Explosive safety boundary intelligent monitoring system based on multi-parameter response analysis

Abstract

The invention relates to the technical field of safety performance evaluation of energetic materials, in particular to an intelligent explosive safety boundary monitoring system based on multi-parameter response analysis. Comprising a granulation mechanism and further comprises a sensor system, and the sensor system comprises a thermocouple sensor, a force sensor and an electrostatic sensor; the thermocouple sensor, the force sensor and the electrostatic sensor are connected with a signal acquisition instrument and then are connected into a computer, and real-time monitoring on the temperature, pressure and electrostatic accumulation situation in the granulation process is realized through the computer. According to the method, the statistical distribution characteristics of the response probability under multiple repeated tests are considered in the determination of the safety boundary, and the obtained safety boundary error is smaller. The intelligent monitoring system for the granulation process can accurately monitor the temperature, pressure and electrostatic quantity change of the explosive in real time, and the potential safety hazards such as thermal runaway, mechanical overload and electrostatic discharge risks in the production process are effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of energetic material safety performance assessment, in particular to an intelligent explosive safety boundary monitoring system based on multi-parameter response analysis. Background Art

[0002] In the research and development and production of energetic materials, accurately evaluating their safety performance and achieving safe and controllable manufacturing processes are core issues that the industry has long been concerned about. At present, the determination of the safety boundary of explosives mainly relies on sensitivity testing under single stimulus conditions, such as obtaining the critical stimulus threshold through an impact sensitivity meter or a friction sensitivity meter. However, this method has significant limitations: (1) Traditional tests often use a single test with a fixed sample size (for example, the 50% ignition probability method recommended by the United Nations), ignoring the statistical distribution characteristics of the response probability under multiple repeated tests, resulting in large errors in the safety threshold; (2) Test data are often processed through linear interpolation or empirical formulas, lacking nonlinear fitting methods based on probability statistical models, resulting in insufficient scientificity of the safety threshold.

[0003] In the field of explosive granulation, existing granulation equipment generally has insufficient safety monitoring capabilities. Although conventional granulation devices (such as rotary extrusion granulators) can form explosive granules, their monitoring systems have the following problems: (1) Thermocouple sensors are often located on the outer wall of the equipment or far away from the reaction zone, and cannot capture the local temperature rise caused by frictional heat in the granulation chamber in real time; (2) Pressure detection relies on overall torque monitoring, making it difficult to analyze the dynamic pressure pulsation characteristics in the contact area between the scraper and the screen. These problems lead to safety hazards such as thermal runaway and mechanical overload during the production process. Summary of the Invention

[0004] The present invention aims to determine the safety boundary of traditional explosives by using a single test with a fixed sample size, which leads to a large error in the safety threshold. In the field of explosives granulation technology, existing granulation equipment generally has the problem of insufficient safety monitoring capabilities. This paper provides an intelligent monitoring system for the safety boundary of explosives based on multi-parameter response analysis.

[0005] The present invention adopts the following technical solution: an intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis, including a granulation mechanism and a sensor system, wherein the sensor system includes: Thermocouple sensors, force sensors, and electrostatic sensors; The thermocouple sensor, force sensor and electrostatic sensor are connected to the signal acquisition instrument and then connected to the computer, so as to realize real-time monitoring of the temperature, pressure and static electricity accumulation situation in the granulation process through the computer.

[0006] In some embodiments, the monitored temperature is compared with the temperature safety boundary of the explosive. If the monitored temperature exceeds the temperature safety boundary, the explosive granulation process is in a dangerous state and an alarm is immediately issued. The monitored pressure is compared with the safety limit of the impact stimulation amount and the safety limit of the friction stimulation amount. If the monitored pressure exceeds the safety limit of the impact stimulation amount and the safety limit of the friction stimulation amount, the explosive granulation process is in a dangerous state and an alarm is immediately issued; The monitored static electricity accumulation situation is compared with the safety limit of the static electricity stimulation amount. If the monitored static electricity accumulation situation exceeds the safety limit of the static electricity stimulation amount, the explosive granulation process is in a dangerous state and an alarm is immediately issued.

[0007] In some embodiments, the temperature safety margin of the explosive is the initial temperature of thermal decomposition of the explosive.

[0008] In some embodiments, the initial temperature of thermal decomposition of the explosive sample is calculated by the following steps: S11: Take the explosive sample and place it in the crucible of the differential scanning calorimeter, record the heat flow curve and extract the thermal decomposition peak temperature T p ; S12: Calculate the activation energy E a : Where: is the heating rate; is the heating rate of the explosive The decomposition peak temperature at ; A is the pre-exponential factor; is the activation energy; R is the gas constant; S13: Calculate the initial temperature of thermal decomposition : Where: is the critical temperature of thermal explosion; is the initial temperature of thermal decomposition; b, c, and d are constants.

[0009] In some embodiments, the heating rate of the differential scanning calorimeter is 5-20°C / min, and the test temperature range is 50-400°C.

[0010] In some embodiments, the impact stimulus safety margin and the friction stimulus safety margin are obtained by the following steps: Apply mechanical impact and friction with different stimulation amounts to the explosive sample, repeat the test for each stimulation amount, and obtain a scatter distribution diagram of the impact stimulation amount-response probability and a scatter distribution diagram of the friction stimulation amount-response probability of the explosive sample; Nonlinear curve fitting was performed on the impact stimulus amount-response probability scatter distribution diagram and the friction stimulus amount-response probability scatter distribution diagram respectively, and the stimulus amount corresponding to the intersection of the fitting curve and the abscissa was extracted as the safety boundary of the explosive under impact stimulation or friction stimulation.

[0011] In some embodiments, the electrostatic stimulation safety margin is calculated by the following steps: Apply electrostatic spark stimulation to explosive samples and conduct repeated tests to obtain a scatter plot of electrostatic stimulation amount-response probability of the explosive samples; A nonlinear curve fitting was performed on the electrostatic stimulation amount-response probability scatter plot, and the stimulation amount corresponding to the intersection of the fitting curve and the abscissa was extracted as the safety boundary of the explosive under electrostatic stimulation.

[0012] In some embodiments, the process of fitting the impact stimulus amount, the friction stimulus amount, and the electrostatic stimulus amount to obtain the safety margin includes: The data of each stimulus amount-response probability were imported into Origin software, and nonlinear fitting was performed using the ExpDec1 model; The confidence level was set to 95%, the confidence interval of the fitting curve was calculated, and the stimulation amount corresponding to the intersection of the fitting curve and the abscissa was extracted as the safety boundary of the explosive under impact stimulation, friction stimulation, or electrostatic stimulation. The stimulation amount corresponding to the response probability of 2% was taken as the safety threshold of the explosive under impact stimulation, friction stimulation, or electrostatic stimulation.

[0013] In some embodiments, the granulation mechanism comprises: Upper feeding funnel; A cylindrical cylinder, which is arranged at the bottom of the upper feeding funnel and has a screen with replaceable aperture at the bottom of the cylindrical cylinder; The thermocouple sensor is set on the side wall of the cylinder, and the probe is inserted into the material area above the screen through the small hole on the side wall; A force sensor is provided on the screen; The lower layer forming funnel is arranged below the cylindrical barrel.

[0014] In some embodiments, the electrostatic sensor is arranged on the side of the granulation mechanism, and the probe detection end of the electrostatic sensor points to the central axis of the cylindrical barrel.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The safety margin determined by this invention takes into account the statistical distribution characteristics of response probabilities under repeated tests, resulting in a smaller safety margin error. The intelligent monitoring system for the granulation process can accurately monitor changes in explosive temperature, pressure, and static electricity in real time, effectively avoiding safety hazards such as thermal runaway, mechanical overload, and electrostatic discharge during production. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis of the present invention; Figure 2 It is a schematic diagram of the granulation mechanism of the present invention; Figure 3 is the impact stimulus-response probability curve; Figure 4 is the friction stimulus amount-response probability curve; Figure 5 is the electrostatic stimulus amount-response probability curve; In the figure, 1-granulation mechanism, 1.1-upper feeding funnel, 1.2-cylindrical barrel, 1.3-lower forming funnel, 2-signal acquisition instrument, 3-computer, 4-electrostatic sensor, 5-thermocouple sensor, 6-force sensor. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, an intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis includes a granulation mechanism 1 and a sensor system, wherein the sensor system includes: Thermocouple sensor 5, force sensor 6 and electrostatic sensor 4; The thermocouple sensor 5, the force sensor 6 and the electrostatic sensor 4 are connected to the signal acquisition device 2 and then connected to the computer 3, so that the temperature, pressure and electrostatic accumulation state during the granulation process can be monitored in real time through the computer 3.

[0019] like Figure 2 As shown, the granulation mechanism 1 includes: Upper feeding funnel 1.1; A cylindrical drum 1.2 is provided at the bottom of the upper feeding funnel 1.1 and is provided with a screen with a replaceable aperture at the bottom of the cylindrical drum 1.2; The thermocouple sensor 5 is set on the side wall of the cylinder 1.2, and the probe is inserted into the material area above the screen through the small hole on the side wall; A force sensor 6 is provided on the screen; The lower layer forming funnel 1.3 is arranged below the cylindrical barrel 1.2.

[0020] The electrostatic sensor 4 is arranged on the side of the granulating mechanism 1, and the probe detection end of the electrostatic sensor 4 points to the central axis of the cylindrical barrel 1.2.

[0021] Example 1: Implementation structure configuration of granulation process monitoring device (1) Granulation mechanism The upper feeding funnel 1.1 is made of stainless steel, with an inlet diameter of 500mm, an outlet diameter of 300mm, and a height of 100mm; the outlet of the upper feeding funnel 1.1 is connected to a cylindrical barrel 1.2 with a diameter of 300mm and a height of 70mm; below the cylindrical barrel 1.2 is a stainless steel screen with replaceable aperture; the lower forming funnel 1.3 has an inlet diameter of 300mm, an outlet diameter of 160mm, and a height of 270mm.

[0022] (2) Sensor system Thermocouple sensor 5: located on the side wall of the cylindrical barrel 1.2 connected to the outlet of the upper feeding funnel 1.1. The probe is inserted into the material area of ​​the granulator through the small hole on the side wall.

[0023] Force sensor 6: located on the screen, and the force-bearing area of ​​the force sensor 6 is flush with the screen.

[0024] Electrostatic sensor 4: It is placed on the side of the granulation mechanism 1 through a bracket, and the probe detection end points to the central axis of the cylindrical barrel 1.2.

[0025] (3) Data acquisition and control All sensors are connected to a signal acquisition device 2 and then to a computer 3. Computer software enables real-time monitoring of temperature, pressure, and static electricity accumulation during the granulation process. Safety margins are set as warning values. If a sensor detects a value exceeding the safety margin during production, an immediate warning is issued.

[0026] The monitored temperature is compared with the temperature safety limit of the explosive. If the monitored temperature exceeds the temperature safety limit, the explosive granulation process is in a dangerous state and an alarm is immediately issued. The monitored pressure is compared with the safety limit of the impact stimulation amount and the safety limit of the friction stimulation amount. If the monitored pressure exceeds the safety limit of the impact stimulation amount and the safety limit of the friction stimulation amount, the explosive granulation process is in a dangerous state and an alarm is immediately issued; The monitored static electricity accumulation situation is compared with the safety limit of the static electricity stimulation amount. If the monitored static electricity accumulation situation exceeds the safety limit of the static electricity stimulation amount, the explosive granulation process is in a dangerous state and an alarm is immediately issued.

[0027] The temperature safety boundary of the explosive is the initial temperature of the thermal decomposition of the explosive; the temperature safety threshold of the explosive is the critical temperature of the thermal explosion of the explosive.

[0028] The initial temperature of thermal decomposition and the critical temperature of thermal explosion of explosive samples are calculated by the following steps: S11: 0.15 mg of explosive sample was placed in a crucible of a differential scanning calorimeter and heated from 50°C to 400°C at a heating rate of 10°C / min under a nitrogen atmosphere. The heat flow curve was recorded and the peak thermal decomposition temperature T was extracted. p ; S12: Calculate the activation energy E a : Where: is the heating rate (K·min -1 ); is the heating rate of the explosive The decomposition peak temperature at ; A is the pre-exponential factor (min -1 ); is the activation energy (kJ·min -1 ); R is the gas constant (8.314 J·min -1 ·K -1 ); S13: Calculate the critical temperature of thermal explosion and : Where: is the critical temperature of thermal explosion; is the initial temperature of thermal decomposition, b, c, and d are constants.

[0029] Calculate the initial temperature of thermal decomposition and the critical temperature of thermal explosion of the sample. The initial temperature of thermal decomposition of the explosive is used as the temperature safety boundary of the explosive, and the critical temperature of thermal explosion of the explosive is used as the temperature safety threshold of the explosive.

[0030] The safety margin of the impact stimulus and the safety margin of the friction stimulus are obtained by the following steps: Apply mechanical impact and friction with different stimulation amounts to the explosive sample, repeat the test for each stimulation amount, and obtain a scatter distribution diagram of the impact stimulation amount-response probability and a scatter distribution diagram of the friction stimulation amount-response probability of the explosive sample; Specifically, using an impact sensitivity tester, different stimulation amounts were selected, and 50 tests were conducted at each stimulation amount to obtain impact stimulation amount-response probability data; using a friction sensitivity tester, different stimulation amounts were selected, and 50 tests were conducted at each stimulation amount to obtain friction stimulation amount-response probability data; using an electrostatic spark sensitivity tester, different stimulation amounts were selected, and 50 tests were conducted at each stimulation amount to obtain electrostatic stimulation amount-response probability data.

[0031] Nonlinear curve fitting was performed on the scatter distribution diagrams of impact stimulus amount-response probability and the scatter distribution diagrams of friction stimulus amount-response probability, respectively. The stimulus amount corresponding to the intersection of the fitting curve and the abscissa was extracted as the safety boundary of the explosive under impact stimulation or friction stimulation. The stimulus amount corresponding to the response probability of 2% was taken as the safety threshold of the explosive under impact stimulation or friction stimulation.

[0032] The safety margin of electrostatic stimulation is calculated by the following steps: Apply electrostatic spark stimulation to explosive samples and conduct repeated tests to obtain a scatter plot of electrostatic stimulation amount-response probability of the explosive samples; A nonlinear curve fitting was performed on the electrostatic stimulation amount-response probability scatter plot, and the stimulation amount corresponding to the intersection of the fitting curve and the abscissa was extracted as the safety boundary of the explosive under electrostatic stimulation.

[0033] Safety margin fitting and verification (1) Import the stimulus-response probability data into Origin software and use the ExpDec1 model for nonlinear fitting: Where: A is the amplitude, is the decay constant, is the baseline offset.

[0034] (2) Set the confidence level to 95%, calculate the confidence interval of the fitting curve, extract the stimulus amount corresponding to the intersection of the fitting curve and the horizontal axis, and use it as the safety boundary of the explosive under impact stimulation, friction stimulation, or electrostatic stimulation. The stimulus amount corresponding to the response probability of 2% is used as the safety threshold of the explosive under impact stimulation, friction stimulation, or electrostatic stimulation.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis, comprising a granulation mechanism (1), characterized in that: Also included is a sensor system comprising: a thermocouple sensor (5), a force sensor (6), and an electrostatic sensor (4); The thermocouple sensor (5), the force sensor (6) and the electrostatic sensor (4) are connected to the signal acquisition device (2) and then connected to the computer (3), and the computer (3) is used to realize real-time monitoring of the temperature, pressure and static electricity accumulation during the granulation process.

2. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 1 is characterized in that: The monitored temperature is compared with the temperature safety limit of the explosive. If the monitored temperature exceeds the temperature safety limit, the explosive granulation process is in a dangerous state and an alarm is immediately issued. The monitored pressure is compared with the safety limit of the impact stimulation amount and the safety limit of the friction stimulation amount. If the monitored pressure exceeds the safety limit of the impact stimulation amount and the safety limit of the friction stimulation amount, the explosive granulation process is in a dangerous state and an alarm is immediately issued; The monitored static electricity accumulation situation is compared with the safety limit of the static electricity stimulation amount. If the monitored static electricity accumulation situation exceeds the safety limit of the static electricity stimulation amount, the explosive granulation process is in a dangerous state and an alarm is immediately issued.

3. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 2 is characterized in that: The temperature safety margin of the explosive is the initial temperature of thermal decomposition of the explosive.

4. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 3 is characterized in that: The initial temperature of thermal decomposition of the explosive sample is calculated by the following steps: S11: Take the explosive sample and place it in the crucible of the differential scanning calorimeter, record the heat flow curve and extract the thermal decomposition peak temperature T p ; S12: Calculate the activation energy E a : Where: is the heating rate; is the heating rate of the explosive The decomposition peak temperature at ; A is the pre-exponential factor; is the activation energy; R is the gas constant; S13: Calculate the initial temperature of thermal decomposition : Where: is the critical temperature of thermal explosion; is the initial temperature of thermal decomposition, b, c, and d are constants.

5. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 4 is characterized in that: The heating rate of the differential scanning calorimeter is 5-20°C / min, and the test temperature range is 50-400°C.

6. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 2 is characterized in that: The safety margin of the impact stimulus and the safety margin of the friction stimulus are obtained by the following steps: Apply mechanical impact and friction with different stimulation amounts to the explosive sample, repeat the test for each stimulation amount, and obtain a scatter distribution diagram of the impact stimulation amount-response probability and a scatter distribution diagram of the friction stimulation amount-response probability of the explosive sample; Nonlinear curve fitting was performed on the impact stimulus amount-response probability scatter distribution diagram and the friction stimulus amount-response probability scatter distribution diagram respectively, and the stimulus amount corresponding to the intersection of the fitting curve and the abscissa was extracted as the safety boundary of the explosive under impact stimulation or friction stimulation.

7. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 6 is characterized in that: The electrostatic stimulation safety margin is calculated by the following steps: Apply electrostatic spark stimulation to explosive samples and conduct repeated tests to obtain a scatter plot of electrostatic stimulation amount-response probability of the explosive samples; A nonlinear curve fitting was performed on the electrostatic stimulation amount-response probability scatter plot, and the stimulation amount corresponding to the intersection of the fitting curve and the abscissa was extracted as the safety boundary of the explosive under electrostatic stimulation.

8. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 7 is characterized in that: The process of fitting the impact stimulus amount, the friction stimulus amount, and the electrostatic stimulus amount to obtain the safety margin includes: The data of each stimulus amount-response probability were imported into Origin software, and nonlinear fitting was performed using the ExpDec1 model; The confidence level was set to 95%, the confidence interval of the fitting curve was calculated, and the stimulation amount corresponding to the intersection of the fitting curve and the abscissa was extracted as the safety boundary of the explosive under impact stimulation, friction stimulation, or electrostatic stimulation. The stimulation amount corresponding to the response probability of 2% was taken as the safety threshold of the explosive under impact stimulation, friction stimulation, or electrostatic stimulation.

9. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 1 is characterized in that: The granulation mechanism (1) comprises: Upper feeding funnel (1.1); A cylindrical cylinder (1.2), the cylindrical cylinder (1.2) being arranged at the bottom of the upper feeding funnel (1.1), and a screen with a replaceable aperture being provided at the bottom of the cylindrical cylinder (1.2); The thermocouple sensor (5) is arranged on the side wall of the cylindrical barrel (1.2), and the probe is extended into the material area above the screen through the small hole on the side wall; A force sensor (6) is provided on the screen; The lower-layer forming funnel (1.3) is arranged below the cylindrical barrel (1.2).

10. The intelligent monitoring system for explosive safety boundaries based on multi-parameter response analysis according to claim 9 is characterized in that: The electrostatic sensor (4) is arranged on the side of the granulation mechanism (1), and the probe detection end of the electrostatic sensor (4) points to the central axis of the cylindrical barrel (1.2).

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