Method for determining the gas storage efficiency of a hydrate promoter and device therefor

Abstract

The application discloses a method and device for determining gas storage efficiency of a hydrate promoter, the method comprising: constructing a gas-liquid two-phase molecular simulation system, the molecular simulation system comprising a filled gas box and a water solution box spliced in sequence along a set direction; inputting a plurality of test parameter groups into the gas-liquid two-phase molecular simulation system to form parameter regulation under a multivariate grid simulation framework, and realizing simulation of hydrate nucleation or growth of a gas; obtaining trajectory data of molecular dynamics simulation in the process of simulation of hydrate nucleation or growth of the gas, the trajectory data comprising the number of newly formed hydrate cages, the mass of the gas, the promoter and water in the hydrate within a set sampling time; and calculating the gas storage efficiency of the hydrate under the action of the promoter according to the trajectory data of the molecular dynamics simulation and a pre-constructed gas storage efficiency mathematical model. Based on the calculation result of the gas storage efficiency of the hydrate, the reliability of the promoter screening can be improved, and the screening process of the promoter can be accelerated.

Description

Technical Field

[0001] This application relates to the technical field of computational materials science, and in particular to a method and apparatus for determining the gas storage efficiency of a hydrate promoter. Background Technology

[0002] Gas hydrates, also known as cage-like hydrates, are non-stoichiometric crystalline compounds formed by the self-assembly of water molecules through a hydrogen bond network, creating a porous cage-like framework structure (such as cubic sI type or face-centered cubic sII type). This cage-like framework structure (also known as hydrate cages) can trap gas molecules. This unique hydrate cage endows gas hydrates with extremely high gas storage density: under standard conditions (273 K, 101.3 kPa), 1 volume of hydrate can store the equivalent of 160-180 volumes of gas. This characteristic makes gas hydrates have broad application potential in the field of energy storage and transportation. In the context of the clean energy transition, gas hydrates are not only considered as potential carriers for the safe storage and transportation of hydrogen and natural gas, but can also be used for CO2 capture and storage technologies, which is of great significance for my country to achieve its "dual carbon" goals.

[0003] The nucleation and growth of pure gas hydrates are limited by harsh thermodynamic and kinetic conditions. Typical phase equilibrium lines require low temperatures (<250 K) and high pressures (>30 MPa), resulting in high costs for large-scale applications. To make hydrate formation conditions more flexible and facilitate commercial applications, researchers have extensively explored and introduced highly efficient hydrate promoters, including thermodynamic promoters that lower the temperature and pressure thresholds for hydrate formation (such as tetrahydrofuran (THF), propylene oxide, or methylcyclohexane), kinetic promoters that accelerate hydrate nucleation or growth rates (such as surfactants like sodium dodecyl sulfate or nanoparticles), and composite dual-effect promoters that combine thermodynamic and kinetic promotion functions (such as cyclopentane, 1,3-dioxolane (DIOX)). While hydrate promoters exhibit superior performance in promoting the nucleation and growth of gas hydrates, screening, designing, and optimizing them experimentally presents numerous challenges. The workload is immense, requiring the evaluation of the synergistic effects of thousands of candidates (such as organic molecules, inorganic molecules, surfactants, bio-based polymers, and nanoparticles) on the growth rate and gas storage capacity of gas hydrates under multivariate conditions (temperature, pressure, concentration, etc.). Furthermore, the experimental methods are highly complex and demanding, requiring customized synthetic routes for different promoters, resulting in poor adaptability. Technical personnel must possess diverse professional skills, and improper operation can easily introduce impurities, affecting experimental results and leading to poor reproducibility. Moreover, the calculation of gas storage capacity in experiments is mostly determined by pressure difference or reduction in the amount of gas introduced, representing an indirect or comprehensive detection method that cannot accurately calculate the actual gas storage capacity within the hydrate. Therefore, existing experimental methods struggle to rapidly, economically, and accurately assess the impact of promoters on the gas storage effect of hydrates. Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for determining the gas storage efficiency of a hydrate promoter.

[0005] This application provides a method for determining the gas storage efficiency of a hydrate promoter, including:

[0006] A gas-liquid two-phase molecular simulation system is constructed. The constructed gas-liquid two-phase molecular simulation system includes a gas box filled with a first set number of gas molecules, and an aqueous solution box filled with a second set number of promoter molecules and a third set number of water molecules. The filled gas box and aqueous solution box are sequentially spliced ​​along a set direction.

[0007] Multiple test parameter sets are input into the constructed gas-liquid two-phase molecular simulation system to form parameter control under a multivariable grid simulation framework, so as to realize the simulation of gas hydrate nucleation or growth.

[0008] In simulating the nucleation or growth of gas hydrates, trajectory data from molecular dynamics simulations are obtained; wherein, the trajectory data from molecular dynamics simulations includes the number of newly formed hydrate cages within a set sampling time, and the mass of gas, promoter, and water in the newly formed hydrates;

[0009] Based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency, the gas storage efficiency of hydrates under the action of a promoter is obtained.

[0010] Furthermore, the process of obtaining the gas storage efficiency of hydrates under the action of a promoter, based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency, includes:

[0011] The growth rate of hydrates is calculated based on the number of newly formed hydrate cages within a set sampling time.

[0012] The gas storage capacity of the hydrate is calculated based on the mass of gas, promoter, and water in the newly formed hydrate.

[0013] The gas storage efficiency of hydrates under the action of a promoter is calculated based on the growth rate and gas storage capacity of hydrates with a promoter, and the growth rate and gas storage capacity of pure gas hydrates without a promoter.

[0014] Furthermore, the gas storage efficiency of the hydrate under the action of the promoter is calculated according to the following conditional formula:

[0015]

[0016] In the formula, R and Q represent the growth rate and gas storage capacity of the hydrate under the action of the promoter, respectively, and R0 and Q0 represent the growth rate and gas storage capacity of the pure gas hydrate without the promoter, respectively. and These are the corresponding weight parameters.

[0017] Furthermore, the method also includes:

[0018] The gas-liquid two-phase molecular simulation system is subjected to energy minimization processing. The steepest descent algorithm is used to eliminate high-energy conflicts in the gas-liquid two-phase molecular simulation system. The optimization process is terminated when the maximum interaction force between molecules in the gas-liquid two-phase molecular simulation system is less than the preset convergence threshold.

[0019] A temperature-pressure coupling algorithm is used to perform pre-equilibrium simulation calculations on the gas-liquid two-phase molecular simulation system that has undergone energy minimization, so that the temperature and pressure of the gas-liquid two-phase molecular simulation system reach the initial conditions of the target simulation.

[0020] Furthermore, the test parameter set includes temperature, pressure, and promoter concentration. The input of multiple test parameter sets into the constructed gas-liquid two-phase molecular simulation system to form parameter control within a multivariable grid simulation framework, thereby simulating gas hydrate nucleation or growth, includes:

[0021] Set the temperature, pressure, and accelerator concentration ranges, and set the corresponding step sizes for the temperature, pressure, and accelerator concentration ranges, as well as the corresponding test baseline conditions.

[0022] Under the corresponding test benchmark conditions, the set temperature, pressure and accelerator concentration ranges are combined according to the corresponding step sizes to obtain the multiple test parameter groups;

[0023] Submit the multiple test parameter sets according to the set time step, and perform the target simulation calculation task on the gas-liquid two-phase molecular simulation system after the pre-equilibrium simulation calculation is completed;

[0024] The multiple test parameter sets are repeatedly submitted multiple times to perform the target simulation calculation task on the gas-liquid two-phase molecular simulation system, in order to eliminate random errors in the calculation results of the target simulation; wherein,

[0025] The computational task of the target simulation includes performing gas hydrate nucleation or growth simulation in the gas-liquid two-phase molecular simulation system under the temperature, pressure and promoter concentration conditions corresponding to the multiple test parameter groups, and generating trajectory data for molecular dynamics simulation.

[0026] Furthermore, the construction of the gas-liquid two-phase molecular simulation system includes:

[0027] An empty gas box is constructed with a first predetermined volume, and an empty aqueous solution box is constructed with a second predetermined volume.

[0028] A first predetermined number of gas molecules are inserted into an empty gas box to obtain a filled gas box; a second predetermined number of accelerator molecules and a third predetermined number of water molecules are inserted into an empty aqueous solution box to obtain a filled aqueous solution box; and

[0029] A filled aqueous solution box is placed between two filled gas boxes, and the filled gas boxes and aqueous solution boxes are sequentially spliced ​​together along a set direction to obtain the gas-liquid two-phase molecular simulation system.

[0030] Further, the construction of an empty gas box with a first predetermined volume and an empty aqueous solution box with a second predetermined volume includes:

[0031] The size of the empty aqueous solution box is determined based on the size of the hydrate cell and the number of hydrate cages; wherein the size of the empty aqueous solution box includes the length extending along the first direction, the width extending along the second direction, and the depth extending along the third direction.

[0032] The dimensions of the empty gas box are determined based on the dimensions of the empty water solution box; wherein the length and width of the empty gas box are equal to the length and width of the empty water solution box.

[0033] Furthermore, the construction of the gas-liquid two-phase molecular simulation system also includes:

[0034] Based on the force field models of gas molecules, promoter molecules, and water molecules, corresponding force field parameters are matched to the gas molecules, promoter molecules, and water molecules contained in the gas-liquid two-phase molecular simulation system, respectively; wherein, the force field parameters include collision radius and potential well depth;

[0035] The diffusion coefficient, solubility, and hydrate melting point of the gas-liquid two-phase molecular simulation system after matching force field parameters were calculated.

[0036] The calculated values ​​of diffusion coefficient, solubility, and hydrate melting point are compared with experimental values, and the matched force field parameters are optimized based on the comparison results.

[0037] This application provides an apparatus for determining the gas storage efficiency of a hydrate promoter, comprising:

[0038] The construction module is used to construct a gas-liquid two-phase molecular simulation system. The gas-liquid two-phase molecular simulation system includes a gas box filled with a first set number of gas molecules and an aqueous solution box filled with a second set number of promoter molecules and a third set number of water molecules. The filled gas box and the filled aqueous solution box are sequentially spliced ​​along a set direction.

[0039] The parameter control module is used to input multiple sets of different test parameters into the constructed gas-liquid two-phase molecular simulation system to form parameter control under the multivariable grid simulation framework, so as to realize the simulation of gas hydrate nucleation or growth.

[0040] The generation module is used to acquire trajectory data from molecular dynamics simulations during the simulated nucleation or growth of gas hydrates. The trajectory data from molecular dynamics simulations includes the number of newly formed hydrate cages within a set sampling time, and the mass of gas, promoter, and water in the newly formed hydrates.

[0041] The calculation module is used to obtain the gas storage efficiency of hydrates under the action of a promoter, based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency.

[0042] This application provides an electronic device, which includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the steps of the method described above.

[0043] This application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps of the method described above.

[0044] This application provides a computer program product, which includes computer program instructions that, when executed by a processor, implement the steps of the method described above.

[0045] The above-mentioned technical solution of this application has the following beneficial technical effects:

[0046] In this embodiment, trajectory analysis based on molecular dynamics simulations can obtain data such as the number of newly formed hydrate cages within a set sampling time, as well as the mass of gas, promoter, and water captured in the hydrate. Based on the trajectory data from the molecular dynamics simulations, the growth rate and gas storage capacity of the hydrate promoter can be calculated. The pre-constructed mathematical model for gas storage efficiency integrates two evaluation indicators—the growth rate and gas storage capacity of the hydrate under the action of the promoter—and assigns flexible weighting factors. These weights can be adjusted according to the economic efficiency of thermodynamic conditions and the magnitude of the impact of growth rate and gas storage capacity on economic effectiveness. Therefore, by substituting the corresponding trajectory data into the pre-constructed mathematical model for gas storage efficiency calculations, the results can be obtained under the action of the promoter. By using the gas storage efficiency of hydrates, a comprehensive and quantitative evaluation of the accelerator's performance can be achieved. Compared to experimental methods, this saves experimental costs. Moreover, the simulation calculation method in this application is repeatable, accurately calculating the gas storage efficiency of hydrates under the action of the accelerator and under different test conditions. This is not only beneficial for multi-dimensional comparison of the same hydrate accelerator to determine the optimal gas storage conditions, but also allows for comparison of different types of hydrate accelerators based on the gas storage efficiency of hydrates. The quantitative comparison results can improve the reliability of accelerator screening and accelerate the screening process for different types of accelerators. This can guide the optimization and iteration of accelerators in practical production applications, and promote the commercial application of hydrate gas storage. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings of the embodiments of this application will be briefly described below.

[0048] Figure 1 This is a schematic diagram of the structure of a testing device for accelerator performance in related technologies.

[0049] Figure 2This is a flowchart illustrating a method for determining the gas storage efficiency of a hydrate promoter according to an embodiment of this application.

[0050] Figure 3 This is a schematic diagram of the fragment structure and the overall molecular simulation structure of the promoter molecule BSS8 in an embodiment of this application.

[0051] Figure 4 This is a schematic diagram of the initial gas-liquid two-phase molecular simulation system architecture after splicing in an embodiment of this application.

[0052] Figure 5 This is a structural block diagram of a device for determining the gas storage efficiency of a hydrate promoter according to an embodiment of this application.

[0053] Figure 6 This is a schematic diagram of an electronic device used to implement the hydrate promoter gas storage efficiency method of the embodiments of this application. Detailed Implementation

[0054] The principles and spirit of this application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided to make the principles and spirit of this application clearer and more thorough, enabling those skilled in the art to better understand and implement the principles and spirit of this application. The exemplary embodiments provided herein are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments described herein without inventive effort are within the scope of protection of this application.

[0055] In this document, terms such as first, second, and third are used only to distinguish one entity (or operation) from another entity (or operation), and are not intended to require or imply any order or relationship between these entities (or operations).

[0056] Currently, the gas storage performance evaluation of hydrate promoters mainly relies on experimental methods, referencing... Figure 1The testing apparatus for accelerator performance includes a first high-pressure gas cylinder 11, a second high-pressure gas cylinder 12, a gas pump 13, a circulating cooler 14, a hydrate high-pressure reactor 15, a high-pressure micro-differential scanning calorimeter 16, a data acquisition unit 17, a high-precision temperature and pressure sensor, and complex equipment such as Raman spectroscopy, nuclear magnetic resonance, and X-ray diffractometers. The hydrate high-pressure reactor 15 may include a reference cell 151, a sample cell 152, and a Peltier cooler 153. For example, the first high-pressure gas cylinder 11 stores hydrogen, and the second high-pressure gas cylinder 12 stores nitrogen. Therefore, the testing apparatus for accelerator performance involves significant equipment investment, stringent experimental environment requirements, and complex operating procedures, resulting in long experimental times and high costs. The main steps of the testing process include gas injection, temperature / pressure control, real-time monitoring of conversion rate, and auxiliary characterization of crystal form. However, the above experimental method suffers from the following technical problems:

[0057] (1) High experimental cost: It is difficult to coordinate multiple devices, and the equipment has limited accuracy in low temperature and high pressure resistance. The applicable temperature fluctuation range of the equipment is ±0.1 K, and the pressure fluctuation range is ±0.01 K. MPa, heavy equipment cost burden, high cost of a single experiment; (2) High threshold for experimental method synthesis and operation: different promoters require customized synthesis paths, poor adaptability, and technicians need to have professional skills. If the operation is not done properly, impurities are easily introduced and affect the experimental results, resulting in poor repeatability; (3) Not suitable for large-scale screening: there are a variety of existing promoters, but in the experiment, only one condition point can be tested, and the time for a single test is often several weeks, the overall cycle is long, and it is impossible to screen a large number of candidate promoters in parallel, resulting in high overall screening experiment time cost; (4) Insufficient microscopic resolution: since the nucleation and growth of hydrates is a nanosecond-micrometer level process, it cannot be directly observed. In the experiment, it can only be indirectly characterized by temperature and pressure changes; (5) Indirect quantitative bias: the gas storage volume is based on the total gas consumption, ignoring the dissolution in the aqueous solution and the free gas in the pores. For example, the CO2 solubility in the experiment can account for 10-20% of the total consumption, resulting in overestimation and inaccurate evaluation results. The aforementioned defects result in high testing costs and poor accuracy of hydrate accelerators, which in turn leads to slow optimization and iteration of accelerators in actual production applications, failing to meet the need for efficient and rapid evaluation of accelerator performance under the "dual carbon" target.

[0058] Therefore, embodiments of this application provide a method for determining the gas storage efficiency of a hydrate promoter, referring to... Figure 2 The method includes the following steps:

[0059] S110: Construct a gas-liquid two-phase molecular simulation system, the gas-liquid two-phase molecular simulation system including a gas box filled with a first predetermined number of gas molecules, and an aqueous solution box filled with a second predetermined number of promoter molecules and a third predetermined number of water molecules, the filled gas box and aqueous solution box being sequentially spliced ​​along a predetermined direction.

[0060] Specifically, gases such as methane, hydrogen, or carbon dioxide, and accelerators such as DIOX or THF, can be input by the user on the terminal device into the molecular simulation platform. Alternatively, the user can retrieve the chemical formula, two-dimensional or three-dimensional molecular structure of the accelerator from the server's database. The system determines whether the accelerator's molecular structure is neutral or contains charged groups. If it is determined to be charged, the charge quantity is calculated. By optimizing the accelerator's molecular structure, an optimized accelerator molecular structure with an accurate charge distribution can be generated, which is particularly suitable for complex multi-charged ions. The construction of optimized molecular structures for ionization accelerators involves determining the set number of gas molecules, water molecules, and accelerator molecules to be evaluated based on the concentration ratio of gas, accelerator, and water, as well as the amount of hydrates generated. The set number of gas molecules are inserted into an empty gas box, and the set number of accelerator and water molecules are inserted into an empty aqueous solution box. The filled gas box and the filled aqueous solution box are then sequentially assembled along a set direction to form a gas-liquid two-phase molecular simulation system with a gas-liquid interface. This provides a "realistic" environment for subsequent simulations of gas dissolution in aqueous solution and / or hydrate formation, improving the accuracy and efficiency of the simulation results.

[0061] S120: Input multiple test parameter sets into the constructed gas-liquid two-phase molecular simulation system to form parameter control under a multivariable grid simulation framework, so as to realize the simulation of gas hydrate nucleation or growth.

[0062] Specifically, for example, the test parameter set may include multiple variables such as temperature, pressure, and promoter concentration. By using a grid design within the set temperature range, pressure range, and promoter concentration range, and combining the parameter values ​​of multiple variables, different test parameter sets can be obtained, thereby covering the parameter control of multiple variables. In the process of simulating the nucleation or growth of gas hydrates, the variable factors that dominate the changes in the gas storage efficiency of the promoter can be identified, thereby reducing the blindness of the experiment.

[0063] S130: During the simulated nucleation or growth of gas hydrates, acquire trajectory data from molecular dynamics simulations; the trajectory data from molecular dynamics simulations includes the number of newly formed hydrate cages within a set sampling time, and the mass of gas, promoter, and water in the newly formed hydrates.

[0064] S140: Based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency, the gas storage efficiency of hydrates under the action of a promoter is obtained.

[0065] Specifically, through trajectory analysis of molecular dynamics simulations, the number of newly formed hydrate cages within a set sampling time, as well as data such as the mass of gas, promoter, and water captured in the newly formed hydrates, can be obtained. Based on the trajectory data from molecular dynamics simulations, the growth rate and gas storage capacity of the hydrate under the action of the promoter can be calculated. For example, the growth rate of the hydrate can be calculated based on the number of newly formed hydrate cages within a set sampling time, and the accurate result of the gas storage capacity of the hydrate can be obtained based on the mass of gas, promoter, and water in the hydrate. The pre-constructed mathematical model of gas storage efficiency integrates the two evaluation indicators of hydrate growth rate and gas storage capacity, and assigns flexible weighting factors. The weighting factors can be adjusted according to the economic efficiency of thermodynamic conditions and the magnitude of the impact of growth rate and gas storage capacity on economic efficiency. Therefore, by substituting the corresponding trajectory data into the pre-constructed mathematical model of gas storage efficiency, the gas storage efficiency of hydrates under the action of the promoter can be obtained. This allows for a comprehensive and quantitative evaluation of the promoter's performance, saving experimental costs compared to experimental methods. Moreover, the simulation calculation method in this application is repeatable, accurately calculating the gas storage efficiency of hydrates under the action of the promoter and different test conditions. This is not only beneficial for multi-dimensional comparison of the same hydrate promoter to determine the optimal gas storage conditions for the hydrate promoter, but also allows for comparison of different types of hydrate promoters based on the gas storage efficiency of hydrates. The quantitative comparison results can improve the reliability of promoter screening and accelerate the screening process of different types of promoters, thereby guiding the optimization and iteration of promoters in actual production applications and promoting the commercial application of hydrate gas storage.

[0066] In some embodiments, step S140, which involves obtaining the gas storage efficiency of hydrates under the action of a promoter based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency, includes the following specific steps:

[0067] S141: The growth rate of hydrates under the action of the promoter is calculated based on the number of newly formed hydrate cages within the set sampling time.

[0068] S142: The gas storage capacity of the hydrate is calculated based on the mass of gas, promoter and water in the newly formed hydrate;

[0069] S143: The gas storage efficiency of hydrates is calculated based on the growth rate and gas storage capacity of hydrates under the action of promoters, and the growth rate and gas storage capacity of pure gas hydrates without promoters.

[0070] Specifically, the growth rate R of hydrates under the action of the promoter can be calculated according to the following conditional formula:

[0071]

[0072] In the formula, To set the sampling time The number of newly formed hydrate cages, and the structure of the hydrate cages, for example, can be 5. 12 , 5 12 6 2 , 5 12 6 4 The growth rate of hydrates is the same for any of them, according to calculations.

[0073] The gas storage capacity Q of the hydrate under the action of the promoter can be calculated according to the following conditional formula:

[0074]

[0075] In the formula, To capture the mass of gas in the hydrate, and These represent the mass of the promoter and the mass of water in the newly formed hydrate, respectively.

[0076] In some embodiments, the gas storage efficiency E of the hydrate under the action of the promoter is calculated according to the following conditional formula:

[0077]

[0078] In the formula, R and Q represent the growth rate and gas storage capacity of the hydrate under the action of the promoter, respectively, and R0 and Q0 represent the growth rate and gas storage capacity of the pure gas hydrate without the promoter, respectively. and These are the corresponding weighting parameters. These can be flexibly adjusted based on the economic efficiency of thermodynamic conditions, as well as the magnitude of the impact of growth rate and gas storage capacity on economic effectiveness. and Weight, if set =0.3, =0.7, meaning that the gas storage capacity factor is more important for evaluating the gas storage efficiency of hydrate promoters.

[0079] In some embodiments, the method further includes the following specific steps:

[0080] S118: Perform energy minimization processing on the gas-liquid two-phase molecular simulation system, use the steepest descent algorithm to eliminate high-energy conflicts in the gas-liquid two-phase molecular simulation system, and terminate the optimization process when the maximum interaction force between molecules in the gas-liquid two-phase molecular simulation system is less than the preset convergence threshold.

[0081] S119: The temperature-pressure coupling algorithm is used to perform pre-equilibrium simulation calculations on the gas-liquid two-phase molecular simulation system after energy minimization, so that the temperature and pressure of the gas-liquid two-phase molecular simulation system reach the initial conditions of the target simulation.

[0082] Specifically, energy minimization can be performed on the gas-liquid two-phase molecular simulation system. The steepest descent method can be used to eliminate high-energy conflicts in the initial stage. The optimization process is terminated when the maximum interaction force between molecules in the gas-liquid two-phase molecular simulation system is less than a preset convergence threshold, such as 1000 KJ / mol / nm. This eliminates undesirable contacts and unreasonable geometric structures in the gas-liquid two-phase molecular simulation system, such as undesirable contacts that may occur when there are three-dimensional conflicts between atoms, so that the gas-liquid two-phase molecular simulation system is in a local minimum energy state. Before the formal simulation calculation, a pre-equilibrium simulation calculation is performed on the gas-liquid two-phase molecular simulation system that has undergone energy minimization treatment, using a temperature-pressure coupling algorithm, such as the Berendsen thermostat temperature-pressure coupling algorithm. This algorithm has fast convergence and high computational efficiency, which can enable the gas-liquid two-phase molecular simulation system to quickly reach the target temperature in the initial stage. The time step of the pre-equilibrium simulation calculation can be set to 1 femtosecond to ensure the accuracy of the calculation. After setting parameters such as temperature, pressure, promoter concentration and time step, a pre-equilibrium simulation calculation of at least 2 nanoseconds is performed on the gas-liquid two-phase molecular simulation system to allow the temperature and pressure of the gas-liquid two-phase molecular simulation system to gradually transition to the initial conditions that meet the formal simulation.

[0083] In some embodiments, the test parameter set includes temperature, pressure, and promoter concentration. Step S120: Inputting different sets of test parameters into the constructed gas-liquid two-phase molecular simulation system to form parameter control under a multivariable grid simulation framework to achieve simulated gas hydrate nucleation or growth includes the following specific steps:

[0084] S121: Set the temperature, pressure and accelerator concentration ranges, and set the corresponding step sizes for the temperature, pressure and accelerator concentration ranges, as well as set the corresponding test baseline conditions.

[0085] S122: Under the corresponding test reference conditions, the set temperature, pressure and accelerator concentration range are combined according to the corresponding step size to obtain the multiple test parameter groups;

[0086] S123: Submit the multiple test parameter groups according to the set time step, and perform the target simulation calculation task on the gas-liquid two-phase molecular simulation system after the pre-equilibrium simulation calculation is completed;

[0087] S124: Submit the multiple test parameter sets repeatedly to perform the target simulation calculation task on the gas-liquid two-phase molecular simulation system, in order to eliminate random errors in the calculation results of the target simulation; wherein,

[0088] The computational task of the target simulation includes performing gas hydrate nucleation or growth simulation in the gas-liquid two-phase molecular simulation system under the temperature, pressure and promoter concentration conditions corresponding to the multiple test parameter groups, and generating trajectory data for molecular dynamics simulation.

[0089] Specifically, for example, the temperature range can be set to 230-300 K with a corresponding step size of 10 K; the pressure range to 0.1-50 MPa with a corresponding step size of 5 MPa; and the promoter concentration range to 0.1-10 mol% with a corresponding step size of 2 mol%. The baseline conditions can be parameter values ​​near the phase equilibrium curve or determined based on previous experimental / simulation results. Orthogonally combining temperature, pressure, and promoter concentration according to the set step sizes generates multiple test parameter sets. A full factorial design can be used to cover all parameter combinations, and scanning the test parameter sets with the baseline conditions as the center allows for rapid identification of the sensitivity of parameters to simulation results. Through the full factorial design approach, the influence of each parameter on hydrate nucleation / growth can be systematically analyzed, such as whether increased temperature inhibits hydrate nucleation or whether increased promoter concentration accelerates hydrate growth, avoiding the limitations of single-factor experiments. Simulation tasks can be submitted in batches according to test parameter sets using script tools, leveraging parallel computing resources to shorten simulation time. Initial conditions in molecular dynamics simulations (such as molecular position and velocity) can affect the simulation results. These results can include trajectory data from the molecular dynamics simulation, such as hydrate nucleation time, gas mass in the hydrate, mass of the promoter, and mass of water. Repeatedly submitting test parameter sets can eliminate random errors and improve the reliability of the simulation results. If the simulation results differ significantly after repeatedly submitting test parameter sets, it indicates instability in the gas-liquid two-phase molecular simulation system. In such cases, simulation conditions can be adjusted, such as extending the pre-equilibrium simulation time or optimizing the force field parameters.

[0090] In some embodiments, step S110: constructing the gas-liquid two-phase molecular simulation system includes the following specific steps:

[0091] S112: Construct an empty gas box with a first predetermined volume and an empty aqueous solution box with a second predetermined volume;

[0092] S113: Insert a first predetermined number of gas molecules into an empty gas box to obtain a filled gas box; insert a second predetermined number of accelerator molecules and a third predetermined number of water molecules into an empty aqueous solution box to obtain a filled aqueous solution box; and

[0093] S114: The filled aqueous solution box is placed between two filled gas boxes. The filled gas boxes and aqueous solution boxes are arranged and spliced ​​in sequence along a set direction to obtain the gas-liquid two-phase molecular simulation system.

[0094] Specifically, for example, by keeping the types and quantities of molecules in the filled aqueous solution box constant, and changing the different gas molecules and their set quantities, the gas storage efficiency of the promoter for different gases can be evaluated and analyzed; or, by keeping the types and quantities of gases constant and changing the type and quantity of promoter molecules in the aqueous solution box, the gas storage efficiency of different promoters can be evaluated. The above simulation system can be used to evaluate and analyze the gas storage efficiency of different types of promoters for multiple gases, possessing universality, and providing a "realistic" environment for simulating the dissolution and / or hydrate formation of gases in aqueous solutions, thus improving the accuracy and efficiency of simulation results.

[0095] In some embodiments, step S112: constructing an empty gas box with a first predetermined volume and an empty aqueous solution box with a second predetermined volume includes the following specific steps:

[0096] S1121: Determine the size of the empty aqueous solution box based on the size of the hydrate cell and the number of hydrate cages generated; wherein, the size of the empty aqueous solution box includes the length extending along the first direction, the width extending along the second direction, and the depth extending along the third direction.

[0097] S1122: Determine the dimensions of the empty gas box based on the dimensions of the empty water solution box; wherein the length and width of the empty gas box are equal to the length and width of the empty water solution box.

[0098] Specifically, the first direction is, for example, the x-axis, the second direction is, for example, the y-axis, and the third direction is, for example, the z-axis. Based on the size of the hydrate cage-like crystal, an empty-water solution box is designed to accommodate the growth of the hydrate cage-like crystal while maintaining the stability of the simulation system. For example, the volume of a single unit cell can be calculated based on the lattice constant of the target hydrate (such as type SII hydrate). Then, based on the number of hydrate unit cells required for the simulation and the volume of a single unit cell, the total volume of the crystal is calculated. This precise matching of the hydrate crystal size ensures sufficient space within the water solution box to accommodate crystal growth, avoiding the crystal being "squeezed" and deformed due to an undersized box, which would affect the realism of the simulation results. Based on the size of the water solution box, a matching gas box is designed to form a stable gas-liquid interface. The length and width of the empty gas box are the same as those of the empty water solution box, for example, both are 30 × 30 Å, ensuring a smooth interface when the two are joined.

[0099] In some embodiments, step S113, inserting a first predetermined number of gas molecules into an empty gas box to obtain a filled gas box, and inserting a second predetermined number of promoter molecules and a third predetermined number of water molecules into an empty aqueous solution box to obtain a filled aqueous solution box, may include the following specific steps:

[0100] S1131: Insert a second predetermined number of accelerator molecules into an empty aqueous solution box, forming gaps between adjacent accelerator molecules;

[0101] S1132: Water molecules are inserted into the gaps to obtain a filled aqueous solution box. This optimizes space filling by first inserting macromolecular promoter molecules into an empty aqueous solution box and then filling the gaps with water molecules. The inserted molecular structure can be, for example, a space-filling model, which more closely approximates the actual spatial structure of the molecules.

[0102] In some embodiments, step S114: The filled gas box and the filled aqueous solution box are joined together to obtain the gas-liquid two-phase molecular simulation system, including:

[0103] The gas-filled boxes and the aqueous solution-filled boxes are arranged alternately and spliced ​​along a third direction, with a predetermined gap between adjacent boxes. (Reference) Figure 4 When gas and solution boxes of the same length and width are spliced ​​together along the z-direction, a flat gas-liquid interface can be formed, which can simulate the gas dissolution process in actual gas-liquid systems, such as H2 diffusing from the gas phase to the liquid phase and interacting with the promoter. Furthermore, by reserving a set gap between adjacent gas and aqueous solution boxes, such as 0.5 nm, the resulting void layer can prevent edge molecules from overlapping. If edge molecules overlap, the simulation is prone to collapse, causing the simulation calculation to fail.

[0104] In some embodiments, step S110: the construction of the gas-liquid two-phase molecular simulation system further includes:

[0105] Based on the simulated temperature, pressure, and promoter concentration, the size of the hydrate cell and the number of hydrate cages generated, a first set number of gas molecules, a second set number of optimized promoter molecules, and a third set number of water molecules are determined.

[0106] Specifically, based on the input of the simulated temperature, pressure, and promoter concentration, the size of the hydrate cell, and the number of hydrate cages generated, the system can output a first set number of gas molecules, a second set number of promoter molecules, and a third set number of water molecules. For example, for the DIOX-H2 hydrate simulation system, the preset sII hydrate is a 2×2×2 unit cell. First, based on the hydrate crystal structure and the number of unit cells, the third set number of water molecules is calculated. An sII hydrate unit cell contains 136 water molecules, and a 2×2×2 unit cell corresponds to 8 unit cells, so the required number of water molecules is 8×136=1088. Second, based on the concentration ratio of promoter and water and the number of water molecules, the optimized number of promoter molecules (the second set number) can be obtained. For example, when the DIOX concentration is 5.56 mol%, the number of promoter molecules = number of water molecules × (concentration ratio / (1 - concentration ratio)), so the required number of DIOX molecules is 192. Finally, based on the initial temperature and pressure of the simulation system, for example at 270 K and 50 °C, the system can output the third set number of water molecules. Under MPa conditions, the calculated number of H2 molecules was 384. If more molecules are needed for hydrate growth, the number of each type of molecule can be dynamically adjusted. The above operations improve the accuracy, efficiency, and repeatability of the molecular simulation system, and ensure that the obtained gas-liquid two-phase molecular simulation system is consistent with the experimental conditions, thereby improving the reliability of the molecular simulation system.

[0107] In some embodiments, step S110: the construction of the gas-liquid two-phase molecular simulation system further includes the following specific steps:

[0108] S115: Based on the gas molecule force field model, the promoter molecule force field model, and the water molecule force field model, the corresponding force field parameters are matched to the gas molecules, promoter molecules, and water molecules contained in the gas-liquid two-phase molecular simulation system, respectively; wherein, the force field parameters include the collision radius and the potential well depth;

[0109] S116: Diffusion coefficient, solubility, and hydrate melting point of the gas-liquid two-phase molecular simulation system after calculating the matched force field parameters;

[0110] S117: Compare the calculated values ​​of diffusion coefficient, solubility, and hydrate melting point with experimental values, and optimize the matched force field parameters based on the comparison results.

[0111] Specifically, gas molecules can be modeled using the Lennard-Jones (LJ) force field model, water molecules using the TIP4P / ice force field model, and promoter molecules using the OPLS-AA force field model. By inputting the molecular structure into the corresponding force field model, the corresponding force field parameters can be output, such as the collision radius of the molecules. It can describe the spatial size of molecules and the depth of potential wells. It can describe the strength of intermolecular interactions and assign force field parameters to each simulated molecular structure in a gas-liquid two-phase molecular simulation system, by matching accurate collision radii. Sum of potential well depth Parameters can accurately reflect the steric hindrance of molecules (e.g., the collision radius of H2). (The smaller the value, the easier it is to diffuse) and the strength of the interaction.

[0112] The diffusion coefficient describes the diffusion ability of gas molecules in aqueous solution (unit: cm² / s), reflecting the kinematic activity of molecules; solubility measures the amount of gas molecules dissolved in aqueous solution (unit: mol / L or wt%), which is related to gas storage capacity; the hydrate melting point is the melting temperature of the hydrate (unit: K), reflecting the thermodynamic stability of the hydrate. These parameters are used to evaluate core indicators reflecting the storage performance of hydrates, such as gas storage capacity and growth rate. Calculated values ​​(e.g., simulated solubility) are compared with experimental values ​​to calculate the relative error. The force field parameters are adjusted based on the calculated relative error, and then recalculated until the relative error is less than a preset threshold. This iterative optimization of the force field parameters by comparing calculated and experimental values ​​makes the simulation results closer to the real system, improving simulation accuracy. The experimental values ​​are obtained through experimental evaluation methods using equipment such as high-pressure reactors, high-pressure differential scanning calorimeters, and spectrometers.

[0113] Currently, constructing promoter models is quite difficult. Typically, only simple neutral or less than two-charge promoter molecules can be constructed, without considering complex ionic promoters, thus limiting the range of promoters that can be analyzed. To address this technical problem, the embodiments of this application may include the following specific steps when constructing a gas-liquid two-phase molecular simulation system:

[0114] S111: Based on the chemical composition and molecular structure of the accelerator to be evaluated, an optimized molecular structure of the accelerator is generated. The optimized molecular structure of the accelerator carries the charge distribution results. Step S111 includes the following specific steps:

[0115] S1111: Draw an initial structural model based on the chemical composition and molecular structure of the promoter molecule to be evaluated;

[0116] S1112: Determine the charge parameters of the charged groups based on the initial structural model drawn;

[0117] S1113: Based on the charge parameters of charged groups and the segmented generation strategy, the molecular structure of the promoter is split into multiple structural fragments;

[0118] S1114: The endpoints of each structural fragment are spliced ​​together to obtain the optimized molecular structure of the promoter.

[0119] Specifically, the system inputs the chemical formula, two-dimensional or three-dimensional molecular structure of the accelerator to be evaluated from the user, or retrieves the chemical composition and molecular structure of the accelerator from the server's database. Based on the retrieved chemical composition and molecular structure of the accelerator molecule, an initial structural model of the accelerator is drawn. Based on the drawn initial structural model, it can be determined whether the molecular structure of the accelerator is a neutral molecule or a charged group. If the accelerator's molecular structure is a charged group, the charge parameters of the charged group can be determined. If the number of charges of the charged groups exceeds two, based on the molecular topology, and according to the charge parameters of the charged groups and a segmentation generation strategy, the accelerator's molecular structure is divided into multiple structural fragments to achieve charge continuity at the boundaries of the structural fragments. By splicing the endpoints of each structural fragment, the optimized molecular structure of the accelerator can be output. Therefore, by breaking down the molecular structure of the promoter into multiple structural fragments, it is particularly suitable for constructing optimized molecular structures of complex multi-charge ionization promoters, thereby enhancing the versatility of the molecular simulation system. Furthermore, by connecting each structural fragment with covalent bonds at its endpoints and recombining them into a complete promoter molecular model, its geometric stability and charge balance can be verified, laying the foundation for improving the accuracy of simulation results.

[0120] In some embodiments, step S1113: Based on the charge parameters of the charged groups and the segmentation generation strategy, the molecular structure of the promoter is split into multiple structural fragments, including the following specific steps:

[0121] S11131: If it is determined that the number of charges of a charged group is greater than 2, the number of charges will be distributed.

[0122] S11132: Based on the distribution of charge, the molecular structure of the promoter is split into multiple structural fragments, where the charge of each structural fragment is less than or equal to 2.

[0123] Specifically, charge models can be stored in a server database. These models may include RESP models, AM1-BCC models, etc. RESP models are suitable for charge fitting after quantum chemical calculations, while AM1-BCC models are suitable for charge correction using semi-empirical quantum chemical methods. The chemical properties of charged groups include polarity and functional group type. Appropriate charge models can be selected based on the chemical properties of the charged groups to calculate and obtain the charge quantity of each group. Reasonable splitting of multi-charged groups ensures that each structural fragment contains complete functional groups or chemical bonds. This not only ensures the chemical rationality of the structural fragments but also makes the charge distribution closer to the actual chemical environment, thereby improving the accuracy of charge distribution and enhancing the reliability of the simulation. For example, for a promoter molecule structure containing a +3 charge, the three split +1 charge structural fragments can more realistically simulate its diffusion behavior in solution, avoiding the formation of "ion pairs" caused by concentrated charges. Furthermore, by setting the charge quantity of each structural segment to ≤2, the computational complexity in the simulation can be reduced. If the complex molecular structure of the multi-charge ionization promoter is not broken down, the computational workload of the electrostatic interaction of the multi-charge structure increases exponentially, the simulation is prone to crashing, and the simulation calculation cannot proceed normally.

[0124] Taking the complex type of molecular promoter BSS8 (C16H24N2O12S2Na4) as an example, promoter BSS8 carries four negative charges, and the particle type can be selected as 1.14. The CM1A charge model analyzes charged groups and breaks down molecular structures into structural segments carrying ≤2 charges, such as... Figure 3 As shown, a promoter A with four negative charges is split into A1 and A2, generating two structural fragments and charge distribution results. Each structural fragment is then covalently linked at its endpoints to recombine into a complete promoter molecular model, generating an optimized promoter molecular structure with accurate charge distribution. This expands the universality of the molecular structure model, especially applicable to constructing gas-liquid two-phase molecular simulation systems using complex promoters, including bio-based polymers or ionic surfactants. Furthermore, the simulation results can be used to analyze the effect of promoter molecules on the concentration of gas in aqueous solution, determining whether the promoter promotes or inhibits gas dissolution in aqueous solution. This is significant for studying hydrate nucleation or growth and gas storage applications, and can broaden the applicability of simulation calculations.

[0125] For the promoter DIOX (1,3-dioxolane), which has a neutral molecular structure, there is no need to split the structural fragments.

[0126] The implementation methods and advantages of the embodiments of this application are described. Taking the accelerator DIOX as an example, the gas storage efficiency of the hydrate for methane gas under the action of the accelerator is calculated. The specific processing procedures of the embodiments of this application are described in detail below with specific examples.

[0127] Another method for determining the gas storage efficiency of hydrate promoters provided in this application embodiment may specifically include the following steps:

[0128] S1: Constructing a gas-liquid two-phase molecular simulation system may include the following specific steps:

[0129] S11: Construct an empty gas box and an empty aqueous solution box.

[0130] For example, an empty gas cell with a size of x,y,z = 4 × 4 × 2 nm³ can be constructed, resulting in the file gas_initial.pdb. Similarly, an empty aqueous solution cell with a size of x,y,z = 4 × 4 × 5 nm³ can be constructed, resulting in the file solution_initial.pdb.

[0131] S12: Insert gas molecules into an empty gas box, and insert promoter molecules and water molecules into an empty aqueous solution box; preferably, insert macromolecular promoter molecules first, and then fill the pores with water molecules to optimize space filling.

[0132] For example, inserting 192 CH4 gas molecules into an empty gas box results in a filled gas box (gas.pdb). Similarly, inserting 64 DIOX accelerator molecules followed by 1088 water molecules into an empty aqueous solution box results in a filled aqueous solution box (solution.pdb).

[0133] S13: Using a custom script, the gas and aqueous solution boxes that have been constructed separately are spliced ​​along the z-axis to obtain the initial gas-liquid two-phase molecular simulation system system.pdb. A 0.5nm gap layer is left between different boxes to prevent edge molecules from overlapping.

[0134] S2: Simulation force field parameter setting and optimization;

[0135] Based on the molecular categories in the initial gas-liquid two-phase molecular simulation system, the corresponding force field parameter model is selected. For example, water molecules use the Tip4p / Ice force field model, CH4 uses the OPLS-UA force field model, and H2 and CO2 use the TraPPE force field model. For promoter molecules, due to their numerous types and complex structures, there are no readily available force field parameters. Force field parameters can be generated based on the OPLS-AA force field model, and these generated force field parameters are used on the LigParGen molecular simulation platform to improve accuracy.

[0136] S3: Simulation calculations within a multivariable (temperature, pressure, and concentration) grid simulation framework may include the following specific steps:

[0137] S31: Minimize the energy of the initial gas-liquid two-phase molecular simulation system.

[0138] The preferred algorithm is the steepest descent method, which is particularly suitable for eliminating high-energy conflicts in the initial stage. The optimization process stops when the maximum interaction force between molecules in the gas-liquid two-phase molecular simulation system is less than the preset convergence threshold, for example, the preset convergence threshold is set to 1000 KJ / mol / nm.

[0139] S32: Pre-balancing simulation calculation.

[0140] The Berendsen thermostat temperature-pressure coupling algorithm was selected because it converges quickly and is computationally efficient. It is suitable for rapidly reaching the target temperature in the initial equilibrium stage and is often used for rapid relaxation of gas-liquid two-phase molecular simulation systems. A time step of 1 femtosecond was used to ensure the accuracy of the calculation.

[0141] S33: Multi-parameter simulation calculation, including:

[0142] S331: First, adjust the temperature coupler to Nose-Hoover and the pressure coupler to Parrinello-Rahman. The new temperature and pressure control algorithm is more refined, which can make the simulation results more accurate.

[0143] Preferably, for example, the temperature range can be set to 230-300 K with a corresponding setting step of 10 K, the pressure range to 0.1-50 MPa with a corresponding setting step of 5 MPa, and the DIOX accelerator concentration range to 0.1-10 mol%, with a corresponding setting step of 2 mol; the baseline simulation conditions are a temperature of 270 K, a pressure of 10 MPa, and a DIOX accelerator concentration of 5.56 mol%. The above test conditions can also be adjusted according to the actual focus of attention.

[0144] S332: Adjust the time step to 2 femtoseconds for a formal simulation of 500 nanoseconds. A batch task submission script can be set up, with the temperature, pressure, and accelerator concentration ranges set above submitted in batches according to the set step intervals.

[0145] S333: Submitting each test parameter group three times can prevent random errors in the simulation results and record the trajectory data of the molecular dynamics simulation.

[0146] S4. Calculate the growth rate, gas storage capacity, and gas storage efficiency of the hydrate. The first 100 nanoseconds of the formal simulation are set as the simulation time to stabilize. The trajectory data of the molecular dynamics simulation for the last 400 nanoseconds can be selected for analysis.

[0147] S41: Calculate the hydrate growth rate and gas storage capacity based on trajectory data from molecular dynamics simulations. The hydrate growth rate R can be calculated using the following conditional equation:

[0148]

[0149] In the formula, To set the sampling time The number of newly formed hydrate cages, and the structure of the hydrate cages, for example, can be 5. 12 , 5 12 6 2 , 5 12 6 4 Any of these hydrate promoters, calculated to have the same growth rate, will produce the same result. For example, the structure of the DIOX-CH4 hydrate cage uses a 5-cell... 12 6 2 .

[0150] The gas storage capacity Q of the hydrate can be calculated according to the following conditional formula:

[0151]

[0152] In the formula, To capture the mass of gas in the hydrate, and These represent the mass of the promoter and the mass of water in the newly formed hydrate, respectively.

[0153] S42: The gas storage efficiency of hydrates can be calculated according to the following conditional formula:

[0154]

[0155] In the formula, E represents the gas storage efficiency of the hydrate under the action of the promoter, R and Q represent the growth rate and gas storage capacity of the hydrate under the action of the promoter, respectively, and R0 and Q0 represent the growth rate and gas storage capacity of the pure gas hydrate without the promoter, respectively. and These are the corresponding weighting parameters. These can be flexibly adjusted based on the economic efficiency of thermodynamic conditions, as well as the magnitude of the impact of growth rate and gas storage capacity on economic effectiveness. and Weight, if set =0.3, =0.7, meaning that the gas storage factor is more important for assessing the gas storage efficiency of hydrates.

[0156] In summary, a gas-liquid two-phase molecular simulation system was constructed using DIOX accelerator and methane gas. The simulation results under different temperature, pressure, and accelerator concentration test parameters are shown in Tables 1-3 below:

[0157] Table 1. Quantitative evaluation of gas storage efficiency of hydrates based on DIOX promoter at different temperatures.

[0158] (Under the condition that the pressure is 10 MPa and the concentration is 5.56 mol% unchanged)

[0159]

[0160] Table 2. Quantitative evaluation of gas storage efficiency of hydrates based on DIOX promoter under different pressures.

[0161] (Under the condition that the temperature is 270K and the concentration is 5.56 mol% constant)

[0162]

[0163] Table 3. Quantitative evaluation of gas storage efficiency of hydrates based on DIOX promoter at different concentrations.

[0164] (Under the condition that the temperature is 270K and the pressure is 10MPa)

[0165]

[0166] Table 1-3 presents the specific simulation results, showing the calculated growth rate, gas storage capacity, and gas storage efficiency under different temperature, pressure, and concentration series of test parameter sets. The error is the standard deviation of the calculation results from three repeated submissions of the test parameter set. Here, `pure` represents the calculation results without a hydrate promoter at a temperature of 270 K and a pressure of 10 MPa. The baseline simulation conditions are, for example, a temperature of 270 K, a pressure of 10 MPa, and a DIOX concentration of 5.56 mol%. To emphasize the greater importance of gas storage capacity to promoter performance, the weighting factor for growth rate is set to 0.3, and the weighting factor for gas storage capacity is set to 0.7 in this embodiment. By comparing the gas storage efficiency E under different conditions, if the gas storage efficiency E is greater than 1, then under the corresponding temperature, pressure, and promoter concentration conditions, the promoter has a positive impact on hydrate gas storage; otherwise, it has a negative impact. Table 1 clearly shows that the DIOX accelerator is beneficial for hydrate gas storage at temperatures of 260-280 K, where the gas storage efficiency E is greater than 1. Table 2 also shows that the DIOX accelerator is beneficial for hydrate gas storage at pressures of 1-20 MPa, with a gas storage efficiency E greater than 1. Furthermore, Table 2 indicates that the DIOX accelerator achieves the highest gas storage efficiency E greater than 1 at a concentration of 5.56 mol%. Therefore, it can be concluded that the DIOX accelerator exhibits the highest gas storage efficiency at 270 K, 10 MPa, and a concentration of 5.56 mol%, and these technical indicators can provide guidance for subsequent industrial applications.

[0167] Comparative Example 1

[0168] use Figure 1 An experiment was conducted using a testing apparatus for the accelerator performance of DIOX to test the amount of CH4 gas stored by the accelerator under different temperature and pressure conditions. The consumption was calculated based on the total amount of CH4 gas to obtain the corresponding storage capacity. The experimental data obtained are shown below:

[0169] At a temperature of 288 K, a pressure of 6.6 MPa, and a DIOX promoter concentration of 5.56 mol%, the gas storage capacity of DIOX-CH4 hydrate is 90.96 v / v, or 5.91 wt%. In the hydrate gas storage experiment, the gas storage capacity is the volume ratio under standard conditions, i.e., gas volume / hydrate volume (v / v); in the hydrate simulation calculation, the gas storage capacity is the mass percentage (wt%) calculated by dividing the mass of gas in the hydrate by the total mass of the hydrate. For the DIOX-CH4 hydrate gas storage system, after unit conversion, 1 v / v = 0.065 wt%.

[0170] Based on the specific simulation results in Tables 1-3, the simulated gas storage capacity at 290 K, 10 MPa, and 5.56 mol% is 5.23 wt%. Since the experimental statistics in Comparative Example 1 include dissolved or pore-filled methane gas, the results may be slightly higher. Therefore, this verifies the accuracy of the simulation results in the embodiments of this application.

[0171] Comparative Example 2

[0172] use Figure 1 An experiment was conducted using a testing apparatus for the accelerator performance of DIOX to test the amount of CH4 gas stored by the accelerator under different temperature and pressure conditions. The consumption was calculated based on the total amount of CH4 gas to obtain the corresponding storage capacity. The experimental data obtained are shown below:

[0173] At a temperature of 283 K, a pressure of 7.2 MPa, and a DIOX promoter concentration of 5.56 mol%, the gas storage capacity of DIOX-CH4 hydrate is 83.81 v / v, or 5.45 wt%.

[0174] According to the specific simulation results in Table 1-3, the simulated gas storage capacity at 290 K, 10 MPa, and 5.56 mol% is 5.23 wt%. Verification shows that the simulation results in this application embodiment are basically consistent with the experimental results.

[0175] Comparative Example 3

[0176] use Figure 1 Experiments were conducted using a testing device for the performance of the DIOX accelerator. The gas storage capacity of the DIOX accelerator under different temperature and pressure conditions was tested. The consumption was calculated based on the total amount of CH4 gas to obtain the corresponding gas storage capacity. The hydrate growth rate was calculated based on the consumption of CH4 gas per unit time. The experimental data obtained are as follows:

[0177] At 11.2 MPa and 5.56 mol%, the time for DIOX-CH4 hydrate to reach 90% gas storage capacity increased from 72.78 to 128.44 minutes as the temperature rose to 283-293 K, with the time becoming increasingly longer, thus the hydrate growth rate showed a decreasing trend.

[0178] According to the specific simulation results in Table 1-3, the growth rate continuously decreases at temperatures of 280-300 K, 10 MPa, and 5.56 mol%. Therefore, it has been verified that the simulated growth rate in the embodiments of this application shows a consistent trend with the experimental results.

[0179] Comparative Example 4

[0180] use Figure 1Experiments were conducted using a testing device for the performance of the DIOX accelerator. The gas storage capacity of the DIOX accelerator under different temperature and pressure conditions was tested. The consumption was calculated based on the total amount of CH4 gas to obtain the corresponding gas storage capacity. The hydrate growth rate was calculated based on the CH4 gas consumption per unit time, as shown below:

[0181] The growth rate of DIOX-CH4 hydrate at 290 K, 4 MPa, and DIOX concentrations ranging from 1 to 10 mol% initially increased and then decreased, with the maximum growth rate occurring at a DIOX concentration of 5 mol%.

[0182] According to the specific simulation results in Table 1-3, at 270K and 10MPa, the growth rate of DIOX-CH4 hydrate first increases and then decreases when the DIOX concentration ranges from 1.45 to 10.53 mol%, with the maximum growth rate occurring at a DIOX concentration of 5.56 mol%. Therefore, it has been verified that the simulation results of the growth rate in the embodiments of this application are basically consistent with the experimental results.

[0183] Corresponding to the method embodiments of this application, this application also provides an apparatus for determining the gas storage efficiency of a hydrate promoter, such as... Figure 5 As shown, it includes:

[0184] The construction module 510 is used to construct a gas-liquid two-phase molecular simulation system. The gas-liquid two-phase molecular simulation system includes a gas box filled with a first set number of gas molecules, and an aqueous solution box filled with a second set number of promoter molecules and a third set number of water molecules. The filled gas box and aqueous solution box are sequentially spliced ​​along a set direction.

[0185] The parameter control module 520 is used to input multiple test parameter sets into the constructed gas-liquid two-phase molecular simulation system to form parameter control under the multivariable grid simulation framework, so as to realize the simulation of gas hydrate nucleation or growth.

[0186] The generation module 530 is used to generate trajectory data of molecular dynamics simulation during the simulated nucleation or growth of gas hydrates; wherein the trajectory data of molecular dynamics simulation includes the number of newly formed hydrate cages within a set sampling time, the mass of gas, promoter and water in the newly formed hydrates;

[0187] The calculation module 540 is used to obtain the gas storage efficiency of hydrates under the action of a promoter based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency.

[0188] The electronic device in this application embodiment may be a user terminal device, a server, other computing devices, or a cloud server. Figure 6 The diagram illustrates the hardware structure of an electronic device according to an embodiment of this application. The electronic device may include a processor 601 and a memory 602 storing computer program instructions. When the processor 601 executes the computer program instructions, it implements the process or function of any of the methods described above.

[0189] Specifically, processor 601 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. Memory 602 may include a mass storage device for data or instructions. For example, memory 602 may be at least one of the following: a hard disk drive (HDD), read-only memory (ROM), random access memory (RAM), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, universal serial bus (USB) drive, or other physical / tangible memory storage device. Alternatively, memory 602 may include removable or non-removable (or fixed) media. Furthermore, memory 602 may be internal or external to the integrated gateway disaster recovery device. Memory 602 may be non-volatile solid-state memory. In other words, typically memory 602 includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with computer-executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in the methods of the embodiments of this application. The processor 601 implements the process or function of any of the methods described in the above embodiments by reading and executing computer program instructions stored in the memory 602.

[0190] In one example Figure 6The illustrated electronic device may also include a communication interface 603 and a bus 610. The processor 601, memory 602, and communication interface 603 are connected via bus 610 and communicate with each other. Communication interface 603 is primarily used to enable communication between modules, devices, units, and / or equipment in the embodiments of this application. Bus 610 may include hardware, software, or both, and can couple components of the online data traffic billing device together. For example, the bus may include at least one of the following: Accelerated Graphics Port (AGP) or other graphics bus, Enhanced Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), HyperTransport (HT) Interconnect, Industry Standard Architecture (ISA) bus, Infinite Bandwidth Interconnect, Low Pin Count (LPC) bus, memory bus, Microchannel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus, or other suitable buses. Bus 610 may include one or more buses. Although specific buses are described or illustrated in the embodiments of this application, any suitable bus or interconnection method may be considered in the embodiments of this application.

[0191] In conjunction with the methods in the above embodiments, this application also provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the process or function of any of the methods in the above embodiments.

[0192] In addition, this application also provides a computer program product that stores computer program instructions, which, when executed by a processor, implement the process or function of any of the methods described above.

[0193] The flowcharts and / or block diagrams of methods, apparatuses, systems, and computer program products according to embodiments of this application have been exemplarily described above, and related aspects have been described. It should be understood that each block or combination thereof in the flowcharts and / or block diagrams may be implemented by computer program instructions, by dedicated hardware performing a specified function or action, or by a combination of dedicated hardware and computer instructions. For example, these computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to form a machine that enables the implementation of the function / action specified in each block or combination thereof in the flowcharts and / or block diagrams, executable via such processor. Such a processor may be a general-purpose processor, a dedicated processor, a special-purpose application processor, or a field-programmable logic circuit.

[0194] The functional blocks shown in the structural block diagrams of this application can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc.; when implemented in software, they are programs or code segments used to perform the required tasks. Programs or code segments can be stored in memory or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. Code segments can be downloaded via computer networks such as the Internet or intranets.

[0195] It should be noted that this application is not limited to the specific configurations and processes described above or shown in the figures. The above descriptions are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described systems, devices, modules, or units can be referred to the corresponding processes in the method embodiments, and need not be repeated here. It should be understood that the scope of protection of this application is not limited thereto. Any person skilled in the art can conceive of various equivalent modifications or substitutions within the scope of the technology disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application.

Claims

1. A method for determining the gas storage efficiency of a hydrate promoter, characterized in that, include: A gas-liquid two-phase molecular simulation system is constructed, comprising a gas box filled with a first predetermined number of gas molecules, and an aqueous solution box filled with a second predetermined number of promoter molecules and a third predetermined number of water molecules. The gas box and the aqueous solution box are sequentially assembled along a predetermined direction. The promoter to be evaluated includes an ionic promoter. If the ionic promoter carries a charge greater than 2, an optimized molecular structure of the promoter is generated based on its chemical composition and molecular structure. The optimized molecular structure of the promoter is then filled into the aqueous solution box. The optimized molecular structure of the promoter includes multiple structural segments, each of which carries a charge less than or equal to 2. Multiple test parameter sets are input into the constructed gas-liquid two-phase molecular simulation system to form parameter control under a multivariable grid simulation framework, so as to realize the simulation of gas hydrate nucleation or growth. In simulating the nucleation or growth of gas hydrates, trajectory data from molecular dynamics simulations are obtained; wherein, the trajectory data from molecular dynamics simulations includes the number of newly formed hydrate cages within a set sampling time, and the mass of gas, promoter, and water in the newly formed hydrates; Based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency, the gas storage efficiency of hydrates under the action of a promoter is obtained; wherein, the gas storage efficiency of hydrates under the action of a promoter is calculated according to the following conditional formula: ， In the formula, E represents the gas storage efficiency of the hydrate under the action of the promoter, R and Q represent the growth rate and gas storage capacity of the hydrate under the action of the promoter, respectively, and R0 and Q0 represent the growth rate and gas storage capacity of the pure gas hydrate without the promoter, respectively. and These are the corresponding weight parameters.

2. The method according to claim 1, characterized in that, The process of obtaining the gas storage efficiency of hydrates under the action of a promoter, based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency, includes: The growth rate of hydrates is calculated based on the number of newly formed hydrate cages within a set sampling time. The gas storage capacity of the hydrate is calculated based on the mass of gas, promoter, and water in the newly formed hydrate. The gas storage efficiency of hydrates under the action of a promoter is calculated based on the growth rate and gas storage capacity of hydrates with a promoter, and the growth rate and gas storage capacity of pure gas hydrates without a promoter.

3. The method according to claim 1, characterized in that, Also includes: The gas-liquid two-phase molecular simulation system is subjected to energy minimization processing. The steepest descent algorithm is used to eliminate high-energy conflicts in the gas-liquid two-phase molecular simulation system. The optimization process is terminated when the maximum interaction force between molecules in the gas-liquid two-phase molecular simulation system is less than the preset convergence threshold. A temperature-pressure coupling algorithm is used to perform pre-equilibrium simulation calculations on the gas-liquid two-phase molecular simulation system that has undergone energy minimization, so that the temperature and pressure of the gas-liquid two-phase molecular simulation system reach the initial conditions of the target simulation.

4. The method according to claim 3, characterized in that, The test parameter set includes temperature, pressure, and promoter concentration. Multiple test parameter sets are input into the constructed gas-liquid two-phase molecular simulation system to form parameter control within a multivariable grid simulation framework, thereby simulating gas hydrate nucleation or growth. This includes: Set the temperature, pressure, and accelerator concentration ranges, and set the corresponding step sizes for the temperature, pressure, and accelerator concentration ranges, as well as the corresponding test baseline conditions. Under the corresponding test benchmark conditions, the set temperature, pressure and accelerator concentration ranges are combined according to the corresponding step sizes to obtain the multiple test parameter groups; Submit the multiple test parameter sets according to the set time step, and perform the target simulation calculation task on the gas-liquid two-phase molecular simulation system after the pre-equilibrium simulation calculation is completed; The multiple test parameter sets are repeatedly submitted multiple times to perform the target simulation calculation task on the gas-liquid two-phase molecular simulation system, in order to eliminate random errors in the calculation results of the target simulation; wherein, The computational task of the target simulation includes performing gas hydrate nucleation or growth simulation in the gas-liquid two-phase molecular simulation system under the temperature, pressure and promoter concentration conditions corresponding to the multiple test parameter groups, and generating trajectory data for molecular dynamics simulation.

5. The method according to claim 1, characterized in that, The construction of the gas-liquid two-phase molecular simulation system includes: An empty gas box is constructed with a first predetermined volume, and an empty aqueous solution box is constructed with a second predetermined volume. A first predetermined number of gas molecules are inserted into an empty gas box to obtain a filled gas box; a second predetermined number of accelerator molecules and a third predetermined number of water molecules are inserted into an empty aqueous solution box to obtain a filled aqueous solution box; and A filled aqueous solution box is placed between two filled gas boxes. The filled gas boxes and aqueous solution boxes are arranged and spliced ​​in sequence along a set direction to obtain the gas-liquid two-phase molecular simulation system.

6. The method according to claim 5, characterized in that, The construction of an empty gas box with a first predetermined volume and an empty aqueous solution box with a second predetermined volume includes: The size of the empty aqueous solution box is determined based on the size of the hydrate cell and the number of hydrate cages generated; wherein the size of the empty aqueous solution box includes the length extending along the first direction, the width extending along the second direction, and the depth extending along the third direction. The dimensions of the empty gas box are determined based on the dimensions of the empty water solution box; wherein the length and width of the empty gas box are equal to the length and width of the empty water solution box.

7. The method according to claim 5, characterized in that, The construction of the gas-liquid two-phase molecular simulation system also includes: Based on the force field models of gas molecules, promoter molecules, and water molecules, corresponding force field parameters are matched to the gas molecules, promoter molecules, and water molecules contained in the gas-liquid two-phase molecular simulation system, respectively; wherein, the force field parameters include collision radius and potential well depth; The diffusion coefficient, solubility, and hydrate melting point of the gas-liquid two-phase molecular simulation system after matching force field parameters were calculated. The calculated values ​​of diffusion coefficient, solubility, and hydrate melting point are compared with experimental values, and the matched force field parameters are optimized based on the comparison results.

8. A device for determining the gas storage efficiency of a hydrate promoter, characterized in that, include: A construction module is used to construct a gas-liquid two-phase molecular simulation system. The gas-liquid two-phase molecular simulation system includes a gas box filled with a first predetermined number of gas molecules and an aqueous solution box filled with a second predetermined number of promoter molecules and a third predetermined number of water molecules. The gas box and the aqueous solution box are sequentially assembled along a predetermined direction. The promoter to be evaluated includes an ionic promoter. If the ionic promoter carries a charge greater than 2, an optimized molecular structure of the promoter is generated based on the chemical composition and molecular structure of the promoter to be evaluated. The optimized molecular structure of the promoter is then filled into the aqueous solution box. The optimized molecular structure of the promoter includes multiple structural fragments, and each structural fragment carries a charge less than or equal to 2. The parameter control module is used to input multiple test parameter sets into the constructed gas-liquid two-phase molecular simulation system to form parameter control under the multivariable grid simulation framework, so as to realize the simulation of gas hydrate nucleation or growth. The generation module is used to acquire trajectory data from molecular dynamics simulations during the simulated nucleation or growth of gas hydrates. The trajectory data from molecular dynamics simulations includes the number of newly formed hydrate cages within a set sampling time, and the mass of gas, promoter, and water in the newly formed hydrates. The calculation module is used to obtain the gas storage efficiency of hydrates under the action of a promoter based on trajectory data from molecular dynamics simulations and a pre-constructed mathematical model of gas storage efficiency; wherein, the gas storage efficiency of hydrates under the action of a promoter is calculated according to the following conditional formula: ， In the formula, E represents the gas storage efficiency of the hydrate under the action of the promoter, R and Q represent the growth rate and gas storage capacity of the hydrate under the action of the promoter, respectively, and R0 and Q0 represent the growth rate and gas storage capacity of the pure gas hydrate without the promoter, respectively. and These are the corresponding weight parameters.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory storing computer program instructions; when the electronic device executes the computer program instructions, it implements the method as described in any one of claims 1-7.

Images