Organic dust explosion suppression dynamics test analysis method based on reaction force field

Abstract

The invention relates to the technical field of organic dust explosion suppression simulation, and provides an organic dust explosion suppression dynamics test analysis method based on a reaction force field, which is based on a representation experiment, a reaction force field molecular dynamics simulation technology and a data post-processing analysis method. Systematically carrying out modeling and quantitative research on microscopic reaction behaviors in the organic dust explosion / explosion suppression process; according to the invention, the explosion evolution process of the organic dust and the microscopic explosion suppression mechanism of the explosion suppressant on the organic dust can be accurately simulated and quantitatively analyzed from the molecular scale; compared with a traditional macroscopic experiment means, the method has the advantages of being low in simulation cost, wide in application range and high in species decomposition tracking precision, and is particularly suitable for analysis of a reaction path of key intermediate products and free radicals in a complex dust system.

Description

Technical Field

[0001] This invention relates to the field of organic dust explosion suppression simulation technology, and more specifically, to a method for testing and analyzing the dynamics of organic dust explosion suppression based on a reactive force field. Background Technology

[0002] In the production and processing of plastics, rubber, pharmaceuticals, and coatings, since raw materials and intermediate products are mostly combustible organic powders, a certain concentration of organic dust suspension or sediment is inevitably generated during transportation, crushing, and mixing. Once exposed to an ignition source, organic dust explosions are highly likely to occur, causing equipment damage, production interruptions, and even casualties. Compared to single-phase gaseous fuel explosions, organic dust explosions involve complex solid-gas multiphase heat and mass transfer processes, with high heat release intensity, long reaction chains, and the release of large amounts of toxic and harmful gases and ultrafine particles, making them far more dangerous. Therefore, the prevention and control of organic dust explosions, the development of highly efficient explosion suppressants, and the elucidation of their microscopic explosion suppression mechanisms and evolutionary characteristics have become urgent scientific challenges.

[0003] To reduce the losses caused by organic dust explosions and mitigate their harm to production equipment, infrastructure, the environment, and personnel, "explosion suppression" is often used as an effective method to protect overall equipment. However, the explosion flames and powerful shock waves generated by suppression inevitably cause secondary damage to the external environment. Given the characteristics of organic dust explosions and the resulting hazards, explosion suppression is an effective form of protection. This has significant practical implications for preventing industrial flammable medium explosions and provides high theoretical guidance for basic research in the field of explosion suppression / resistance.

[0004] Existing research largely relies on devices such as 20L explosion spheres and vertical explosion pipes to evaluate the macroscopic explosion suppression characteristics and performance of various explosion suppressants. However, macroscopic experimental methods are insufficient to reveal the kinetic mechanisms by which powder explosion suppressants inhibit organic dust explosions at the microscopic scale. Furthermore, traditional numerical simulations cannot describe the reaction pathways between explosion suppressants and organic dust, failing to accurately reflect the key action sites and reaction pathway evolution of the explosion suppressant during the suppression process. ReaxFF-MD (Reactive Force Field Molecular Dynamics) simulations can characterize molecular structure evolution, chemical bond breaking and formation, and reaction pathway maps at the atomic scale, while fully considering the influence of various external conditions such as temperature, pressure, and heating rate. To date, reactive force field simulations have been successfully applied to the reaction pathway analysis of pyrolysis combustion processes of hydrocarbons, coal, polymers, and metals. Combined with thermal analysis and physicochemical property analysis techniques to verify the rationality of simulation results, this provides a novel research method and theoretical support for the study of organic dust explosion suppression mechanisms and the prediction of explosion suppressant performance, which is of great significance for improving and developing the theory of dust explosion prevention and control.

[0005] Reaction molecular dynamics simulations can reveal the structural evolution and energy conversion laws of reaction systems at the atomic scale, elucidate the mechanisms of processes such as explosion, pyrolysis, flame retardation, and explosion suppression, effectively reduce research costs and time, overcome experimental limitations, and provide support for multi-scale mechanism research and optimization of control strategies.

[0006] Chinese invention patent application CN119360998A discloses a method for studying the mechanism of action of powder explosion suppressants based on molecular simulation. This method combines machine learning force fields with ab initio molecular dynamics simulations, utilizing tools such as ChemTraYzer and Chemkin to analyze pyrolysis trajectories, elementary reactions, and free radical evolution. It further employs DFT and TST calculations to reveal the suppression mechanism of powder explosion suppressants on gas-coal dust composite explosions. Chinese invention patent application CN115831241A discloses a method and system for predicting cellulose pyrolysis products based on deep learning. This method constructs and relaxes the cellulose molecular structure, generates training data using ab initio molecular dynamics simulations, employs a multi-layer neural network for iterative training to obtain the deep learning potential function for cellulose pyrolysis, and predicts the types and structures of products in the molecular dynamics simulation.

[0007] Chinese invention patent application CN116759002A discloses a molecular dynamics-based method for simulating the reaction of nano-aluminothermic agents. This method obtains the microstructural evolution and dynamic characteristics of the explosion process of nano-aluminothermic agents by establishing an initial model, selecting a suitable reaction force field, and performing thermally induced self-sustaining reaction simulation after relaxation.

[0008] Chinese invention patent application CN114722736B discloses an optimization method for coal chemical looping gasification based on experiments and multi-scale simulations. This method uses coal as raw material, prepares an oxygen carrier, and conducts experiments in a tubular furnace reactor. Optimal operating parameters are determined through data analysis. The pyrolysis and gasification temperature results are verified using ReaxFF-MD, and kinetic parameters are obtained. The experimental results are then validated by combining CFD with flow, mass transfer, heat transfer, and kinetics.

[0009] Chinese invention patent application CN119294096A discloses a post-processing method for reaction force field simulation results. This method extracts molecules and reaction paths based on simulation results, constructs a directed graph with molecules as nodes and paths as edges, and generates a reaction network through hierarchical and numbered arrangement. This intuitively displays the chemical reaction process within the system, facilitating path analysis and understanding, and improving the efficiency and visualization level of reaction mechanism research.

[0010] However, the above-mentioned existing technologies still have the following shortcomings:

[0011] 1. Existing research lacks sufficient experimental verification, mostly relying on simulation and prediction, and lacks systematic data support, making it difficult to fully guarantee the accuracy and applicability of the results;

[0012] 2. The quantitative characterization of key active sites and reaction pathways of the knock suppressant is insufficient, making it difficult to achieve performance optimization design;

[0013] 3. The coupling between multi-scale simulation and experiment is not deep, and there is a lack of cross-scale verification and feedback mechanisms, which limits the transformation of theoretical results into engineering applications.

[0014] To address the aforementioned issues, this application proposes a dynamic testing and analysis method for suppressing organic dust explosions based on a reactive force field. Summary of the Invention

[0015] The purpose of this invention is to provide a dynamic testing and analysis method for suppressing organic dust explosions based on a reaction force field. This method enables multi-scale and multi-parameter analysis of the thermal decomposition, explosion reaction, and explosion evolution of organic dust under the action of powder explosion suppressants. The method simulates the dynamic evolution of the explosion reaction of organic dust by constructing a mixed reaction system of organic dust and explosion suppressant under periodic boundary conditions. By combining experimental characterization techniques (FTIR, XPS, NMR) with computational simulation tools (Materials Studio, Gaussian, LAMMPS), the method achieves accurate inversion of the explosion process at the molecular level. Furthermore, by utilizing a self-developed Python script and the open-source reaction trajectory analysis tool ChemTraYzer, the trajectory data generated during the reaction process was processed in depth to identify the chain explosion reaction path and the site of explosion suppression, extract the evolution of key products, elucidate the microscopic mechanism by which the explosion suppressant interrupts the free radical chain reaction, and verify the reliability of the simulation results from both thermogravimetric-infrared spectroscopy (TG-FTIR) and gas phase product types. This invention can systematically reveal the evolution law of organic dust explosion reaction under high temperature conditions and the microscopic suppression mechanism of explosion suppressant, providing theoretical support and simulation basis for the design optimization of explosion suppressant materials and the prevention and control of organic dust disasters.

[0016] To achieve the above objectives, this invention provides the following technical solution: a dynamic testing and analysis method for suppressing organic dust explosions based on reactive force fields. This method systematically models and quantitatively studies the microscopic reaction behavior during organic dust explosions / suppression, based on characterization experiments, reactive force field molecular dynamics simulation technology, and data post-processing analysis methods. The method specifically includes the following steps:

[0017] Molecular structure information of organic dust and powder explosion suppressants was obtained by means of Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and nuclear magnetic resonance (NMR).

[0018] Three-dimensional molecular models of organic dust and explosion suppressants were created using software such as Material Studio and Ligpargen. Chemical position information was supplemented based on a materials database to complete the molecular configuration construction and optimization.

[0019] Select a target reactive force field that can cover all atomic types in organic dust and powder explosion suppressant molecules for force field optimization;

[0020] The molecular structures of organic dust and explosion suppressants were simplified. Molecular structures containing key chemical groups were selected and imported into Gaussian software. The bond angles and bond lengths marked in the structure were rigidly scanned using the b3lyp / 6-31g(d) basis set to obtain the relative energies corresponding to different bond angles and bond lengths.

[0021] The coordinate files corresponding to the calibrated bond angles and bond lengths are converted into the Data format supported by LAMMPS using a Python script. Simulation calculations are then performed under a pre-selected target reaction force field that can cover all atomic types in organic dust and powder explosion suppressant molecules to obtain the relative energies corresponding to different bond angles and bond lengths.

[0022] The relative energies obtained from the Gaussian scans were compared with those calculated from the reaction force field. The reaction force field with high bond energy matching and good accuracy of interatomic forces was selected as the calculation force field for subsequent simulations.

[0023] A model of an organic dust-suppressant mixed explosion system under periodic boundary conditions was constructed using the Amorphous Cell module in Material Studio and the Moltemplate software.

[0024] The formula for calculating the proportion of the anti-knock agent is as follows:

[0025]

[0026] In the formula, n is the amount of substance, m is the mass, M is the molar mass, N is the quantity, and Na is Avogadro's constant.

[0027] The formula for calculating the volume parameters of the simulated box under periodic boundary conditions is as follows:

[0028]

[0029] In the formula, To simulate the volume parameters of the box under periodic boundary conditions, The relative molecular mass of the reaction system is... The volume of the pyrolysis chamber. For system quality, is Avogadro's constant.

[0030] The reaction system model coordinate file was further exported and converted into a Data file format using the LAMMPS msi2lmp tool. Molecular dynamics simulation calculations were then performed based on the reaction force field.

[0031] Reaction force field simulation dynamically determines bonding relationships by calculating the bond order between arbitrary atomic pairs in real time, and expresses interatomic interactions based on bond order functions, thereby realizing the breaking and formation of chemical bonds. The relationship between bond order and atomic distance is as follows:

[0032]

[0033]

[0034] In the formula, The bond order between atoms The distance between atoms. For the balance bond length, Empirical parameters.

[0035] The total energy of the calculated system includes terms such as bond, bond angle, dihedral angle, conjugate, Coulomb, and van der Waals. The total energy is expressed as:

[0036]

[0037]

[0038] In the formula, For total energy, For bond energy, For the sake of being alone, For overcoordination energy correction term, For undermatched potential energy, For bond angle energy, This is a bond angle energy penalty term. It is the conjugate term of the three bodies. To correct energy, For triple bond correction items, For dihedral energy, This is a four-body conjugate term. For hydrogen bond energy, For van der Waals, It is coulomb energy.

[0039] The simulation calculation includes the following steps:

[0040] Energy minimization: By calculating the forces and energies between atoms, the gradient descent algorithm is used to minimize the energy of the organic dust-explosion suppressant model in the reaction system, thereby obtaining the stable configuration of the reaction system model;

[0041] Low-temperature relaxation: Further, the organic dust-explosion suppressant reaction system model was subjected to low-temperature relaxation treatment to obtain the optimized configuration of the reaction system model;

[0042] High-temperature calculations: Simulation calculations of the organic dust / organic dust-explosion suppressant reaction system under high-temperature conditions;

[0043] In the low-temperature relaxation and high-temperature calculation stages, the NVT (isothermal and isochoric) or NPT (isothermal and isobaric) control ensemble, Nose-Hoover or Berendson temperature control method can be selected according to the requirements.

[0044] A self-written Python script was used to extract the classification and quantity of decomposition species in organic dust and the organic dust-explosion suppressant reaction system;

[0045] The species are defined as: coke (C40+), tar (C12-C39), light tar (C5-C12), and light gases (C1-C4);

[0046] Furthermore, the decomposition rate and mass loss rate of organic dust during the reaction process were calculated, and the types and quantitative evolution of the main gaseous products were obtained.

[0047] Based on the reaction rate constant of reactant decomposition, the activation energy of the organic dust and organic dust-explosion suppressant reaction was solved using the Arrhenius equation.

[0048]

[0049] In the formula, For any given time, the amount of reactants is [value]. The initial amount of reactants, As the rate constant, For any given moment, This is the initial time.

[0050]

[0051] In the formula, As the rate constant, Pre-exponential factor, The activation energy of the reaction is expressed in J / mol. molar gas constant ( ), Thermodynamic temperature (K);

[0052] Thermogravimetric curves and gas type information of organic dust and its mixture with explosion suppressant were obtained by TG-FTIR coupled experiments.

[0053] The activation energy of the reaction was calculated based on thermogravimetric data and compared with the thermodynamic parameters extracted from the simulation. The accuracy of the simulation results was verified from both the composition of the pyrolysis gas and the kinetic parameters.

[0054] After verification, a series of simulation calculations were carried out under different simulated temperatures, simulated pressures, oxygen contents and the proportion of detonator added, to obtain the system coordinates, intermediate products and gaseous product state information recorded every 1000 steps;

[0055] Post-processing and calculations are performed on the simulation results, specifically including:

[0056] The evolution of the number of key explosion products and active free radicals during the statistical reaction process was studied. The product formation rate and final quantity were compared under different simulation conditions and under different doping ratios of the explosion suppressant, revealing the regulatory role of the explosion suppressant in the reaction process.

[0057] Using the open-source automated reaction path analysis toolkit ChemTraYzer, a detailed chain reaction network of organic dust / organic dust-explosion suppressant was constructed to depict the process from organic dust molecules to small molecules. By comparing the differences in the reaction network with and without the addition of explosion suppressant, high-frequency reaction types were extracted, the migration, exchange and destruction paths of free radicals in the reaction were tracked, the key nodes of the interaction of explosion suppressant were identified, and the explosion suppression kinetic mechanism was revealed.

[0058] By combining the visualization tool Ovito to dynamically render atomic trajectories and reaction hotspots during high-temperature reactions, the spatiotemporal action of the explosion suppressant is demonstrated, providing a comprehensive explanation of the explosion suppression kinetics mechanism of organic dust.

[0059] The beneficial effects of this invention are:

[0060] This invention can accurately simulate and quantify the evolution process of organic dust explosions at the molecular scale, as well as the microscopic explosion suppression mechanism of explosion suppressants. Compared with traditional macroscopic experimental methods, the method of this invention has the advantages of low simulation cost, wide applicability, and high accuracy in species decomposition tracking. It is especially suitable for the analysis of key intermediate products and free radical reaction pathways in complex dust systems.

[0061] This invention provides a theoretical basis and simulation reference for the optimization of molecular structure, formulation design and explosion-proof safety assessment of explosion suppressants. It has important engineering application value and academic research significance, and plays an important role in ensuring the safety of industrial dust environment and preventing dust explosion accidents. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 Construct a flow chart for the reaction system;

[0064] Figure 2 This is a force field verification diagram for key functional groups;

[0065] Figure 3 Evolution diagram of the main gaseous products of pyrolysis / explosion of polyethylene and ammonium dihydrogen phosphate;

[0066] Figure 4 This is the pyrolysis mass loss rate curve;

[0067] Figure 5 This is a distribution diagram of the main gaseous products from pyrolysis;

[0068] Figure 6 Explosive chain reaction pathway diagram

[0069] Figure 7 A pathway diagram for an explosion suppression chain reaction;

[0070] Figure 8 A distribution diagram showing the formation and consumption pathways of major gaseous products;

[0071] Figure 9 This diagram shows the evolution of the main products at different temperatures. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0073] Please see Figures 1-9 This invention provides a method for testing and analyzing the kinetics of organic dust explosion suppression based on a reactive force field. Specifically, taking the suppression of polyethylene dust explosion by ammonium dihydrogen phosphate as an example, the implementation steps of the method are systematically described by combining molecular dynamics simulation and data post-processing and analysis procedures.

[0074] Specifically, such as Figure 1 As shown, the construction of this reaction system model specifically includes the following steps:

[0075] The degree of polymerization of polyethylene was determined to be 50 using experimental results such as Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and nuclear magnetic resonance (NMR). The monomer structures of ammonium dihydrogen phosphate, polyethylene, and oxygen were plotted using Ligpargen software.

[0076] Simplify the molecules of polyethylene and ammonium dihydrogen phosphate to , , Molecular bond lengths were scanned using the Gaussian software b3lyp / 6-31g(d) basis set. ) and key angle ( The potential surface energy of a surface is used to obtain the corresponding energy, expressed in Hartree units.

[0077] The coordinate files corresponding to the calibrated bond angles and bond lengths were converted into the Data format supported by LAMMPS using a Python script. Simulation calculations were performed in the CHONSSiPNa force field of the ReaxFF-MD force field combustion partition to obtain the energy of the corresponding bond angles and bond lengths, in Kcal / mol.

[0078] The energy conversion relationship is 1 Hartree = 625.7 Kcal / mol. After standardizing the energy unit scale, the relative energies of the two are compared (i.e., the minimum energy value among all energies in each group). The summary of the relative energy comparison is as follows: Figure 2 As shown;

[0079] Given a polyethylene to ammonium dihydrogen phosphate mass ratio of 1:1 and a polyethylene quantity of 10, calculate the quantity of ammonium dihydrogen phosphate using the following formula:

[0080]

[0081] In the formula, n is the amount of substance; m is the mass; M is the molar mass; N is the quantity; and Na is Avogadro's constant.

[0082] In the Moltemplate software, execute the monomer structure array command to construct a model of the pyrolysis / explosion initial reaction system of polyethylene monomer and its mixture with ammonium dihydrogen phosphate;

[0083] To ensure the scientific nature of the model configuration, the completed system was subjected to 20 ps of low-temperature relaxation under a classical force field using an NPT (constant pressure and temperature) system. The Nose-Hoover control method was used to maintain the temperature and pressure at 300 K and 1 MPa, respectively, with a time step of 0.5 fs.

[0084] Based on the final density of the system (0.25 g / cm³), the volume parameters of the simulated box under periodic boundary conditions are calculated using the following formula:

[0085]

[0086] In the formula, To simulate the volume parameters of the box under periodic boundary conditions, The relative molecular mass of the reaction system is... The volume of the pyrolysis chamber. For system quality, is Avogadro's constant.

[0087] The relaxed system was then imported into the large-scale molecular parallel simulator—Lammps—for energy minimization calculations.

[0088] A low-temperature (300K) equilibrium calculation was performed at 25 ps using the Nose-Hoover control method under the control of an NVT (constant volume temperature) system. The time step and temperature damping coefficient for this stage were set to 0.25 fs and 0.025 ps, respectively.

[0089] Finally, pyrolysis / explosion calculations were performed at 2300K, 2600K, and 2900K for 500 ps, ​​with species and atomic trajectory information collected every 1000 steps. The molecular recognition bond sequence cutoff value was [value missing]. The specific parameters are shown in Table 1 below;

[0090] Table 1 ReaxFF-MD Simulation Parameters

[0091]

[0092] The evolution of the number of major gaseous products in the polyethylene reaction system and the polyethylene-ammonium dihydrogen phosphate mixed reaction system was extracted using a self-written Python script, such as... Figure 3 As shown.

[0093] Simultaneously, the prepared polyethylene sample and the polyethylene-ammonium dihydrogen phosphate (1:1 ratio) mixture were purged into a German-NETZSCH-STA449F3 reactor by nitrogen / air at a flow rate of 60 mL / min for pyrolysis testing. The heating rate was 20 °C / min, and the heating range was 30-800 °C. The pyrolysis weight loss and heat flux curves of polyethylene and its mixture with ammonium dihydrogen phosphate under different atmospheric conditions were obtained, such as... Figure 4 As shown;

[0094] In this process, the pyrolysis volatiles under a nitrogen atmosphere are transported via connecting pipelines to a Fourier transform infrared spectrometer (Thermo Fisher Scientific FTIR iS50) for detection, obtaining the types and distribution of the main gaseous products of polyethylene and its pyrolysis with ammonium dihydrogen phosphate, such as... Figure 5 As shown;

[0095] The main pyrolysis gas types obtained by comparing the simulation and experimental results of polyethylene and polyethylene-ammonium dihydrogen phosphate are used to support the rationality of the simulation results.

[0096] The reaction process at high temperatures was studied, with a reaction system at 2600 K selected as the analysis object. The reaction process was divided into two stages (initial decomposition stage: polyethylene molecular bonds break to form smaller molecules and produce some gas; main reaction stage: continuous molecular decomposition and violent intermolecular reactions). Molecular dynamics simulation results were post-processed and analyzed.

[0097] The quantities of major gaseous products from the pyrolysis / explosion of polyethylene and polyethylene-ammonium dihydrogen phosphate were extracted using a self-written Python script, and an evolution trend diagram of the major gaseous products was plotted, such as... Figure 3 As shown;

[0098] ChemTraYzer was used to focus on tracing the initial decomposition pathway of a polyethylene chain in a pyrolysis / explosion system, and the self-decomposition pathway of ammonium dihydrogen phosphate was plotted. The key sites of the influence of ammonium dihydrogen phosphate and its generated free radicals on the polyethylene chain reaction pathway were identified. The polyethylene explosion and suppression pathways are as follows: Figure 6 and Figure 7 As shown;

[0099] Table 2 Details of the initial reaction of polyethylene

[0100]

[0101] Table 3 Details of the initial reaction of polyethylene

[0102]

[0103] Extract and count , , , The formation and consumption pathways of major gaseous products were analyzed, and the top six pathways in the order of occurrence were selected as the main reaction routes. By examining the evolution of gaseous products, the influence of ammonium dihydrogen phosphate on the main reaction stages of polyethylene was revealed. The pathways for the formation and consumption of ethylene and water under the action of ammonium dihydrogen phosphate were also analyzed. Figure 8 As shown;

[0104] A series of simulations were conducted at temperatures of 2300K, 2600K, and 2900K to extract the quantities of major gaseous products from the pyrolysis / explosion of polyethylene and polyethylene-ammonium dihydrogen phosphate. The evolution of pyrolysis / explosion gas quantities at different temperatures was obtained, and the regulatory role of temperature in the explosion and suppression processes was explored. Figure 9 As shown.

[0105] This invention utilizes experimental techniques such as FTIR, XPS, and NMR, combined with computational tools like Material Studio and Gaussian, to construct high-fidelity molecular models of organic dust and explosion suppressants. By comparing the energy of Gaussian and LAMMPS simulation results, the scientific optimization of the reaction force field is achieved, improving the accuracy of molecular dynamics simulations. A mixed reaction system is constructed under periodic boundary conditions, and the thermal decomposition and explosion reactions of the organic dust-explosion suppressant are dynamically tracked based on the reaction force field. A self-developed Python script and the ChemTraYzer path analysis tool are introduced to construct a detailed chain reaction network, identifying the evolution of intermediate free radicals and the key action sites of the explosion suppressant. Combined with TG-FTIR experiments, the accuracy of the main gaseous products and thermodynamic parameters during the simulation is verified, achieving bidirectional verification between simulation results and experimental data. Ovito is used to visualize atomic trajectories and reaction regions, intuitively demonstrating the temporal and spatial effects of the explosion suppressant. This invention not only significantly improves the simulation capability of organic dust pyrolysis and explosion suppression reaction processes, but also provides a theoretical basis and simulation reference for the optimization of molecular structure, formulation design and explosion safety assessment of explosion suppressants. It has important engineering application value and academic research significance, and plays an important role in ensuring the safety of industrial dust environment and preventing dust explosion accidents.

[0106] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inner", "around", etc., which indicate orientation or positional relationship, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting this invention.

[0107] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for the analysis of organic dust explosion suppression kinetics testing based on the reaction force field, characterized in that, The method comprises the following steps: S1. Obtain molecular structure information of organic dust and explosion inhibitor by experimental characterization means; S2. Construct and optimize the molecular model of organic dust and explosion inhibitor; S3. Prefer and verify the reaction force field suitable for the organic dust and the explosion inhibitor; S4. Establish the model of the organic dust-explosion inhibitor mixed reaction system under the periodic boundary condition; S5. Carry out molecular dynamics simulation to obtain atomic trajectory and species evolution data in the reaction process; S6. Extract and analyze the key reaction path, product distribution and explosion inhibition mechanism; S7. Verify the accuracy of the simulation results by experiment.

2. The reactive force field based organic dust explosion suppression kinetics test analysis method of claim 1, wherein: The experimental characterization means include at least one of Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and nuclear magnetic resonance (NMR).

3. The reactive force field based organic dust explosion suppression kinetics test analysis method of claim 1, wherein: The preference of the reaction force field includes obtaining potential energy surface data by Gaussian software for bond length / bond angle scanning, comparing the energy calculated by LAMMPS under the candidate force field, and selecting the force field with high matching degree.

4. The reactive force field based organic dust explosion suppression kinetics test analysis method of claim 1, wherein: The construction of the mixed reaction system model includes using Amorphous Cell module or Moltemplate software, and determining the amount of each component according to the explosion inhibitor addition ratio formula.

5. The reactive force field based organic dust explosion suppression kinetics test analysis method of claim 1, wherein: The molecular dynamics simulation includes energy minimization, low temperature relaxation and high temperature reaction simulation steps, adopts NVT or NPT ensemble, and uses Nose-Hoover or Berendson temperature control method.

6. The reactive force field based organic dust explosion suppression dynamics test analysis method of claim 1, wherein: The data analysis includes using self-programmed Python script to count the evolution of species number, using ChemTraYzer toolkit to construct reaction network and identify key reaction path.

7. The reactive force field based organic dust explosion suppression dynamics test analysis method of claim 1, wherein: It also includes obtaining the thermal weight loss curve and gas product information by thermal gravimetric-infrared spectrometry (TG-FTIR) experiment, and comparing and verifying with the simulation results.

8. The reactive force field based organic dust explosion suppression dynamics test analysis method of claim 1, wherein: The organic dust is polyethylene, and the explosion inhibitor is ammonium dihydrogen phosphate.

9. The reactive force field based organic dust explosion suppression dynamics test analysis method of claim 1, wherein: The analysis of the key reaction path includes: constructing a detailed chain reaction network by means of ChemTraYzer toolkit, comparing the difference of the reaction network when adding and not adding the explosion inhibitor, extracting high-frequency reaction types, tracking the migration, exchange and extinction path of free radicals, so as to determine the key nodes of the interaction of the explosion inhibitor.

10. The reactive force field based organic dust explosion suppression kinetics test analysis method of claim 7, wherein: The verification by experiment includes solving the reaction activation energy based on the thermal gravimetric-infrared spectrometry experiment, and comparing it with the thermodynamic parameters extracted from the molecular dynamics simulation trajectory.

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