Process optimization method and device for vapor deposition of aluminosilicate fibers

Abstract

The present application relates to the field of chemical vapor deposition technology, and discloses a process optimization method and device for chemical vapor deposition of aluminosilicate fibers. The method includes: establishing a chemical reaction mass transfer model through the component diffusion parameters of the reaction chamber 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 using the mass transfer conditions to obtain a kinetic prediction model; setting equilibrium parameters according to the prediction model to obtain the optimal reaction conditions; treating the substrate surface according to the reaction conditions to obtain an oriented growth interface; designing a control system based on the growth interface to obtain aluminosilicate fibers with controllable structures. The present application realizes the control of the uniformity of the aluminosilicate fiber structure, establishes an accurate mass transfer prediction model, optimizes the substrate surface treatment process, and realizes the real-time monitoring and control of the deposition process by optimizing the chemical vapor deposition process parameters and process control methods.

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, widely used in fields such as aerospace, metallurgy, and petrochemical industry. Traditional preparation methods of aluminosilicate fibers mainly include melt spinning method, sol-gel method, and chemical vapor deposition method. Among them, chemical vapor deposition method has received extensive attention due to its advantages such as controllable fiber structure and high purity. In the existing chemical vapor deposition process, and are usually used as precursors, which 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 structural uniformity and orientation consistency. This is mainly because there is a lack of in-depth understanding of the gas mass transfer process and surface growth kinetics in the traditional process, 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, thereby affecting 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 achieve the control of the structural uniformity 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 the 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 coefficient of the reaction chamber, and using the gas-phase precursor and Numerically simulate the kinetic 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 response surface analysis to obtain optimized chemical reaction mass transfer conditions; use the optimized chemical reaction mass transfer conditions to construct a kinetic equation for precursor conversion, 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 best reaction conversion conditions; chemically activate the surface of the reaction substrate according to the best 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; design a process control system based on the reaction interface with directional growth activity, and adjust the reaction mass transfer process using a 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 vapor deposition of aluminosilicate fibers. The process optimization device for vapor deposition of aluminosilicate fibers includes:

[0007] 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 kinetic characteristics of the mixed gas of the gas-phase precursor and to obtain a mass transfer prediction model in the reaction chamber;

[0008] 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 response surface analysis to obtain optimized chemical reaction mass transfer conditions;

[0009] A calculation module for constructing a kinetic equation for precursor conversion 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;

[0010] A determination 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 best reaction conversion conditions;

[0011] An activation module for chemically activating the surface of the reaction substrate according to the best reaction conversion conditions, forming surface active sites through plasma treatment, and constructing a seed layer using the sol-gel method to obtain a reaction interface with directional growth activity;

[0012] A monitoring module is used to regulate a 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 a feedback control algorithm to adjust the reaction mass transfer process, aluminosilicate fibers with controllable structures are obtained.

[0013] 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, a quantitative relationship between the gas-phase component diffusion characteristics and process parameters is established, significantly improving the chemical reaction mass transfer conditions. By deeply analyzing 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. Combining 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 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 whole 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] 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, other drawings can be obtained based on these drawings without creative efforts.

[0015] 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;

[0016] 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;

[0017] Figure 3 Schematic diagram of an embodiment of the process optimization device for vapor deposition of aluminosilicate fibers in the embodiments of the present application;

[0018] Reference numerals: simulation module 201, setting module 202, calculation module 203, measurement module 204, activation module 205, monitoring module 206. Detailed implementation manners

[0019] The embodiments of the present application provide a process optimization method and device for 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 necessarily need to be used to describe a specific order or sequence. It should be understood that such data 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 term "comprising" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily 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.

[0020] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the process optimization method for vapor deposition of aluminosilicate fibers in the embodiments of the present application includes:

[0021] Step S101: Establish a chemical reaction mass transfer model based on 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;

[0022] Step S102: 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;

[0023] 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;

[0024] 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 method, and optimize using the gas-solid interface mass transfer coefficient to obtain the optimal reaction conversion conditions;

[0025] 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;

[0026] Step S106: Design a process control system based on the reaction interface with directional growth activity. By real-time monitoring of the precursor conversion rate and product morphology parameters, adjust the reaction mass transfer process using a feedback control algorithm to obtain aluminosilicate fibers with controllable structures.

[0027] 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 used as the execution subject for illustration.

[0028] Specifically, establish a chemical reaction mass transfer model for the reaction chamber. By measuring the diffusion coefficients of gaseous precursors and at different temperatures, establish a component diffusion parameter database. In the temperature range of 800 - 1000 °C, collect a set of diffusion coefficient data every 50 °C, and plot the data points into a diffusion coefficient - temperature relationship curve. At the same time, determine the variation law of the thermal conductivity coefficient of the mixed gas in this temperature range and establish a thermal conductivity coefficient data set. Based on these basic data, use the Maxwell-Stefan diffusion equation to describe the component transfer process and combine the energy conservation equation to characterize the heat transfer characteristics. Optimize the process parameters through the established mass transfer prediction model. Design parameter combinations within the ranges of carrier gas flow rate from 5 - 20 L / min, reaction temperature from 800 - 1100 °C, and chamber pressure from 50 - 200 kPa. Adopt the Box-Behnken experimental design scheme to determine 17 groups of process parameter combinations. Conduct response surface analysis on the gas-phase component diffusion behavior under each set of parameter conditions to obtain the diffusion characteristic data of gas-phase components under different conditions. Determine the optimal process parameter combination through the analysis of response values such as diffusion coefficient and mass transfer rate.

[0029] Based on the optimized mass transfer conditions of chemical reactions, the transformation kinetic characteristics of the precursor are studied. First, the influence of gas-phase mass transfer resistance on the reaction rate is analyzed. By measuring the mass transfer coefficients under different pressure gradients, the correlation between mass transfer resistance and reaction rate is established. At the same time, quantum chemical calculation methods are used to analyze the surface adsorption potential energy distribution, and the adsorption sites and adsorption energy of precursor molecules on the substrate surface are determined. 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 transformation conditions are optimized. The activation energy of the reaction is measured by programmed temperature rise thermal analysis, which is measured 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, the variation law of the reaction rate constant with temperature is analyzed to determine the optimal reaction temperature range. At the same time, the mass transfer characteristics of the gas-solid interface are studied, and the interfacial mass transfer coefficient is optimized by adjusting the gas flow rate and pressure.

[0030] 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 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 sol-gel method, and the size and orientation of the seeds are adjusted by controlling the pH value of the sol, aging time and drying conditions. A process control system is established to achieve precise control of the deposition process. Spectral analysis methods are used to monitor the conversion rate of the precursor in real time, and the morphology characteristics and orientation distribution of the products are observed by scanning electron microscopy. According to the monitoring data, the reaction conditions are adjusted in real time by using feedback control algorithms to ensure that the products have uniform structure and performance.

[0031] 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 real-time monitoring the change of gas-phase component concentration, it is found that the conversion rate of the precursor reaches 85%. At this time, through scanning electron microscopy, it is observed 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 monitoring 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 growth environment is optimized by slightly adjusting the reaction temperature and pressure. This dynamic regulation ensures the stability and consistency of product quality.

[0032] In the embodiments of this application, by establishing a chemical reaction mass transfer model including component diffusion parameters and thermal conductivity 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 is carried out. 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 mass transfer conditions of the chemical reaction, a coupled calculation model of gas-phase mass transfer resistance and surface adsorption potential energy is established, and an accurate deposition reaction kinetic prediction model is obtained, providing a theoretical basis for the precise regulation of process parameters. On this basis, the reaction activation energy is determined by thermal analysis, 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 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 whole scheme, 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.

[0033] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0034] (1) Establish the Maxwell-Stefan diffusion equation based on the molecular mass and collision cross-section of the reactant gas, and combine with the calculation formula of the thermal conductivity of the gas mixture to obtain the component diffusion parameters;

[0035] (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;

[0036] (3) Establish the energy conservation equation using the momentum transfer coefficient, solve the temperature field distribution by numerical iteration, and obtain the thermal conductivity;

[0037] (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;

[0038] (5) Construct the 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;

[0039] (6) Establish the 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.

[0040] Specifically, analyze the molecular characteristics of the gas-phase precursors and Based on the kinetic theory of gases, establish the Maxwell-Stefan diffusion equation:

[0041]

[0042] where, represents the mass fraction of component i, is the velocity vector of the mixed gas, is the density of the mixed 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.

[0043] 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 equation:

[0044]

[0045] where, is the pressure, is the dynamic viscosity, is the acceleration due to gravity, is the external body force.

[0046] Take the momentum transfer coefficient as the input parameter and construct the energy conservation equation:

[0047]

[0048] where, is the specific heat capacity at constant pressure, is the temperature, is the thermal conductivity, is the heat source term.

[0049] For the chemical reaction kinetics equation:

[0050]

[0051] 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.

[0052] Based on the precursor conversion rate, establish a chemical reaction mass transfer model. Through Fick's diffusion law:

[0053]

[0054] Among them, is the diffusion flux of component i, is the diffusion coefficient, is the concentration.

[0055] Construct a material balance equation:

[0056]

[0057] Among them, is the interfacial mass transfer source term.

[0058] By numerical solution, the distribution of key parameters in the gas-phase 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.

[0059] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0060] (1) Convert the momentum transfer data in the mass transfer prediction model into a flow matrix, determine the change range of the carrier gas flow rate through hierarchical analysis, and obtain a flow parameter table;

[0061] (2) Construct a temperature gradient field for the flow parameter table, determine the response range of the reaction temperature through thermodynamic equilibrium calculation, and obtain a temperature distribution sequence;

[0062] (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;

[0063] (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;

[0064] (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;

[0065] (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.

[0066] Specifically, as Figure 2 shown, it is a timing schematic diagram for obtaining the diffusion characteristics of gas-phase components through the response surface analysis method in the embodiment of this application. For the processing of momentum transfer data, first establish a flow matrix expression:

[0067]

[0068] where is the flow 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.

[0069] The construction of the temperature gradient field adopts the thermodynamic equilibrium equation:

[0070]

[0071] 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.

[0072] The establishment of the pressure field data set is based on the state equation:

[0073]

[0074] 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.

[0075] For the Box-Behnken experimental design, construct the response surface equation:

[0076]

[0077] where is the response value, is the constant term, is the coefficient of the linear term, is the coefficient of the quadratic term, is the coefficient of the interaction term, and are the factor variables.

[0078] The response value calculation of the mass transfer process adopts the partial differential equation:

[0079]

[0080] wherein is a function of component concentration, is the effective diffusion coefficient matrix, is the reaction source term.

[0081] The multi-objective optimization adopts the objective function:

[0082]

[0083] wherein is the comprehensive optimization objective, is the weight coefficient, is the l-th optimization objective function, is the optimization variable.

[0084] Analyze the gas flow in the reaction chamber, and establish a flow rate 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 variation law of the chamber pressure. Adopt the Box-Behnken experimental design method, with the carrier gas flow rate, reaction temperature, and chamber pressure as independent variables, to construct a three-factor response surface model. Obtain experimental data through orthogonal experiments, and calculate the diffusion coefficient of the 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.

[0085] For example, in a specific optimization process, first calculate through the flow rate 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 the thermodynamic equilibrium calculation to obtain the 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 the diffusion coefficient data of the gas-phase components. 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 12 L / min, reaction temperature 900 °C, chamber pressure 100 kPa.

[0086] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0087] (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 parameters;

[0088] (2)Construct a surface potential energy distribution function based on the mass transfer resistance parameters, determine the molecular adsorption energy through quantum chemical calculations, and obtain the surface adsorption potential energy value;

[0089] (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;

[0090] (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;

[0091] (5)Construct an interfacial mass transfer flux distribution based on the reaction rate constant, calculate the component concentration field through reaction microkinetics, and obtain the gas-solid interface mass transfer coefficient;

[0092] (6)Correlate and calculate the gas-solid interface mass transfer coefficient with the surface reaction rate, determine the kinetic parameter group through nonlinear regression analysis, and obtain the kinetic prediction model of the deposition reaction.

[0093] Specifically, in the optimization process of the gas-phase deposition of aluminosilicate fibers, by quantitatively analyzing the optimized chemical reaction mass transfer conditions, first establish the concentration gradient mass transfer resistance equation:

[0094]

[0095] 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.

[0096] Surface potential energy distribution It is expressed by the molecular potential field equation:

[0097]

[0098] where is the surface active potential energy, is the molecular spacing, is the characteristic length, is the weight coefficient, is the surface state energy.

[0099] The molecular migration path matrix is described by the interfacial mass transfer kinetic equation:

[0100]

[0101] wherein is the molecular migration path matrix, is the interfacial mass transfer coefficient, is the coordination number correction factor, is the reaction rate term.

[0102] The energy transfer efficiency is expressed by an improved Maxwell-Boltzmann distribution:

[0103]

[0104] wherein is the number of energy states, E is the transferred energy, is the ground state energy, is the Boltzmann constant, is the surface temperature.

[0105] The interfacial mass transfer flux distribution adopts a microkinetic equation:

[0106]

[0107] wherein 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 vapor deposition process of aluminosilicate fibers is achieved through numerical solution. In practical applications, first, the process conditions (such as reaction temperature 900 °C, pressure 100 kPa) are 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 the molecular migration path matrix is established. The energy transfer efficiency is analyzed by combining the Maxwell-Boltzmann distribution, and the kinetic parameter group is determined through the interfacial mass transfer flux distribution.

[0108] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0109] (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;

[0110] (2) Conduct isothermal thermogravimetric measurement on the chemical reaction equilibrium parameters, analyze the trend of the pre-exponential factor according to the mass loss curve, and obtain the kinetic activation parameters;

[0111] (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;

[0112] (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;

[0113] (5) Evaluate the mass transfer resistance based on the interfacial mass transfer coefficient, measure the mass transfer rate through the concentration field distribution, and obtain the mass transfer optimization parameters;

[0114] (6) Construct a combination of process parameters based on the mass transfer optimization parameters, screen the optimal conditions by combining the mass transfer-kinetic equilibrium relationship, and obtain the best reaction conversion conditions.

[0115] Specifically, convert the kinetic prediction model into an equilibrium state data set, including 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 set of chemical reaction equilibrium parameters, including key parameters such as equilibrium constant and equilibrium conversion rate. Input the obtained chemical reaction equilibrium parameters into a thermogravimetric analyzer for isothermal section measurement. Keep it for 30 minutes at each temperature point, and record the change curve of the sample mass over time. According to the slope change of the mass loss curve, analyze the variation law of the pre-exponential factor with temperature. For each isothermal section, calculate the mass loss rate, establish the relationship between the pre-exponential factor and temperature, and obtain the kinetic activation parameters.

[0116] Based on the obtained kinetic activation parameters, design a programmed heating process. The heating rate is set at 10 °C / min, rising from room temperature to 1200 °C, while performing differential thermal analysis. Calculate the enthalpy change during the reaction process through the peak position and peak area on the differential thermal curve. Combine the reaction rate data at different temperatures and use the Arrhenius equation to calculate the reaction activation energy. Use the reaction activation energy data to analyze the transfer process at the gas-solid two-phase interface. By establishing a concentration gradient field, measure the gas phase component concentrations at different positions. Based on Fick's diffusion law, calculate the diffusion flux at the interface, thereby determining the interfacial mass transfer coefficient. This process needs to consider the gas flow state and boundary layer effect.

[0117] Conduct a systematic evaluation of the obtained interfacial mass transfer coefficient and establish a mass transfer resistance model. Calculate the local mass transfer resistance by measuring the concentration distribution at different positions in the reaction chamber. Combine fluid mechanics theory to analyze the resistance factors during the mass transfer process and determine the mass transfer optimization parameters. Based on the mass transfer optimization parameters, design a process parameter combination scheme. Consider factors such as carrier gas flow rate, temperature, and pressure, and establish a multivariable optimization model. By analyzing the equilibrium relationship between mass transfer and kinetics, screen the best combination of process parameters. This process needs to comprehensively consider multiple objectives such as reaction rate, conversion rate, and product uniformity.

[0118] For example, in actual process optimization, first analyze the reaction equilibrium at 900°C. Measure the mass loss curve at this temperature through thermogravimetric analysis and find that the mass loss rate is relatively fast within the first 10 minutes and then tends to be stable. Combine the differential thermal analysis results to calculate the reaction activation energy. Based on the activation energy data, measure the interfacial mass transfer coefficient and find that the mass transfer effect is optimal at a pressure of 100 kPa. Through parameter optimization, determine the optimal 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.

[0119] In a specific embodiment, the process of performing step S105 may specifically include the following steps:

[0120] (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;

[0121] (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;

[0122] (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;

[0123] (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;

[0124] (5) Heat-treat the seed layer in a nitrogen atmosphere, and regulate the crystal phase structure by controlling the heating rate and heat treatment time to obtain an oriented growth substrate;

[0125] (6) Perform surface morphology analysis on the oriented growth substrate, and monitor the distribution of surface active sites through a scanning probe microscope to obtain a reaction interface with oriented growth activity.

[0126] 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.

[0127] The obtained surface-cleaned substrate is subjected to radio frequency plasma treatment with a radio frequency power in the range of 100 - 300 W and a treatment time of 5 - 15 minutes. By adjusting the radio frequency power and exposure time, the hydroxyl density on the substrate surface is controlled. The change in the intensity of the surface hydroxyl peak is monitored in real time using attenuated total reflection infrared spectroscopy (ATR-FTIR). When the -OH stretching vibration peak at [the specific position] reaches a predetermined intensity, the desired surface active sites are obtained. Subsequently, the surface active sites are surface-modified with an aluminum alkoxide sol. The aluminum alkoxide sol is prepared using aluminum n-butoxide as a precursor, and the hydrolysis process is adjusted by controlling the pH value of the sol (4 - 6) and the hydrolysis conditions (water-to-alcohol ratio of 1:10 - 1:20). The evolution of the sol particle size is monitored using dynamic light scattering (DLS). When the particle size distribution reaches 50 - 100 nm, the seed precursor is obtained.

[0128] The seed precursor is aged at a temperature of 60 °C for 24 - 72 hours. By adjusting the aging time and drying temperature (100 - 200 °C), the size distribution of the seeds is controlled. The morphology and size of the seeds are observed using transmission electron microscopy (TEM), and the crystal phase structure is analyzed in combination with X-ray diffraction (XRD) to ensure a uniform seed layer is obtained. The heat treatment of the seed layer is carried out in a nitrogen atmosphere with a gas flow rate of 50 sccm, a heating rate controlled at 1 - 5 °C / min, and a temperature of 600 - 800 °C. The heat treatment time is 2 - 6 hours, and the evolution of the crystal phase is monitored by in-situ XRD until the desired crystal phase structure is formed, obtaining an oriented growth substrate.

[0129] The surface morphology of the oriented growth substrate is analyzed. The surface morphology is scanned using atomic force microscopy (AFM) with a scanning range of 5 μm × 5 μm and a resolution of 512 × 512 points. The distribution and intensity of the surface active sites are measured through force spectroscopy curves, and in combination with Kelvin probe force microscopy (KPFM) to measure the surface potential distribution, the spatial distribution characteristics of the active sites are determined.

[0130] For example, in an actual process, when the reaction conversion temperature is determined to be 900 °C, argon plasma with a power of 200 W is first 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, radio frequency plasma treatment at 200 W for 10 minutes is carried out, and ATR-FTIR shows that the intensity of the hydroxyl peak reaches the predetermined value. The pH value of the aluminum alkoxide sol is controlled at 5.0, and the water-to-alcohol ratio is 1:15. 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 size is uniform. Heating to 700 °C at 2 °C / min in a nitrogen atmosphere and holding for 4 hours, XRD shows that a good crystal phase structure is formed. By analyzing the surface active site distribution using AFM and KPFM, a reaction interface with oriented growth activity is obtained.

[0131] In a specific embodiment, the process of performing step S106 may specifically include the following steps:

[0132] (1) Taking the reaction interface with directional growth activity as the initial condition, monitoring the change of the concentration of the gas-phase precursor by spectroscopic analysis to obtain the precursor conversion data;

[0133] (2) Collecting the precursor conversion data in real time, observing the fiber growth morphology and orientation angle by scanning electron microscopy to obtain the product morphology parameters;

[0134] (3) Inputting the product morphology parameters into the feedback loop, adjusting the carrier gas flow rate and reaction temperature through the deviation signal to obtain the process parameter correction value;

[0135] (4) Adjusting the mass transfer process of the process parameter correction value, and obtaining the optimized mass transfer conditions by regulating the reaction chamber pressure and gas flow field distribution;

[0136] (5) Monitoring the crystal growth rate according to the optimized mass transfer conditions, and determining the lattice orientation and crystallinity by X-ray diffraction to obtain the structure characteristic data;

[0137] (6) Optimizing the growth environment based on the structure characteristic data, and obtaining the aluminosilicate fiber with controllable structure by feedback control to regulate the reaction mass transfer process.

[0138] Specifically, taking the reaction interface with directional growth activity as the initial condition, an infrared spectrometer is used to and monitor the concentration change of the gas-phase precursor in real time. The conversion rate of the precursor is calculated through the change of the characteristic peak intensity, where at the Si-Cl stretching vibration peak and at the Al-Cl stretching vibration peak are used as the basis for quantitative analysis. The spectral data is collected once per minute to establish a concentration-time relationship curve.

[0139] The collected precursor conversion data is processed, and at the same time, the field emission scanning electron microscope is used to observe the product morphology in real time. Under the condition of an acceleration voltage of 15 kV, the secondary electron images are collected once every 5 minutes, and the diameter distribution and orientation angle of the fiber are measured. The image analysis is processed using professional software to extract the morphology characteristic parameters of the fiber, including data such as the 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.

[0140] Adjust the mass transfer process for the corrected process parameter values, with a focus on the reaction chamber pressure and gas flow field distribution. Regulate the intake gas volume through a mass flow controller and monitor the chamber pressure with a pressure sensor. Use numerical simulation to calculate the gas flow field distribution, adjust the gas inlet position and flow direction, and optimize the mass transfer conditions.

[0141] Based on the optimized mass transfer conditions, monitor the crystal growth process. Conduct in-situ analysis using an X-ray diffractometer and collect diffraction spectra every 10 minutes. Calculate the lattice parameters and preferred orientation coefficients based on the changes in the position and intensity of the diffraction peaks. Calculate the grain size using the Scherrer formula and evaluate the crystallinity. Dynamically optimize the growth environment based on the structural characteristic data. Establish a feedback control loop, use the change trend of the structural parameters as the control basis, and adjust the reaction mass transfer process in real time. When the crystal orientation deviates, correct the growth direction by adjusting the gas flow field; when the crystallinity decreases, improve the crystallization quality by adjusting the temperature.

[0142] 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 microscopy 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 slightly adjusted from 900 °C to 920 °C. The adjusted mass transfer conditions increase the diffraction peak intensity of the (001) crystal plane in the X-ray diffraction pattern, indicating an improvement in orientation. Through continuous monitoring and adjustment, aluminosilicate fibers with uniform structure and consistent orientation are obtained.

[0143] The above describes the process optimization method for chemical vapor deposition of aluminosilicate fibers in the embodiments of the present application. Next, the process optimization device for chemical vapor deposition of aluminosilicate fibers in the embodiments of the present application will be described. Please refer to Figure 3 One embodiment of the process optimization device for chemical vapor deposition of aluminosilicate fibers in the embodiments of the present application includes:

[0144] A simulation module 201 for establishing a chemical reaction mass transfer model through the component diffusion parameter and heat conduction coefficient of the reaction chamber, and performing 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;

[0145] A setting module 202 for setting 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 through the response surface analysis method, and obtaining the optimized chemical reaction mass transfer conditions;

[0146] A calculation module 203, configured to construct a kinetic equation for precursor conversion by using 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;

[0147] A determination 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 the gas-solid interface mass transfer coefficient to obtain the optimal reaction conversion conditions;

[0148] 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 by sol-gel method to obtain a reaction interface with directional growth activity;

[0149] A monitoring module 206, configured to design a process control system based on the reaction interface with directional growth activity, obtain aluminosilicate fibers with controllable structures by regulating the reaction mass transfer process using a feedback control algorithm through real-time monitoring of the precursor conversion rate and product morphology parameters.

[0150] 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 kinetic characteristics of the gas-phase precursor and mixed gas are accurately described, 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, a quantitative relationship between the gas-phase component diffusion characteristics and process parameters is established, significantly improving the chemical reaction mass transfer conditions. By deeply analyzing 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 kinetic prediction model for the deposition reaction, providing a theoretical basis for the precise regulation of process parameters. On this basis, the reaction activation energy is measured by thermal analysis method, combined with the optimization of the gas-solid interface mass transfer coefficient, the optimal reaction conversion conditions are determined, and the reaction efficiency is improved. The surface of the reaction substrate is chemically activated, a seed layer is constructed by plasma treatment and sol-gel method, and a reaction interface with directional growth activity is obtained, 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 achieved, obtaining aluminosilicate fibers with controllable structures. 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.

[0151] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting 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 on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions 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 aluminosilicate fibers, characterized in that The process optimization method for vapor deposition of aluminosilicate fiber includes: A chemical reaction mass transfer model is established based on the component diffusion parameters and heat conduction coefficients of the reaction chamber, and numerical simulation is carried out using the kinetic characteristics of the mixed gas of the gas-phase precursors and to obtain a mass transfer prediction model in the reaction chamber; Setting parameters for carrier gas flow rate, reaction temperature and chamber pressure according to the mass transfer prediction model, obtaining the diffusion characteristics of gas-phase components through response surface analysis method, and obtaining optimized chemical reaction mass transfer conditions; Constructing a precursor conversion kinetic equation using the optimized chemical reaction mass transfer conditions, and obtaining a kinetic prediction model for deposition reaction through coupled calculation of gas-phase mass transfer resistance and surface adsorption potential energy; Setting chemical reaction equilibrium parameters according to the kinetic prediction model of the deposition reaction, measuring the reaction activation energy through thermal analysis method, and optimizing with the gas-solid interface mass transfer coefficient 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 through plasma treatment, and constructing a seed layer using sol-gel method to obtain a reaction interface with directional growth activity; Designing a process control system based on the reaction interface with directional growth activity, monitoring the precursor conversion rate and product morphology parameters in real time, and adjusting the reaction mass transfer process using feedback control algorithm to obtain aluminosilicate fiber with controllable structure.

2. The process optimization method for vapor deposition of aluminosilicate fibers according to claim 1, characterized in that The chemical reaction mass transfer model is established based on the component diffusion parameter and heat conduction coefficient of the reaction chamber, and numerical simulation is carried out using the kinetic characteristics of the mixed gas of the gas-phase precursor and to obtain the mass transfer prediction model in the reaction chamber, including: Establishing a Maxwell-Stefan diffusion equation based on the molecular mass and collision cross-section of reactant gases, and combining with the calculation formula of the thermal conductivity of gas mixture to obtain component diffusion parameters; Constructing a mass transfer control equation based on the component diffusion parameters, calculating the flow characteristics of gas-phase mixture through Navier-Stokes equation, and obtaining the momentum transfer coefficient; Establishing an energy conservation equation using the momentum transfer coefficient, solving the temperature field distribution through numerical iteration, and obtaining the thermal conductivity; Substituting the thermal conductivity into the chemical reaction kinetic equation, calculating the reaction rate constant through finite difference method, and obtaining the precursor conversion rate; Constructing a chemical reaction mass transfer model based on the precursor conversion rate, calculating the component concentration gradient through Fick's diffusion law, and obtaining the gas-phase diffusion flux; Establishing a material balance equation using the gas-phase diffusion flux, analyzing the mass transfer resistance in combination with boundary layer theory, and obtaining a mass transfer prediction model.

3. The process optimization method for vapor deposition of aluminosilicate fiber according to claim 1, characterized in that, The setting parameters for carrier gas flow rate, reaction temperature and chamber pressure according to the mass transfer prediction model, obtaining the diffusion characteristics of gas-phase components through response surface analysis method, and obtaining optimized chemical reaction mass transfer conditions include: Converting the momentum transfer data in the mass transfer prediction model into a flow matrix, determining the change range of carrier gas flow rate through hierarchical analysis, and obtaining a flow parameter table; Constructing a temperature gradient field for the flow parameter table, determining the response range of reaction temperature through thermodynamic equilibrium calculation, and obtaining a temperature distribution sequence; Establishing a pressure field data set using the temperature distribution sequence, calculating the change rule of chamber pressure through thermodynamic state equation, and obtaining pressure control parameters; Inputting the pressure control parameters into the Box-Behnken experimental design scheme, constructing a three-factor interaction relationship through response surface analysis method, and obtaining the gas-phase component diffusion coefficient; Calculating the response value of the mass transfer process based on the gas-phase component diffusion coefficient, solving the spatial distribution of component concentration through partial differential equation, and obtaining the component diffusion characteristics; Perform 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.

4. The process optimization method for vapor deposition of aluminosilicate fibers according to claim 1, characterized in that Construct the precursor conversion kinetic equation using the optimized chemical reaction mass transfer conditions, and obtain the kinetic prediction model of the deposition reaction through the coupled calculation of gas-phase mass transfer resistance and surface adsorption potential energy, including: Decompose the optimized chemical reaction mass transfer conditions into concentration gradient data, calculate the gas-phase diffusion resistance coefficient through the material transfer equation, and obtain the mass transfer resistance parameter; Construct the surface potential energy distribution function based on the mass transfer resistance parameter, determine the molecular adsorption energy through quantum chemical calculation, and obtain the surface adsorption potential energy value; Establish the 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; Perform mass transfer-kinetic coupling analysis using the precursor conversion kinetic equation, calculate the energy transfer efficiency through the Maxwell-Boltzmann distribution, and obtain the reaction rate constant; Construct the interfacial mass transfer flux distribution based on the reaction rate constant, calculate the component concentration field through the reaction microkinetics, and obtain the gas-solid interfacial mass transfer coefficient; Perform correlation calculation between the gas-solid interfacial mass transfer coefficient and the surface reaction rate, determine the kinetic parameter group through nonlinear regression analysis, and obtain the kinetic prediction model of the deposition reaction.

5. The process optimization method for vapor deposition of aluminosilicate fibers according to claim 1, characterized in that, Set the chemical reaction equilibrium parameters according to the kinetic prediction model of the deposition reaction, measure the reaction activation energy through thermal analysis method, and optimize using the gas-solid phase interfacial mass transfer coefficient to obtain the optimal reaction conversion conditions, including: 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; Perform isothermal thermogravimetric measurement 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; Set the temperature heating 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; 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; 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 parameter; Construct the process parameter combination based on the mass transfer optimization parameter, screen the optimal conditions by combining the mass transfer-kinetic equilibrium relationship, and obtain the optimal reaction conversion conditions.

6. The process optimization method of vapor deposition aluminosilicate fiber according to claim 1, characterized in that 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, including: Map the temperature range corresponding to the optimal reaction conversion conditions to the substrate surface treatment parameters, perform surface cleaning through argon plasma bombardment, and obtain a surface-clean substrate; Perform radio frequency plasma treatment on the surface-clean substrate, control the surface hydroxyl density by adjusting the radio frequency power and exposure time, and obtain surface active sites; Mix the surface active sites with an aluminum alkoxide sol, and regulate the crystal nucleation process by controlling the pH value and hydrolysis rate of the sol to obtain a seed precursor; Perform a 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; Heat-treat the seed layer in a nitrogen atmosphere, and regulate the crystal phase structure by controlling the heating rate and heat-treatment time to obtain an oriented growth substrate; Analyze the surface morphology of the oriented growth substrate, and monitor the distribution of surface active sites by a scanning probe microscope to obtain a reaction interface with oriented growth activity.

7. The process optimization method for vapor deposition of aluminosilicate fibers according to claim 1, characterized in that, The process control system for designing the reaction interface based on the oriented growth activity regulates the reaction mass transfer process by monitoring the precursor conversion rate and product morphology parameters in real time and using a feedback control algorithm to obtain aluminosilicate fibers with controllable structures, including: Use the reaction interface with oriented growth activity as the initial condition, and monitor the change in the concentration of the gas-phase precursor by spectroscopic analysis to obtain precursor conversion rate data; Collect the precursor conversion rate data in real time, and observe the growth morphology and orientation angle of the fibers by a scanning electron microscope to obtain product morphology parameters; Input the product morphology parameters into a feedback loop, and adjust the carrier gas flow rate and reaction temperature by a deviation signal to obtain a process parameter correction value; Adjust the mass transfer process according to the process parameter correction value, and regulate the reaction chamber pressure and gas flow field distribution to obtain optimized mass transfer conditions; Monitor the crystal growth rate according to the optimized mass transfer conditions, and determine the lattice orientation and crystallinity by X-ray diffraction to obtain structure characteristic data; Optimize the growth environment based on the structure characteristic data, and regulate the reaction mass transfer process by feedback control to obtain aluminosilicate fibers with controllable structures.

8. An apparatus for optimizing the process of vapor-depositing aluminosilicate fibers, which is used to implement the method for optimizing the process of vapor-depositing aluminosilicate fibers according to any one of claims 1-7, is characterized in that, The process optimization device for vapor deposition of aluminosilicate fibers includes: A simulation module is used to establish a chemical reaction mass transfer model based on the component diffusion parameters 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 for setting parameters of the carrier gas flow rate, reaction temperature, and chamber pressure according to the mass transfer prediction model, and obtaining the diffusion characteristics of gas-phase components by a response surface analysis method to obtain optimized chemical reaction mass transfer conditions; A calculation module for constructing a kinetic equation for precursor conversion by 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 determination module for setting chemical reaction equilibrium parameters according to the kinetic prediction model of the deposition reaction, determining the reaction activation energy by a thermal analysis method, and optimizing 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 by plasma treatment, and constructing a seed layer by a sol-gel method to obtain a reaction interface with oriented growth activity; A monitoring module for regulating the reaction mass transfer process by monitoring the precursor conversion rate and product morphology parameters in real time based on the process control system for designing the reaction interface with oriented growth activity and using a feedback control algorithm to obtain aluminosilicate fibers with controllable structures.

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