Chemical reaction kettle temperature control method and system for chemical production

Abstract

The present application relates to the technical field of non-electric variable control, in particular to a chemical reaction kettle temperature control method and system for chemical production. The method comprises: at each time, predicting a predicted temperature at the next time according to actual temperatures at each time experienced; determining prediction errors at each time in a prediction time domain according to the actual temperatures and the predicted temperatures at each time experienced; determining system adjustment parameters according to variation of the actual temperatures at each time experienced; obtaining a temperature control parameter value at the current time by solving a target function; the target function comprises a prediction error term and a control term; the prediction error term is determined based on the prediction errors and the predicted temperatures at each time in the prediction time domain; the control term is determined based on the system adjustment parameters and the temperature control parameters to be solved at each time in a control time domain; and proportional integral differential control is performed based on the temperature control parameter value at the current time. The method can improve the accuracy of chemical reaction kettle temperature control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of non-electric variable control, in particular to a chemical reaction kettle temperature control method and system for chemical production. BACKGROUND

[0002] In the chemical production process, reaction kettle temperature control is a key link to ensure product quality, production safety and process efficiency. Many chemical reactions are extremely sensitive to temperature, and excessive temperature may lead to uncontrolled reaction and sudden pressure rise, and even cause explosion or harmful gas leakage. Some reactions (such as polymerization, nitration, etc.) have strong exothermic characteristics, and if heat dissipation is not timely, it may cause "thermal runaway" accidents. Temperature directly affects reaction rate, selectivity and conversion rate. Temperature fluctuations may lead to increased side reactions and decreased product purity, and even generate unqualified products. Chemical reaction kettle temperature control not only concerns production safety, product quality and economic benefits, but also is a key technology to realize intelligent and green production.

[0003] Traditional temperature control methods mainly rely on manual adjustment or simple PID control (proportional-integral-derivative control), which adjusts the flow or power of heating or cooling medium to achieve temperature control by monitoring the temperature signal in the reaction kettle. However, chemical reactions often have nonlinear, time-varying and large lag characteristics, especially in complex reactions or large-scale production, traditional PID control is difficult to adapt to dynamic working condition changes, and is prone to overshoot, oscillation or response lag, etc. Temperature control is inaccurate, affecting reaction selectivity and yield. SUMMARY

[0004] In order to solve the technical problem of inaccurate chemical reaction kettle temperature control, the purpose of the present application is to provide a chemical reaction kettle temperature control method and system for chemical production, and the technical solution adopted is as follows:

[0005] The present application provides a chemical reaction kettle temperature control method for chemical production, which comprises:

[0006] At each time, the actual temperature of each time experienced is used to predict the predicted temperature of the next time;

[0007] The actual temperature and the predicted temperature of each time experienced are used to determine the prediction error of each time in the prediction time domain; the prediction time domain includes the experienced time and the next time;

[0008] The change of the actual temperature of each time experienced is used to determine the system adjustment parameter;

[0009] Solving the objective function to obtain the temperature control parameter value at the current time; the objective function includes a prediction error term and a control term; the prediction error term is determined based on the prediction error and the predicted temperature at each time in the prediction time domain; the control term is determined based on the system adjustment parameter and the temperature control parameter to be solved at each time in the control time domain;

[0010] Performing proportional-integral-derivative control based on the temperature control parameter value at the current time.

[0011] According to the present application, the temperature control method for the chemical reaction kettle for chemical production is provided, and the prediction error at each time in the prediction time domain is determined based on the actual temperature and the predicted temperature at each time.

[0012] The prediction error coefficient at each time is determined based on the actual temperature and the predicted temperature at each time.

[0013] The prediction error function is obtained by function fitting based on the prediction error coefficient at each time.

[0014] The prediction error at each time in the prediction time domain is determined based on the prediction error function.

[0015] According to the present application, the temperature control method for the chemical reaction kettle for chemical production is provided, and at each time, the predicted temperature at the next time is predicted based on the actual temperature at each time.

[0016] Starting from the initial time period, at each time, the predicted temperature at the next time is predicted based on the actual temperature at each time.

[0017] The prediction error coefficient at each time is determined based on the actual temperature and the predicted temperature at each time.

[0018] The temperature difference between the actual temperature and the predicted temperature at each time except the initial time period, and the time difference between each time except the initial time period and the initial time period are determined.

[0019] The prediction error coefficient at each time except the initial time period is determined based on the temperature difference and the time difference at each time except the initial time period.

[0020] According to the present application, the temperature control method for the chemical reaction kettle for chemical production is provided, and the prediction error coefficient at each time except the initial time period is determined based on the temperature difference and the time difference at each time except the initial time period.

[0021] respectively for each time that has been experienced except the initial time period, according to the product of the temperature difference and the time difference of the time, determine the prediction error coefficient of the current time.

[0022] According to the present application, the temperature control method of the chemical reaction kettle for chemical production is provided, and the system regulation parameters are determined according to the actual temperature change of each time that has been experienced, including:

[0023] The slope between the data points corresponding to the actual temperatures of adjacent times in each time that has been experienced is determined.

[0024] The slopes are clustered to obtain a plurality of clustering clusters.

[0025] For each chemical reaction stage corresponding to each clustering cluster, the temperature change degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the slope and the temperature in the chemical reaction stage.

[0026] The system regulation parameters are determined according to the temperature change degree of the chemical reaction stage corresponding to each clustering cluster.

[0027] According to the present application, the temperature control method of the chemical reaction kettle for chemical production is provided, and the temperature change degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the slope and the temperature in the chemical reaction stage, including:

[0028] For each chemical reaction stage corresponding to each clustering cluster, the discrete degree and the concentration tendency of the slope in the chemical reaction stage, and the temperature span in the chemical reaction stage are determined.

[0029] The temperature change degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the discrete degree, the concentration tendency and the temperature span.

[0030] According to the present application, the temperature control method of the chemical reaction kettle for chemical production is provided, and the temperature change degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the discrete degree, the concentration tendency and the temperature span, including:

[0031] The temperature change degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the product of the discrete degree, the concentration tendency and the temperature span.

[0032] According to the present application, the temperature control method of the chemical reaction kettle for chemical production is provided, and the system regulation parameters are determined according to the temperature change degree of the chemical reaction stage corresponding to each clustering cluster, including:

[0033] Determine a system adjustment parameter according to the mean of the temperature variation degree of the chemical reaction stage corresponding to each of the clustering groups.

[0034] The method for controlling the temperature of the chemical reaction kettle for chemical production provided by the application comprises the following steps of:

[0035] The method for controlling the temperature of the chemical reaction kettle for chemical production provided by the application comprises the following steps of:

[0036] The method for controlling the temperature of the chemical reaction kettle for chemical production provided by the application comprises the following steps of:

[0037] The method for controlling the temperature of the chemical reaction kettle for chemical production provided by the application comprises the following steps of: BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0039] Figure 1 A flowchart of a method for controlling the temperature of a chemical reaction kettle for chemical production provided by an embodiment of the application;

[0040] Figure 2The overall control flow of the chemical reaction kettle temperature control method for chemical production provided by an embodiment of the present application is shown in the figure.

[0041] Figure 3 The flowchart for determining the prediction error at each time in the prediction time domain provided by an embodiment of the present application is shown in the figure.

[0042] Figure 4 The flowchart for determining the system adjustment parameter provided by an embodiment of the present application is shown in the figure.

[0043] Figure 5 The architecture diagram of the chemical reaction kettle temperature control system for chemical production provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0044] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purposes, the specific embodiments, structures, features and effects of the chemical reaction kettle temperature control method and system for chemical production according to the present application are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0046] The specific scheme of the chemical reaction kettle temperature control method and system for chemical production provided by the present application is specifically described below in combination with the accompanying drawings.

[0047] Please refer to Figure 1 which shows the flowchart of the chemical reaction kettle temperature control method for chemical production provided by an embodiment of the present application, including the following steps:

[0048] Step 101, at each time, the prediction temperature at the next time is predicted according to the actual temperature at each time experienced.

[0049] Among them, the experienced times include historical times and the current time. The time interval between each time can be set according to the actual situation, for example: one time per second.

[0050] In an embodiment, a temperature sensor can be installed in the chemical reaction kettle to collect the real-time actual temperature of the chemical reaction kettle through the temperature sensor.

[0051] In an embodiment, multiple temperature sensors can be installed at different positions of the chemical reaction kettle to avoid measurement deviation caused by local temperature unevenness. For example, sensors are installed at the top, middle and bottom of the chemical reaction kettle respectively. Temperature data collected by multiple temperature sensors can be integrated by methods such as weighted average or Kalman filtering to improve measurement reliability.

[0052] In an embodiment, the temperature sensor can be a thermocouple or a thermal resistance (RTD). For example, the thermal resistance can be Pt100 or Pt1000. The principle of the thermocouple is based on the Seebeck effect, that is, a potential difference is generated between two different metal conductors when the temperature changes, and the temperature is calculated by measuring the voltage. The characteristics of the thermocouple are wide measurement range (-200℃~1800℃), fast response, low cost, but low precision (±1~2℃), cold end compensation is required, and it is easy to be affected by electromagnetic interference. The application scenario of the thermocouple is high temperature and fast changing reaction, such as polymerization or cracking reaction, etc. The principle of the thermal resistance is to measure temperature by using the temperature change characteristics of the metal resistance. The characteristics of the thermal resistance are high precision (±0.1~0.5℃), good stability, high linearity, narrow measurement range (-200℃~600℃), slow response, and higher cost than the thermocouple. The application scenario of the thermal resistance is low-temperature precise temperature control, such as pharmaceutical or fine chemical industry, etc.

[0053] In an embodiment, the temperature sensor can transmit the collected temperature data to a programmable logic controller (PLC) or a distributed control system (DCS) for filtering and linearization compensation. For example, the linearization compensation can be cold end compensation for the thermocouple. Then the PLC or DCS can transmit the filtered and linearized temperature data to a SCADA (Supervisory Control And Data Acquisition) or a cloud platform, and execute the chemical reaction kettle temperature control method for chemical production provided by the present application through the SCADA or the cloud platform to realize real-time monitoring, historical data storage and alarm functions.

[0054] In an embodiment, the transmission of temperature data can use analog signals or digital signals. The analog signal can use 4~20mA current signal, which has strong anti-interference ability and is suitable for industrial field. The digital signal can use RS485 protocol, HART protocol or wireless transmission. For example, wireless transmission can use LoRa (a long-distance low-power radio technology standard) or ZigBee (a wireless communication technology applied to short distance and low speed).

[0055] In one embodiment, the predicted temperature of the next time point can be predicted according to the actual temperatures of the time points that have been experienced, at each time point, after an initial time period. The initial time period is a time period of a preset duration when the chemical reaction just starts. For example, the preset duration can be 10 seconds.

[0056] In one embodiment, the predicted temperature of the next time point can be predicted according to the actual temperatures of the time points that have been experienced, by a time series prediction model. For example, the time series prediction model includes ARIMA (autoregressive integrated moving average model) and the like.

[0057] Step 102, determining the prediction error of each time point in the prediction time domain according to the actual temperature and the predicted temperature of each time point that has been experienced; the prediction time domain includes the time points that have been experienced and the next time point.

[0058] In one embodiment, a prediction error function is determined according to the actual temperature and the predicted temperature of each time point that has been experienced, and the prediction error of each time point in the prediction time domain is determined according to the prediction error function.

[0059] In one embodiment, a prediction error function can be determined according to the actual temperature and the predicted temperature of each time point that has been experienced after the initial time period, and the prediction error of each time point in the prediction time domain is determined according to the prediction error function. The prediction time domain includes the time points that have been experienced after the initial time period and the next time point.

[0060] Step 103, determining the system adjustment parameter according to the change of the actual temperature of each time point that has been experienced.

[0061] It can be understood that because the temperature change rate in the reaction kettle is not constant, if the temperature changes too fast, it will cause serious safety hazards, especially for strong exothermic reactions, which may lead to thermal runaway and pressure surge, and even cause explosion accidents; secondly, it will cause the product quality to decline, such as increase of side reactions, uneven molecular weight distribution or catalyst deactivation. When the temperature changes too slowly, not only the reaction period is prolonged, the production efficiency is reduced, but also the reaction may be incomplete, the intermediate is accumulated or the crystal defects are caused. Therefore, it is necessary to analyze the temperature change of different reaction processes to obtain the system adjustment parameter.

[0062] In one embodiment, the slope between the data points corresponding to the actual temperatures of adjacent time points in the time points that have been experienced is determined, and the system adjustment parameter at the current time point is determined according to the slope.

[0063] Step 104, obtaining the temperature control parameter value at the current time point by solving the objective function; the objective function includes a prediction error term and a control term; the prediction error term is determined based on the prediction error and the predicted temperature of each time point in the prediction time domain; the control term is determined based on the system adjustment parameter and the temperature control parameter to be solved at each time point in the control time domain.

[0064] Temperature control parameters are parameters used to control temperature. For example, temperature control parameters can be the opening degree of a steam valve or the heating power. Temperature control parameter values ​​refer to the specific values ​​that the temperature control parameters take.

[0065] Model predictive control (MPC) is an advanced control strategy based on dynamic models, rolling optimization, and feedback correction, widely used in chemical engineering, energy, and process control. Its core idea is to predict the system's behavior over a future period using a dynamic model and solve a finite-time optimal control problem within each control cycle to determine the optimal control action at the current moment. The core process includes three steps: First, based on the current system state and mathematical model, predict the output response for multiple future time steps, such as the temperature change trajectory of a reactor. Second, calculate the control sequence through rolling optimization to minimize the objective function, such as the weighted sum of temperature tracking error and control variable changes, while satisfying process constraints, such as temperature safety limits and valve opening ranges. Finally, execute the first step of the optimized control input and perform feedback correction in the next cycle using real-time measurements to compensate for model errors and external disturbances.

[0066] In one embodiment, the prediction error term is obtained by summing the product of the prediction error and the predicted temperature at each time point in the prediction time domain. The temperature control parameters to be solved at each time point in the control time domain are summed, and the summation result is multiplied by the system adjustment coefficient at the current time point to obtain the control term.

[0067] In one embodiment, the objective function can be the sum of the prediction error term and the control term. The temperature control parameters at the current moment can be obtained by minimizing the value of the objective function.

[0068] In one embodiment, the objective function can be expressed as follows:

[0069]

[0070] in, This represents the objective function. This represents the prediction error term. Indicates a control item. Indicates the first The prediction error at each time point. Indicates the first The predicted temperature at each moment. This indicates the prediction time domain. Indicates the first System adjustment parameters at each time point. Indicates the first temperature control parameter at the current time. denotes the control time domain. denotes taking the minimum value.

[0071] reflects the first time prediction error and the predicted temperature in the reaction kettle, the greater the correction degree of the temperature in the reaction kettle. In the prediction time domain, the prediction error of the system mainly depends on the change in the reaction steady stage, so the time with the minimum error is selected as the output of the objective function, the smaller, the smaller the error input. The system adjustment parameter is used to control the stability and response speed of temperature adjustment, which mainly depends on the change of temperature. If the temperature changes faster, the corresponding system adjustment parameter is larger, and vice versa. The slower the temperature changes, the smaller the system adjustment parameter. denotes the temperature control parameter at the current time. for example: the opening of the steam valve.

[0072] In one embodiment, the prediction time domain includes the time that has passed after the initial time period and the next time. The control time domain includes the time that has passed after the initial time period. For example: assuming that the initial time period is 10 seconds when the chemical reaction just starts, and assuming that the current time is the 15th second, the prediction time domain is from the 11th second to the 16th second, and the control time domain is from the 11th second to the 15th second.

[0073] In one embodiment, the formula for solving the objective function is as follows:

[0074]

[0075] wherein, denotes the target temperature control parameter sequence. denotes the set of independent variables that makes the objective function take the minimum value.

[0076] Step 105, proportional-integral-derivative control based on the temperature control parameter value at the current time.

[0077] Wherein, proportional-integral-derivative control (PID control, proportional-integral-derivative control), is a control method that constructs a deviation according to a given value and an actual output value, and constructs a control amount by linear combination according to the deviation, proportional, integral and differential, to control the controlled object.

[0078] It can be understood that solving the objective function can obtain the temperature control parameter value at each time in the control time domain (Target temperature control parameter sequence), which includes the temperature control parameter values ​​at the current time t. The temperature control parameter value at the current time t The control is sent to the actuator for proportional-integral-derivative (PID) control. For example, it is sent to the regulating valve based on the current steam valve opening value. Proportional-integral-derivative (PID) control is used to control the opening of the steam valve, thereby accurately controlling the temperature. Here, m represents the end time of the initial time period, m+1 represents the first time after the initial time period, and t represents the current time. For example, in the above example, solving the objective function yields the temperature control parameter values ​​at each time point from the 11th to the 15th second (control time domain), and then PID control is performed based on the temperature control parameter value at the 15th second (the current time).

[0079] like Figure 2 The diagram shows the overall control flow, which generates the target temperature control parameter sequence through MPC rolling optimization. Extract the temperature control parameters at the current moment from it. The data is sent to the PID controller as an implicit target for PID control, outputting the change in the temperature control parameter value. This enables temperature control and then provides feedback on the current temperature. Continue with MPC rolling optimization.

[0080] The proportional-integral-derivative control formula is as follows:

[0081]

[0082] in, This represents the change in the temperature control parameter value at time t. This represents the proportional gain coefficient. This represents the integral gain coefficient. This represents the differential gain coefficient. This represents the predicted temperature at time t. This represents the actual temperature at time t. This represents the temperature deviation at time t, i.e. . This represents the number of moments that have elapsed up to time t.

[0083] As an indirect target of PID control, temperature deviation is used. Indirect impact, if the actual temperature If the PID deviates from the predicted trajectory, then it passes. Real-time fine-tuning of temperature control parameters, such as fine-tuning the opening of the steam valve, to make the temperature control parameter value approximate... .

[0084] The chemical reaction kettle temperature control method for chemical production determines the prediction error of each time in the prediction time domain according to the actual temperature and the prediction temperature of each time that has been experienced, determines the system adjustment parameter according to the change of the actual temperature of each time that has been experienced, then determines the prediction error term based on the prediction error of each time in the prediction time domain and the prediction temperature, determines the control term based on the system adjustment parameter and the temperature control parameter to be solved at each time in the control time domain, constructs the objective function based on the prediction error term and the control term and solves the objective function to obtain the temperature control parameter value at the current time, and performs proportional integral differential control based on the temperature control parameter value at the current time, so that the large lag effect is compensated in advance by predicting the future temperature trend, the temperature error is corrected in real time, the passive correction of the traditional PID control method is changed into active intervention, and the accuracy of the chemical reaction kettle temperature control is improved.

[0085] In one embodiment, referring to Figure 3 , step 102 determines the prediction error of each time in the prediction time domain according to the actual temperature and the prediction temperature of each time that has been experienced, including the following steps:

[0086] Step 1021 determines the prediction error coefficient of each time that has been experienced according to the actual temperature and the prediction temperature of each time that has been experienced.

[0087] The prediction error coefficient is used to represent the size of the prediction error.

[0088] Step 1022 performs function fitting to obtain a prediction error function according to the prediction error coefficient of each time that has been experienced.

[0089] In one embodiment, the prediction error coefficients of each time that has been experienced are used to form a data sequence, and a least square fitting algorithm is used to obtain a fitting function as the prediction error function. .

[0090] Step 1023 determines the prediction error of each time in the prediction time domain according to the prediction error function.

[0091] The time value of each time in the prediction time domain can be substituted into the prediction error function to obtain the prediction error of each time in the prediction time domain. For example, assuming that the prediction time domain is from the 11th second to the 16th second, the prediction errors of each time in the prediction time domain are , , , , , .

[0092] In the above embodiment, since the temperature changes differently in different stages, the accuracy of the temperature prediction is also different, and thus the prediction error function can be obtained by function fitting according to the prediction error coefficients of the time points that have been experienced, so that the prediction error of each time point in the prediction time domain can be accurately determined based on the prediction error function.

[0093] In one embodiment, the prediction of the predicted temperature of the next time point according to the actual temperature of each time point that has been experienced at each time point includes: starting from after the initial time period, the prediction of the predicted temperature of the next time point according to the actual temperature of each time point that has been experienced at each time point. The determination of the prediction error coefficient of each time point that has been experienced according to the actual temperature and the predicted temperature of each time point that has been experienced includes: determining the temperature difference between the actual temperature and the predicted temperature of each time point that has been experienced except the initial time period, and the time difference between each time point that has been experienced except the initial time period and the end time point of the initial time period; determining the prediction error coefficient of each time point that has been experienced except the initial time period according to the temperature difference and the time difference of each time point that has been experienced except the initial time period.

[0094] In one embodiment, the difference between the actual temperature and the predicted temperature of each time point that has been experienced except the initial time period can be taken as the temperature difference between the actual temperature and the predicted temperature of each time point that has been experienced except the initial time period.

[0095] In one embodiment, the length of time from each time point that has been experienced except the initial time period to the end time point of the initial time period is taken as the time difference between each time point that has been experienced except the initial time period and the initial time period.

[0096] In one embodiment, the prediction error coefficient of each time point that has been experienced except the initial time period is positively correlated with the temperature difference of each time point that has been experienced except the initial time period, and is positively correlated with the time difference of each time point that has been experienced except the initial time period.

[0097] In the above embodiment, the greater the difference between the predicted temperature and the actual temperature, and the farther the current time point is from the initial time period, the less accurate the current prediction is, that is, the greater the prediction error coefficient of the current time point, and thus the prediction error coefficient of each time point that has been experienced except the initial time period can be accurately determined according to the temperature difference and the time difference of each time point that has been experienced except the initial time period.

[0098] In one embodiment, the determination of the prediction error coefficient of each time point that has been experienced except the initial time period according to the temperature difference and the time difference of each time point that has been experienced except the initial time period includes: for each time point that has been experienced except the initial time period, the prediction error coefficient of the current time point is determined according to the product of the temperature difference and the time difference of the time point.

[0099]

[0100] wherein, represents the prediction error coefficient of the th time point. represents the actual temperature of the th time point. represents the predicted temperature of the th time point. represents the temperature difference of the th time point. represents the time difference of the th time point from the initial time period.

[0101] In the above embodiment, the greater the difference between the predicted temperature and the actual temperature, and the farther the current time point is from the initial time period, the less accurate the current prediction is, i.e., the greater the prediction error coefficient of the current time point is, and therefore, the product of the temperature difference of the time point and the time difference of the time point from the initial time period can accurately determine the prediction error coefficient of the current time point.

[0102] In one embodiment, referring to Figure 4 , step 103 determines the system adjustment parameter according to the change of the actual temperature of each time point that has been experienced, including the following steps:

[0103] Step 1031, determining the slope between the data points corresponding to the actual temperatures of adjacent time points in each time point that has been experienced.

[0104] Step 1032, clustering the slopes to obtain a plurality of clustering clusters.

[0105] In one embodiment, the slopes between the data points corresponding to the actual temperatures of adjacent time points in each time point that has been experienced form a slope sequence, and the slope sequence is clustered by a hierarchical clustering algorithm to obtain a plurality of clustering clusters. In one embodiment, the preset clustering level in the hierarchical clustering algorithm can be set to 2, i.e., the clustering stops after 2 iterations.

[0106] Step 1033, for each chemical reaction stage corresponding to each clustering cluster, determining the temperature variation degree of the chemical reaction stage corresponding to the clustering cluster according to the slope and the temperature in the chemical reaction stage.

[0107] wherein, the temperature variation degree represents the degree of temperature variation in the chemical reaction stage.

[0108] ​It can be understood that the temperature change rate of the chemical reaction kettle is different in different chemical reaction stages. In the initial stage of the reaction (the initiation stage), the temperature change rate is usually slow, and at this time, the activation energy barrier of the reaction needs to be overcome, and the temperature change presents obvious hysteresis characteristics. When the reaction enters the acceleration stage, the temperature rises exponentially due to chain reaction or autocatalysis. In the stable reaction stage, the system reaches dynamic equilibrium, and the temperature fluctuation tends to be stable, at which time the heat release rate is basically equal to the heat dissipation rate. In the final stage of the reaction (the decay stage), the temperature needs to be slowly reduced according to the process requirements, especially for crystallization or polymerization reactions, and stepwise temperature reduction can avoid product defects. Therefore, each cluster corresponds to a chemical reaction stage.

[0109] In step 1034, the system adjustment parameter is determined according to the temperature change degree of the chemical reaction stage corresponding to each cluster.

[0110] In one embodiment, the system adjustment parameter is positively correlated with the temperature change degree of the chemical reaction stage corresponding to each cluster.

[0111] In the above embodiment, since the temperature change rate of the chemical reaction kettle is different in different chemical reaction stages, the chemical reaction stages are divided by clustering according to the slope between the actual temperatures of adjacent time points, and then the temperature change degree of each chemical reaction stage is determined. According to the temperature change degree of the chemical reaction stage corresponding to each cluster, the system adjustment parameter can be accurately determined.

[0112] In one embodiment, for each chemical reaction stage corresponding to each cluster, the temperature change degree of the chemical reaction stage corresponding to the cluster is determined according to the slope and the temperature in the chemical reaction stage, including: for each chemical reaction stage corresponding to each cluster, the dispersion degree and the concentration tendency of the slope in the chemical reaction stage, and the temperature span in the chemical reaction stage are determined; and according to the dispersion degree, the concentration tendency and the temperature span, the temperature change degree of the chemical reaction stage corresponding to the cluster is determined.

[0113] In one embodiment, the dispersion degree can be variance or standard deviation, etc.

[0114] In one embodiment, the concentration tendency can be mean or median, etc.

[0115] In one embodiment, the temperature span can be determined according to the difference between the maximum temperature and the minimum temperature.

[0116] In one embodiment, the temperature change degree of the chemical reaction stage is positively correlated with the dispersion degree of the slope in the chemical reaction stage, positively correlated with the concentration tendency of the slope in the chemical reaction stage, and positively correlated with the temperature span in the chemical reaction stage.

[0117] In the above embodiments, a greater degree of dispersion indicates that the rate of temperature change fluctuates wildly, with periods of significant temperature increase followed by periods of smaller increase. Therefore, the greater the dispersion of the slope in a chemical reaction stage, the greater the degree of temperature change within that stage. A larger temperature span within a chemical reaction stage indicates a greater degree of temperature change in that stage. The mean of the slope in a chemical reaction stage represents the overall rate of temperature change at that chemical reaction node; a larger mean indicates a faster overall temperature change. Therefore, based on the dispersion and central tendency of the slope in a chemical reaction stage, as well as the temperature span within that stage, the degree of temperature change in the corresponding chemical reaction stage can be accurately determined.

[0118] In one embodiment, determining the degree of temperature change of the chemical reaction stage corresponding to the cluster based on the degree of dispersion, central tendency, and temperature span includes: determining the degree of temperature change of the chemical reaction stage corresponding to the cluster based on the product of the degree of dispersion, central tendency, and temperature span.

[0119] In the above embodiments, since the degree of temperature change in the chemical reaction stage is positively correlated with the degree of dispersion, central tendency, and temperature span of the slope in the chemical reaction stage, the degree of temperature change in the chemical reaction stage corresponding to the cluster can be accurately determined by the product of the degree of dispersion, central tendency, and temperature span.

[0120] In one embodiment, determining system adjustment parameters based on the degree of temperature change in the chemical reaction stage corresponding to each cluster includes: determining system adjustment parameters based on the average degree of temperature change in the chemical reaction stage corresponding to each cluster.

[0121] In one embodiment, the system adjustment parameters can be determined according to the following formula:

[0122]

[0123] in, This indicates the system adjustment parameters. Indicates the first The variance of the slope in each stage of a chemical reaction. Indicates the first The maximum temperature during each stage of a chemical reaction. Indicates the first The minimum temperature during each stage of a chemical reaction. Indicates the first The average slope of each chemical reaction stage. This indicates the total number of chemical reaction stages, i.e., the total number of clusters. This indicates normalization processing.

[0124] In the above embodiment, the system regulation parameter can be accurately determined according to the mean value of the temperature variation degree of the chemical reaction stage corresponding to each cluster.

[0125] In one embodiment, the temperature control parameter value at the current time is obtained by solving the target function, including: obtaining the temperature control parameter value at each time in the control time domain by solving the target function, and extracting the temperature control parameter value at the current time from the temperature control parameter value at each time in the control time domain; the control time domain includes the time that has been experienced.

[0126] The target function is solved to obtain the temperature control parameter value at each time in the control time domain , which includes the temperature control parameter value at the current time t , the temperature control parameter value at the current time t is extracted and is issued to the actuator for proportional integral derivative control. For example, the temperature control parameter value at each time from the 11th second to the 15th second (control time domain) is obtained by solving the target function, and then proportional integral derivative control is performed based on the temperature control parameter value at the 15th second (current time).

[0127] In the above embodiment, the temperature control parameter value at the current time is obtained by solving the target function, and the temperature control parameter value at the current time is extracted from the temperature control parameter value at each time in the control time domain for proportional integral derivative control, so that the current temperature can be controlled in real time and accurately.

[0128] Referring to Figure 5 , the present application provides a chemical reaction kettle temperature control system for chemical production, which comprises a memory and a processor; the memory is used for storing executable program code; the processor is used for calling and running the executable program code from the memory to realize the following steps: at each time, the actual temperature at each time that has been experienced is used to predict the predicted temperature at the next time; the actual temperature and the predicted temperature at each time that has been experienced are used to determine the prediction error at each time in the prediction time domain; the prediction time domain includes the time that has been experienced and the next time; the system regulation parameter is determined according to the change of the actual temperature at each time that has been experienced; the temperature control parameter value at the current time is obtained by solving the target function; the target function includes a prediction error term and a control term; the prediction error term is determined based on the prediction error and the predicted temperature at each time in the prediction time domain; the control term is determined based on the system regulation parameter and the temperature control parameter to be solved at each time in the control time domain; proportional integral derivative control is performed based on the temperature control parameter value at the current time.

[0129] In one embodiment, the predicted error of each time point in the prediction time domain is determined according to the actual temperature and the predicted temperature of each time point that has been experienced, comprising: determining the predicted error coefficient of each time point that has been experienced according to the actual temperature and the predicted temperature of each time point that has been experienced; performing function fitting according to the predicted error coefficient of each time point that has been experienced to obtain a predicted error function; and determining the predicted error of each time point in the prediction time domain according to the predicted error function.

[0130] In one embodiment, at each time point, the predicted temperature of the next time point is predicted according to the actual temperature of each time point that has been experienced, comprising: starting from an initial time period, at each time point, the predicted temperature of the next time point is predicted according to the actual temperature of each time point that has been experienced; the predicted error coefficient of each time point that has been experienced is determined according to the actual temperature and the predicted temperature of each time point that has been experienced, comprising: determining the temperature difference between the actual temperature and the predicted temperature of each time point that has been experienced except the initial time period, and the time difference between each time point that has been experienced except the initial time period and the initial time period; and the predicted error coefficient of each time point that has been experienced except the initial time period is determined according to the temperature difference and the time difference of each time point that has been experienced except the initial time period.

[0131] In one embodiment, the predicted error coefficient of each time point that has been experienced except the initial time period is determined according to the temperature difference and the time difference of each time point that has been experienced except the initial time period, comprising: for each time point that has been experienced except the initial time period, the predicted error coefficient of the current time point is determined according to the product of the temperature difference and the time difference of the time point.

[0132] In one embodiment, the system adjustment parameter is determined according to the change of the actual temperature of each time point that has been experienced, comprising: determining the slope between the data points corresponding to the actual temperatures of adjacent time points in each time point that has been experienced; clustering the slopes to obtain a plurality of clustering clusters; for each chemical reaction stage corresponding to each clustering cluster, the temperature change degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the slope and the temperature in the chemical reaction stage; and the system adjustment parameter is determined according to the temperature change degree of the chemical reaction stage corresponding to each clustering cluster.

[0133] In one embodiment, for each chemical reaction stage corresponding to each clustering cluster, the temperature change degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the slope and the temperature in the chemical reaction stage, comprising: for each chemical reaction stage corresponding to each clustering cluster, the dispersion degree and the concentration tendency of the slope in the chemical reaction stage, and the temperature span in the chemical reaction stage are determined; and the temperature change degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the dispersion degree, the concentration tendency and the temperature span.

[0134] In one embodiment, the temperature variation degree of the chemical reaction stage corresponding to the clustering cluster is determined according to the dispersion degree, the concentration tendency and the temperature span, including: determining the temperature variation degree of the chemical reaction stage corresponding to the clustering cluster according to the product of the dispersion degree, the concentration tendency and the temperature span.

[0135] In one embodiment, the system adjustment parameter is determined according to the temperature variation degree of the chemical reaction stage corresponding to each clustering cluster, including: determining the system adjustment parameter according to the mean of the temperature variation degree of the chemical reaction stage corresponding to each clustering cluster.

[0136] In one embodiment, the temperature control parameter value at the current time is obtained by solving the objective function, including: obtaining the temperature control parameter value at each time in the control time domain by solving the objective function, and extracting the temperature control parameter value at the current time from the temperature control parameter value at each time in the control time domain; the control time domain includes the elapsed time.

[0137] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not contradict, they should be considered within the scope of the present application.

[0138] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application.

[0139] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0140] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. A chemical reaction vessel temperature control method for chemical production, characterized by, The method comprises: at each time, predicting a predicted temperature of a next time according to actual temperatures of times experienced; determining prediction errors of times in a prediction time domain according to the actual temperatures and the predicted temperatures of the times experienced; the prediction time domain comprises the times experienced and the next time; determining system adjustment parameters according to changes of the actual temperatures of the times experienced; solving a target function to obtain a temperature control parameter value at the current time; the target function comprises a prediction error term and a control term; the prediction error term is determined based on the prediction errors and the predicted temperatures of the times in the prediction time domain; the control term is determined based on the system adjustment parameters and the temperature control parameters to be solved at times in a control time domain; performing proportional-integral-derivative control based on the temperature control parameter value at the current time; wherein the method for obtaining the system adjustment parameters comprises: determining slopes between data points corresponding to actual temperatures of adjacent times among the times experienced; clustering the slopes to obtain a plurality of clustering clusters; determining temperature variation degrees of chemical reaction stages corresponding to each of the clustering clusters according to the slopes and temperatures in the chemical reaction stages; and determining the system adjustment parameters according to mean values of the temperature variation degrees of the chemical reaction stages corresponding to the clustering clusters; wherein the method for obtaining the temperature variation degrees comprises: determining a dispersion degree and a concentration tendency of the slopes in the chemical reaction stages corresponding to each of the clustering clusters, and a temperature span in the chemical reaction stages; and determining the temperature variation degrees of the chemical reaction stages corresponding to the clustering clusters according to the dispersion degree, the concentration tendency and the temperature span.

2. The temperature control method for a chemical reaction vessel for chemical production according to claim 1, characterized by, The method for determining the prediction errors of the times experienced according to the actual temperatures and the predicted temperatures of the times experienced comprises: determining prediction error coefficients of the times experienced according to the actual temperatures and the predicted temperatures of the times experienced; performing function fitting according to the prediction error coefficients of the times experienced to obtain a prediction error function; determining the prediction errors of the times in the prediction time domain according to the prediction error function.

3. The temperature control method for a chemical reaction vessel for chemical production according to claim 2, characterized by, The method for predicting the predicted temperature of the next time according to the actual temperatures of the times experienced at each time comprises: starting from an initial time period, predicting the predicted temperature of the next time according to the actual temperatures of the times experienced at each time; The method for determining the prediction error coefficients of the times experienced according to the actual temperatures and the predicted temperatures of the times experienced comprises: determining temperature differences between the actual temperatures and the predicted temperatures of the times experienced except the initial time period, and time differences between the times experienced except the initial time period and the initial time period; determining the prediction error coefficients of the times experienced except the initial time period according to the temperature differences and the time differences.

4. The temperature control method for a chemical reaction vessel for chemical production according to claim 3, characterized by, The determining the prediction error coefficient of each time period experienced other than the initial time period according to the temperature difference and the time difference of the time period includes: The determining the prediction error coefficient of the current time period according to the product of the temperature difference and the time difference of the time period respectively for each time period experienced other than the initial time period.

5. The temperature control method for a chemical reaction vessel for chemical production according to claim 1, characterized by, The determining the temperature variation degree of the chemical reaction stage corresponding to the clustering cluster according to the dispersion degree, the central tendency and the temperature span includes: The determining the temperature variation degree of the chemical reaction stage corresponding to the clustering cluster according to the product of the dispersion degree, the central tendency and the temperature span.

6. The temperature control method for a chemical reaction vessel for chemical production according to claim 1, characterized by, The solving the objective function to obtain the temperature control parameter value at the current time includes: The solving the objective function to obtain the temperature control parameter value at the current time includes:

7. A chemical reaction vessel temperature control system for chemical production, characterized by, The system includes a memory and a processor; the memory is used to store executable program code; the processor is used to call and run the executable program code from the memory to realize the chemical reaction kettle temperature control method for chemical production in any one of claims 1 to 6.

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