Amine-based auto-cooling system and cooling method for insecticidal mono preparation

Abstract

The present application relates to the technical field of cooling control, and in particular to an automatic cooling system and method for the preparation of a single insecticidal amine. The temperature change rate and heat release timing data of the current amination process are obtained, and the temperature change rate of the historical temperature rise stage is used as a comparison. The current temperature change rate data is divided into multiple temperature change segments, the differences in length and temperature change rate of each segment and the historical comparison data are quantified, and the cooling effect value is obtained to evaluate the cooling performance. The temperature change segments are divided into multiple lengths to obtain interval data segments, the temperature fluctuation differences are analyzed, and the cooling effect value is combined to determine the optimal division method and the optimal interval data segment. Finally, based on the temperature change characteristic position of the optimal interval data segment and the heat release cumulative, the preset flow rate of the cooling liquid is adjusted to obtain an adaptive flow rate value, effectively improving the matching degree of the cooling effect and the reaction demand.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cooling control, in particular to an automatic cooling system and method for preparing a pesticide monomer. BACKGROUND

[0002] The pesticide monomer is a green, safe and low-toxic pesticide, which is mainly used for preventing and treating pests of rice, vegetables, fruit trees, sugarcane and corn. In the field of chemical production, the amination reaction is an important process link for preparing nitrogen-containing heterocyclic compounds such as the pesticide monomer, and is a dangerous chemical process listed as a key supervision by the Emergency Management Bureau. The reaction is an exothermic reaction, and the temperature control precision directly affects the reaction efficiency, product purity and production safety.

[0003] The whole dropping process of chloropropene in the amination process needs to be carried out in a cooling environment to prevent violent reaction, and then a warming-up stage is used to ensure the reaction is complete. In the prior art, when the actual temperature exceeds the temperature threshold, a preset cooling liquid flow rate or a stepwise adjustment strategy is usually used. However, since the heat accumulation and temperature fluctuation at different times in the actual reaction process will be different, if the dynamic thermodynamic characteristics and other factors in the reaction process are ignored, the matching degree of the final cooling effect and the reaction demand will be low, and the cooling effect will be poor. SUMMARY

[0004] In order to solve the technical problem that the heat accumulation and temperature fluctuation at different times in the actual reaction process will be different, if the dynamic thermodynamic characteristics and other factors in the reaction process are ignored, the matching degree of the final cooling effect and the reaction demand will be low, and the cooling effect will be poor, the purpose of the present application is to provide an automatic cooling system and method for preparing a pesticide monomer, and the technical solution is as follows:

[0005] An automatic cooling method for preparing a pesticide monomer, comprising:

[0006] Obtaining temperature change rate time series data and heat release time series data in the current amination process, and obtaining temperature change rate time series data in the warming-up stage of the historical amination process as a comparison data segment;

[0007] Segmenting the temperature change rate time series data in the current amination process to obtain a temperature change segment; analyzing the length difference and temperature change rate fluctuation difference between each temperature change segment and the comparison data segment to determine a cooling effect value of each temperature change segment;

[0008] The temperature change section is divided into multiple length divisions to obtain interval data segments; the fluctuation difference of the temperature change rate between the interval data segments is analyzed, the positional relationship in time sequence is analyzed, and the optimal division method is determined and the optimal interval data segment is obtained in combination with the cooling effect value corresponding to the interval data segment;

[0009] Based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the cumulative heat dissipation in the corresponding period, the preset flow rate of the cooling liquid is adjusted to obtain an adaptive flow rate value.

[0010] Further, the cooling effect value acquisition method comprises:

[0011] The temperature change section and the comparison data segment are collectively referred to as a to-be-analyzed data segment;

[0012] The variance of all data values in each to-be-analyzed data segment is combined with the length of each to-be-analyzed data segment to determine the fluctuation density value of each to-be-analyzed data segment.

[0013] The difference between the fluctuation density value of the comparison data segment and the fluctuation density value of each temperature change section is normalized to obtain the cooling effect value of each temperature change section.

[0014] Further, the fluctuation density value acquisition method comprises:

[0015] The ratio of the variance of all data values in each to-be-analyzed data segment to the length of each to-be-analyzed data segment is taken as the fluctuation density value of each to-be-analyzed data segment.

[0016] Further, the optimal interval data segment acquisition method comprises:

[0017] In each temperature change section, the ratio of the variance of the temperature change rate in each interval data segment to the cooling effect value of the temperature change section is normalized to obtain the change composite factor of each interval data segment under each length division method.

[0018] The difference of the change composite factors between the interval data segments in different temperature change sections is analyzed to determine the error extraction accuracy of each length division method.

[0019] Under 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.

[0020] Further, the error extraction accuracy acquisition method comprises:

[0021] Optionally, any one interval data segment in a temperature change segment is taken as a target data segment, and based on the sequence number of the target data segment in the temperature change segment, an interval data segment with the same sequence number in other temperature change segments is determined as a matching data segment of the target data segment;

[0022] The absolute value of the difference between the change compound factor of the target data segment and each matching data segment is taken as a deviation factor, and the mean value of all deviation factors corresponding to the target data segment is taken as a mean deviation feature value;

[0023] Under each length division method, the mean value of the mean deviation feature values of all interval data segments is negatively correlated and normalized, and the value after the negative correlation and normalization is taken as the error extraction accuracy under each length division method.

[0024] Further, the method for obtaining the temperature change segment comprises:

[0025] In the current amination process, the temperature time series data is obtained, the time point at which the temperature value is greater than the preset temperature threshold value is taken as a breakpoint, and the temperature change rate time series data in the current amination process is segmented based on the breakpoint, so that all temperature change segments are obtained.

[0026] Further, the method for obtaining the adaptive flow rate value comprises:

[0027] Based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the cumulative heat release in the corresponding period, a reaction cumulative degree value of each optimal interval data segment is determined;

[0028] The reaction cumulative degree value of each optimal interval data segment is taken 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 a prediction data segment;

[0029] If the temperature change rate at the current time in the current amination process is greater than the temperature change rate at the corresponding time in the prediction data segment, the preset flow rate of the cooling liquid is adjusted based on the change of the reaction cumulative degree value of all optimal interval data segments in the current amination process, to obtain the adaptive flow rate value at the current time.

[0030] Further, the method for obtaining the reaction cumulative degree value comprises:

[0031] In each optimal interval data segment, the value after negatively correlating the mean value of all temperature change rates is taken as a temperature stability performance value of each optimal interval data segment;

[0032] The sum of the heat release values at all time points corresponding to the period of each optimal interval data segment is taken as a heat accumulation value of each optimal interval data segment;

[0033] For any one optimal interval data segment, the previous preset number of optimal interval data segments adjacent to the optimal interval data segment are taken as reference segments;

[0034] The sum of the thermal accumulation values of all the reference segments corresponding to the optimal interval data segment is normalized by the product of the temperature stability performance value of the optimal interval data segment, and the normalized value is taken as the reaction accumulation degree value of the optimal interval data segment;

[0035] If a certain optimal interval data segment is in the warming-up stage of the current amination reaction process, the reaction accumulation degree value of the optimal interval data segment is a preset value.

[0036] Further, the preset flow rate of the cooling liquid is adjusted based on the change of the reaction accumulation degree values of all the optimal interval data segments in the current amination process to obtain an adaptive flow rate value at the current time, which comprises:

[0037] In the current amination process, the range of the reaction accumulation degree values of all the optimal interval data segments is taken as an adjustment degree value;

[0038] The adjustment degree value is multiplied by a preset minimum adjustment amplitude, and the sum of the product and the preset flow rate of the cooling liquid is taken as the adaptive flow rate value at the current time.

[0039] An amination automatic cooling system for preparing a pesticide monomer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the amination automatic cooling method for preparing a pesticide monomer when executing the computer program.

[0040] The present application has the following beneficial effects:

[0041] The temperature change rate time series data in the current amination process is acquired, and the heat release time series data in the current amination process is also acquired so that the cooling control can be matched with the actual reaction requirement, and the temperature change rate time series data in the temperature rising stage in the historical amination process is taken as a comparison data segment. First, the temperature change rate time series data in the current amination process is segmented to obtain a plurality of temperature change segments. Since the cooling link actually weakens the temperature change in the amination process, the length and temperature change difference features between each temperature change segment and the comparison data segment are quantified to obtain the cooling effect value of each temperature change segment in the current amination process, which is used to evaluate the cooling effect. Further, since the cooling link usually needs to quickly realize temperature control in a short time, each temperature change segment is divided into a plurality of length intervals to obtain interval data segments, then the fluctuation difference of the temperature change rate between the interval data segments is analyzed, and the cooling effect value corresponding to the interval data segments is fused to measure the overall control performance of the cooling link in a short time under all temperature change segments, so as to determine an optimal length division method to obtain optimal interval data segments. Because violent boiling is easy to occur in the amination reaction, which will cause excessive heat accumulation, finally, the preset flow rate of the cooling liquid is adjusted based on the optimal interval data segments to analyze the heat release accumulation and the numerical features of the temperature change rate in the corresponding period, so that the adaptive flow rate value can better match the reaction requirement in the current amination reaction, and the cooling effect is effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present 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 present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0043] Figure 1 A method flowchart of an amination automatic cooling method for preparing a single insecticide provided by an embodiment of the present application;

[0044] Figure 2 A method flowchart of a cooling effect value acquisition method provided by an embodiment of the present application;

[0045] Figure 3 A method flowchart of an optimal interval data segment acquisition method provided by an embodiment of the present application;

[0046] Figure 4 A method flowchart of an adaptive flow rate value acquisition method provided by an embodiment of the present application;

[0047] Figure 5 A system block diagram of an automatic cooling system for preparing an insecticide single preparation amine provided by an embodiment of the present application;

[0048] Figure 6 A system structure schematic diagram of an automatic cooling system for preparing an insecticide single preparation amine provided by an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object, the specific embodiments, structures, features and effects of the automatic cooling system and cooling method for preparing an insecticide single preparation amine 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.

[0050] 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.

[0051] The specific scheme of the automatic cooling system and cooling method for preparing an insecticide single preparation amine provided by the present application is specifically described below in combination with the accompanying drawings.

[0052] Please refer to Figure 1 , which shows a method flowchart of an automatic cooling method for preparing an insecticide single preparation amine provided by an embodiment of the present application. The method comprises the following steps:

[0053] Step S1: Obtain the temperature change rate time series data and the heat release time series data in the current amine process, and obtain the temperature change rate time series data in the heating stage of the historical amine process as a comparison data segment.

[0054] The amine stage process in the preparation process of monodiamine mainly includes the following steps: in the initial stage, the chloropropylene is added dropwise into the reaction kettle in a cooling environment (0-10 DEG C) to prevent violent reaction; in the heating stage, the required reaction temperature is maintained by self-heating after the cooling is closed, and the temperature is naturally raised to 30-35 DEG C, and the reaction is kept for 3 hours to ensure that the reaction is complete, and the reaction is completed and then the layers are separated. In the process of adding chloropropylene, heat is continuously released, which easily causes the temperature fluctuation in the amine stage to change greatly. Therefore, in the embodiment of the present application, the temperature change rate time series data and the heat release time series data in the current amine stage are obtained, 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, which is arranged on the kettle wall of the reaction kettle, and the accuracy of the sensor is 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 to obtain the heat release time series data, and the heat release time series data and the temperature change rate time series data should be collected synchronously to ensure consistent time resolution; then in the database, the temperature change rate time series data of the heating stage in the amine process is extracted as a comparison data segment.

[0055] It should be noted that the time interval of the time series data collection in the embodiment of the present application is 1 second, and the specific interval can be adjusted according to the implementation scene, which is not limited here.

[0056] Step S2: segment the temperature change rate time series data in the current amine process to obtain a temperature change segment; analyze the length difference and the fluctuation difference of the temperature change rate between each temperature change segment and the comparison data segment to determine the cooling effect value of each temperature change segment.

[0057] The temperature change of the amination reaction is not a uniform process. For example, in the initial stage, the whole dropwise addition process of chloropropene is in a boiling state, which is unstable, so the temperature change fluctuates greatly, and the exothermic reaction during the dropwise addition process will have a more significant impact on the temperature change. The temperature change in different stages has different effects on the actual amination reaction. For the initial stage and the natural warming stage, the cooling link actually weakens the temperature change in the reaction kettle. In the warming stage, the reaction temperature is maintained by self-warming, and the temperature change rate is usually greater, which characterizes the temperature change state that the amination reaction should have without the cooling link. Therefore, in the embodiment of the present application, the temperature change rate time series data in the current amination reaction process is first segmented to obtain the temperature change segment. The temperature change segment can help to determine which stage the current amination reaction is in, so as to realize independent analysis of the temperature change and cooling of different stages. Then, the length difference and the fluctuation difference of the temperature change rate of the temperature change segment in the current amination process and the comparison data segment are analyzed to obtain the cooling effect value of each temperature change segment in the current amination process, which is used to measure the weakening degree of the cooling link to the temperature change.

[0058] Preferably, in an embodiment of the present application, the method for obtaining the temperature change segment comprises:

[0059] Since the cooling requirements of different stages are different, when the temperature at a certain moment exceeds the set temperature threshold, the cooling liquid valve needs to be opened for cooling. Therefore, the temperature change rate time series data in the current amination process can be segmented based on whether the temperature exceeds the set temperature threshold to obtain the temperature change segment. In the current amination process, the temperature time series data is obtained (which can be obtained by a temperature sensor, and is collected synchronously with the temperature change rate time series data and the heat release amount 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 taken as the breakpoint, and the temperature change rate time series data in the current amination process is segmented based on the breakpoint to obtain all the temperature change segments.

[0060] It should be noted that in the embodiment of the present application, the preset temperature threshold of the initial stage in the amination process is 10℃, and the preset temperature threshold of the warming stage is 35℃.

[0061] After obtaining the multiple temperature change segments, the length and the fluctuation difference of the temperature change rate of the temperature change segment and the comparison data segment can be compared to obtain the cooling effect value of each temperature change segment.

[0062] Preferably, in an embodiment of the present application, the method for obtaining the cooling effect value comprises:

[0063] Please refer to Figure 2, which shows a method flow chart of the method for obtaining the cooling effect value in one embodiment of the present application, the method comprising the following steps:

[0064] 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 segment to obtain the fluctuation density value of the comparison data segment and each temperature change segment respectively.

[0065] For the convenience of description, the temperature change segment and the comparison data segment are collectively referred to as the to-be-analyzed data segment.

[0066] In each to-be-analyzed data segment, the fluctuation of the temperature change rate in each to-be-analyzed data segment can be combined with the duration of the temperature change rate, thereby eliminating the influence of the length of the to-be-analyzed data segment and enhancing the comparability between data segments of different lengths.

[0067] The fluctuation of the temperature change rate can be characterized by variance, so the ratio of the variance of all data values in each to-be-analyzed data segment to the length of each to-be-analyzed data segment is taken as the fluctuation density value of each to-be-analyzed data segment. The greater the fluctuation density value, the more significant the fluctuation of the temperature change rate in the to-be-analyzed data segment.

[0068] Step S202: Comparing 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.

[0069] Based on the foregoing analysis, the comparison data segment represents the temperature change state that the amination reaction should have without the cooling link, so the difference between the fluctuation density value of the comparison data segment and the fluctuation density value of each temperature change segment is calculated. The greater the difference, the greater the degree of inhibition or weakening of the cooling link on the temperature fluctuation. The value after normalization of the difference is taken as the cooling effect value of each temperature change segment. The greater the cooling effect value, the better the cooling effect under the temperature change segment. The normalization is a technical means familiar to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited herein.

[0070] It should be noted that in the embodiment of the present application, the fluctuation density value of the comparison data segment is always greater than the fluctuation density value of the temperature change segment. If there is a special case, the normalization can use function.

[0071] Step S3: Dividing each temperature change segment into multiple lengths to obtain interval data segments; analyzing the fluctuation difference of the temperature change rate between the interval data segments, the positional relationship in time sequence, and combining the cooling effect value corresponding to the interval data segments to determine the optimal division method and obtain the optimal interval data segment.

[0072] For the cooling control link, its control process is more based on the instantaneous rate of temperature change, that is, it needs to achieve temperature control in a shorter time interval, and since the temperature change rate of amination reaction may contain characteristics of different time scales, in this embodiment of the present application, the temperature change segment is divided into multiple length divisions to obtain interval data segments, and then under each length division method, the fluctuation difference of the temperature change rate between the interval data segments is analyzed, and the cooling effect value of the temperature change segment to which the upper interval data segment belongs is combined to determine the error accuracy of the fluctuation extraction of the temperature change rate value under each length division method, which is used to screen an optimal division method under all length division methods, so as to obtain the optimal interval data segment.

[0073] Preferably, the method for obtaining the optimal interval data segment in an embodiment of the present application comprises:

[0074] Referring to Figure 3 , a method flow chart of the method for obtaining the optimal interval data segment in an embodiment of the present application is shown, and the method comprises the following steps:

[0075] 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 combined with the cooling effect value of each temperature change segment to obtain the change composite factor of each interval data segment.

[0076] In this embodiment of the present application, the length range is set to 5-10 minutes, and the step size is set to 1 minute, so there are 6 length division methods, which are 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes and 10 minutes for the length of the interval data segment. The specific length range and step size can be adjusted according to the actual scene, which is not limited here, and during 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 of the two methods can be selected.

[0077] Under each length division method, in each temperature variation section, the ratio of the variance of the temperature variation rate in each interval data section to the cooling effect value of the temperature variation section is normalized, and the value after normalization is taken as the change composite factor of each interval data section. The variance of the temperature variation rate in the interval data section reflects the stability of the temperature control in the interval data section. The greater the variance, the worse the stability. The cooling effect value reflects the cooling performance of the whole temperature variation section. The greater the value, the better the cooling effect. Therefore, the greater the change composite factor obtained based on the ratio of the two, the more obvious the temperature variation fluctuation in the interval data section, and the worse the cooling effect. The normalization is a technical means familiar to those skilled in the art. The selection of the normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0078] It should be noted that if the cooling effect value is 0, a preset parameter 0.001 needs to be added to the denominator when calculating the ratio here to prevent the denominator from being 0.

[0079] Step S302: Analyze the difference of the change composite factors between the interval data sections in different temperature variation sections to determine the error extraction accuracy of each length division method.

[0080] Based on the foregoing steps, each temperature variation section can be divided into several interval data sections. If the interval data sections at the same position in time have relatively consistent temperature variation characteristics, it means that the length division method can stably extract the temperature variation in the amination process, and the division effect is the best.

[0081] Therefore, any interval data section in a temperature variation section is selected as a target data section. Based on the serial number of the target data section in the temperature variation section, the interval data section with the same serial number in other temperature variation sections is determined as the matching data section of the target data section. For example, if the target data section is arranged as the second interval data section in time in its own temperature variation section, the serial number of the target data section is 2. The interval data section with the serial number 2 in other temperature variation sections is taken as the matching data section of the target data section.

[0082] Then, the absolute value of the difference between the change composite factors of the target data section and each matching data section is taken as a deviation factor, and the mean value of all deviation factors between the target data section and all matching data sections is taken as a mean deviation characteristic value. The smaller the deviation factor, the smaller the mean deviation characteristic value representing the average level, which means that the temperature variation fluctuation and the cooling effect between the target data section and the matching data section are more consistent, and the effect of the current length division method is better.

[0083] 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 mapped and normalized in a negative correlation to correct the logical relationship, and the error extraction accuracy under each length division method is obtained. Based on the foregoing analysis, the greater the error extraction accuracy, the better the effect of the current length division method, and the temperature change characteristics in the amination reaction can be stably extracted. The negative correlation mapping and normalization processing can be performed by the formula , wherein, represents an exponential function with a natural constant e as the base, and x represents the independent variable.

[0084] Step S303: Under all length division methods, the optimal method is screened based on the error extraction accuracy, and the optimal interval data segment is determined.

[0085] Based on the analysis in step S302, the greater the error extraction accuracy of a certain length division method, the better the effect of the length division method. Therefore, under all length division methods, the length division method with the greatest 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.

[0086] Step S4: Based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the cumulative amount of heat released in the corresponding period, the preset flow rate of the cooling liquid is adjusted to obtain an adaptive flow rate value.

[0087] During the amination reaction, part of the chloropropene accumulates in the system without reaction due to the slow reaction rate in the early stage of dropwise addition, but the overall heat release rate of the system shows an upward trend. As the reaction proceeds, the temperature of the system gradually rises, and the unreacted chloropropene accumulated in the reaction kettle reacts violently, and the instantaneous performance of the heat release rate is obvious. Therefore, during the reaction process, the phenomenon of violent boiling is prone to occur, and more is due to the rapid change stage of the reaction rate caused by the cumulative effect of heat, so for the divided optimal interval data segment, the greater the heat accumulation performance in the interval, the greater the demand for temperature control. Therefore, in this embodiment of the present application, not only the numerical characteristics of the temperature change rate in the optimal interval data segment are analyzed, but also the cumulative amount of heat released in the corresponding period is combined to adjust the preset flow rate of the cooling liquid, so that the final adaptive flow rate can better adapt to the reaction requirements in the current amination reaction process.

[0088] Preferably, in an embodiment of the present application, based on the numerical characteristics of the temperature change rate in the optimal interval data segment and the cumulative amount of heat released in the corresponding period, the preset flow rate of the cooling liquid is adjusted to obtain an adaptive flow rate value, comprising:

[0089] Please refer toFigure 4 which shows a method flow chart of the method for acquiring the adaptive flow rate value in one embodiment of the present application, the method comprising the following steps:

[0090] Step S401: determining 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 cumulative situation of heat release in the corresponding time period.

[0091] Since the greater the temperature change rate, the greater the change in temperature, the heat change is not stable, therefore in each optimal interval data segment, the value after the mean value of all temperature change rates is negatively correlated mapping is taken as 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 adopt the formula wherein x represents the independent variable, and 0.01 is used to prevent the denominator from being 0.

[0092] The sum value of the heat release at all time points corresponding to the time period of each optimal interval data segment is taken as the heat accumulation value of each optimal interval data segment. Then for any one optimal interval data segment, the previous preset number of optimal interval data segments adjacent to the optimal interval data segment are taken as reference segments, and the sum value of the heat accumulation values of the optimal interval data segment and all corresponding reference segments is taken as the heat accumulation sum value of the optimal interval data segment, the greater the heat accumulation sum value, the more likely it is to accumulate more heat when the ammination reaction occurs to the optimal interval data segment.

[0093] Finally, for any one optimal interval data segment, the product of the heat accumulation sum value of the optimal interval data segment and the temperature stability performance value of the optimal interval data segment after normalization is taken as the reaction accumulation degree value of the optimal interval data segment, the greater the reaction accumulation degree value, the greater the influence of heat reaction accumulation on the optimal interval data segment, and the more stable the temperature fluctuation change. The normalization is a technology known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0094] It should be noted that the preset number in the embodiment of the present application is set to 2, and the specific value can be adjusted according to the implementation scene, which is not limited herein. For the first optimal interval data segment, the reference segment of the optimal interval data segment is itself, and the thermal cumulative sum is the thermal cumulative value of itself. 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 thermal cumulative sum of the optimal interval data segment is the sum of the thermal cumulative value of itself and the thermal cumulative value of the first optimal interval data segment. If a certain optimal interval data segment is in the heating stage of the current amination reaction process, the reaction cumulative degree value of the optimal interval data segment is a preset value, which is 1.

[0095] Step S402: obtaining a predicted data segment based on the reaction cumulative degree values of all optimal interval data segments and a time series prediction algorithm.

[0096] Based on the foregoing analysis, the greater the reaction cumulative degree value, the greater the influence of thermal reaction accumulation on the optimal interval data segment, so higher attention is required. Therefore, the reaction cumulative degree value of each optimal interval data segment is taken as a weight, and the temperature change rate in the next interval corresponding to the time period is obtained based on the ARIMA time series prediction algorithm as a predicted data segment.

[0097] It should be noted that the ARIMA time series prediction algorithm is a known technology, and the specific process is not described herein.

[0098] Step S403: comparing the temperature change rate at the current time in the current amination process with the temperature change rate in the predicted data segment to determine whether cooling needs to be started. If so, the preset flow rate of the cooling liquid is adjusted based on the change of the reaction cumulative degree value of all optimal interval data segments in the current amination process to obtain an adaptive flow rate value at the current time.

[0099] If the temperature change rate at the current time in the current amination process is greater than the temperature change rate at the corresponding time in the predicted data segment, it means that the temperature may soon rise, and the flow rate of the cooling liquid needs to be increased in advance.

[0100] Therefore, in the current amination process, the range of the reaction cumulative degree values of all optimal interval data segments is taken as an adjustment degree value. The greater the adjustment degree value, the greater the fluctuation of the thermal reaction between the optimal interval data segments in the current amination process, and the greater the thermal cumulative difference. Therefore, the flow rate of the cooling liquid needs to be appropriately increased to adapt to the current reaction state. Finally, the product obtained by multiplying the adjustment degree value by a preset minimum adjustment amplitude is taken as the adaptive flow rate value at the current time.

[0101] It should be noted that the preset minimum adjustment amplitude is 1ml / s; the preset flow rate is set to 8ml / s; the specific values can be adjusted according to the implementation scene, which is not limited here.

[0102] In order to facilitate operation, all index data involved in operation in the embodiment of the application are pre-processed, and the dimensional influence is cancelled. The dimensional influence cancelling means is a technical means familiar to those skilled in the art, which is not limited here.

[0103] As described above, the temperature change rate time series data in the current amination process is obtained, and at the same time, in order to match the cooling control and the actual reaction requirement, the heat release time series data in the current amination process is also obtained, and the temperature change rate time series data in the temperature rising stage of the historical amination process is taken as a comparison data segment. First, the temperature change rate time series data in the current amination process is segmented to obtain a plurality of temperature change segments. Since the cooling link actually weakens the temperature change in the amination process, the length and temperature change difference characteristics between each temperature change segment and the comparison data segment are quantified to obtain the cooling effect value of each temperature change segment in the current amination process, which is used to evaluate the cooling effect. Further, since the cooling link usually needs to quickly realize temperature control in a short time, each temperature change segment is divided into a plurality of lengths in the current amination process to obtain interval data segments, then the fluctuation difference of the temperature change rate between the interval data segments is analyzed, and the cooling effect value corresponding to the interval data segment is fused, which can measure the overall control performance of the cooling link in a short time under all temperature change segments, so as to determine an optimal length division method to obtain the optimal interval data segment. Because violent boiling is easy to occur in the amination reaction, which will produce excessive heat accumulation, finally, on the basis of the optimal interval data segment, the heat release accumulation and the numerical characteristics of the temperature change rate in the corresponding period are analyzed, and the preset flow rate of the cooling liquid is adjusted, so that the adaptive flow rate value can better match the reaction requirement in the current amination reaction, and the cooling effect is effectively improved.

[0104] The embodiment of the application also provides an amination automatic cooling system for preparing monosultap, please refer to Figure 5 which shows a system block diagram, including a data acquisition module 501 for realizing the step S1 in the above method embodiment; a cooling analysis module 502 for realizing the step S2 in the above method embodiment; an interval segment division module 503 for realizing the step S3 in the above method embodiment; and a cooling liquid flow rate adjustment module 504 for realizing the step S4 in the above method embodiment.

[0105] It should be noted that the system provided by the above embodiment is only used as an example for the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the above described functions. In addition, the ammination automatic cooling system for the preparation of insecticidal single and the ammination automatic cooling method for the preparation of insecticidal single provided by the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0106] Please refer to Figure 6 Fig. 1 shows a system structure diagram of an ammination cooling system for the preparation of insecticidal single provided by an embodiment of the present application, which includes a processor 600, a memory 601, a bus 602 and a communication interface 603, and the processor 600, the communication interface 603 and the memory 601 are connected through the bus 602; wherein the memory 601 can contain a high-speed random access memory, the bus 602 can be an ISA bus, a PCI bus or an EISA bus, etc., the processor 600 can be an integrated circuit chip with signal processing capability; the memory 601 stores a computer program, and the computer program is loaded and executed by the processor to realize the steps in the ammination automatic cooling method for the preparation of insecticidal single.

[0107] 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 can be advantageous.

[0108] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. An automated cooling process for the amination of a single preparation of a pesticide, characterized in that, The method comprises: Obtain the temperature change rate time series data and the heat release time series data in the current amination process, and obtain the temperature change rate time series data without the cooling link as a comparison data segment; Segment the temperature change rate time series data in the current amination process to obtain temperature change segments; analyze the length difference and the fluctuation difference of the temperature change rate between each temperature change segment and the comparison data segment 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 difference of the temperature change rate between the interval data segments, the positional relationship in time sequence, and combine the cooling effect value corresponding to the interval data segments to determine the optimal division method and obtain the optimal interval data segment; Based on the temperature change rate in the optimal interval data segment and the cumulative heat release in the corresponding period, adjust the preset flow rate of the cooling liquid to obtain an adaptive flow rate value; The method for obtaining the optimal interval data segment comprises: In each length division method, in each temperature change segment, 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 normalized to obtain a change composite factor of each interval data segment; Analyze the difference of the change composite factors between the interval data segments in different temperature change segments to determine the error extraction accuracy of each length division method; Under all length division methods, the length division method with the highest error extraction accuracy is determined as the optimal division method, and the interval data segment under the optimal division method is determined as the optimal interval data segment; The method for obtaining the error extraction accuracy comprises: Optionally, any interval data segment in a temperature change segment is taken as a target data segment, and based on the serial number of the target data segment in the temperature change segment, interval data segments with the same serial number in other temperature change segments are determined as matching data segments of the target data segment; The absolute value of the difference between the change composite factors of the target data segment and each matching data segment is taken as a deviation factor, and the mean value of all deviation factors corresponding to the target data segment is taken as a mean deviation characteristic value; Under each length division method, the mean value of the mean deviation characteristic values of all interval data segments is negatively correlated and normalized to obtain the error extraction accuracy under each length division method.

2. An automated cooling process for the amination of a single insecticide preparation as claimed in claim 1, characterized in that, The method for obtaining the cooling effect value comprises: All temperature change segments and comparison data segments are collectively referred to as analysis data segments; The variance of all data values in each analysis data segment is combined with the length of each analysis data segment to determine the fluctuation density value of each analysis data segment; The difference between the fluctuation density value of the comparison data segment and the fluctuation density value of each temperature change segment is normalized to obtain the cooling effect value of each temperature change segment.

3. An automated cooling process for the amination of a single pesticide preparation as claimed in claim 2, characterized in that, The method for obtaining the fluctuation density value comprises: The ratio of the variance of all data values in each analysis data segment to the length of each analysis data segment is taken as the fluctuation density value of each analysis data segment.

4. An automated cooling process for the amination of a single pesticide preparation as claimed in claim 1, characterized in that, The method for obtaining the temperature change segment comprises: In the current amination process, time series data of temperature is obtained, a time point at which a temperature value is greater than a preset temperature threshold is taken as a breakpoint, and time series data of a temperature change rate in the current amination process is segmented based on the breakpoint, so that all temperature change segments are obtained.

5. An automated cooling process for the preparation of amines for insecticidal single use as claimed in claim 1, wherein, The method for obtaining the adaptive flow rate value comprises: determining a reaction accumulation degree value of each optimal interval data segment based on the temperature change rate in the optimal interval data segment and the cumulative heat release in the corresponding time period; taking the reaction accumulation degree value of each optimal interval data segment as a weight, obtaining a temperature change rate in a corresponding time period of a next interval based on an ARIMA time series prediction algorithm, and taking the temperature change rate as a prediction data segment; if the temperature change rate at the current time point in the current amination process is greater than the temperature change rate at the corresponding time point in the prediction data segment, adjusting a preset flow rate of the cooling liquid based on changes of the reaction accumulation degree values of all the optimal interval data segments in the current amination process, and obtaining an adaptive flow rate value at the current time point; The method for obtaining the reaction accumulation degree value comprises: in each optimal interval data segment, taking a value obtained by negatively correlating all mean values of the temperature change rates as a temperature stability performance value of each optimal interval data segment; taking a sum value of the heat release at all time points corresponding to the time period of each optimal interval data segment as a heat accumulation value of each optimal interval data segment; for any one optimal interval data segment, taking a preset number of optimal interval data segments adjacent to the optimal interval data segment as reference segments; taking a value obtained by normalizing a product of the heat accumulation value of the optimal interval data segment and all corresponding reference segments and the temperature stability performance value of the optimal interval data segment as a reaction accumulation degree value of the optimal interval data segment; if a certain optimal interval data segment is in a temperature rising stage in the current amination reaction process, the reaction accumulation degree value of the optimal interval data segment is 1.

6. An automated cooling process for the preparation of amines for insecticidal single use according to claim 5, characterized in that, The method for adjusting the preset flow rate of the cooling liquid based on changes of the reaction accumulation degree values of all the optimal interval data segments in the current amination process to obtain an adaptive flow rate value at the current time point comprises: in the current amination process, taking a range of the reaction accumulation degree values of all the optimal interval data segments as an adjustment degree value; multiplying the adjustment degree value by a preset minimum adjustment amplitude, and taking a sum value of the product and the preset flow rate of the cooling liquid as the adaptive flow rate value at the current time point.

7. An automated cooling system for the preparation of an insecticidal mono comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the amination automatic cooling method for preparing monosulfiram according to any one of claims 1-6. The processor executes the computer program to implement the steps of the amination automatic cooling method for preparing monosulfiram according to any one of claims 1-6.

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