Automatic amination cooling system and cooling method for monosultap preparation

Abstract

The invention relates to the technical field of cooling control, in particular to an amination automatic cooling system and method for monosultap preparation. Acquiring the temperature change rate and heat release time sequence data of the current amination process, and comparing the temperature change rate and heat release time sequence data with the temperature change rate of the historical temperature rise stage; and segmenting the current temperature change rate data into a plurality of temperature change sections, quantifying the difference between each section and historical comparison data in length and temperature change rate, and obtaining a cooling effect value to evaluate the cooling performance. And performing multi-length division on each temperature change section to obtain interval data sections, analyzing temperature fluctuation differences of the interval data sections, and determining an optimal division method and an optimal interval data section in combination with a cooling effect value. And finally, the preset flow velocity of the cooling liquid is adjusted based on the temperature change feature position and the heat release accumulation of the optimal interval data segment, so that a self-adaptive flow velocity value is obtained, and the matching degree of the cooling effect and the reaction requirement is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cooling control, and in particular to an automatic cooling system and a cooling method for amination of insecticide monohydrate. Background Art

[0002] Insecticide is a green, safe, and low-toxic pesticide primarily used to control pests in crops such as rice, vegetables, fruit trees, sugarcane, and corn. In the chemical industry, amination is a key step in the preparation of nitrogen-containing heterocyclic compounds like insecticide. It is a hazardous chemical process designated as a key regulatory step by the Emergency Management Bureau. This exothermic reaction directly impacts reaction efficiency, product purity, and production safety.

[0003] The entire dropwise addition process of allyl chloride during the amination process needs to be carried out in a cooling environment to prevent a violent reaction, and then a heating stage is performed to ensure the complete reaction. When controlling the cooling link in the amination process, the existing technology usually adopts a preset coolant flow rate or a step-by-step adjustment strategy when the actual temperature exceeds the temperature threshold. However, since heat accumulation and temperature fluctuations at different times in the actual reaction process will vary, ignoring factors such as the dynamic thermodynamic characteristics of the reaction process will result in a poor match between the final cooling effect and the reaction requirements, resulting in a poor cooling effect. Summary of the Invention

[0004] In order to solve the technical problem that heat accumulation and temperature fluctuations at different times in the actual reaction process are different, if factors such as the dynamic thermodynamic characteristics of the reaction process are ignored, the final cooling effect will not match the reaction requirements, resulting in poor cooling effect. The purpose of the present invention is to provide an automatic amination cooling system and cooling method for the preparation of insecticide monochloride. The technical solutions adopted are as follows: An amination automatic cooling method for preparing insecticide monohydrate, comprising: Obtain the time series data of the temperature change rate and the heat release rate in the current amination process, and obtain the time series data of the temperature change rate in the heating stage of the historical amination process as a comparison data segment; Segment the temperature change rate time series data during the current amination process to obtain temperature change segments; analyze the length difference between each temperature change segment and the comparison data segment and the fluctuation difference in the temperature change rate to determine the cooling effect value of each temperature change segment; Divide each temperature change segment into multiple lengths to obtain interval data segments; analyze the fluctuation differences in the temperature change rates between the interval data segments and their positional relationships in time series, and combine the cooling effect values ​​corresponding to the interval data segments to determine the optimal division method and obtain the optimal interval data segments; Based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the accumulation of heat released in the corresponding period, the preset flow rate of the coolant is adjusted to obtain an adaptive flow rate value.

[0005] Furthermore, the method for obtaining the cooling effect value includes: The temperature change segment and the comparison data segment are collectively referred to as the data segment to be analyzed; Combining the variance of all data values ​​in each data segment to be analyzed with the length of each data segment to be analyzed to determine the fluctuation density value of each data segment to be analyzed; The value after normalizing the difference between the fluctuation density value of the comparison data segment and the fluctuation density value of each temperature change segment is used as the cooling effect value of each temperature change segment.

[0006] Furthermore, the method for obtaining the fluctuation density value includes: The ratio of the variance of all data values ​​in each data segment to be analyzed to the length of each data segment to be analyzed is used as the fluctuation density value of each data segment to be analyzed.

[0007] Furthermore, the method for obtaining the optimal interval data segment includes: Under each length division method, in each temperature change segment, the normalized value of the ratio of the variance of the temperature change rate in each interval data segment to the cooling effect value of the temperature change segment is used as the change composite factor of each interval data segment; Analyze the differences in the change composite factors between interval data segments in different temperature change segments to determine the error extraction accuracy of each length division method; Among all length division methods, the length division method with the highest error extraction accuracy is taken as the optimal division method, and the interval data segment under the optimal division method is taken as the optimal interval data segment.

[0008] Furthermore, the method for obtaining the error extraction accuracy includes: Select any interval data segment in a temperature change segment as a target data segment, and based on the sequence number of the target data segment in the temperature change segment, determine the interval data segment with the same sequence number in other temperature change segments as the matching data segment of the target data segment; The absolute value of the difference between the target data segment and each matching data segment's change composite factor is used as the deviation factor, and the mean of all deviation factors corresponding to the target data segment is used as the mean deviation feature value; Under each length division method, the mean of the mean deviation eigenvalues ​​of all interval data segments is negatively correlated and normalized to obtain the value as the error extraction accuracy under each length division method.

[0009] Furthermore, the method for obtaining the temperature change section includes: In the current amination process, temperature time series data is obtained, and the moment when the temperature value is greater than the preset temperature threshold is used as a breakpoint. The temperature change rate time series data in the current amination process is segmented based on the breakpoint to obtain all temperature change segments.

[0010] Furthermore, the method for obtaining the adaptive flow rate value includes: Determine the reaction accumulation degree value of each optimal interval data segment based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the accumulation of heat released in the corresponding period; The reaction accumulation value of each optimal interval data segment is used as the weight, and the temperature change rate in the corresponding period of the next interval is obtained as the predicted data segment based on the ARIMA time series prediction algorithm; If the temperature change rate at the current moment in the current amination process is greater than the temperature change rate at the corresponding moment in the predicted data segment, the preset flow rate of the coolant is adjusted based on the change in the reaction cumulative degree values ​​of all optimal interval data segments in the current amination process to obtain the adaptive flow rate value at the current moment.

[0011] Furthermore, the method for obtaining the reaction cumulative degree value includes: In each optimal interval data segment, the value after negative correlation mapping of the mean values ​​of all temperature change rates is used as the temperature stability performance value of each optimal interval data segment; The sum of the heat release values ​​at all moments corresponding to the time period of each optimal interval data segment is taken as the heat accumulation value of each optimal interval data segment; For any optimal interval data segment, a preset number of optimal interval data segments adjacent to the optimal interval data segment are used as reference segments; The sum of the heat accumulation values ​​of the optimal interval data segment and all corresponding reference segments, and the product of the temperature stability performance value of the optimal interval data segment, are normalized as the reaction accumulation degree value of the optimal interval data segment; If a certain optimal interval data segment is in the temperature rising stage during the current amination reaction, the reaction accumulation degree value of the optimal interval data segment is a preset value.

[0012] Furthermore, the preset flow rate of the coolant is adjusted based on the change in the reaction cumulative degree values ​​of all optimal interval data segments in the current amination process to obtain the adaptive flow rate value at the current moment, including: In the current amination process, the range of the cumulative reaction degree values ​​of all optimal interval data segments is used as the adjustment degree value; After multiplying the adjustment degree value by the preset minimum adjustment amplitude, the sum of the obtained product and the preset flow rate of the coolant is used as the adaptive flow rate value at the current moment.

[0013] An automatic cooling system for the amination of insecticide preparation comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the automatic cooling method for the amination of insecticide preparation are implemented.

[0014] The present invention has the following beneficial effects: Time series data on the rate of temperature change during the current amination process are obtained. To align cooling control with actual reaction requirements, time series data on the heat release during the current amination process are also obtained. The time series data on the rate of temperature change during the heating phase of a historical amination process are used as comparison data segments. The time series data on the rate of temperature change during the current amination process are first segmented to produce multiple temperature variation segments. Given that temperature control during the cooling phase effectively mitigates temperature fluctuations during the amination process, the cooling effect value for each temperature variation segment during the current amination process is determined by quantifying the length and temperature variation differences between each temperature variation segment and the comparison data segment. This is used to evaluate the cooling effect. Furthermore, since the cooling phase typically requires rapid temperature control within a short period of time, each temperature variation segment in the current amination process is segmented into various lengths to produce interval data segments. The temperature change rate fluctuations between the interval data segments are then analyzed and the corresponding cooling effect values ​​are combined. This allows for a comprehensive assessment of the cooling phase's overall control performance over a short period of time across all temperature variation segments. This allows for the determination of an optimal length segmentation method and the generation of optimal interval data segments. Because bumping is prone to occur in the amination reaction, which also results in excessive heat accumulation, the present invention finally analyzes the accumulation of heat released in the corresponding time period and the numerical characteristics of the temperature change rate based on the optimal interval data segment, and adjusts the preset flow rate of the coolant so that the adaptive flow rate value can better match the reaction requirements of the current amination reaction, effectively improving the cooling effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1A flow chart of a method for an automatic cooling method for the preparation of insecticide monohydrate provided by one embodiment of the present invention; Figure 2 A flow chart of a method for obtaining a cooling effect value provided by one embodiment of the present invention; Figure 3 A flow chart of a method for obtaining an optimal interval data segment provided by one embodiment of the present invention; Figure 4 A flow chart of a method for obtaining an adaptive flow velocity value provided by one embodiment of the present invention; Figure 5 A system block diagram of an automatic amination cooling system for preparing insecticide monochloride provided by one embodiment of the present invention; Figure 6 A schematic diagram of the system structure of an amination cooling system for preparing insecticide monohydrate provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of an automatic amination cooling system and cooling method for preparing insecticide monochloride according to the present invention. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following describes in detail the specific scheme of the automatic cooling system and cooling method for the preparation of insecticide provided by the present invention in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a method flow chart of an amination automatic cooling method for preparing cypermethrin provided by one embodiment of the present invention, the method comprising the following steps: Step S1: obtaining the time series data of the temperature change rate and the heat release time series data during the current amination process, and obtaining the time series data of the temperature change rate during the heating stage of the historical amination process as a comparison data segment.

[0021] The amination process in the preparation of insecticide can be divided into the following steps: the initial stage: carried out in a cooling environment (0-10°C), adding allyl chloride to the reactor to prevent violent reaction; the heating stage: after turning off the cooling, self-heating is adopted to maintain the required reaction temperature, naturally heating to 30-35°C, and keeping warm for 3 hours to ensure complete reaction. After the reaction is completed, the reaction is allowed to stand and separate. Among them, heat is continuously released during the dropwise addition of allyl chloride, which easily causes large temperature fluctuations during the amination process. Therefore, in this embodiment of the present invention, time series data of the temperature change rate and time series data of the heat release are obtained during the current amination process. The horizontal axis of the temperature change rate time series data is time, and the vertical axis is the temperature change rate. A thermocouple sensor can be used and arranged on the wall of the reactor. The accuracy of the sensor can be adjusted to ensure accurate capture of the temperature change rate. The horizontal axis of the heat release time series data is time, and the vertical axis is the heat release. A heat sensor can be used for acquisition. The heat release time series data and the temperature change rate time series data should be collected synchronously to ensure consistent time resolution. Then, the temperature change rate time series data of the heating stage of a historical amination process are extracted from the database as a comparison data segment.

[0022] It should be noted that the time interval for collecting time series data in the embodiment of the present invention is 1 second. The specific interval can be adjusted according to the implementation scenario and is not limited here.

[0023] Step S2: Segment the temperature change rate time series data during the current amination process to obtain temperature change segments; analyze the length difference between each temperature change segment and the comparison data segment and the fluctuation difference of the temperature change rate to determine the cooling effect value of each temperature change segment.

[0024] The temperature change in the amination reaction is not a uniform process. For example, in the initial stage, the entire dropwise addition process of allyl chloride is in a boiling state and unstable, resulting in large temperature fluctuations. Furthermore, the exothermic reaction during the dropwise addition process has a more significant impact on the temperature fluctuation. Temperature fluctuations at different stages have different effects on the actual amination reaction conditions. For the initial stage and the natural heating stage, the temperature control during the cooling step actually mitigates the temperature fluctuations within the reactor. During the heating stage, the reaction temperature is maintained by self-heating, and the temperature change rate is generally greater, representing the temperature change state that the amination reaction would have if the cooling step were not performed. Therefore, in this embodiment of the present invention, the temperature change rate time series data during the current amination reaction is first segmented to obtain temperature change segments. The temperature change segments can help clarify the current stage of the amination reaction, thereby enabling subsequent independent analysis of the temperature changes and cooling conditions at different stages. The temperature change segments during the current amination process are then analyzed with the comparison data segments for length differences and temperature change rate fluctuations to obtain a cooling effect value for each temperature change segment during the current amination process, which is used to measure the degree to which the cooling step mitigates the temperature fluctuations.

[0025] Preferably, in one embodiment of the present invention, the method for obtaining the temperature change section includes: Since the cooling requirements vary in different stages, when the temperature at a certain moment exceeds the set temperature threshold, it is necessary to open the coolant valve for cooling. Therefore, the temperature change rate time series data of the current amination process can be segmented based on whether the temperature exceeds the set temperature threshold to obtain temperature change segments: in the current amination process, temperature time series data is obtained (which can be obtained through a temperature sensor and is synchronously collected with the temperature change rate time series data and the heat release time series data in step S1, and the collection frequency is consistent), the moment when the temperature value is greater than the preset temperature threshold is used as a breakpoint, and the temperature change rate time series data of the current amination process is segmented based on the breakpoint to obtain all temperature change segments.

[0026] It should be noted that, in the embodiment of the present invention, the preset temperature threshold in the initial stage of the amination process is 10°C; the preset temperature threshold in the temperature rising stage is 35°C.

[0027] After obtaining multiple temperature change segments, the lengths and temperature change rate fluctuation differences of the segments can be compared with those of the comparison data segments to obtain the cooling effect value of each temperature change segment.

[0028] Preferably, in one embodiment of the present invention, a method for obtaining a cooling effect value includes: See also Figure 2 , which shows a flow chart of a method for obtaining a cooling effect value in one embodiment of the present invention, the method comprising the following steps: Step S201: In the comparison data segment and each temperature change segment, the fluctuation characteristics of the data values ​​are analyzed and combined with the length characteristics of the data segments to obtain the fluctuation density values ​​of the comparison data segment and each temperature change segment respectively.

[0029] For the convenience of description, the temperature change segment and the comparison data segment are collectively referred to as the data segment to be analyzed.

[0030] In each data segment to be analyzed, the fluctuation of the temperature change rate in each data segment to be analyzed can be combined with the duration of the temperature change rate, thereby eliminating the influence of the length of the data segment to be analyzed and enhancing the comparability between data segments of different lengths.

[0031] The fluctuation of the temperature change rate can be characterized by variance, so the ratio of the variance of all data values ​​in each data segment to be analyzed to the length of each data segment to be analyzed is used as the fluctuation density value of each data segment to be analyzed. The larger the fluctuation density value, the more significant the fluctuation of the temperature change rate in the data segment to be analyzed.

[0032] Step S202: Compare the fluctuation density value of each temperature change segment with the fluctuation density value of the comparison data segment to determine the cooling effect value of each temperature change segment.

[0033] Based on the above analysis, it can be seen that the comparative data segment represents the temperature variation state that the amination reaction should have experienced without the cooling step. Therefore, the difference between the fluctuation density value of the comparative data segment and the fluctuation density value of each temperature variation segment is calculated. The larger the difference, the greater the degree of suppression or weakening of temperature fluctuation by the cooling step. The difference is normalized and used as the cooling effect value for each temperature variation segment. The larger the cooling effect value, the better the cooling effect in that temperature variation segment. Normalization is a technical means well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0034] It should be noted that, in the embodiment of the present invention, it is assumed that the fluctuation density value of the comparison data segment is always greater than the fluctuation density value of the temperature change segment. If there are special circumstances, the normalization can be adopted. function.

[0035] Step S3: Divide each temperature change segment into multiple lengths to obtain interval data segments; analyze the fluctuation differences in the temperature change rates between the interval data segments and the positional relationships in time series, and combine the cooling effect values ​​corresponding to the interval data segments to determine the optimal division method and obtain the optimal interval data segments.

[0036] For the cooling control link, its control process is more based on the instantaneous rate of temperature change, that is, it is necessary to achieve temperature control within a shorter time interval. In addition, since the temperature change rate of the amination reaction may contain characteristics of different time scales, in this embodiment of the present invention, each temperature change segment is divided into multiple lengths to obtain interval data segments. Then, under each length division method, the fluctuation difference of the temperature change rate between the interval data segments is analyzed. Combined with the cooling effect value of the temperature change segment to which the upper interval data segment belongs, the error accuracy of the fluctuation extraction of the temperature change rate value under each length division method is determined, which is used to screen out an optimal division method under all length division methods, thereby obtaining the optimal interval data segment.

[0037] Preferably, in one embodiment of the present invention, a method for obtaining an optimal interval data segment includes: See also Figure 3 , which shows a flow chart of a method for obtaining an optimal interval data segment in one embodiment of the present invention, the method comprising the following steps: Step S301: Under each length division method, in each temperature change segment, the fluctuation characteristics of the temperature change rate in each interval data segment are integrated with the cooling effect value of each temperature change segment to obtain the change composite factor of each interval data segment.

[0038] In this embodiment of the present invention, the length range is set to 5 to 10 minutes, and the step size is set to 1 minute, so there are 6 length division methods, namely, the length of the interval data segment is 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes and 10 minutes. The specific length range and step size can be adjusted according to the actual scenario and are not limited here. In the division process, if the length of the remaining data segment is less than the length of the interval data segment, it can be discarded or regarded as an interval data segment. Either method can be selected.

[0039] Under each length division method, in each temperature change segment, the normalized value of the ratio of the variance of the temperature change rate in each interval data segment to the cooling effect value of the temperature change segment is used as the change composite factor of each interval data segment. The variance of the temperature change rate of the interval data segment reflects the stability of the temperature control in the interval data segment. The larger the variance, the worse the stability. The cooling effect value reflects the overall cooling performance of the temperature change segment. The larger the value, the better the cooling effect. Therefore, when the change composite factor obtained based on the ratio of the two is larger, it means that the temperature change fluctuation in the interval data segment is more obvious and the cooling effect is worse. Normalization is a technical means well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0040] It should be noted that if the cooling effect value is 0, a preset parameter of 0.001 needs to be added to the denominator when calculating the ratio here to prevent the denominator from being 0.

[0041] Step S302: Analyze the differences in the change composite factors between the interval data segments in different temperature change segments, so as to determine the error extraction accuracy of each length division method.

[0042] Based on the above steps, each temperature change segment can be divided into several interval data segments. If the interval data segments at the same position in time series have relatively consistent temperature change characteristics, it means that this length division method can stably obtain the temperature change situation during the extraction amination process, and the division effect is the best.

[0043] Therefore, any interval data segment in a temperature change segment is selected as the target data segment. Based on the sequence number of the target data segment in the temperature change segment, the interval data segments with the same sequence number in other temperature change segments are determined as the matching data segments of the target data segment. For example, if the target data segment is arranged as the second interval data segment in the temperature change segment to which it belongs in terms of time sequence, then the sequence number of the target data segment is 2, and the interval data segments with sequence number 2 in other temperature change segments are used as the matching data segments of the target data segment.

[0044] Then, the absolute value of the difference between the changing composite factors of the target data segment and each matching data segment is used as the deviation factor, and the mean of all deviation factors between the target data segment and all matching data segments is used as the mean deviation eigenvalue. The smaller the deviation factor, the smaller the mean deviation eigenvalue representing the average level. This means that the temperature change fluctuation and cooling effect between the target data segment and the matching data segment are more consistent, and the effect of the current length division method is better.

[0045] At this point, the mean deviation characteristic value of each interval data segment can be obtained. Finally, under each length division method, the mean of the mean deviation characteristic values ​​of all interval data segments is negatively correlated and normalized to achieve logical relationship correction, and the error extraction accuracy under each length division method is obtained. Based on the above analysis, it can be seen that the greater the error extraction accuracy, the better the effect of the current length division method, and the more stable the temperature change characteristics in the amination reaction can be extracted. The negative correlation mapping and normalization here can be used as formula ,in, It represents the exponential function with the natural constant e as the base, and x represents the independent variable.

[0046] Step S303: Under all length division methods, the optimal method is selected based on the error extraction accuracy, and the optimal interval data segment is determined.

[0047] Based on the analysis in step S302, it can be seen that the greater the error extraction accuracy of a certain length division method, the better the effect of the length division method. Therefore, among all length division methods, the length division method with the highest error extraction accuracy is used as the optimal division method, and the interval data segment under the optimal division method is used as the optimal interval data segment.

[0048] Step S4: Based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the accumulation of heat released in the corresponding time period, the preset flow rate of the coolant is adjusted to obtain an adaptive flow rate value.

[0049] During the amination reaction, due to the slow reaction rate in the early stages of the dropwise addition, some allyl chloride accumulates unreacted in the system, but the overall heat release rate of the system shows an upward trend. As the reaction proceeds, the system temperature gradually rises, and the unreacted allyl chloride accumulated in the reactor reacts violently, resulting in a significant instantaneous increase in the heat release rate. Therefore, bumping is prone to occur during the reaction process, and more often, the reaction rate rapidly changes due to the cumulative effect of heat. Therefore, for the optimal interval data segments after division, the more significant the heat accumulation, the greater the temperature control requirement within the interval. Therefore, in this embodiment of the present invention, not only the numerical characteristics of the temperature change rate in the optimal interval data segments are analyzed, but also the accumulation of heat release in the corresponding time period is combined to adjust the preset flow rate of the coolant, so that the final adaptive flow rate can better adapt to the reaction requirements of the current amination reaction process.

[0050] Preferably, in one embodiment of the present invention, based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the accumulation of heat released in the corresponding period, the preset flow rate of the coolant is adjusted to obtain an adaptive flow rate value, including: See also Figure 4 , which shows a flow chart of a method for obtaining an adaptive flow rate value in one embodiment of the present invention, the method comprising the following steps: Step S401: Based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the accumulation of heat released in the corresponding time period, the reaction accumulation degree value of each optimal interval data segment is determined.

[0051] Since the greater the temperature change rate, the greater the temperature change, and the less stable the heat change, in each optimal interval data segment, the mean of all temperature change rates is negatively correlated and mapped to the value of the temperature stability performance value of each optimal interval data segment. The greater the temperature stability performance value, the more stable the temperature change. The negative correlation mapping here can be used with the formula , where x represents the independent variable and 0.01 is used to prevent the denominator from being zero.

[0052] The sum of the heat release values ​​at all times corresponding to each optimal interval data segment is used as the heat accumulation value of each optimal interval data segment. For any optimal interval data segment, a preset number of adjacent optimal interval data segments are used as reference segments, and the sum of the heat accumulation values ​​of the optimal interval data segment and all corresponding reference segments is used as the heat accumulation sum value of the optimal interval data segment. A larger heat accumulation sum value indicates that the amination reaction is more likely to have accumulated more heat when it reaches the optimal interval data segment.

[0053] Finally, for any optimal interval data segment, the product of the thermal accumulation sum value of the optimal interval data segment and the temperature stability performance value of the optimal interval data segment is normalized to obtain the reaction accumulation degree value of the optimal interval data segment. A larger reaction accumulation degree value indicates that the optimal interval data segment is subject to a greater thermal reaction accumulation effect and more stable temperature fluctuations. Normalization is a technical means well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0054] It should be noted that in this embodiment of the present invention, the preset number is set to 2. The specific value can be adjusted according to the implementation scenario and is not limited here. For the first optimal interval data segment, the reference segment of the optimal interval data segment is itself, and the heat accumulation sum value is its own heat accumulation value. For the second optimal interval data segment, the reference segment of the optimal interval data segment is the first optimal interval data segment, and the heat accumulation sum value of the optimal interval data segment is the sum of its own heat accumulation value and the heat accumulation value of the first optimal interval data segment. If an optimal interval data segment is in the temperature rise stage of the current amination reaction process, the reaction accumulation degree value of the optimal interval data segment is the preset value, which is 1.

[0055] Step S402: obtaining predicted data segments based on the reflection accumulation degree values ​​of all optimal interval data segments and the time series prediction algorithm.

[0056] Based on the above analysis, it can be seen that the larger the reaction accumulation degree value, the greater the cumulative impact of the thermal reaction on the optimal interval data segment, so it requires higher attention. Therefore, the reaction accumulation degree value of each optimal interval data segment is used as a weight, and the temperature change rate in the corresponding period of the next interval is obtained based on the ARIMA time series prediction algorithm as the predicted data segment.

[0057] It should be noted that the ARIMA time series prediction algorithm is a well-known technology, and the specific process will not be described here in detail.

[0058] Step S403: Compare the temperature change rate at the current moment in the current amination process with the temperature change rate in the predicted data segment to determine whether cooling is required. If required, adjust the preset flow rate of the coolant based on the change in the reaction accumulation degree values ​​of all optimal interval data segments in the current amination process to obtain an adaptive flow rate value at the current moment.

[0059] If the temperature change rate at the current moment in the current amination process is greater than the temperature change rate at the corresponding moment in the predicted data segment, it indicates that the temperature may be about to rise and the flow rate of the coolant needs to be increased in advance.

[0060] Therefore, in the current amination process, the range of the reaction accumulation degree values ​​of all the optimal interval data segments is used as the adjustment degree value. The larger the adjustment degree value, the greater the fluctuation of the thermal reaction conditions between the optimal interval data segments in the current amination process, and the greater the difference in heat accumulation. Therefore, it is necessary to appropriately increase the flow rate of the coolant to adapt to the current reaction state. Therefore, after multiplying the adjustment degree value by the preset minimum adjustment amplitude, the sum of the obtained product and the preset flow rate of the coolant is used as the adaptive flow rate value at the current moment.

[0061] It should be noted that the preset minimum adjustment range is 1 ml / s; the preset flow rate is set to 8 ml / s; the specific values ​​can be adjusted according to the implementation scenario and are not limited here.

[0062] To facilitate calculations, all indicator data involved in the calculations in the embodiments of the present invention are pre-processed to eliminate dimension effects. Specific means for eliminating dimension effects are well known to those skilled in the art and are not limited here.

[0063] In summary, the temperature change rate time series data for the current amination process is obtained. To align cooling control with actual reaction requirements, the heat release time series data for the current amination process is also obtained. The temperature change rate time series data for the heating phase of the historical amination process is used as a comparison data segment. First, the temperature change rate time series data for the current amination process is segmented to obtain multiple temperature change segments. Given that temperature control during the cooling phase effectively weakens temperature fluctuations during the amination process, the cooling effect value for each temperature change segment in the current amination process is determined by quantifying the length and temperature variation differences between each temperature change segment and the comparison data segment, thereby evaluating the cooling effect. Furthermore, since the cooling phase typically requires rapid temperature control within a short period of time, each temperature change segment in the current amination process is segmented into various lengths to obtain interval data segments. The temperature change rate fluctuation differences between the interval data segments are then analyzed and the corresponding cooling effect values ​​are combined. This allows the overall control performance of the cooling phase within a short period of time to be measured across all temperature change segments. This allows the optimal length segmentation method to be determined, resulting in the optimal interval data segments. Because bumping is prone to occur during the amination reaction, which also results in excessive heat accumulation, the embodiment of the present invention analyzes the accumulation of heat released during the corresponding time period and the numerical characteristics of the temperature change rate based on the optimal interval data segment, and adjusts the preset flow rate of the coolant so that the adaptive flow rate value can better match the reaction requirements of the current amination reaction, effectively improving the cooling effect.

[0064] The present invention also provides an automatic cooling system for the preparation of insecticide monohydrate. Figure 5 , which shows a system block diagram, including a data acquisition module 501, used to implement step S1 in the above method embodiment; a cooling analysis module 502, used to implement step S2 in the above method embodiment; an interval segment division module 503, used to implement step S3 in the above method embodiment; and a coolant flow rate adjustment module 504, used to implement step S4 in the above method embodiment.

[0065] It should be noted that the system provided in the above embodiment is merely illustrative of the division of the aforementioned functional modules. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, i.e., the internal structure of the computer device can be divided into different functional modules to perform all or part of the functions described above. Furthermore, the above embodiment provides an automatic amination cooling system for preparing insecticide monochloride and an automatic amination cooling method for preparing insecticide monochloride, each of which is based on the same concept. The specific implementation process is detailed in the method embodiments and will not be further described here.

[0066] See also Figure 6, which shows a system structure diagram of an amination cooling system for preparing insecticide provided by one embodiment of the present invention, including a processor 600, a memory 601, a bus 602 and a communication interface 603, wherein the processor 600, the communication interface 603 and the memory 601 are connected via the bus 602; wherein the memory 601 may include a high-speed random access memory, the bus 602 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 600 may be an integrated circuit chip with signal processing capabilities; the memory 601 stores a computer program, which, when loaded and executed by the processor, implements the steps of an amination automatic cooling method for preparing insecticide.

[0067] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. An automatic cooling method for the preparation of insecticide monohydrate, characterized in that: The method comprises: Obtain the time series data of the temperature change rate and the heat release rate in the current amination process, and obtain the time series data of the temperature change rate in the heating stage of the historical amination process as a comparison data segment; Segment the temperature change rate time series data during the current amination process to obtain temperature change segments; analyze the length difference between each temperature change segment and the comparison data segment and the fluctuation difference in the temperature change rate to determine the cooling effect value of each temperature change segment; Divide each temperature change segment into multiple lengths to obtain interval data segments; analyze the fluctuation differences in the temperature change rates between the interval data segments and their positional relationships in time series, and combine the cooling effect values ​​corresponding to the interval data segments to determine the optimal division method and obtain the optimal interval data segments; Based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the accumulation of heat released in the corresponding period, the preset flow rate of the coolant is adjusted to obtain an adaptive flow rate value.

2. The method for automatic cooling by amination for preparing insecticide monohydrate according to claim 1, characterized in that: The method for obtaining the cooling effect value includes: The temperature change segment and the comparison data segment are collectively referred to as the data segment to be analyzed; Combining the variance of all data values ​​in each data segment to be analyzed with the length of each data segment to be analyzed to determine the fluctuation density value of each data segment to be analyzed; The value after normalizing the difference between the fluctuation density value of the comparison data segment and the fluctuation density value of each temperature change segment is used as the cooling effect value of each temperature change segment.

3. The method for automatic cooling by amination for preparing insecticide monohydrate according to claim 2, characterized in that: The method for obtaining the fluctuation density value includes: The ratio of the variance of all data values ​​in each data segment to be analyzed to the length of each data segment to be analyzed is used as the fluctuation density value of each data segment to be analyzed.

4. The method for automatic cooling by amination for preparing insecticide monohydrate according to claim 1, characterized in that: The method for obtaining the optimal interval data segment includes: Under each length division method, in each temperature change segment, the normalized value of the ratio of the variance of the temperature change rate in each interval data segment to the cooling effect value of the temperature change segment is used as the change composite factor of each interval data segment; Analyze the differences in the change composite factors between interval data segments in different temperature change segments to determine the error extraction accuracy of each length division method; Among all length division methods, the length division method with the highest error extraction accuracy is taken as the optimal division method, and the interval data segment under the optimal division method is taken as the optimal interval data segment.

5. The method for automatic cooling by amination for preparing insecticide monohydrate according to claim 4, characterized in that: The method for obtaining the error extraction accuracy includes: Select any interval data segment in a temperature change segment as a target data segment, and based on the sequence number of the target data segment in the temperature change segment, determine the interval data segment with the same sequence number in other temperature change segments as the matching data segment of the target data segment; The absolute value of the difference between the target data segment and each matching data segment's change composite factor is used as the deviation factor, and the mean of all deviation factors corresponding to the target data segment is used as the mean deviation feature value; Under each length division method, the mean of the mean deviation eigenvalues ​​of all interval data segments is negatively correlated and normalized to obtain the value as the error extraction accuracy under each length division method.

6. The method for automatic cooling by amination for preparing insecticide monohydrate according to claim 1, characterized in that: The method for obtaining the temperature change section includes: In the current amination process, temperature time series data is obtained, and the moment when the temperature value is greater than the preset temperature threshold is used as a breakpoint. The temperature change rate time series data in the current amination process is segmented based on the breakpoint to obtain all temperature change segments.

7. The method for automatic cooling by amination for preparing insecticide monohydrate according to claim 1, characterized in that: The method for obtaining the adaptive flow rate value includes: Determine the reaction accumulation degree value of each optimal interval data segment based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the accumulation of heat released in the corresponding period; The reaction accumulation value of each optimal interval data segment is used as the weight, and the temperature change rate in the corresponding period of the next interval is obtained as the predicted data segment based on the ARIMA time series prediction algorithm; If the temperature change rate at the current moment in the current amination process is greater than the temperature change rate at the corresponding moment in the predicted data segment, the preset flow rate of the coolant is adjusted based on the change in the reaction cumulative degree values ​​of all optimal interval data segments in the current amination process to obtain the adaptive flow rate value at the current moment.

8. The method for automatic cooling by amination for preparing insecticide monohydrate according to claim 7, characterized in that: The method for obtaining the reaction cumulative degree value includes: In each optimal interval data segment, the value after negative correlation mapping of the mean values ​​of all temperature change rates is used as the temperature stability performance value of each optimal interval data segment; The sum of the heat release values ​​at all moments corresponding to the time period of each optimal interval data segment is taken as the heat accumulation value of each optimal interval data segment; For any optimal interval data segment, a preset number of optimal interval data segments adjacent to the optimal interval data segment are used as reference segments; The sum of the heat accumulation values ​​of the optimal interval data segment and all corresponding reference segments, and the product of the temperature stability performance value of the optimal interval data segment, are normalized as the reaction accumulation degree value of the optimal interval data segment; If a certain optimal interval data segment is in the temperature rising stage during the current amination reaction, the reaction accumulation degree value of the optimal interval data segment is a preset value.

9. The method for automatic cooling by amination for preparing insecticide monohydrate according to claim 7, characterized in that: The preset flow rate of the coolant is adjusted based on the change in the reaction cumulative degree values ​​of all optimal interval data segments in the current amination process to obtain the adaptive flow rate value at the current moment, including: In the current amination process, the range of the cumulative reaction degree values ​​of all optimal interval data segments is used as the adjustment degree value; After multiplying the adjustment degree value by the preset minimum adjustment amplitude, the sum of the obtained product and the preset flow rate of the coolant is used as the adaptive flow rate value at the current moment.

10. An automatic amination cooling system for preparing insecticide monohydrate, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the amination automatic cooling method for preparing insecticide monohydrate as claimed in any one of claims 1 to 9 are implemented.

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