains based on temperature range, heating rate range, and control command range, filters parameter candidates, and outputs control commands that meet the constraint conditions.

[0131] The stage identification module monitors the operating data and determines whether the reaction process is in the heating stage, the isothermal stage, or the cooling stage, and transmits the stage determination result to the adaptive search module to determine the corresponding search variable.

[0132] The control and execution module sends control commands to the actuator to adjust the temperature of the jacket valve or refrigerant, thereby regulating the temperature of the reactor.

[0133] The abnormal rollback module monitors the running data during control execution. When the running data exceeds the preset range, the control parameters are switched to the safety parameter set, a new control command is generated, and it is transmitted to the control execution module.

[0134] Specifically, the intelligent temperature control system for fine chemical reactors based on adaptive search acquires real-time reactor operating information through a data acquisition module, establishes a heat balance equation and calculates the heat released by the reaction and the equivalent heat transfer coefficient through a heat balance calculation module, corrects the prediction model based on these parameters and generates future temperature change trends, calculates candidate control commands based on constraints, iteratively optimizes control parameters within the parameter space, selects control commands that meet safety conditions through safety barriers and trust domains, identifies reaction stages based on operating data and adjusts search variables, and sends commands to the actuators to adjust the jacket or refrigerant to achieve temperature control. The anomaly fallback module switches to a safety parameter set and regenerates control commands when operating data exceeds limits, thus achieving intelligent, adaptive, and safe temperature control of the reactor. Its effect is to ensure the stability, accuracy, and safety of temperature regulation under complex operating conditions.

[0135] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent temperature control of a fine chemical reactor based on adaptive search, characterized in that, include: S1. Data acquisition: Acquire reactor operating data, including reactor internal temperature, jacket inlet and outlet temperatures, fluid flow rate, and valve opening signals. S2. Heat balance calculation: Based on the collected data, establish the heat balance equation and calculate the heat released by the reaction and the equivalent heat transfer coefficient. S3. Model update: The calculated heat release and heat transfer coefficient of the reaction are used to update the prediction model and generate a predicted value for the future change of the kettle temperature. S4. Predictive control solution: Based on the output of the predictive model, execute model predictive control to obtain the control input candidates; S5. Adaptive search: An adaptive search algorithm is introduced for control command candidates. The prediction time domain length, control time domain length, weighting coefficients and temperature set trajectory parameters are iteratively searched to generate new parameter candidates. S6. Safety constraints: During the adaptive search process, temperature range, heating rate range, and control command range are set as safety barriers, and the new parameter candidates are filtered through the trust domain to obtain control command candidates that meet the constraints. S7. Stage Identification: Identify the stages of the reaction process, including the heating stage, the isothermal stage, and the cooling stage, and switch the search variables and optimization objectives according to the stage. S8. Control execution: Apply the control input, after constraint screening, to the reactor to complete the temperature control process.

2. The intelligent temperature control method for fine chemical reactors based on adaptive search according to claim 1, characterized in that, The heat balance calculation includes: Calculate the heat exchange between the jacket and the reactor based on the jacket inlet and outlet temperatures and flow rates; Substituting the heat exchange and the rate of change of the vessel temperature into the heat balance equation, the heat released by the reaction is obtained, and the equivalent heat transfer coefficient is estimated by the recursive least squares method.

3. The intelligent temperature control method for fine chemical reactors based on adaptive search according to claim 1, characterized in that, The model update includes: The heat transfer coefficient and heat release term in the prediction model are corrected based on the heat release of the reaction and the equivalent heat transfer coefficient to obtain the updated prediction model. The updated prediction model is used to calculate the future changes in reactor temperature within a given prediction time domain.

4. The intelligent temperature control method for fine chemical reactors based on adaptive search according to claim 1, characterized in that, The predictive control solution includes: The updated prediction model is used as the control prediction model, with the reactor temperature as the controlled variable and the jacket valve opening or refrigerant inlet temperature as the control variables. Set upper and lower limits for temperature, temperature rise rate constraint, and range of control variable values ​​within the prediction time domain. An optimization problem is constructed based on the constraints, and the control input candidates that satisfy the constraints are obtained by solving the problem in the control time domain.

5. The intelligent temperature control method for fine chemical reactors based on adaptive search according to claim 1, characterized in that, The adaptive search includes: An adaptive search algorithm is introduced for the candidate control commands to determine the parameters to be optimized. The parameters include the prediction time domain length, the control time domain length, the weighting coefficient, and the temperature setting trajectory parameters. New parameter candidates are generated using an adaptive search algorithm and then input into the predictive control solution process. The predictive control problem is resolved based on the new parameter candidates to obtain the corresponding control command candidates.

6. The intelligent temperature control method for fine chemical reactors based on adaptive search according to claim 1, characterized in that, The security constraints include: During the adaptive search process, temperature range, temperature change rate range, and control command range are set as safety barriers. A trust domain is established based on the safety barrier to limit the range of values ​​for candidate parameters; The parameter candidates generated by the adaptive search are filtered, and those that exceed the safety barrier or trust domain are removed, while the control command candidates that meet the conditions are retained.

7. The intelligent temperature control method for fine chemical reactors based on adaptive search according to claim 1, characterized in that, The stage identification includes: Obtain operational data of the reaction process, and determine whether the reaction is in the heating stage, the isothermal stage, or the cooling stage based on the operational data; When it is determined to be in the heating stage, the temperature setting trajectory parameters and the prediction time domain are selected as search variables; When the temperature is determined to be in a constant temperature stage, the weighting coefficient is selected as the search variable. When the temperature is determined to be in the cooling phase, the control time domain and temperature setting trajectory parameters are selected as search variables.

8. The intelligent temperature control method for a fine chemical reactor based on adaptive search according to claim 1, characterized in that, The control execution includes: The control commands, after being filtered by safety constraints, are sent to the actuators. The actuator includes a valve for regulating the jacket fluid or a heat exchanger for regulating the refrigerant temperature. The control commands are applied to the reaction vessel through the actuator.

9. The intelligent temperature control method for fine chemical reactors based on adaptive search according to claim 1, characterized in that, During the execution process, the running data is monitored; When the operating data is detected to exceed the preset range, the control parameters are switched to the safety parameter set, and control commands are generated based on the safety parameter set and applied to the reactor.

10. A temperature intelligent control system for a fine chemical reactor based on adaptive search, characterized in that, The system for the intelligent temperature control method of a fine chemical reactor based on adaptive search as described in any one of claims 1-9 includes: The data acquisition module collects operating data from the reactor, including internal temperature, jacket inlet and outlet temperatures, fluid flow rate, and valve opening signals. The heat balance calculation module establishes a heat balance equation based on operational data and calculates the heat released by the reaction and the equivalent heat transfer coefficient. The model update module corrects the parameters of the prediction model based on the heat balance equation by using the heat released by the reaction and the equivalent heat transfer coefficient, and generates a predicted value for the future change of the reactor temperature. The predictive control solution module constructs an optimization problem based on the prediction results and the set temperature range, heating rate range, and valve opening range, solves the control command candidates, and passes them to the adaptive search module. The adaptive search module performs an adaptive search on the control command candidates, determines the parameters to be optimized, generates new parameter candidates, and feeds them back to the predictive control solution module for re-solving. The safety constraint module generates new parameter candidates, establishes safety barriers and trust domains based on temperature range, heating rate range, and control command range, filters parameter candidates, and outputs control commands that meet the constraint conditions. The stage identification module monitors the operating data and determines whether the reaction process is in the heating stage, the isothermal stage, or the cooling stage, and transmits the stage determination result to the adaptive search module to determine the corresponding search variable. The control and execution module sends control commands to the actuator to adjust the temperature of the jacket valve or refrigerant, thereby regulating the temperature of the reactor. The abnormal rollback module monitors the running data during control execution. When the running data exceeds the preset range, the control parameters are switched to the safety parameter set, a new control command is generated, and it is transmitted to the control execution module.

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