Method, device and program product for obtaining solid self-accelerating decomposition temperature

Abstract

The present disclosure provides a method, device and program product for obtaining a solid self-accelerating decomposition temperature, comprising: obtaining a material thermal decomposition kinetics model; constructing a heat conduction model according to a material package; and obtaining the solid self-accelerating decomposition temperature of the material based on the material thermal decomposition kinetics model and the heat conduction model. The method for obtaining a solid self-accelerating decomposition temperature according to the present disclosure simulates the heat release behavior of the material itself by using a thermal decomposition kinetics model, describes the heat exchange behavior between the object in the package and the outside world by using a heat conduction model, and obtains the SADT of the fixed material by combining the two models. Moreover, the thermal decomposition kinetics model of the material involved in the drug synthesis process is established, and the heat conduction model is further established for the solid material. The thermal decomposition kinetics model and the heat conduction model are combined to further obtain the SADT of the solid material and predict the possible runaway reaction.

Description

Technical Field

[0001] This application relates to the field of thermal risk analysis and assessment technology for drug synthesis processes in the chemical industry, and in particular to a method for obtaining the Self Accelerating Decomposition Temperature (SADT). Background Technology

[0002] The chemical industry, which uses chemical methods to produce products, is more dangerous than other industries due to its flammability, explosiveness, toxicity, high temperature, high pressure, and high corrosivity. Currently, the raw materials, intermediates, and products used in the chemical industry generally release energy during chemical reactions; these reactions are exothermic. If left uncontrolled, they can lead to the decomposition of substances, releasing even more energy and causing catastrophic consequences. Therefore, reaction heat risk assessment is necessary. The Self-Accelerating Decomposition Temperature (SADT) refers to the lowest ambient temperature at which reactive chemicals in actual packaged goods will undergo self-accelerated decomposition within seven days. It is related to the chemical properties of the reactive substances, as well as the packaging size and material characteristics. It is a crucial parameter in reaction heat risk assessment and an important reference indicator for designing material storage and transportation conditions.

[0003] The United Nations Expert Committee on the Transport of Dangerous Goods (JETRO) recommends four methods for testing SADT: the US SADT test, the isothermal storage test, the adiabatic storage test, and the heat accumulation storage test (Dewar flask test). The US SADT test provides the most direct SADT value, but it uses hundreds of grams of reagent and has an extremely long testing period—up to 7 days for a single test, and even longer for multiple tests. While the isothermal and adiabatic storage tests use laboratory-scale reagents, the testing period still requires 4-5 days or more. Furthermore, both of these tests determine SADT based on Semenov theory, which is not suitable for complex thermal decomposition reactions; the heat accumulation storage test (Dewar flask test) is more suitable for liquid materials.

[0004] Furthermore, for highly hazardous materials, large-scale testing using traditional thermal analysis instruments is not only time-consuming but also prone to accidents. If thermal decomposition occurs, it can cause significant damage to the instrument. For expensive materials, thermal stability testing is prohibitively costly. Traditional methods for determining SADT (Self-Standardized Decomposition Time) for these two types of reactions suffer from high testing costs, long cycles, and the need for large quantities of materials. In addition, from a theoretical analysis perspective, the thermal decomposition of some materials involves complex reaction types, potentially combining multiple reaction types, which are difficult to explain with a single formula or model. Establishing a more accurate thermal decomposition kinetic model for complex reactions without conducting large-scale, multi-type experiments has always been a challenge. For the complex heat transfer process between solid materials in packaging and the external environment, involving numerous thermal stability parameters, establishing a suitable heat conduction model is also crucial. Summary of the Invention

[0005] The technical problem to be solved by this disclosure is to overcome the deficiencies in the prior art and provide a method, apparatus, electronic device, medium and program product for rapidly and accurately obtaining the self-accelerating decomposition temperature of solids.

[0006] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0007] A method for obtaining the temperature of solid self-accelerating decomposition includes,

[0008] Obtain the material thermal decomposition kinetic model;

[0009] Construct a heat conduction model based on the material packaging;

[0010] Based on the material thermal decomposition kinetics model and heat conduction model, the fixed self-accelerating decomposition temperature of the material is obtained.

[0011] Preferably, the step of obtaining the material thermal decomposition kinetic model includes,

[0012] Obtain thermal decomposition data of materials;

[0013] Based on the thermal decomposition data, determine the type of thermal decomposition reaction;

[0014] A material thermal decomposition kinetic model was constructed by fitting the thermal decomposition data.

[0015] Preferably, the acquisition of thermal decomposition data of the material specifically includes,

[0016] Acquire thermal decomposition data of materials at different heating rates;

[0017] The thermal decomposition data is preprocessed.

[0018] Preferably, the thermal decomposition reaction type includes autocatalytic and non-autocatalytic types;

[0019] The determination of the thermal decomposition reaction type based on the thermal decomposition data includes:

[0020] Acquire the first thermal decomposition reaction data and the second thermal decomposition reaction data of the material. The first thermal decomposition reaction data is the data scanned according to the normal temperature rise procedure. The second thermal decomposition reaction data is the data scanned after first raising the temperature from the test temperature starting point to near the decomposition starting temperature and then rapidly cooling it back to the starting test temperature. Then, the normal temperature rise procedure is repeated.

[0021] In response to the second thermal decomposition reaction data, the onset temperature of the exothermic peak is significantly lower than that of the first thermal decomposition reaction data, confirming that the thermal decomposition reaction type is autocatalytic.

[0022] Preferably, the step of fitting the thermal decomposition data to construct a material thermal decomposition kinetic model includes,

[0023] Based on the pre-processed thermal decomposition numbers, a kinetic model of material thermal decomposition is obtained through kinetic fitting:

[0024]

[0025] Among them, different thermal decomposition reaction types f(α) can be represented by different equations:

[0026]

[0027] In the above expressions, from top to bottom, they are the N-order model, the model of generalized autocatalysis, the Proto model, the Erofeev's topochemical model, and the generalized Erofeev's topochemical model.

[0028] r is the reaction rate, E is the activation energy, Ez is the difference between the activation energies of the initiation reaction and the autocatalytic reaction, R is the molar gas constant, T is the thermodynamic temperature, n, n1 and n2 are the reaction orders, k0 is the pre-exponential factor, α is the thermal conversion rate, z0 is the ratio of the pre-exponential factors of the initiation reaction and the autocatalytic reaction, and is a dimensionless parameter.

[0029] Preferably, the step of constructing a heat conduction model based on the material packaging includes:

[0030] Obtain the type of material packaging;

[0031] Determine the relevant parameters based on the type of packaging;

[0032] A heat conduction model is constructed based on the type of packaging.

[0033] Preferably, the step of obtaining the fixed self-accelerating decomposition temperature of the material based on the material thermal decomposition kinetic model and the heat conduction model specifically includes:

[0034] Combining the aforementioned material thermal decomposition kinetics model and heat conduction model, the following model is obtained:

[0035]

[0036] r represents the characteristic value of different types of packaging sizes, in meters (m); Cp represents the specific heat capacity of the material, in J / (kg·K). The change in material temperature over time is calculated using a thermal decomposition kinetic model, where q is the heat release rate per unit material, in J / (m³). 3 ·sec), calculated through a thermal decomposition kinetic model; div(λgradT) describes the temperature distribution of the material in a specific package, where λ is the thermal conductivity of the material, in units of J / (m·sec·K);

[0037] Based on the above model, the internal temperature of the material in the packaging is simulated over time to obtain the fixed self-accelerating decomposition temperature of the material.

[0038] Another aspect of this disclosure provides an apparatus for obtaining the temperature of a solid self-accelerating decomposition, comprising,

[0039] The first acquisition module is used to acquire the material thermal decomposition kinetics model;

[0040] The second acquisition module is used to construct a heat conduction model based on the material packaging.

[0041] The fusion module is used to obtain the fixed self-accelerating decomposition temperature of the material based on the material thermal decomposition kinetic model and the heat conduction model.

[0042] In another aspect of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the method for obtaining the solid self-accelerating decomposition temperature as described above.

[0043] In another aspect of this disclosure, a computer program product is provided, comprising a computer program, characterized in that, when the computer program is executed by a processor, it implements the method for obtaining the solid self-accelerating decomposition temperature as described above.

[0044] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.

[0045] The positive and progressive effects of this disclosure are as follows: The technical solution of this disclosure uses a thermal decomposition kinetic model to simulate the exothermic behavior of the material itself, and a heat conduction model to describe the heat exchange behavior between the object inside the packaging and the outside environment. The SADT of a fixed material is obtained by combining the two models. Moreover, a thermal decomposition kinetic model of the materials involved in the drug synthesis process is established, and a heat conduction model is further established for solid materials. The combination of the thermal decomposition kinetic model and the heat conduction model further obtains the SADT of solid materials and predicts possible runaway reactions. Attached Figure Description

[0046] Figure 1 A flowchart of the method for obtaining the solid self-accelerating decomposition temperature provided in Embodiment 1 of this disclosure;

[0047] Figure 2 The exothermic curves of the same material at different heating rates provided in Embodiment 1 of this disclosure;

[0048] Figure 3 A schematic diagram of the frame of the device for obtaining the accelerated decomposition temperature of the stationary phase provided in Embodiment 2 of this disclosure;

[0049] Figure 4 This is a schematic diagram of an electronic device provided in Embodiment 3 of this disclosure. Detailed Implementation

[0050] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0051] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0052] In this embodiment of the disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good morals.

[0053] The heat flow differential scanning calorimeter (DSC) is a thermal analysis instrument from Mettler Toledo, Switzerland. It allows observation of the heat flow power difference between the material and a reference material under a controlled temperature program, enabling the acquisition of information on endothermic and exothermic changes and other related thermal effects during the temperature program. This instrument is widely used in the field of thermal risk analysis and assessment. Its significant advantages include requiring minimal reagent dosage and high testing speed.

[0054] The Thermal Safety Series (TSS) is a software suite developed by the St. Petersburg Chemical Information Company in Russia. It can process experimental data, build thermodynamic models, and simulate chemical reactions. TSS can simulate runaway reactions using small-scale experimental data obtained from laboratory DSC measurements. It verifies the accuracy of the established reaction model by simulating the thermal decomposition process of materials, and provides thermodynamic parameters through simulation. Based on the parameters of the thermal decomposition reaction, it can assess runaway reactions that may occur in various environments. For complex thermal decomposition reactions, it can couple or segmentally fit the thermodynamic behavior to accurately fit the results, obtain more relevant data, and build a more accurate thermal analysis kinetic model.

[0055] This disclosure establishes a thermal decomposition kinetic model for materials involved in the drug synthesis process, and further establishes a heat conduction model for solid materials. The combination of the thermal decomposition kinetic model and the heat conduction model further obtains the SADT of the solid material and predicts possible runaway reactions. Preferably, this can be done using TSS software.

[0056] Example 1

[0057] Figure 1 A flowchart of a method for obtaining the self-accelerating decomposition temperature of a solid provided in Embodiment 1 of this disclosure; including,

[0058] S10, Obtain the material thermal decomposition kinetics model;

[0059] S20, construct a heat conduction model based on the material packaging;

[0060] S30, based on the material thermal decomposition kinetic model and the heat conduction model, the fixed self-accelerating decomposition temperature of the material is obtained.

[0061] Wherein, step S10 includes

[0062] S11, Obtain thermal decomposition data of the material;

[0063] S12, Based on the thermal decomposition data, determine the type of thermal decomposition reaction;

[0064] S13, Fit the thermal decomposition data to construct a material thermal decomposition kinetic model;

[0065] The thermal decomposition kinetic model describes the heat release behavior of the object itself. The heat conduction model describes the heat exchange behavior between the object inside the packaging and the outside environment. In step S11, thermal decomposition data of the material is obtained, specifically, thermal decomposition data of the material at different heating rates is obtained, and the thermal decomposition data is preprocessed.

[0066] Specifically, such as Figure 2 As shown, DSC software can be used to scan materials at different heating rates. The lowest and highest scan data differ by more than ten times, and the mass of each scanned material remains the same. Figure 2 The figure shows the heat release curves of the same material at different heating rates.

[0067] The obtained data undergoes preprocessing, specifically including deconvolution and data correction. Specifically,

[0068]

[0069] W is the corrected actual heat generation rate of the material, τ is the time lag constant, and q is the actual heat release rate of the material, which can be obtained by actual measurement using DSC software.

[0070] m represents the crucible mass plus the material mass, Cp represents the specific heat capacity of the material, and Cp for the metal crucible is chosen to be 0.5 J / (g·K). β represents the temperature rise rate. The Cp of the sample is ignored because the mass of the sample in the metal crucible is approximately 5 mg, while the mass of the metal crucible is approximately 940 mg, which is much greater than the sample mass, and therefore is ignored.

[0071] Then, the thermal resistance for each test is calculated. The formula for calculating thermal resistance Rt is as follows:

[0072] Rt=τ / (m·Cp);

[0073] τ is the time lag constant, m is the crucible mass + material mass, Cp is the specific heat capacity of the material, and Cp = 0.5 J / (g·K) is chosen for the metal crucible. Rt is the thermal resistance (K / W).

[0074] After obtaining the thermal resistance, the actual temperature of the material is corrected using the following formula:

[0075] Ts = Tr + Rt × q;

[0076] Ts is the material temperature, Tr is the reference temperature, and Q is the actual heat release rate of the material.

[0077] Step S12: Based on the thermal decomposition data, determine the type of thermal decomposition reaction; specifically including,

[0078] Acquire the first thermal decomposition reaction data and the second thermal decomposition reaction data of the material. The first thermal decomposition reaction data is the data scanned according to the normal temperature rise procedure. The second thermal decomposition reaction data is the data scanned after first raising the temperature from the test temperature starting point to near the decomposition starting temperature and then rapidly cooling it back to the starting test temperature. Then, the normal temperature rise procedure is repeated.

[0079] In response to the second thermal decomposition reaction data, the onset temperature of the exothermic peak is significantly lower than that of the first thermal decomposition reaction data, confirming that the thermal decomposition reaction type is autocatalytic.

[0080] To determine whether a substance decomposition is an autocatalytic reaction, the specific method is to perform two DSC tests on the same sample. The first scan follows the normal temperature ramping procedure. The second scan starts from the test temperature and rises to near the decomposition initiation temperature (ensuring that a small portion of the sample decomposes), then rapidly cools back to the initial test temperature. Finally, the scan is performed again following the normal temperature ramping procedure.

[0081] For substances with autocatalytic properties, if a small portion of the substance decomposes first, the onset temperature of its exothermic peak will be significantly lower than that of the first test.

[0082] Step S13, fitting the thermal decomposition data to construct a material thermal decomposition kinetic model; including,

[0083] Based on the pre-processed thermal decomposition numbers, a kinetic model of material thermal decomposition is obtained through kinetic fitting:

[0084] Preferably, the processed thermal data at different heating rates can be imported into the TSS model method kinetic fitting module (Software for evaluation of Formal Kinetics). The decomposition reaction rate is related to the reaction activation energy E, k0f(α), as shown in the following equation. Different thermal decomposition reaction types can be represented by different equations.

[0085]

[0086] r is the reaction rate, E is the activation energy. The generalized autocatalysis model actually includes two parts: the initiation reaction and the autocatalysis reaction. Ez is the difference between the activation energies of the initiation reaction and the autocatalysis reaction, in kJ / mol. R is the molar gas constant, T is the thermodynamic temperature, and n, n1, and n2 are all reaction orders. Some models have only a single reaction order, while others have multiple reaction orders. k0 is the pre-exponential factor, α is the thermal conversion rate, and z0 is the ratio of the pre-exponential factors of the initiation reaction and the autocatalysis reaction, which are dimensionless parameters.

[0087] The expressions for the reaction mechanism function f(α) for different types of reactions are as follows.

[0088]

[0089] In the above expressions, from top to bottom, they represent the N-order model, the model of generalized autocatalysis, the Proto model, the Erofeev's topochemical model, and the generalized Erofeev's topochemical model. Except for the N-order model, which is an n-order kinetic model, the other models represent autocatalytic reactions, with generalized autocatalysis being the most generalized type. For a single type of reaction, one reaction type can be selected for fitting. For reactions suspected of being autocatalytic, DSC isothermal or DSC interrupted backscan method can be used to confirm (specific methods will not be elaborated here). If it is not an autocatalytic reaction, an N-order model is selected for fitting. If it is an autocatalytic reaction, multiple autocatalytic models are tried. If the fitting result has poor overlap with the reaction data (fit index R2 < 0.99), other types of reactions can be considered for refitting. If the fitting result has high overlap with the reaction data (fit index R2 > 0.99), the reaction type is considered to fit the model well, and more accurate activation energy, reaction order, and other values ​​are obtained.

[0090] For complex reaction types, the reaction can be broken down into multiple stages, and then the reaction type of each stage can be defined and fitted.

[0091] Step S20 involves constructing a heat conduction model based on the material packaging; specifically, this includes:

[0092] S21, Obtain the type of material packaging;

[0093] S22, Determine the relevant parameters according to the type of packaging;

[0094] S23, Construct a heat conduction model based on the type of packaging.

[0095] During material storage, the release of heat from the material itself and the dissipation of heat to the surrounding environment occur simultaneously. The heat release behavior of the material has been simulated by constructing a thermal decomposition kinetic model, while heat dissipation involves heat transfer between the material and the environment. Therefore, it is necessary to construct a heat conduction model and then combine it with dynamic numerical fitting of the thermal decomposition kinetic model to obtain the SADT (Sustainable Temperature Detection). The heat transfer process between the material and the environment is quite complex. It is intuitively represented by the dynamic distribution of temperature inside the material. This process is related to the heat release parameters of the material itself, as well as the size (including thickness), shape, and material of the packaging. Therefore, establishing an accurate heat conduction model is also crucial for the accuracy of SADT calculation.

[0096] The TSS solid material thermal explosion simulation module (Software for simulation of thermal explosion in TE standard module) is applied. First, the packaging size and shape are selected based on actual needs. Commonly used packaging sizes and shapes are categorized into six types:

[0097] Plate: The material is stored in the space between two infinitely parallel plates. The distance between the parallel plates needs to be entered.

[0098] Sphere: The material is stored inside the sphere. The diameter of the sphere must be entered.

[0099] Infinitely long cylinder: Materials are stored inside the cylinder. The radius of the circular base must be entered.

[0100] Cylindrical bucket: Materials are stored inside the bucket; the height and radius of the circular base must be entered.

[0101] Rectangular box: Materials are stored inside the rectangular box. The box's length, width, and height must be entered.

[0102] Multiple cuboid boxes: Materials are stored inside individual cuboid boxes. Multiple identical cuboid boxes are stacked to form a larger cuboid. Input is required for the length, width, and height of each individual cuboid, as well as the number of cuboids in each of the three dimensions.

[0103] After selecting a packaging shape and entering the relevant parameters, specify the packaging shell and its thickness. For shapes with limited space, such as cylindrical barrels, calculate the loading rate based on the amount of material, which is the ratio of the space occupied by the material to the total storage space, and input this value.

[0104] Define boundary conditions. Input the ambient temperature and the heat transfer coefficient (HTC, W / m2 / K) of the packaging surface. The heat transfer coefficient is related to the characteristic value of the packaging dimensions, i.e., the bottom diameter of the cylindrical barrel or the diameter of the sphere.

[0105] Determine the density, specific heat capacity, and heat transfer coefficient of the solid material, input these values ​​into the software, and substitute them into the following formula. Preferably, TSS software can be used to import the values ​​into the TSS software.

[0106] In step S30, preferably, the material thermal decomposition kinetic model and heat conduction model can be imported into the TSS software to perform numerical simulation and obtain the SADT.

[0107] Specifically, by combining the material thermal decomposition kinetics model and the heat conduction model, the following model is obtained to represent the solid decomposition and heat exchange process:

[0108]

[0109] r represents the characteristic value of different types of packaging dimensions, such as half the distance between two flat plates, the radius of a sphere, the radius of the bottom surface of a cylinder, etc., in meters; Cp represents the specific heat capacity of the material, in J / (Kg·K). The change in material temperature over time is calculated using a thermal decomposition kinetic model, where q is the heat release rate per unit material (J / (m²)). 3 ·sec), calculated through a thermal decomposition kinetic model; div(λgradT) describes the temperature distribution of the material in a specific package, where λ is the thermal conductivity of the material, in units of J / (m·sec·K).

[0110] For a plate (K=0), a sphere (K=2), and an infinitely long cylinder (K=1), div(λgradT) is expressed by the following equation:

[0111]

[0112] For a cylindrical barrel, div(λgradT) is expressed by the following formula:

[0113]

[0114] Where z is the height along the Z-axis.

[0115] For a cuboid box and a collection of multiple cuboid boxes, div(λgradT) is expressed by the following formula.

[0116]

[0117] Where x, y, and z represent length, width, and height, respectively.

[0118] Based on the above model, the internal temperature of the material in the packaging is simulated over time to obtain the fixed self-accelerating decomposition temperature of the material.

[0119] The principle of this method is to maintain the packaging at a constant temperature. If the highest internal temperature of the material inside the packaging exceeds the packaging temperature by 6°C after exactly 7 days, then this constant temperature of the packaging is considered to be the material's SADT.

[0120] The simulation method is based on the "SADT experimental method" from the United States. A temperature detector is installed at the center of the material inside the packaging. A constant temperature is applied to the packaging, and energy transfer is performed to observe whether it triggers thermal decomposition of the material inside the packaging. If thermal decomposition occurs, heat transfer occurs between the material and the packaging, as well as between the packaging and the external environment. The temperature rise due to thermal decomposition of the material itself is determined by thermal decomposition kinetics, while the temperature change due to heat transfer is determined by a heat conduction model. By continuously applying a constant packaging temperature, when the detector at the center of the material detects a 6°C increase in temperature over 7 days, the applied packaging temperature represents the required SADT. Therefore, based on this model, the accelerated decomposition temperature of the solid material can be simulated and determined.

[0121] The technical solution of this embodiment utilizes a thermal decomposition kinetic model to simulate the exothermic behavior of the material itself, and a heat conduction model to describe the heat exchange behavior between the object inside the packaging and the outside environment. The SADT of a fixed material is obtained by combining the two models. Furthermore, a thermal decomposition kinetic model of the materials involved in the drug synthesis process is established using TSS software, and a heat conduction model is further established for solid materials. The combination of the thermal decomposition kinetic model and the heat conduction model further obtains the SADT of solid materials and predicts possible runaway reactions.

[0122] Example 2

[0123] Corresponding to the aforementioned embodiments of the method for obtaining the solid self-accelerating decomposition temperature, this disclosure also provides embodiments of the apparatus for obtaining the solid self-accelerating decomposition temperature.

[0124] Figure 3 A schematic diagram of a module for obtaining the temperature of solid self-accelerating decomposition, provided as an exemplary embodiment of this disclosure, is provided. The device includes:

[0125] The first acquisition module 1 is used to acquire the material thermal decomposition kinetic model;

[0126] The second acquisition module 2 is used to construct a heat conduction model based on the material packaging.

[0127] The fusion module 3 is used to obtain the fixed self-accelerating decomposition temperature of the material based on the material thermal decomposition kinetic model and the heat conduction model.

[0128] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0129] Example 3

[0130] Figure 4 This is a schematic diagram of the structure of an electronic device according to an example embodiment of the present disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the method for obtaining the solid self-accelerating decomposition temperature described in any of the above embodiments. Figure 4 The electronic device 90 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0131] like Figure 4 As shown, the electronic device 90 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 90 may include, but are not limited to: at least one processor 91, at least one memory 92, and a bus 93 connecting different system components (including memory 92 and processor 91).

[0132] Bus 93 includes a data bus, an address bus, and a control bus.

[0133] Memory 92 may include volatile memory, such as random access memory (RAM) 921 and / It may include a cache memory 922, and may further include a read-only memory (ROM) 923.

[0134] The memory 92 may also include a program tool 925 (or utility) having a set (at least one) program module 924, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0135] The processor 91 executes various functional applications and data processing by running computer programs stored in the memory 92, such as the method for obtaining the solid self-accelerating decomposition temperature provided in any of the above embodiments.

[0136] Electronic device 90 can also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). This communication can be performed through input / output (I / O) interface 95. Furthermore, electronic device 90 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 96. As shown, network adapter 96 communicates with other modules of electronic device 90 via bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0137] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0138] Example 4

[0139] This disclosure also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for obtaining the solid self-accelerating decomposition temperature provided in any of the above embodiments.

[0140] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0141] Example 5

[0142] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the method for obtaining the solid self-accelerating decomposition temperature described in any of the preceding embodiments.

[0143] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0144] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A method for obtaining the temperature of solid self-accelerating decomposition, characterized in that, include, Obtain the material thermal decomposition kinetic model; A heat conduction model is constructed based on the material packaging. Based on the material thermal decomposition kinetics model and heat conduction model, the fixed self-accelerating decomposition temperature of the material is obtained.

2. The acquisition method as described in claim 1, characterized in that, The step of obtaining the material thermal decomposition kinetic model includes, Obtain thermal decomposition data of materials; Based on the thermal decomposition data, determine the type of thermal decomposition reaction; A material thermal decomposition kinetic model was constructed by fitting the thermal decomposition data.

3. The acquisition method as described in claim 2, characterized in that, The acquisition of thermal decomposition data of the material specifically includes... Acquire thermal decomposition data of materials at different heating rates; The thermal decomposition data is preprocessed.

4. The acquisition method as described in claim 2, characterized in that, The thermal decomposition reaction types include autocatalytic and non-autocatalytic types; The step of determining the thermal decomposition reaction type based on the thermal decomposition data includes obtaining the first thermal decomposition reaction data and the second thermal decomposition reaction data of the material. The first thermal decomposition reaction data is the data scanned according to the normal heating procedure, and the second thermal decomposition reaction data is the data scanned after first heating from the test temperature starting point to near the decomposition starting temperature and then rapidly cooling back to the starting test temperature. Then, the data is scanned again according to the normal heating procedure. In response to the second thermal decomposition reaction data, the onset temperature of the exothermic peak is significantly lower than that of the first thermal decomposition reaction data, confirming that the thermal decomposition reaction type is autocatalytic.

5. The acquisition method as described in claim 2, characterized in that, The process of fitting the thermal decomposition data to construct a material thermal decomposition kinetic model includes, Based on the pre-processed thermal decomposition numbers, a kinetic model of material thermal decomposition is obtained through kinetic fitting: Among them, different thermal decomposition reaction types f(α) can be represented by different equations: In the above expressions, from top to bottom, they represent the N-level model, the generalized autocatalytic model, the Proto model, the Erofeev's topological chemistry model, and the generalized Erofeev's topological chemistry model, respectively. r is the reaction rate, E is the activation energy, Ez is the difference between the activation energies of the initiation reaction and the autocatalytic reaction, R is the molar gas constant, T is the thermodynamic temperature, n, n1 and n2 are the reaction orders, k0 is the pre-exponential factor, α is the thermal conversion rate, z0 is the ratio of the pre-exponential factors of the initiation reaction and the autocatalytic reaction, and is a dimensionless parameter.

6. The acquisition method as described in claim 1, characterized in that, The heat conduction model is constructed based on the material packaging; include, Obtain the type of packaging for the material; Determine the relevant parameters based on the type of packaging; A heat conduction model is constructed based on the type of packaging.

7. The acquisition method as described in claim 1, characterized in that, The method for obtaining the fixed self-accelerating decomposition temperature of the material based on the material's thermal decomposition kinetics model and heat conduction model specifically includes: Combining the aforementioned material thermal decomposition kinetics model and heat conduction model, the following model is obtained: r represents the characteristic value of different types of packaging dimensions, in meters (m). Cp: ​​Specific heat capacity of the material, in J / (Kg·K); The change in material temperature over time is calculated using a thermal decomposition kinetic model, where q is the heat release rate per unit material, in J / (m³). 3 ·sec), calculated through a thermal decomposition kinetic model; div(λgradT) describes the temperature distribution of the material in a specific package, where λ is the thermal conductivity of the material, in units of J / (m·sec·K); Based on the above model, the internal temperature of the material in the packaging is simulated over time to obtain the fixed self-accelerating decomposition temperature of the material.

8. A device for obtaining the temperature of solid self-accelerating decomposition, characterized in that, include, The first acquisition module is used to acquire the material thermal decomposition kinetics model; The second acquisition module is used to construct a heat conduction model based on the material packaging. The fusion module is used to obtain the fixed self-accelerating decomposition temperature of the material based on the material thermal decomposition kinetic model and the heat conduction model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the method for obtaining the solid self-accelerating decomposition temperature as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for obtaining the solid self-accelerating decomposition temperature as described in any one of claims 1-7.

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