Process optimization method and device for vapor deposition of aluminum silicate fibers

Abstract

The invention relates to the technical field of vapor deposition, and discloses a process optimization method and device for vapor deposition of aluminum silicate fibers. The method comprises the following steps: establishing a chemical reaction mass transfer model through component diffusion parameters of a reaction cavity to obtain a mass transfer prediction model; setting process parameters based on the mass transfer prediction model to obtain optimized mass transfer conditions; constructing a kinetic equation by using the mass transfer condition to obtain a kinetic prediction model; setting balance parameters according to the prediction model to obtain optimal reaction conditions; processing the surface of the substrate according to reaction conditions to obtain a directional growth interface; and designing a regulation and control system based on a growth interface to obtain the aluminum silicate fiber with a controllable structure. By optimizing vapor deposition process parameters and a process control method, uniformity control over an aluminum silicate fiber structure is achieved, an accurate mass transfer prediction model is established, a substrate surface treatment process is optimized, and real-time monitoring and regulation and control over the deposition process are achieved.

Description

Technical Field

[0001] This application relates to the field of chemical vapor deposition, and particularly to a process optimization method and device for chemical vapor deposition of aluminosilicate fibers. Background Art

[0002] Aluminosilicate fiber is an important high-temperature thermal insulation material, which is widely used in the fields of aerospace, metallurgy, petrochemical, etc. Traditional methods for preparing aluminosilicate fibers mainly include melt spinning method, sol-gel method and chemical vapor deposition method. Among them, chemical vapor deposition method has received wide attention due to its advantages such as controllable fiber structure and high purity. In the existing chemical vapor deposition process, usually and are used as precursors, and are transported to the heating area by a carrier gas for chemical reaction to form aluminosilicate fibers on the substrate surface. During the process, the growth behavior of the fibers is adjusted by controlling parameters such as reaction temperature, pressure, and gas flow rate.

[0003] However, there is a significant technical problem in the existing chemical vapor deposition process: due to the lack of precise control of the reaction process, the generated aluminosilicate fibers show large differences in terms of structural uniformity and orientation consistency. This is mainly because in the traditional process, there is a lack of in-depth understanding of the gas mass transfer process and surface growth kinetics, and precise control of the fiber growth process cannot be achieved. Especially in the reaction chamber, the non-uniform distribution of the gas flow field will lead to differences in local mass transfer conditions, which in turn affect the growth orientation and structural characteristics of the fibers. In addition, the imperfect substrate surface treatment method will also affect the nucleation and growth behavior of the fibers, ultimately resulting in unstable product performance. Summary of the Invention

[0004] This application provides a process optimization method and device for chemical vapor deposition of aluminosilicate fibers, which are used to control the uniformity of the structure of aluminosilicate fibers by optimizing the chemical vapor deposition process parameters and process control methods, establish an accurate mass transfer prediction model, optimize the substrate surface treatment process, and realize real-time monitoring and control of the deposition process.

[0005] In the first aspect, this application provides a process optimization method for chemical vapor deposition of aluminosilicate fibers. The process optimization method for chemical vapor deposition of aluminosilicate fibers includes: establishing a chemical reaction mass transfer model through the component diffusion parameter and thermal conductivity of the reaction chamber, and using the gas-phase precursor and Numerically simulate the gas dynamics characteristics of the mixed gas to obtain a mass transfer prediction model in the reaction chamber; set parameters for the carrier gas flow rate, reaction temperature, and chamber pressure according to the mass transfer prediction model, and obtain the diffusion characteristics of the gas-phase components through the response surface analysis method to obtain the optimized chemical reaction mass transfer conditions; use the optimized chemical reaction mass transfer conditions to construct a precursor conversion kinetic equation, and obtain a kinetic prediction model for the deposition reaction through the coupled calculation of gas-phase mass transfer resistance and surface adsorption potential energy; set chemical reaction equilibrium parameters according to the kinetic prediction model of the deposition reaction, measure the reaction activation energy through thermal analysis, and optimize using the gas-solid interface mass transfer coefficient to obtain the optimal reaction conversion conditions; chemically activate the surface of the reaction substrate according to the optimal reaction conversion conditions, form surface active sites through plasma treatment, and use the sol-gel method to construct a seed layer to obtain a reaction interface with directional growth activity; design a process control system based on the reaction interface with directional growth activity, and adjust the reaction mass transfer process using the feedback control algorithm by real-time monitoring of the precursor conversion rate and product morphology parameters to obtain aluminosilicate fibers with controllable structures.

[0006] Second, the present application provides a process optimization device for gas-phase deposition of aluminosilicate fibers. The process optimization device for gas-phase deposition of aluminosilicate fibers includes: A simulation module for establishing a chemical reaction mass transfer model through the component diffusion parameters and heat conduction coefficient of the reaction chamber, and numerically simulating the gas dynamics characteristics of the mixed gas of the gas-phase precursor and to obtain a mass transfer prediction model in the reaction chamber; A setting module for setting parameters for the carrier gas flow rate, reaction temperature, and chamber pressure according to the mass transfer prediction model, and obtaining the diffusion characteristics of the gas-phase components through the response surface analysis method to obtain the optimized chemical reaction mass transfer conditions; A calculation module for constructing a precursor conversion kinetic equation using the optimized chemical reaction mass transfer conditions, and obtaining a kinetic prediction model for the deposition reaction through the coupled calculation of gas-phase mass transfer resistance and surface adsorption potential energy; A measurement module for setting chemical reaction equilibrium parameters according to the kinetic prediction model of the deposition reaction, measuring the reaction activation energy through thermal analysis, and optimizing using the gas-solid interface mass transfer coefficient to obtain the optimal reaction conversion conditions; An activation module for chemically activating the surface of the reaction substrate according to the optimal reaction conversion conditions, forming surface active sites through plasma treatment, and using the sol-gel method to construct a seed layer to obtain a reaction interface with directional growth activity; The monitoring module is used to regulate the system based on the reaction interface design process of the directional growth activity. By real-time monitoring the precursor conversion rate and product morphology parameters, and using the feedback control algorithm to adjust the reaction mass transfer process, aluminosilicate fibers with controllable structures are obtained.

[0007] In the technical solution provided by this application, by establishing a chemical reaction mass transfer model including component diffusion parameters and thermal conductivity coefficients, the accurate description of the kinetic characteristics of the gas-phase precursor and mixed gas is realized, providing an accurate prediction basis for the mass transfer process in the reaction chamber. Based on the mass transfer prediction model, the parameter optimization of the carrier gas flow rate, reaction temperature, and chamber pressure is carried out. Combining with the response surface analysis method, the quantitative relationship between the gas-phase component diffusion characteristics and process parameters is established, significantly improving the chemical reaction mass transfer conditions. Through in-depth analysis of the optimized chemical reaction mass transfer conditions, a coupled calculation model of gas-phase mass transfer resistance and surface adsorption potential energy is established, obtaining an accurate deposition reaction kinetic prediction model, providing a theoretical basis for the precise regulation of process parameters. On this basis, the reaction activation energy is measured by thermal analysis method, and combined with the optimization of the gas-solid interface mass transfer coefficient, the optimal reaction conversion conditions are determined, improving the reaction efficiency. The surface of the reaction substrate is chemically activated, and a seed layer is constructed by plasma treatment and sol-gel method to obtain a reaction interface with directional growth activity, creating favorable conditions for the directional growth of fibers. Finally, a process regulation system based on real-time monitoring and feedback control is established. By continuously monitoring the precursor conversion rate and product morphology parameters, the dynamic optimization of the reaction mass transfer process is realized, and finally aluminosilicate fibers with controllable structures are obtained. The algorithm features in the entire solution, especially the applications in the construction of the mass transfer prediction model, response surface analysis, and feedback control, significantly improve the accuracy of process parameter optimization and the real-time performance of process control. Description of the Drawings

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0009] Figure 1 It is a schematic diagram of an embodiment of the process optimization method for gas-phase deposition of aluminosilicate fibers in an embodiment of this application; Figure 2 It is a timing diagram of obtaining the diffusion characteristics of gas-phase components by the response surface analysis method in an embodiment of this application; Figure 3 It is a schematic diagram of an embodiment of the process optimization device for gas-phase deposition of aluminosilicate fibers in an embodiment of this application; Reference numerals: simulation module 201, setting module 202, calculation module 203, measurement module 204, activation module 205, monitoring module 206. Detailed implementation manners

[0010] An embodiment of the present application provides a process optimization method and device for chemical vapor deposition of aluminosilicate fibers. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application will be described below. Please refer to Figure 1 , an embodiment of the process optimization method for chemical vapor deposition of aluminosilicate fibers in the embodiment of the present application includes: Step S101: Establish a chemical reaction mass transfer model based on the component diffusion parameter and heat conduction coefficient of the reaction cavity, and perform numerical simulation using the kinetic characteristics of the mixed gas of the gas-phase precursor and to obtain a mass transfer prediction model in the reaction cavity; Step S102: Set parameters for the carrier gas flow rate, reaction temperature and cavity pressure according to the mass transfer prediction model, and obtain the diffusion characteristics of the gas-phase components through the response surface analysis method to obtain the optimized chemical reaction mass transfer conditions; Step S103: Construct a kinetic equation for precursor conversion using the optimized chemical reaction mass transfer conditions, and obtain a kinetic prediction model for the deposition reaction through the coupled calculation of gas-phase mass transfer resistance and surface adsorption potential energy; Step S104: Set chemical reaction equilibrium parameters according to the kinetic prediction model of the deposition reaction, measure the reaction activation energy through thermal analysis, and optimize using the gas-solid interface mass transfer coefficient to obtain the optimal reaction conversion conditions; Step S105: Chemically activate the surface of the reaction substrate according to the optimal reaction conversion conditions, form surface active sites through plasma treatment, and construct a seed layer using the sol-gel method to obtain a reaction interface with directional growth activity; Step S106: Based on the reaction interface design process control system of directional growth activity, by real-time monitoring the precursor conversion rate and product morphology parameters, and using the feedback control algorithm to adjust the reaction mass transfer process, aluminosilicate fibers with controllable structures are obtained.

[0012] It can be understood that the execution subject of this application can be a process optimization device for chemical vapor deposition of aluminosilicate fibers, or a terminal or a server, and specific limitations are not made here. In this embodiment of the application, the server is taken as the execution subject for illustration.

[0013] Specifically, a chemical reaction mass transfer model of the reaction chamber is established. By measuring the diffusion coefficients of gas-phase precursors and at different temperatures, a component diffusion parameter database is established. In the temperature range of 800 - 1000 °C, a set of diffusion coefficient data is collected every 50 °C, and the data points are plotted into a diffusion coefficient - temperature relationship curve. At the same time, the variation law of the thermal conductivity coefficient of the mixed gas in this temperature range is measured, and a thermal conductivity coefficient data set is established. Based on these basic data, the Maxwell - Stefan diffusion equation is used to describe the component transfer process, and the energy conservation equation is combined to characterize the heat transfer characteristics. The process parameters are optimized through the established mass transfer prediction model. Parameter combinations are designed for the carrier gas flow rate in the range of 5 - 20 L / min, the reaction temperature in the range of 800 - 1100 °C, and the chamber pressure in the range of 50 - 200 kPa. The Box - Behnken experimental design scheme is adopted to determine 17 groups of process parameter combinations. Response surface analysis is carried out on the gas-phase component diffusion behavior under each parameter condition to obtain the diffusion characteristic data of gas-phase components under different conditions. By analyzing the response values such as diffusion coefficient and mass transfer rate, the optimal process parameter combination is determined.

[0014] Based on the optimized chemical reaction mass transfer conditions, the conversion kinetic characteristics of the precursor are studied. First, analyze the influence of gas-phase mass transfer resistance on the reaction rate, and establish the correlation between mass transfer resistance and reaction rate by measuring the mass transfer coefficient under different pressure gradients. At the same time, the quantum chemical calculation method is used to analyze the surface adsorption potential energy distribution, and determine the adsorption sites and adsorption energy of precursor molecules on the substrate surface. The gas-phase mass transfer resistance and surface adsorption potential energy are coupled and analyzed to construct a kinetic prediction model. Under the guidance of the kinetic prediction model, the reaction conversion conditions are optimized. The activation energy of the reaction is measured by the programmed heating thermal analysis method, and the measurement is carried out at a heating rate of 10 °C / min in the temperature range of 25 - 1200 °C, and the thermogravimetric curve and differential thermal curve are recorded. According to the Arrhenius equation, analyze the variation law of the reaction rate constant with temperature, and determine the optimal reaction temperature range. At the same time, study the mass transfer characteristics of the gas-solid interface, and optimize the interface mass transfer coefficient by adjusting the gas flow rate and pressure.

[0015] The surface of the reaction substrate is chemically activated, and a uniformly distributed active site is formed on the substrate surface by using radio frequency plasma treatment technology. The density and distribution of the surface active sites are controlled by adjusting the radio frequency power and treatment time. Subsequently, a seed layer is constructed on the activated substrate surface by the sol-gel method, and the size and orientation of the seeds are adjusted by controlling the pH value of the sol, the aging time, and the drying conditions. A process control system is established to achieve precise control of the deposition process. The conversion rate of the precursor is monitored in real time by spectroscopic analysis, and the morphological characteristics and orientation distribution of the product are observed by scanning electron microscopy. According to the monitored data, the reaction conditions are adjusted in real time by using a feedback control algorithm to ensure that the product has uniform structure and performance.

[0016] For example, in practical applications, when the reaction temperature is set at 900 °C, the carrier gas flow rate is 12 L / min, and the chamber pressure is 100 kPa, by monitoring the change in the concentration of gas-phase components in real time, it is found that the conversion rate of the precursor reaches 85%. At this time, it is observed by scanning electron microscopy that the deviation of the fiber orientation angle is less than 15°, and the diameter is distributed in the range of 0.5 - 1 μm. Based on these monitored data, the feedback control system automatically adjusts the process parameters: when it is detected that the conversion rate of the precursor decreases, the system increases the mass transfer efficiency by increasing the carrier gas flow rate; when it is observed that the fiber orientation deviates, the reaction temperature and pressure are finely adjusted to optimize the growth environment. This dynamic regulation ensures the stability and consistency of the product quality.

[0017] In the embodiments of this application, by establishing a chemical reaction mass transfer model including component diffusion parameters and heat conduction coefficients, the gas-phase precursor and The accurate description of the kinetic characteristics of the mixed gas provides an accurate prediction basis for the mass transfer process in the reaction chamber. Based on the mass transfer prediction model, the parameters of the carrier gas flow rate, reaction temperature, and chamber pressure are optimized. Combining with the response surface analysis method, a quantitative relationship between the diffusion characteristics of gas-phase components and process parameters is established, significantly improving the mass transfer conditions of the chemical reaction. Through in-depth analysis of the optimized chemical reaction mass transfer conditions, a coupled calculation model of gas-phase mass transfer resistance and surface adsorption potential energy is established, obtaining an accurate deposition reaction kinetics prediction model, which provides a theoretical basis for the precise regulation of process parameters. On this basis, the reaction activation energy is measured by thermal analysis method, and combined with the optimization of the gas-solid interface mass transfer coefficient, the optimal reaction conversion conditions are determined, improving the reaction efficiency. The surface of the reaction substrate is chemically activated, and a seed layer is constructed by plasma treatment and sol-gel method, obtaining a reaction interface with directional growth activity, creating favorable conditions for the directional growth of fibers. Finally, a process control system based on real-time monitoring and feedback control is established. By continuously monitoring the precursor conversion rate and product morphology parameters, the dynamic optimization of the reaction mass transfer process is realized, and aluminosilicate fibers with controllable structures are obtained. The algorithm features in the entire solution, especially the applications in the construction of mass transfer prediction models, response surface analysis, and feedback control, significantly improve the accuracy of process parameter optimization and the real-time performance of process control.

[0018] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Establish the Maxwell-Stefan diffusion equation based on the molecular mass and collision cross-section of the reactant gas, and combine it with the calculation formula of the thermal conductivity of the gas mixture to obtain the component diffusion parameters; (2) Construct the mass transfer control equation according to the component diffusion parameters, calculate the flow characteristics of the gas-phase mixture through the Navier-Stokes equation, and obtain the momentum transfer coefficient; (3) Establish the energy conservation equation using the momentum transfer coefficient, solve the temperature field distribution through numerical iteration, and obtain the thermal conductivity; (4) Substitute the thermal conductivity into the chemical reaction kinetics equation, calculate the reaction rate constant by the finite difference method, and obtain the precursor conversion rate; (5) Construct a chemical reaction mass transfer model according to the precursor conversion rate, calculate the component concentration gradient through Fick's diffusion law, and obtain the gas-phase diffusion flux; (6) Establish a material balance equation using the gas-phase diffusion flux, analyze the mass transfer resistance in combination with the boundary layer theory, and obtain the mass transfer prediction model.

[0019] Specifically, for the gas-phase precursor and Analyze the molecular characteristics of

[0020] where represents the mass fraction of component i, is the velocity vector of the mixture gas, is the density of the mixture gas, is the diffusion coefficient between components i and j, is the mole fraction of component j, is the chemical reaction source term of component i.

[0021] Based on the obtained component diffusion parameters, further establish an analysis model for the momentum transfer characteristics of the gas-phase mixture. By solving the Navier-Stokes equations:

[0022] where is the pressure, is the dynamic viscosity, is the acceleration due to gravity, is the external body force.

[0023] Take the momentum transfer coefficient as the input parameter and construct the energy conservation equation:

[0024] where is the specific heat capacity at constant pressure, is the temperature, is the thermal conductivity, is the heat source term.

[0025] For the chemical reaction kinetics equation:

[0026] where is the reaction conversion rate, is the reaction rate constant, is the conversion rate function, is the pressure influence function, is the concentration influence function.

[0027] Based on the precursor conversion rate, establish a chemical reaction mass transfer model. By Fick's diffusion law:

[0028] where is the diffusion flux of component i, is the diffusion coefficient, is the concentration.

[0029] Construct a material balance equation:

[0030] where is the interfacial mass transfer source term.

[0031] By numerical solution, the distribution of key parameters in the gas deposition process can be obtained. In practical applications, taking the reaction temperature of 900 °C as an example, first measure and the molecular collision cross-section and diffusion coefficient at this temperature, and calculate the component diffusion parameters by combining gas kinetic data. Subsequently, obtain the gas flow characteristics through flow field simulation, establish the temperature field distribution, and thus determine the heat conduction characteristics of the reaction region. Based on these data, calculate the conversion kinetics of the precursor to obtain an accurate mass transfer prediction model.

[0032] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Convert the momentum transfer data in the mass transfer prediction model into a flow rate matrix, determine the change range of the carrier gas flow rate through hierarchical analysis, and obtain a flow rate parameter table; (2) Construct a temperature gradient field for the flow rate parameter table, determine the response range of the reaction temperature through thermodynamic equilibrium calculation, and obtain a temperature distribution sequence; (3) Use the temperature distribution sequence to establish a pressure field data set, calculate the change law of the chamber pressure through the thermodynamic state equation, and obtain pressure control parameters; (4) Input the pressure control parameters into the Box-Behnken experimental design scheme, construct a three-factor interaction relationship through response surface analysis, and obtain the gas phase component diffusion coefficient; (5) Calculate the response value of the mass transfer process based on the gas phase component diffusion coefficient, solve the spatial distribution of the component concentration through partial differential equations, and obtain the component diffusion characteristics; (6) Conduct multi-objective optimization analysis based on the component diffusion characteristics, determine the optimal process parameter combination through the gradient descent method, and obtain the optimized chemical reaction mass transfer conditions.

[0033] Specifically, as Figure 2 shown, it is a timing diagram for obtaining the diffusion characteristics of gas phase components through response surface analysis in the embodiment of the present application. For the processing of momentum transfer data, first establish a flow rate matrix expression:

[0034] where is the flow rate matrix, represents the flow weight factor of the i-th component, is the component flow rate vector, is the component dynamic viscosity. This equation describes the relationship between the flow characteristics of the carrier gas and the precursor gas.

[0035] The construction of the temperature gradient field adopts the thermodynamic equilibrium equation:

[0036] where is the temperature field distribution function, is the time variable, is the thermal conductivity of the k-th region, is the reaction heat factor, is the enthalpy change of reaction.

[0037] The establishment of the pressure field data set is based on the equation of state:

[0038] where is the pressure field function, is the component pressure coefficient, is the component activity coefficient, is the Gibbs free energy, R is the gas constant, and T is the temperature.

[0039] For the Box-Behnken experimental design, the response surface equation is constructed:

[0040] where is the response value, is the constant term, is the coefficient of the first-order term, is the coefficient of the second-order term, is the coefficient of the interaction term, and are the factor variables.

[0041] The response value calculation of the mass transfer process adopts the partial differential equation:

[0042] where is the component concentration function, is the effective diffusion coefficient matrix, is the reaction source term.

[0043] Multi-objective optimization adopts the objective function:

[0044] where is the comprehensive optimization objective, is the weight coefficient, is the l-th optimization objective function, For optimization variables.

[0045] Analyze the gas flow in the reaction chamber and establish a flow matrix including carrier gas and precursor , . Based on the analytic hierarchy process, determine the key influencing factors of the carrier gas flow rate and set the flow rate change range. Then, using the principle of thermodynamic equilibrium, analyze the reaction equilibrium state at different temperatures and establish a temperature distribution sequence. According to the temperature distribution data, combine with the thermodynamic state equation to calculate the change law of the chamber pressure. Adopt the Box-Behnken experimental design method, taking the carrier gas flow rate, reaction temperature, and chamber pressure as independent variables, and construct a three-factor response surface model. Obtain experimental data through orthogonal experiments and calculate the diffusion coefficient of gas-phase components. Based on the diffusion coefficient data, solve the partial differential equation of the mass transfer process to obtain the spatial distribution characteristics of the component concentration. Through the multi-objective optimization algorithm, comprehensively consider factors such as reaction rate and product uniformity to determine the optimal process parameter combination.

[0046] For example, in a specific optimization process, first calculate through the flow matrix to determine that the change range of the carrier gas flow rate is 5 - 20 L / min. Based on this flow rate range, combine with thermodynamic equilibrium calculations to obtain a temperature response range of 800 - 1100 °C. Calculate through the state equation to determine the corresponding chamber pressure range of 50 - 200 kPa. Adopt the Box-Behnken design scheme, conduct experiments on 17 groups of process parameter combinations, and obtain gas-phase component diffusion coefficient data. Obtain the component concentration distribution by solving the partial differential equation, and determine the optimal process parameters through multi-objective optimization: carrier gas flow rate of 12 L / min, reaction temperature of 900 °C, and chamber pressure of 100 kPa.

[0047] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Decompose the optimized chemical reaction mass transfer conditions into concentration gradient data, calculate the gas-phase diffusion resistance coefficient through the mass transfer equation, and obtain the mass transfer resistance parameter; (2) Construct a surface potential energy distribution function according to the mass transfer resistance parameter, determine the molecular adsorption energy through quantum chemical calculations, and obtain the surface adsorption potential energy value; (3) Establish a molecular migration path matrix based on the surface adsorption potential energy value, calculate the interfacial mass transfer rate through the reaction coordination number, and obtain the precursor conversion kinetic equation; (4) Use the precursor conversion kinetic equation for mass transfer-kinetic coupling analysis, calculate the energy transfer efficiency through the Maxwell-Boltzmann distribution, and obtain the reaction rate constant; (5) Based on the reaction rate constant, construct an interfacial mass transfer flux distribution, calculate the component concentration field through reaction microkinetics, and obtain the gas-solid interface mass transfer coefficient. (6) Correlate the gas-solid interfacial mass transfer coefficient with the surface reaction rate, and determine the kinetic parameter group through nonlinear regression analysis to obtain the kinetic prediction model of the deposition reaction.

[0048] Specifically, in the optimization process of the aluminosilicate fiber chemical vapor deposition process, by quantitatively analyzing the optimized chemical reaction mass transfer conditions, first establish the concentration gradient mass transfer resistance equation:

[0049] where is the concentration of the gas-phase component, is the mass transfer diffusion tensor, is the boundary layer correction coefficient, is the source term, and this equation describes the distribution characteristics of the gas-phase diffusion resistance.

[0050] Surface potential energy distribution is expressed by the molecular potential energy field equation:

[0051] where is the surface active potential energy, is the molecular spacing, is the characteristic length, is the weighting coefficient, is the surface state energy.

[0052] The molecular migration path matrix is described by the interfacial mass transfer kinetic equation:

[0053] where is the molecular migration path matrix, is the interfacial mass transfer coefficient, is the coordination number correction factor, is the reaction rate term.

[0054] The energy transfer efficiency is expressed by the improved Maxwell-Boltzmann distribution:

[0055] where is the number of energy states, E is the transferred energy, is the ground state energy, is the Boltzmann constant, is the surface temperature.

[0056] The interfacial mass transfer flux distribution adopts the microscopic kinetic equation:

[0057] where is the interfacial mass transfer coefficient, is the chemical potential gradient, is the reaction rate constant, is the velocity vector. The accurate prediction of the gas-phase deposition process of aluminosilicate fibers is realized through numerical solution. In practical applications, the process conditions (such as reaction temperature 900 °C, pressure 100 kPa) are first substituted into the concentration gradient mass transfer resistance equation to obtain the mass transfer resistance coefficient. Based on this coefficient, the surface potential energy distribution is calculated, and a molecular migration path matrix is established. Combining the Maxwell-Boltzmann distribution to analyze the energy transfer efficiency, the kinetic parameter group is determined through the interfacial mass transfer flux distribution.

[0058] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Decompose the kinetic prediction model of the deposition reaction into an equilibrium state data set, determine the temperature-pressure change law according to the thermodynamic relationship, and obtain the chemical reaction equilibrium parameters; (2) Conduct isothermal thermogravimetric measurements on the chemical reaction equilibrium parameters, analyze the change trend of the pre-exponential factor according to the mass loss curve, and obtain the kinetic activation parameters; (3) Set the temperature increase process based on the kinetic activation parameters, combine the differential thermal curve to measure the reaction enthalpy change data, and obtain the reaction activation energy; (4) Analyze the gas-solid two-phase interface transfer process using the reaction activation energy, measure the diffusion flux according to the component concentration gradient, and obtain the interfacial mass transfer coefficient; (5) Evaluate the mass transfer resistance according to the interfacial mass transfer coefficient, measure the mass transfer rate through the concentration field distribution, and obtain the mass transfer optimization parameters; (6) Construct a process parameter combination based on the mass transfer optimization parameters, combine the mass transfer-kinetic equilibrium relationship to screen the optimal conditions, and obtain the best reaction conversion conditions.

[0059] Specifically, converting the kinetic prediction model into an equilibrium state data set specifically includes reaction data points every 50 °C within the temperature range of 800 - 1100 °C, and data points every 25 kPa within the corresponding pressure range of 50 - 200 kPa. Through thermodynamic relationship analysis, establish the correlation curve between temperature and pressure, and record the equilibrium pressure value corresponding to each temperature point. Based on these data points, construct a chemical reaction equilibrium parameter set, including key parameters such as equilibrium constant and equilibrium conversion rate. Input the obtained chemical reaction equilibrium parameters into a thermogravimetric analyzer for isothermal measurement. Keep at each temperature point for 30 minutes, and record the change curve of the sample mass over time. Analyze the change law of the pre-exponential factor with temperature according to the slope change of the mass loss curve. For each isothermal section, calculate the mass loss rate, establish the pre-exponential factor-temperature relationship, and obtain the kinetic activation parameters.

[0060] Based on the obtained kinetic activation parameters, a programmed temperature rise process is designed. The heating rate is set at 10 °C / min, rising from room temperature to 1200 °C while performing differential thermal analysis. Through the peak position and peak area on the differential thermal curve, the enthalpy change during the reaction process is calculated. Combining the reaction rate data at different temperatures, the reaction activation energy is calculated using the Arrhenius equation. Using the reaction activation energy data, the transfer process at the gas-solid two-phase interface is analyzed. By establishing a concentration gradient field, the gas-phase component concentrations at different positions are measured. Based on Fick's diffusion law, the diffusion flux at the interface is calculated to determine the interfacial mass transfer coefficient. This process needs to consider the gas flow state and boundary layer effects.

[0061] The obtained interfacial mass transfer coefficient is systematically evaluated, and a mass transfer resistance model is established. By measuring the concentration distribution at different positions in the reaction chamber, the local mass transfer resistance is calculated. Combining fluid mechanics theory, the resistance factors during the mass transfer process are analyzed to determine the mass transfer optimization parameters. Based on the mass transfer optimization parameters, a process parameter combination scheme is designed. Considering factors such as carrier gas flow rate, temperature, and pressure, a multivariable optimization model is established. By analyzing the equilibrium relationship between mass transfer and kinetics, the best process parameter combination is screened. This process needs to comprehensively consider multiple objectives such as reaction rate, conversion rate, and product uniformity.

[0062] For example, in actual process optimization, first, the reaction equilibrium at 900 °C is analyzed. Through thermogravimetric analysis, the mass loss curve at this temperature is measured, and it is found that the mass loss rate is relatively fast within the first 10 minutes and then tends to be stable. Combining the differential thermal analysis results, the reaction activation energy is calculated. Based on the activation energy data, the interfacial mass transfer coefficient is measured, and it is found that the mass transfer effect is the best at a pressure of 100 kPa. Through parameter optimization, the best process parameter combination of a carrier gas flow rate of 12 L / min, a reaction temperature of 900 °C, and a chamber pressure of 100 kPa is determined.

[0063] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Map the temperature range corresponding to the optimal reaction conversion conditions to the substrate surface treatment parameters, and perform surface cleaning through argon plasma bombardment to obtain a surface-clean substrate; (2) Perform radio frequency plasma treatment on the surface-clean substrate, and control the surface hydroxyl density by adjusting the radio frequency power and exposure time to obtain surface active sites; (3) Mix the surface active sites with an aluminum alkoxide sol, and regulate the crystal nucleus formation process by controlling the sol pH value and hydrolysis rate to obtain a seed precursor; (4) Perform gelation treatment on the seed precursor, and control the seed size distribution by adjusting the aging time and drying temperature to obtain a seed layer; (5) Heat-treat the seed layer in a nitrogen atmosphere, and adjust the crystal phase structure by controlling the heating rate and heat-treatment time to obtain an oriented growth substrate; (6) Analyze the surface morphology of the oriented growth substrate, monitor the distribution of surface active sites through a scanning probe microscope, and obtain a reaction interface with oriented growth activity.

[0064] Specifically, take the temperature range (800 - 1100 °C) corresponding to the optimal reaction conversion conditions as the reference parameter for substrate surface treatment, and design surface treatment processes for different temperature points. During the substrate cleaning process, use an argon plasma source, set the argon flow rate to 20 sccm, the radio frequency power to 200 W, and maintain the working pressure at 0.5 Pa. The plasma bombardment process lasts for 10 minutes, and monitor the surface carbon-oxygen ratio through X-ray photoelectron spectroscopy (XPS). When the carbon-oxygen ratio drops below 0.1, it indicates that the surface cleaning is completed.

[0065] Perform radio frequency plasma treatment on the obtained surface-cleaned substrate. The radio frequency power range is 100 - 300 W, and the treatment time is 5 - 15 minutes. Control the hydroxyl density on the substrate surface by adjusting the radio frequency power and exposure time. Use attenuated total reflection infrared spectroscopy (ATR-FTIR) to monitor the intensity change of the surface hydroxyl peak in real time. When the -OH stretching vibration peak at [specific position] reaches the predetermined intensity, the required surface active sites are obtained. Subsequently, modify the surface of the surface active sites with an aluminum alkoxide sol. The preparation of the aluminum alkoxide sol uses aluminum n-butoxide as the precursor, and adjusts the hydrolysis process by controlling the pH value (4 - 6) and hydrolysis conditions (water-alcohol ratio 1:10 - 1:20) of the sol. Use dynamic light scattering (DLS) to monitor the evolution of the sol particle size. When the particle size distribution reaches 50 - 100 nm, the seed precursor is obtained.

[0066] Age the seed precursor, control the aging temperature at 60 °C, and the time is 24 - 72 hours. Control the size distribution of the seeds by adjusting the aging time and drying temperature (100 - 200 °C). Observe the morphology and size of the seeds using transmission electron microscopy (TEM), and analyze the crystal phase structure in combination with X-ray diffraction (XRD) to ensure that a uniform seed layer is obtained. The heat treatment of the seed layer is carried out in a nitrogen atmosphere, the gas flow rate is 50 sccm, the heating rate is controlled at 1 - 5 °C / min, and the temperature is 600 - 800 °C. The heat treatment time is 2 - 6 hours, and monitor the evolution process of the crystal phase through in-situ XRD until the required crystal phase structure is formed to obtain an oriented growth substrate.

[0067] Perform surface topography analysis on the substrate for directional growth. Use an atomic force microscope (AFM) to scan the surface topography, with a scanning range of 5 μm × 5 μm and a resolution of 512 × 512 points. Measure the distribution and intensity of surface active sites through force spectroscopy curves, and combine with Kelvin probe force microscopy (KPFM) to measure the surface potential distribution to determine the spatial distribution characteristics of the active sites.

[0068] For example, in the actual process, when the reaction conversion temperature is determined to be 900 °C, argon plasma with a power of 200 W is preferably used to clean the substrate surface. It is found by XPS monitoring that the surface carbon-oxygen ratio drops from 0.5 to 0.08 after 10 minutes of treatment. Subsequently, 200 W radio frequency plasma treatment is carried out for 10 minutes, and ATR-FTIR shows that the intensity of the hydroxyl peak reaches a predetermined value. The pH value of the aluminum alkoxide sol is controlled at 5.0, the water-alcohol ratio is 1:15, and DLS measurement shows that the average particle size is 75 nm. After aging at 60 °C for 48 hours, TEM observation shows that the seed crystal sizes are uniform. Heat up to 700 °C at 2 °C / min in a nitrogen atmosphere and hold for 4 hours, and XRD shows that a good crystal phase structure is formed. Analyze the surface active site distribution by AFM and KPFM to obtain a reaction interface with directional growth activity.

[0069] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Take the reaction interface with directional growth activity as the initial condition, monitor the change of the concentration of the gas-phase precursor through spectral analysis, and obtain the precursor conversion rate data; (2) Collect the precursor conversion rate data in real time, observe the fiber growth morphology and orientation angle through a scanning electron microscope, and obtain the product morphology parameters; (3) Input the product morphology parameters into the feedback loop, adjust the carrier gas flow rate and reaction temperature through the deviation signal, and obtain the process parameter correction value; (4) Adjust the mass transfer process of the process parameter correction value, and obtain the optimized mass transfer conditions by controlling the reaction chamber pressure and gas flow field distribution; (5) Monitor the crystal growth rate according to the optimized mass transfer conditions, and determine the lattice orientation and crystallinity through X-ray diffraction to obtain the structure characteristic data; (6) Optimize the growth environment based on the structure characteristic data, and obtain aluminosilicate fibers with controllable structures by feedback control to adjust the reaction mass transfer process.

[0070] Specifically, taking the reaction interface with directional growth activity as the initial condition, use an infrared spectrometer to and monitor the change of the concentration of the gas-phase precursor in real time. Calculate the conversion rate of the precursor through the change of the characteristic peak intensity, where at The Si-Cl stretching vibration peak at and the Al-Cl stretching vibration peak at are used as the basis for quantitative analysis. Spectral data is collected once per minute to establish a concentration-time relationship curve.

[0071] The collected precursor conversion rate data is processed, and at the same time, the morphology of the product is observed in real time using a field emission scanning electron microscope. Under the condition of an acceleration voltage of 15 kV, secondary electron images are collected every 5 minutes to measure the diameter distribution and orientation angle of the fibers. Image analysis is processed using professional software to extract the morphological characteristic parameters of the fibers, including data such as average diameter, length distribution, and orientation angle deviation. The obtained product morphology parameters are compared with the preset target values to calculate the deviation signal. For different deviation types, corresponding parameter adjustment strategies are established. When the fiber diameter is too large, the carrier gas flow rate is reduced; when the orientation angle deviation is large, the reaction temperature is adjusted. The adjustment step size of the corrected parameters is dynamically determined according to the size of the deviation to ensure the stability of the adjustment process.

[0072] The mass transfer process is adjusted for the corrected process parameters, with a focus on the reaction chamber pressure and gas flow field distribution. The intake air volume is adjusted through a mass flow controller, and the chamber pressure is monitored by a pressure sensor. The gas flow field distribution is calculated using numerical simulation, and the gas inlet position and flow direction are adjusted to optimize the mass transfer conditions.

[0073] Based on the optimized mass transfer conditions, the crystal growth process is monitored. In-situ analysis is carried out using an X-ray diffractometer, and diffraction spectra are collected every 10 minutes. The lattice parameters and preferred orientation coefficients are calculated through the changes in the position and intensity of the diffraction peaks. The grain size is calculated in combination with the Scherrer formula to evaluate the crystallinity. According to the structural characteristic data, the growth environment is dynamically optimized. A feedback control loop is established, and the change trend of the structural parameters is used as the control basis to adjust the reaction mass transfer process in real time. When the crystal orientation deviates, the growth direction is corrected by adjusting the gas flow field; when the crystallinity decreases, the crystallization quality is improved by adjusting the temperature.

[0074] For example, in the actual process, after the start of chemical vapor deposition, it is found through infrared spectroscopy monitoring that the precursor concentration gradually decreases, and the conversion rate data is calculated. Scanning electron microscope observation shows that the fiber diameter fluctuates in the range of 0.5 - 1 μm, and the orientation angle deviation is 10 - 15°. Based on these data, the carrier gas flow rate is adjusted from 12 L / min to 10 L / min, and the temperature is finely adjusted from 900 °C to 920 °C. The adjusted mass transfer conditions increase the intensity of the diffraction peak of the (001) crystal plane in the X-ray diffraction pattern, indicating that the orientation has been improved. Through continuous monitoring and adjustment, aluminosilicate fibers with uniform structure and consistent orientation are obtained.

[0075] The above describes the process optimization method for vapor deposition of aluminosilicate fibers in the embodiments of the present application. Next, the process optimization device for vapor deposition of aluminosilicate fibers in the embodiments of the present application will be described. Please refer to Figure 3 An embodiment of the process optimization device for vapor deposition of aluminosilicate fibers in the embodiments of the present application includes: A simulation module 201, configured to establish a chemical reaction mass transfer model through the component diffusion parameter and heat conduction coefficient of the reaction chamber, and perform numerical simulation using the kinetic characteristics of the mixed gas of the gas-phase precursor and to obtain a mass transfer prediction model in the reaction chamber; A setting module 202, configured to set parameters of the carrier gas flow rate, reaction temperature, and chamber pressure according to the mass transfer prediction model, and obtain the optimized chemical reaction mass transfer conditions by the response surface analysis method to obtain the diffusion characteristics of the gas-phase components; A calculation module 203, configured to construct a precursor conversion kinetic equation using the optimized chemical reaction mass transfer conditions, and obtain a kinetic prediction model of the deposition reaction through the coupled calculation of the gas-phase mass transfer resistance and surface adsorption potential energy; A measurement module 204, configured to set chemical reaction equilibrium parameters according to the kinetic prediction model of the deposition reaction, measure the reaction activation energy by thermal analysis method, and optimize using the gas-solid interface mass transfer coefficient to obtain the optimal reaction conversion conditions; An activation module 205, configured to chemically activate the surface of the reaction substrate according to the optimal reaction conversion conditions, form surface active sites through plasma treatment, and construct a seed layer using the sol-gel method to obtain a reaction interface with directional growth activity; A monitoring module 206, configured to design a process control system based on the reaction interface with directional growth activity, adjust the reaction mass transfer process using the feedback control algorithm by real-time monitoring of the precursor conversion rate and product morphology parameters, and obtain aluminosilicate fibers with controllable structures.

[0076] Through the collaborative cooperation of the above-mentioned components, by establishing a chemical reaction mass transfer model including component diffusion parameters and heat conduction coefficients, the gas-phase precursor and The accurate description of the kinetic characteristics of the mixed gas provides an accurate prediction basis for the mass transfer process in the reaction chamber. Based on the mass transfer prediction model, the parameter optimization of the carrier gas flow rate, reaction temperature, and chamber pressure, combined with the response surface analysis method, established a quantitative relationship between the diffusion characteristics of gas-phase components and process parameters, significantly improving the mass transfer conditions of chemical reactions. Through in-depth analysis of the optimized mass transfer conditions of chemical reactions, a coupled calculation model of gas-phase mass transfer resistance and surface adsorption potential energy was established, obtaining an accurate deposition reaction kinetics prediction model, providing a theoretical basis for the precise control of process parameters. On this basis, the reaction activation energy was determined by thermal analysis, combined with the optimization of the gas-solid interface mass transfer coefficient, and the optimal reaction conversion conditions were determined, improving the reaction efficiency. The surface of the reaction substrate was chemically activated, and a seed layer was constructed by plasma treatment and sol-gel method to obtain a reaction interface with directional growth activity, creating favorable conditions for the directional growth of fibers. Finally, a process control system based on real-time monitoring and feedback control was established. By continuously monitoring the precursor conversion rate and product morphology parameters, the dynamic optimization of the reaction mass transfer process was achieved, and aluminosilicate fibers with controllable structures were obtained. The algorithm features in the entire solution, especially the applications in the construction of the mass transfer prediction model, response surface analysis, and feedback control, significantly improved the accuracy of process parameter optimization and the real-time performance of process control.

[0077] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A process optimization method for vapor deposition of aluminum silicate fiber, characterized in that: The process optimization method of vapor-deposited aluminum silicate fiber comprises: The chemical reaction mass transfer model is established by using the component diffusion parameters and thermal conductivity coefficient of the reaction chamber. and The mixed gas dynamic characteristics are numerically simulated to obtain the mass transfer prediction model in the reaction chamber; The carrier gas flow rate, reaction temperature and chamber pressure are set according to the mass transfer prediction model, and the diffusion characteristics of the gas phase components are obtained by response surface analysis to obtain the optimized chemical reaction mass transfer conditions; The optimized chemical reaction mass transfer conditions are used to construct a precursor conversion kinetic equation, and a kinetic prediction model of the deposition reaction is obtained by coupling calculation of gas phase mass transfer resistance and surface adsorption potential energy; The chemical reaction equilibrium parameters are set according to the kinetic prediction model of the deposition reaction, the reaction activation energy is determined by thermal analysis, and the gas-solid interface mass transfer coefficient is optimized to obtain the optimal reaction conversion conditions; Chemically activating the surface of the reaction substrate according to the optimal reaction conversion conditions, forming surface active sites by plasma treatment, constructing a seed layer by a sol-gel method, and obtaining a reaction interface with directional growth activity; A process control system is designed based on the reaction interface with directional growth activity. The precursor conversion rate and product morphology parameters are monitored in real time, and the reaction mass transfer process is adjusted using a feedback control algorithm to obtain structurally controllable aluminum silicate fibers.

2. The process optimization method for vapor-deposited aluminum silicate fiber according to claim 1, characterized in that: The chemical reaction mass transfer model is established by using the component diffusion parameters and thermal conductivity coefficient of the reaction chamber, and the gas phase precursor and The mixed gas dynamic characteristics are numerically simulated to obtain the mass transfer prediction model in the reaction chamber, including: The Maxwell-Stefan diffusion equation is established based on the molecular mass and collision cross section of the reactant gas, and the component diffusion parameters are obtained by combining the calculation formula of the thermal conductivity of the gas mixture; A mass transfer control equation is constructed according to the component diffusion parameters, and the flow characteristics of the gas phase mixture are calculated by the Navier-Stokes equation to obtain the momentum transfer coefficient; The energy conservation equation is established by using the momentum transfer coefficient, and the temperature field distribution is solved by numerical iteration to obtain the heat conduction coefficient; Substituting the heat conductivity coefficient into the chemical reaction kinetics equation, calculating the reaction rate constant by the finite difference method, and obtaining the precursor conversion rate; A chemical reaction mass transfer model is constructed according to the precursor conversion rate, and the component concentration gradient is calculated by Fick's diffusion law to obtain the gas phase diffusion flux; The gas phase diffusion flux is used to establish a material balance equation, and the mass transfer resistance is analyzed in combination with the boundary layer theory to obtain a mass transfer prediction model.

3. The process optimization method for vapor-deposited aluminum silicate fiber according to claim 1, characterized in that: The method of setting the parameters of the carrier gas flow rate, reaction temperature and chamber pressure according to the mass transfer prediction model, obtaining the diffusion characteristics of the gas phase components by the response surface analysis method, and obtaining the optimized chemical reaction mass transfer conditions includes: The momentum transfer data in the mass transfer prediction model is converted into a flow matrix, and the variation range of the carrier gas flow is determined by hierarchical analysis to obtain a flow parameter table; Constructing a temperature gradient field for the flow parameter table, determining the response range of the reaction temperature through thermodynamic equilibrium calculation, and obtaining a temperature distribution sequence; The temperature distribution sequence is used to establish a pressure field data set, and the change law of the cavity pressure is calculated by the thermodynamic state equation to obtain the pressure control parameter; The pressure control parameters are input into the Box-Behnken experimental design scheme, and the three-factor interaction relationship is constructed by response surface analysis to obtain the diffusion coefficient of the gas phase component; The response value of the mass transfer process is calculated based on the diffusion coefficient of the gas phase component, and the spatial distribution of the component concentration is solved by a partial differential equation to obtain the component diffusion characteristics; A multi-objective optimization analysis is performed based on the diffusion characteristics of the components, and the optimal process parameter combination is determined by the gradient descent method to obtain the optimized chemical reaction mass transfer conditions.

4. The process optimization method for vapor-deposited aluminum silicate fiber according to claim 1, characterized in that: The method uses the optimized chemical reaction mass transfer conditions to construct a precursor conversion kinetic equation, and obtains a kinetic prediction model of a deposition reaction by coupling calculation of gas phase mass transfer resistance and surface adsorption potential energy, including: Decomposing the optimized chemical reaction mass transfer conditions into concentration gradient data, calculating the gas phase diffusion resistance coefficient through the material transfer equation, and obtaining the mass transfer resistance parameter; Constructing a surface potential energy distribution function according to the mass transfer resistance parameter, determining the molecular adsorption energy through quantum chemical calculation, and obtaining the surface adsorption potential energy value; A molecular migration path matrix is ​​established according to the surface adsorption potential energy value, and the interface mass transfer rate is calculated by the reaction coordination number to obtain the precursor conversion kinetic equation; The precursor conversion kinetic equation is used to perform mass transfer-kinetic coupling analysis, and the energy transfer efficiency is calculated by Maxwell-Boltzmann distribution to obtain the reaction rate constant; Based on the reaction rate constant, the interface mass transfer flux distribution is constructed, and the component concentration field is calculated by the reaction microdynamics to obtain the gas-solid interface mass transfer coefficient; The gas-solid interface mass transfer coefficient is correlated with the surface reaction rate, and a kinetic parameter group is determined by nonlinear regression analysis to obtain a kinetic prediction model for the deposition reaction.

5. The process optimization method for vapor-deposited aluminum silicate fiber according to claim 1, characterized in that: The chemical reaction equilibrium parameters are set according to the kinetic prediction model of the deposition reaction, the reaction activation energy is determined by thermal analysis, and the gas-solid interface mass transfer coefficient is optimized to obtain the optimal reaction conversion conditions, including: Decomposing the kinetic prediction model of the deposition reaction into equilibrium data sets, determining the temperature-pressure variation law according to the thermodynamic relationship, and obtaining the chemical reaction equilibrium parameters; The chemical reaction equilibrium parameters are measured by thermogravimetric isothermal section, and the change trend of the pre-factor is analyzed according to the mass loss curve to obtain the kinetic activation parameters; The temperature rise process is set based on the kinetic activation parameters, and the reaction enthalpy change data is measured in combination with the differential thermal curve to obtain the reaction activation energy; The reaction activation energy is used to analyze the gas-solid two-phase interface transfer process, and the diffusion flux is measured according to the component concentration gradient to obtain the interface mass transfer coefficient; The mass transfer resistance is evaluated according to the interface mass transfer coefficient, the mass transfer rate is measured by the concentration field distribution, and the mass transfer optimization parameters are obtained; A process parameter combination is constructed based on the mass transfer optimization parameters, and the optimal conditions are screened in combination with the mass transfer-kinetic equilibrium relationship to obtain the best reaction conversion conditions.

6. The process optimization method for vapor-deposited aluminum silicate fiber according to claim 1, characterized in that: The method of chemically activating the surface of the reaction substrate according to the optimal reaction conversion conditions, forming surface active sites by plasma treatment, and constructing a seed layer by a sol-gel method to obtain a reaction interface with directional growth activity includes: Mapping the temperature range corresponding to the optimal reaction conversion conditions to the substrate surface treatment parameters, and performing surface cleaning by argon plasma bombardment to obtain a surface-cleaned substrate; Performing radio frequency plasma treatment on the surface clean substrate, controlling the surface hydroxyl density by adjusting the radio frequency power and exposure time, and obtaining surface active sites; The surface active sites are mixed with aluminum alkoxide sol, and the crystal nucleus formation process is adjusted by controlling the pH value and hydrolysis rate of the sol to obtain a seed crystal precursor; Performing gelation treatment on the seed crystal precursor, controlling the seed crystal size distribution by adjusting the aging time and the drying temperature, and obtaining a seed crystal layer; The seed layer is heat-treated in a nitrogen atmosphere, and the crystal phase structure is adjusted by controlling the heating rate and the heat treatment time to obtain an oriented growth substrate; The surface morphology of the directional growth substrate is analyzed, and the distribution of surface active sites is monitored by scanning probe microscopy to obtain a reaction interface with directional growth activity.

7. The process optimization method for vapor-deposited aluminum silicate fiber according to claim 1, characterized in that: The reaction interface design process control system based on the directional growth activity monitors the precursor conversion rate and product morphology parameters in real time, and uses a feedback control algorithm to adjust the reaction mass transfer process to obtain a structure-controllable aluminum silicate fiber, including: Taking the reaction interface with the directional growth activity as the initial condition, monitoring the concentration change of the gas phase precursor by spectral analysis, and obtaining precursor conversion rate data; The precursor conversion rate data is collected in real time, and the fiber growth morphology and orientation angle are observed by scanning electron microscopy to obtain product morphology parameters; The product morphology parameters are input into a feedback loop, and the carrier gas flow rate and the reaction temperature are adjusted by the deviation signal to obtain a process parameter correction value; The process parameter correction value is adjusted for the mass transfer process, and the optimized mass transfer conditions are obtained by adjusting the reaction chamber pressure and the gas flow field distribution; Monitoring the crystal growth rate according to the optimized mass transfer conditions, determining the lattice orientation and crystallinity by X-ray diffraction, and obtaining structural characteristic data; The growth environment is optimized according to the structural characteristic data, and the reaction mass transfer process is adjusted through feedback control to obtain structurally controllable aluminum silicate fibers.

8. A process optimization device for vapor-deposited aluminum silicate fibers, used to implement the process optimization method for vapor-deposited aluminum silicate fibers as described in any one of claims 1 to 7, characterized in that: The process optimization device for vapor-deposited aluminum silicate fibers comprises: The simulation module is used to establish a chemical reaction mass transfer model based on the component diffusion parameters and thermal conductivity of the reaction chamber, using the gas phase precursor and The mixed gas dynamic characteristics are numerically simulated to obtain the mass transfer prediction model in the reaction chamber; A setting module is used to set the parameters of carrier gas flow rate, reaction temperature and chamber pressure according to the mass transfer prediction model, obtain the diffusion characteristics of gas phase components through response surface analysis, and obtain the optimized chemical reaction mass transfer conditions; A calculation module, for constructing a precursor conversion kinetic equation using the optimized chemical reaction mass transfer conditions, and obtaining a kinetic prediction model of a deposition reaction by coupling calculation of gas phase mass transfer resistance and surface adsorption potential energy; A determination module, used to set chemical reaction equilibrium parameters according to the kinetic prediction model of the deposition reaction, determine the reaction activation energy by thermal analysis, and optimize the gas-solid interface mass transfer coefficient to obtain the optimal reaction conversion conditions; An activation module, used to chemically activate the surface of the reaction substrate according to the optimal reaction conversion conditions, form surface active sites by plasma treatment, construct a seed layer by sol-gel method, and obtain a reaction interface with directional growth activity; The monitoring module is used to design a process control system based on the reaction interface of the directional growth activity, and the reaction mass transfer process is adjusted by real-time monitoring of the precursor conversion rate and product morphology parameters using a feedback control algorithm to obtain a structure-controllable aluminum silicate fiber.

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