Method for enhancing flame retardant property of bio-based composite material

Abstract

The invention provides a method for enhancing the flame retardant property of a bio-based composite material, and belongs to the technical field of bio-based composites.The method comprises the steps that wood fibers are pretreated through a sodium hydroxide solution to increase hydroxyl active sites, and a silane coupling agent KH550 is adopted for surface modification to form covalent bond connection; the preparation method comprises the following steps: preparing a nano aluminum oxide and layered double hydroxide compounded nano flame-retardant synergistic system, adding a reaction type phosphorus-nitrogen flame retardant DOPO-HQ in a polyethylene melt blending process to carry out an in-situ polymerization reaction, determining optimal reaction condition parameters by using a greywolf hunting optimization algorithm, and preparing the flame-retardant composite material through compression molding and post-heat treatment processes. A flame retardant-fiber-matrix three-dimensional chemical bonding network is constructed, molecular-level compatibility improvement and uniform dispersion are achieved, and the technical problem that the compatibility of an organic phosphorus flame retardant and wood fibers is poor is solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of bio-based composites, and in particular, relates to a bio-based composite flame retardant performance enhancement method. BACKGROUND

[0002] Bio-based composites, as an environmentally friendly material, are widely used in the fields of construction, packaging, automobiles, etc. The improvement of the flame retardant performance of bio-based composites mainly depends on the addition of various flame retardants in the polymer matrix. Traditional methods usually use physical blending to directly composite organic phosphorus flame retardants with wood fiber reinforced polyethylene matrix. The flame retardant effect is generated by thermal decomposition of the flame retardant to improve the flame retardant performance of the material. However, in the current bio-based composite flame retardant modification technology, due to the polarity difference between the molecular structure of the organic phosphorus flame retardant and the surface chemical groups of the wood fiber, there is a lack of effective intermolecular interaction between the two, and the flame retardant cannot form a stable adsorption layer or chemical bonding on the surface of the wood fiber. Traditional technology is difficult to achieve good interfacial bonding between organic phosphorus flame retardants and wood fibers, resulting in uneven dispersion of the flame retardant in the composite material, forming a phase separation phenomenon, which not only reduces the flame retardant efficiency, but also affects the mechanical properties and processing performance of the composite material, seriously restricting the application and promotion of bio-based composites in high flame retardant requirement occasions. That is, the existing technology has the technical problem of poor compatibility of organic phosphorus flame retardants and wood fibers. SUMMARY

[0003] Therefore, the present application provides a bio-based composite flame retardant performance enhancement method, which can solve the technical problem of poor compatibility of organic phosphorus flame retardants and wood fibers in the prior art.

[0004] The application is implemented in the following manner: the application provides a method for enhancing the flame retardant performance of a bio-based composite material, comprising the following steps: treating wood fibers in a sodium hydroxide solution to remove surface impurities and increase hydroxyl active sites; performing surface modification treatment on the dried wood fibers with silane coupling agent KH550 to form covalent bonds between the silane coupling agent and the hydroxyl groups on the surface of the wood fibers; preparing a nano flame-retardant synergistic system by mixing nano aluminum oxide and layered double hydroxide to obtain a nano flame-retardant particle suspension; melting a polyethylene matrix and blending it with modified wood fibers and the nano flame-retardant synergistic system according to a certain ratio, melting and blending the mixture using a twin-screw extruder, and optimizing the blending parameters through an interfacial reaction kinetics equation set; adding reactive phosphorus-nitrogen flame retardant DOPO-HQ during the melting and blending process, forming chemical bonds between the reactive phosphorus-nitrogen flame retardant DOPO-HQ and the polyethylene molecular chain through in-situ polymerization, and determining the optimal reaction conditions using a multi-objective optimization problem combined with a grey wolf hunting optimization algorithm; forming the blended material into a flame-retardant composite material product through pressure molding, optimizing the molding process parameters through a heat and mass transfer coupling equation set; and performing heat treatment on the obtained flame-retardant composite material product.

[0005] The wood fibers are treated in the sodium hydroxide solution by being placed in a sodium hydroxide solution with a concentration of 8% at 80°C for 2 hours to remove surface impurities and increase hydroxyl active sites, then rinsed with deionized water until neutral and dried to a moisture content of less than 3%.

[0006] The surface modification treatment is performed by mixing the dried wood fibers with silane coupling agent KH550 at a mass ratio of 100:3 for surface modification treatment at 60°C for 90 minutes to form covalent bonds between the silane coupling agent and the hydroxyl groups on the surface of the wood fibers.

[0007] The silane coupling agent KH550 is an amino-containing silane compound with a molecular formula of H2N(CH2)3Si(OC2H5)3, which is used to improve the interfacial compatibility between the wood fibers and the polyethylene matrix.

[0008] The nano flame-retardant synergistic system is prepared by mixing nano aluminum oxide and layered double hydroxide at a mass ratio of 2:1 and obtaining a uniform nano flame-retardant particle suspension through ultrasonic dispersion for 20 minutes.

[0009] The layered double hydroxide is an inorganic compound with a layered structure, with a molecular formula of wherein M 2+ and m 3+ are divalent and trivalent metal ions, respectively, A n- is an interlayer anion, and has excellent flame-retardant synergistic effect.

[0010] The step of melt blending the polyethylene matrix, specifically melt blending the polyethylene matrix at 165 DEG C, is carried out according to a ratio of 100 parts of polyethylene, 30 parts of modified wood fiber, and 15 parts of nano flame-retardant synergistic system, and a twin-screw extruder is used for melt blending for 3 minutes.

[0011] The reaction-type phosphorus-nitrogen flame retardant DOPO-HQ is an addition product of 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide and hydroquinone, and has a molecular formula of C 18 H 13 O4P, belongs to a reaction-type organic phosphorus flame retardant, and is combined with a polymer molecular chain through a phosphorus-oxygen bond.

[0012] The addition amount of the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ is 12% of the mass of the polyethylene, and the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ is chemically bonded to the polyethylene molecular chain through an in-situ polymerization reaction.

[0013] The interface reaction kinetics equation set includes an interface combination reaction equation and a diffusion transfer equation, the interface combination reaction equation is used to describe the reaction rate and conversion rate variation law of the silane coupling agent KH550 and the wood fiber surface hydroxyl group, and the diffusion transfer equation is used to describe the mass transfer process of the silane coupling agent KH550 molecules from the solution phase to the wood fiber surface.

[0014] The multi-objective optimization problem is to optimize the addition amount of the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ, control the reaction temperature, adjust the reaction time, and set the stirring speed as multiple mutually restricted objective functions, and the grey wolf hunting optimization algorithm is used to find a parameter combination that is optimal in flame-retardant effect, minimizes mechanical property loss, and has good processing performance.

[0015] The grey wolf hunting optimization algorithm is a bionic optimization algorithm that simulates the hunting behavior of a grey wolf group, searches for an optimal solution by simulating the social hierarchy system and cooperative hunting strategy of the wolf group, the alpha wolf represents the current optimal solution, the beta wolf and the delta wolf represent suboptimal solutions, and the omega wolf represents other candidate solutions.

[0016] The step of the compression molding process, specifically, the blended material is formed by the compression molding process at 180 DEG C and a pressure of 15 MPa for 5 minutes, and then cooled to room temperature to demold to obtain a flame-retardant composite product.

[0017] The heat and mass transfer coupling equation set includes a heat conduction equation, a mass transfer equation and a phase change heat transfer equation, the heat conduction equation is used to describe the change rule of the temperature field distribution inside the composite material in the compression molding process, the mass transfer equation is used to describe the migration and diffusion behavior of water and volatile components in the molding process, and the phase change heat transfer equation is used to describe the melting and crystallization behavior of the polyethylene matrix in the molding process.

[0018] The heat treatment step is specifically that the prepared flame-retardant composite product is heat treated in a 120 DEG C oven for 24 hours, so as to promote the further crosslinking reaction between the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ and the polyethylene matrix, and improve the durability of the flame-retardant effect.

[0019] The in-situ polymerization reaction refers to a reaction process in which the flame retardant molecules directly participate in chemical reaction in a molten state of the polymer and form covalent bonds with the polymer molecular chain, and the nano flame-retardant synergistic system refers to a synergistic flame-retardant system formed by compounding a nano inorganic flame retardant and a layered material, which improves the flame-retardant efficiency through physical blocking and chemical inhibition mechanisms.

[0020] The multi-objective optimization and grey wolf hunting algorithm determine the optimal conditions of the bio-based composite material flame-retardant reaction, and specifically include: a multi-objective optimization function model is established, the addition amount of the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ, the reaction temperature, the reaction time and the stirring speed are taken as the optimization variables, the addition amount range is 8%-16%, the reaction temperature range is 160 DEG C-185 DEG C, the reaction time range is 2-8 minutes, and the stirring speed range is 150-350 rpm; four objective functions are defined, including a flame-retardant performance maximization function, a mechanical property loss minimization function, a processing performance optimization function and a cost control function, and the weights of the functions are respectively set to 0.4, 0.3, 0.2 and 0.1; the grey wolf hunting optimization algorithm is used for optimization, the population size is set to 50 individuals, each individual represents a set of reaction condition parameter combinations, and the social hierarchy structure of alpha wolf, beta wolf, delta wolf and omega wolf is established; the population is iteratively updated through three behavior stages of surrounding, hunting and attacking, the surrounding coefficient is linearly decreased from 2 to 0 to control the search range, and the maximum iteration number is set to 200 times; when the termination condition is reached, the Pareto optimal solution set is output, and the solution with the best comprehensive performance is selected as the optimal reaction condition parameter combination.

[0021] The implementation process of the grey wolf hunting optimization algorithm includes eight steps of initializing the grey wolf population, establishing a social hierarchy structure, performing a surrounding prey behavior, performing a hunting behavior, performing an attack prey behavior, updating the grey wolf population, judging a termination condition and outputting an optimal solution; the surrounding prey behavior is realized by calculating the distance vector between each omega wolf individual and alpha wolf, beta wolf and delta wolf, and updating the omega wolf position according to a surrounding equation; the hunting behavior calculates the candidate position of the omega wolf according to the position information of the alpha wolf, beta wolf and delta wolf, and determines the new position of the omega wolf through a hunting equation; the attack prey behavior is triggered when the surrounding coefficient is less than 1, and the omega wolf moves to the average position of the alpha wolf, beta wolf and delta wolf, completing the local search of the optimal solution; and the termination condition is that the iteration number reaches 200 times or the Pareto optimal solution set has no significant improvement in 20 continuous iterations.

[0022] The present application solves the technical problem of poor compatibility of organic phosphorus flame retardant and wood fiber through the synergistic effect of silane coupling agent surface modification treatment, nano flame retardant synergistic system construction, in-situ polymerization reaction of reactive phosphorus-nitrogen flame retardant and multi-objective optimization algorithm parameter regulation. The present application uses silane coupling agent KH550 to modify the surface of wood fiber, forms covalent bond connection between the silane coupling agent and the surface hydroxyl group of wood fiber, provides active sites for the subsequent combination of reactive phosphorus-nitrogen flame retardant, strengthens the interface interaction by introducing the nano flame retardant synergistic system, and utilizes the in-situ polymerization reaction of reactive phosphorus-nitrogen flame retardant DOPO-HQ with polyethylene molecular chain during the melt blending process to form chemical bonding, thereby improving the compatibility between the flame retardant and the matrix and the fiber at the molecular level. The present application establishes stable connection between the flame retardant, the fiber and the matrix through chemical bonding, avoids the phase separation problem caused by physical blending, realizes the uniform dispersion and stable existence of the flame retardant in the composite material, thereby effectively improving the flame retardant efficiency and the comprehensive performance of the material, and solving the technical problem of poor compatibility of organic phosphorus flame retardant and wood fiber. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The flowchart of the method of the present application.

[0024] Figure 2 The interface bonding rate curve with reaction time in Example 2.

[0025] Figure 3 The nanoparticle particle size distribution graph in Example 2.

[0026] Figure 4 The grey wolf optimization algorithm fitness value convergence curve graph in Example 2.

[0027] Figure 5Temperature distribution in the compression molding process in Example 2 as a function of time.

[0028] Figure 6 Crosslinking reaction conversion in Example 2 as a function of heat treatment time. DETAILED DESCRIPTION

[0029] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0030] As Figure 1 shown in the figure is a flow chart of a method for enhancing the flame retardant performance of a bio-based composite material provided by the present application, and the method comprises the following steps:

[0031] S01, treating the wood fibers in a sodium hydroxide solution with a concentration of 8% at 80°C for 2 hours to remove surface impurities and increase the hydroxyl active sites, then washing with deionized water to neutral and drying to a moisture content of less than 3%;

[0032] S02, surface modification treatment of the dried wood fibers with silane coupling agent KH550 at a mass ratio of 100:3, reacting at 60°C for 90 minutes to form covalent bonds between the silane coupling agent and the hydroxyl groups on the surface of the wood fibers;

[0033] S03, preparing a nano flame-retardant synergistic system by mixing nano-aluminum oxide and layered double hydroxide at a mass ratio of 2:1, and obtaining a uniform nano flame-retardant particle suspension by ultrasonic dispersion for 20 minutes;

[0034] S04, melting the polyethylene matrix at 165°C, blending according to the ratio of polyethylene 100 parts, modified wood fibers 30 parts, and nano flame-retardant synergistic system 15 parts, using a twin-screw extruder for melt blending for 3 minutes, and optimizing the blending parameters through the interfacial reaction kinetics equation set;

[0035] S05, adding reactive phosphorus-nitrogen flame retardant DOPO-HQ during the melt blending process, the addition amount being 12% of the mass of polyethylene, and forming chemical bonds between the reactive phosphorus-nitrogen flame retardant DOPO-HQ and the polyethylene molecular chain through in-situ polymerization reaction, and this step uses a multi-objective optimization problem combined with grey wolf hunting optimization algorithm to determine the optimal reaction conditions;

[0036] S06, forming the blended material into a flame-retardant composite material product by compression molding process at 180°C and a pressure of 15 MPa for 5 minutes, then cooling to room temperature to demold, and optimizing the molding process parameters through the heat and mass transfer coupling equation set;

[0037] S07、The prepared flame-retardant composite product is post-treated and heat-treated in a 120℃ oven for 24 hours to promote further cross-linking reaction between the reactive phosphorus-nitrogen flame retardant DOPO-HQ and the polyethylene matrix and improve the durability of the flame-retardant effect.

[0038] Among them, silane coupling agent KH550 is a silane compound containing amino groups, with a molecular formula of H2N(CH2)3Si(OC2H5)3, used to improve the interfacial compatibility between wood fibers and the polyethylene matrix.

[0039] Among them, layered double hydroxide is an inorganic compound with a layered structure, with a molecular formula of Among them, M 2+ and M 3+ are divalent and trivalent metal ions, respectively, and A n- is an interlayer anion, with excellent flame-retardant synergistic effect.

[0040] Among them, the reactive phosphorus-nitrogen flame retardant DOPO-HQ is the addition product of 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide and hydroquinone, with a molecular formula of C 18 H 13 O4P, belonging to reactive organic phosphorus flame retardants, which combines with polymer molecular chains through phosphorus-oxygen bonds.

[0041] Among them, the nano flame-retardant synergistic system refers to the synergistic flame-retardant system formed by the combination of nano inorganic flame retardant and layered material, which improves the flame-retardant efficiency through physical barrier and chemical inhibition mechanisms.

[0042] Among them, in-situ polymerization refers to the reaction process in which the flame retardant molecules directly participate in chemical reactions in the molten state of the polymer and form covalent bonds with the polymer molecular chains.

[0043] Among them, interfacial compatibility refers to the degree of molecular-level interaction between different material components, and good interfacial compatibility reduces phase separation and improves the mechanical properties of the composite material.

[0044] The interface reaction kinetics equation set includes an interface bonding reaction equation and a diffusion transfer equation; the interface bonding reaction equation is used for describing the reaction rate and conversion rate variation law of the silane coupling agent KH550 and the wood fiber surface hydroxyl group, and the input includes a reaction temperature of 60 DEG C, a reaction time of 90 minutes, a silane coupling agent KH550 concentration of 3%, a wood fiber surface hydroxyl group density of 4.2 mmol / g, and a catalyst concentration of 0.1%, and the output interface bonding rate is used to determine the dispersion effect of the nano flame-retardant synergistic system in step S05, and the output reaction completion degree is used to judge the timing of the mold forming in step S06; the diffusion transfer equation is used for describing the mass transfer process of the silane coupling agent KH550 molecules from the solution phase to the wood fiber surface and its influence mechanism on the reaction efficiency, and the input includes a diffusion coefficient of 2.1*10 -10 m 2 / s, a concentration gradient of 0.8 mol / m 3 , a mass transfer boundary layer thickness of 15 μm, a fiber surface area of 320 m 2 / g, and a solution viscosity of 1.2*10 -3 Pa*s, and the output mass transfer rate is used to adjust the blending time in step S04, and the output interface concentration distribution is used to optimize the addition order of the reaction type phosphorus-nitrogen flame retardant DOPO-HQ in step S05.

[0045] The heat and mass transfer coupling equation set includes a heat conduction equation, a mass transfer equation, and a phase change heat transfer equation; the heat conduction equation is used for describing the internal temperature field distribution variation law of the composite material in the mold forming process and its influence mechanism on the material performance, and the input includes a thermal conductivity of 0.35 W / (m*K), a specific heat capacity of 2100 J / (kg*K), a density of 1200 kg / m 3 , an initial temperature of 180 DEG C, and a boundary heat transfer coefficient of 45 W / (m 2 *K), and the output temperature field distribution is used to control the setting of the heat treatment temperature in step S07, and the output heat flux density is used to adjust the cooling rate in step S06; the mass transfer equation is used for describing the migration and diffusion behavior of the moisture and volatile components in the forming process and its action law on the material structure formation, and the input includes a diffusion coefficient of 1.8*10 -9 m 2 / s, an initial concentration distribution of 3%, a boundary mass transfer coefficient of 0.025 m / s, and a porosity of 8%, and the output concentration field distribution is used to determine the drying degree requirement of the wood fiber in step S01, and the output mass transfer rate is used to set the pressure maintaining time in step S06; the phase change heat transfer equation is used for describing the melting and crystallization behavior of the polyethylene matrix in the forming process and its regulation mechanism on the microstructure evolution of the composite material, and the input includes a phase change latent heat of 293 kJ / kg, a phase change temperature of 135 DEG C, a crystallinity of 65%, a cooling rate of 5 DEG C / min, and a nucleation density of 2.5*10 12 / m3 The output crystallization kinetics parameters are used to guide the time setting of the post-processing process in step S07, and the output microstructure features are used to evaluate the dispersion uniformity of the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ in step S05.

[0046] In step S05, the multi-objective optimization problem is to optimize the addition amount of the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ, control the reaction temperature, adjust the reaction time, and set the stirring speed as multiple interdependent objective functions. The grey wolf hunting optimization algorithm simulates the social hierarchy and hunting behavior of a wolf pack to find a combination of parameters that optimizes the flame-retardant effect, minimizes the loss of mechanical properties, and has good processing performance. In the algorithm, alpha wolves represent the current optimal solution, beta wolves and delta wolves represent suboptimal solutions, and omega wolves represent other candidate solutions. The algorithm iteratively updates the parameters through the simulation of the surrounding, hunting, and attacking behavior of the wolf pack until it converges to the global optimal solution.

[0047] In step S05, the multi-objective optimization problem is to optimize the addition amount of the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ, control the reaction temperature, adjust the reaction time, and set the stirring speed as multiple interdependent objective functions. The grey wolf hunting optimization algorithm simulates the social hierarchy and hunting behavior of a wolf pack to find a combination of parameters that optimizes the flame-retardant effect, minimizes the loss of mechanical properties, and has good processing performance. In the algorithm, alpha wolves represent the current optimal solution, beta wolves and delta wolves represent suboptimal solutions, and omega wolves represent other candidate solutions. The algorithm iteratively updates the parameters through the simulation of the surrounding, hunting, and attacking behavior of the wolf pack until it converges to the global optimal solution.

[0048] Among them, the particle size distribution in the nano flame-retardant synergistic system includes four ranges. The first range is 10-50 nm ultra-fine particles, which are obtained by sol-gel method. Such particles have extremely high specific surface area and reactivity, with a dispersion conversion rate of 85%, which can significantly improve the flame-retardant efficiency but easily agglomerate to affect the mechanical properties. The second range is 50-100 nm fine particles, which are obtained by chemical precipitation method. Such particles achieve a good balance between dispersion and flame-retardant effect, with a dispersion conversion rate of 75%, which is the main component of the synergistic system. The third range is 100-200 nm medium particles, which are obtained by high-temperature decomposition method. Such particles are relatively easy to disperse but have moderate flame-retardant effect, with a dispersion conversion rate of 65%, which mainly plays a filling and synergistic role. The fourth range is 200-500 nm larger particles, which are obtained by ball milling method. Such particles are easy to disperse but have limited flame-retardant contribution, with a dispersion conversion rate of 55%. Too much of such particles will dilute the flame-retardant effect and increase the material density. The content of larger particles is controlled by ball milling time and screening process to reduce the negative impact.

[0049] Among them, the multi-objective optimization problem refers to a mathematical problem of optimizing multiple conflicting or mutually restrictive objective functions simultaneously, by finding a Pareto optimal solution set to achieve the best balance of multiple objectives.

[0050] Among them, the grey wolf hunting optimization algorithm is a bionic optimization algorithm that simulates the hunting behavior of grey wolf groups, by simulating the social hierarchy and cooperative hunting strategies of wolf packs to search for optimal solutions.

[0051] Among them, the dispersion conversion rate refers to the conversion proportion of nano-particles from agglomerated state to uniformly dispersed state, which is used to evaluate the dispersion effect of nano-particles in composite materials.

[0052] Among them, the contribution rate refers to the percentage of a component's contribution to the overall performance of the composite material, which is used to quantify the influence degree of different components on the final performance.

[0053] The process of determining the optimal reaction conditions by combining the multi-objective optimization problem with the grey wolf hunting optimization algorithm includes the following steps:

[0054] P01, a multi-objective optimization function model is established, taking the addition amount of reactive phosphorus-nitrogen flame retardant DOPO-HQ, reaction temperature, reaction time and stirring speed as optimization variables, setting the addition amount range to 8%-16%, reaction temperature range to 160℃-185℃, reaction time range to 2-8 minutes, and stirring speed range to 150-350 rpm;

[0055] P02、Define four objective functions, the first objective function is the maximum flame retardant performance function, with oxygen index and vertical burning grade as evaluation indexes; the second objective function is the minimum mechanical property loss function, with tensile strength retention rate and impact strength retention rate as evaluation indexes; the third objective function is the processing performance optimization function, with melt flow index and processing temperature as evaluation indexes; the fourth objective function is the cost control function, with raw material cost and energy consumption cost as evaluation indexes;

[0056] P03、Initialize the gray wolf population, set the population size to 50 individuals, each individual represents a set of reaction condition parameter combinations, randomly generate the four optimization variable values of each individual in the initial population, ensure that all variable values are within the set range;

[0057] P04、Establish the gray wolf social hierarchy, sort the individuals according to their multi-objective function fitness values, select the individual with the highest fitness value as the alpha wolf, the individual with the second highest fitness value as the beta wolf, the individual with the third highest fitness value as the delta wolf, and the remaining individuals as omega wolves;

[0058] P05、Perform the gray wolf surrounding prey behavior, calculate the distance vector between each omega wolf individual and the alpha wolf, beta wolf, delta wolf, update the position of the omega wolf according to the surrounding equation, the surrounding coefficient in the surrounding equation decreases linearly from 2 to 0 with the number of iterations;

[0059] P06、Perform the gray wolf hunting behavior, calculate the candidate position of the omega wolf according to the position information of the alpha wolf, beta wolf, delta wolf, determine the new position of the omega wolf through the hunting equation, the random vector in the hunting equation is used to balance the exploration ability and exploitation ability of the algorithm;

[0060] P07、Perform the gray wolf attack prey behavior, when the surrounding coefficient is less than 1, trigger the attack behavior, at this time the omega wolf moves to the average position of the alpha wolf, beta wolf, delta wolf, complete the local search of the optimal solution;

[0061] P08、Update the gray wolf population, re-evaluate the multi-objective function fitness values of all individuals, update the positions of the alpha wolf, beta wolf, delta wolf, record the current iteration of the Pareto optimal solution set;

[0062] P09、Determine the termination condition, when the number of iterations reaches the set maximum number of iterations 200 times or the Pareto optimal solution set has no significant improvement in 20 consecutive iterations, terminate the algorithm, otherwise return to step P05 and continue iteration;

[0063] P10, output the optimal reaction condition parameter combination, select the solution with the best comprehensive performance from the final pareto optimal solution set as the optimal reaction condition, and output the optimal addition amount of the reaction-type phosphorus-nitrogen flame retardant DOPO-HQ, the optimal reaction temperature, the optimal reaction time and the optimal stirring speed.

[0064] Wherein, the multi-objective function fitness value is calculated by weighted summation method, the weight of the flame retardant performance maximization function is 0.4, the weight of the mechanical property loss minimization function is 0.3, the weight of the processing performance optimization function is 0.2, and the weight of the cost control function is 0.1. Each weight is determined according to actual production demand and product performance requirement.

[0065] Wherein, the pareto optimal solution set refers to the set of solutions that cannot improve all objective functions simultaneously in the multi-objective optimization problem. Any solution in the set will inevitably deteriorate at least one other target while improving a certain target.

[0066] Wherein, the enclosure coefficient is a key parameter in grey wolf hunting optimization algorithm for controlling the search range, which is linearly decreased from 2 to 0, used to balance the global exploration ability and local development ability of the algorithm.

[0067] Wherein, the distance vector is a vector describing the spatial position relationship between omega wolf and leader wolf, which is obtained by calculating the square sum of the difference value of each dimension coordinate and then taking the square root.

[0068] Wherein, the candidate position is the selected position coordinate of omega wolf in the next iteration, which is calculated by the hunting equation combined with the position information of alpha wolf, beta wolf and delta wolf.

[0069] Wherein, the solution with the best comprehensive performance refers to the solution with the highest comprehensive evaluation of each objective function value in the pareto optimal solution set, which is selected from the pareto frontier by multi-attribute decision method.

[0070] The specific implementation of the above steps is described in detail as follows.

[0071] The specific implementation of step S01 is to perform alkaline pretreatment and drying treatment on the wood fibers. First, the wood fiber raw material is placed in a sodium hydroxide solution with a concentration of 8%, and through saponification and hydrolysis reactions under alkaline conditions, impurities such as lignin, pectin, and hemicellulose on the fiber surface are removed. At the same time, the strong alkalinity of sodium hydroxide is used to destroy the hydrogen bond network structure on the fiber surface, exposing more hydroxyl active sites. The reaction system temperature is controlled at 80°C for 2 hours, which can ensure that the reaction proceeds sufficiently while avoiding thermal degradation and destruction of the fiber structure. After the reaction is complete, deionized water is used for multiple rinses, and the pH of the rinse water is detected using pH paper. When the pH value reaches the neutral range of 6.5-7.5, the rinsing is stopped. Subsequently, the cleaned fibers are placed in an oven for drying treatment, with the oven temperature set to 105°C. The moisture content is monitored by weight method, and when the moisture content of the fibers is reduced to below 3%, the drying process is completed. The purpose of this step is to provide a clean and active hydroxyl-rich fiber surface for subsequent silane coupling agent modification treatment. Controlling the moisture content below the threshold of 3% is a key condition to ensure that the silane coupling agent can fully react with the fiber surface.

[0072] The specific implementation of step S02 is to perform surface chemical modification of the wood fibers using silane coupling agent KH550. The dried fibers treated in step S01 are mixed with silane coupling agent KH550 in a mass ratio of 100:3. Silane coupling agent KH550 contains amino and ethoxy functional groups, which can form chemical bonds with the hydroxyl groups on the fiber surface and the polymer matrix, respectively. First, the silane coupling agent is diluted with anhydrous ethanol to prepare a solution with a mass fraction of 10%. Then, the fibers are immersed in the solution for impregnation treatment. The reaction system temperature is controlled at 60°C to promote the hydrolysis and condensation reactions of the silane coupling agent molecules. The reaction time is set to 90 minutes to ensure that the coupling agent molecules fully diffuse to the fiber surface and form chemical bonds. Mechanical stirring is used during the reaction to maintain a stirring speed of 120 rpm, ensuring sufficient contact between the coupling agent and the fiber surface. After the reaction is complete, the modified fibers are filtered and dried at 80°C until a constant weight is achieved. The purpose of this step is to form a layer of silane molecules on the fiber surface, improve the interfacial compatibility between the fibers and the polymer matrix, reduce the occurrence of phase separation, and improve the mechanical properties and processing performance of the composite material.

[0073] The specific implementation of step S03 is to prepare the nano flame-retardant synergistic system and achieve uniform dispersion. The nano-aluminum oxide powder and the layered double hydroxide powder are dosed according to a mass ratio of 2:1, and the synergistic mechanism of the two materials is based on the combination of physical barrier effect and chemical inhibition effect. The nano-aluminum oxide mainly provides a dense inorganic protective layer, while the layered double hydroxide releases water vapor to dilute the concentration of combustible gas at high temperature. First, the two powders are mechanically mixed in a high-speed mixer for 10 minutes to preliminarily achieve macroscopic uniform distribution of the particles. Then, the mixed powder is dispersed into deionized water to prepare a suspension with a mass fraction of 15%, and a ultrasonic dispersion device is used for micro-dispersion treatment. The ultrasonic power is set to 300W, the frequency is 40kHz, the dispersion time is 20 minutes, and every 5 minutes during the ultrasonic process is stopped for 1 minute to avoid overheating of the system causing particle re-agglomeration. The principle of ultrasonic dispersion is to use the cavitation effect of ultrasonic waves to generate strong shear force and impact force, which can destroy the van der Waals force and electrostatic interaction between nanoparticles, and achieve single dispersion of the particles. After dispersion is completed, the particle size distribution is detected by a laser particle size analyzer, and when the D90 value is less than 200nm and the particle size distribution index PDI is less than 0.3, it is considered that the dispersion effect meets the requirements.

[0074] The specific implementation of step S04 is to use a twin-screw extruder for melt blending of the polyethylene matrix and the modified fiber. First, the polyethylene resin is preheated to 80℃ in the hopper to reduce the water content and improve the flowability, then the twin-screw extruder is started and the temperature of each section is set: hopper section temperature 120℃, plasticizing section temperature 150℃, homogenizing section temperature 165℃, discharge section temperature 160℃. The components are weighed and added according to the mass ratio of polyethylene 100 parts, modified wood fiber 30 parts, and nano flame-retardant synergistic system 15 parts, and a loss-on-ignition feeder is used to accurately control the addition rate of each component. The screw speed is set to 150rpm, and the residence time of the material in the barrel is controlled to be 3 minutes, which can ensure sufficient mixing of the components and avoid thermal degradation of the fiber. During the blending process, the process parameters are optimized by an interfacial reaction kinetics equation set, which describes the reaction kinetics process of silane coupling agent with fiber surface hydroxyl groups and the diffusion mass transfer behavior of coupling agent molecules in the melt. The input parameters of the interfacial bonding reaction equation include the reaction temperature 165℃, the concentration of silane coupling agent and the density of fiber surface hydroxyl groups, and the output interface bonding rate is used to judge the bonding degree of the fiber and the matrix. The input parameters of the diffusion transfer equation include the diffusion coefficient, concentration gradient and mass transfer boundary layer thickness, and the output mass transfer rate guides the adjustment of the blending time.

[0075] The specific implementation of step S05 is to add the reactive phosphorus-nitrogen flame retardant DOPO-HQ during the melt blending process and determine the optimal reaction conditions through a multi-objective optimization algorithm. The addition amount of the reactive phosphorus-nitrogen flame retardant DOPO-HQ is set to 12% of the mass of the polyethylene. The flame retardant contains active phosphorus-oxygen groups and nitrogen heterocyclic structures, which can undergo in-situ polymerization reaction with the polyethylene molecular chain at high temperature to form chemical bonding. The addition method adopts a side feeding mode, and the flame retardant is quantitatively added to the main stream through a branch runner to avoid decomposition caused by long-term residence of the flame retardant at high temperature. The optimization of the reaction conditions is realized by combining a multi-objective optimization problem with a grey wolf predation optimization algorithm. The algorithm simulates the social hierarchy system and cooperative hunting behavior of the grey wolf group to find the optimal solution. The algorithm first establishes a mathematical model containing four objective functions of maximum flame retardant performance, minimum mechanical performance loss, processing performance optimization, and cost control. The optimization variables include the addition amount of the flame retardant, the reaction temperature, the reaction time, and the stirring speed. The algorithm initializes the grey wolf population and establishes the hierarchical structure of alpha wolves, beta wolves, delta wolves, and omega wolves. The algorithm simulates the behaviors of surrounding, hunting, and attacking to iteratively search for the optimal parameter combination. The convergence criterion of the algorithm is that there is no significant improvement in the Pareto optimal solution set in 20 consecutive iterations or the maximum number of iterations is reached, which is 200. The final output is the reaction condition parameter combination with the best comprehensive performance.

[0076] The specific implementation of step S06 is to prepare the flame-retardant composite material product by using a mold pressing forming process. First, the blend material obtained in step S05 is made into uniform particles with a particle size of 3-5 mm through a granulator, and then the particles are preheated to 120°C to remove residual moisture and improve flowability. The mold pressing forming equipment uses a flat vulcanizing machine, and the mold is preheated to a forming temperature of 180°C and coated with a release agent. The preheated material is uniformly laid in the mold, and the laying thickness is slightly greater than the product thickness to compensate for the shrinkage deformation during the forming process. After closing the mold, a pre-pressure of 5 MPa is applied for 30 seconds to remove air in the material and preliminarily compact the material, and then the pressure is increased to 15 MPa for formal forming. The holding pressure time is set to 5 minutes, which is determined by a heat and mass transfer coupled equation set, which includes a heat conduction equation, a mass transfer equation, and a phase change heat transfer equation. The heat conduction equation describes the distribution change of the temperature field during the forming process, and the input parameters include thermal physical parameters such as thermal conductivity, specific heat capacity, and density. The output temperature field distribution is used to control the subsequent heat treatment temperature. The mass transfer equation describes the migration behavior of water and volatile components, and the output mass transfer rate guides the setting of the holding time. The phase change heat transfer equation describes the melting and crystallization process of polyethylene, and the output crystallization kinetics parameters affect the microstructure of the material. After the forming is completed, the heating system is turned off and circulating cooling water is passed for forced cooling, and the cooling rate is controlled at 5°C / min. When the mold temperature drops below 40°C, the mold is opened and the composite material product is obtained.

[0077] The specific implementation of step S07 is to post-treat the shaped product to improve the persistence of the flame-retardant effect. After demolding, the composite product is placed in a forced air drying oven for heat treatment, and the heat treatment temperature is set to 120°C, which is lower than the melting point of polyethylene but higher than the glass transition temperature, which can activate the molecular chain segment movement to promote the further crosslinking reaction between the reactive flame retardant and the matrix. The heat treatment time is set to 24 hours, which is determined according to the crystallization kinetics parameters output by the phase change heat transfer equation in step S06. Long-term heat treatment is beneficial to the diffusion of the flame retardant molecules and the formation of stable chemical bonds with the polymer molecular chain. The air circulation speed in the oven is maintained at 0.5 m / s during the heat treatment to ensure uniform heating of all parts of the product. After heat treatment, the product is naturally cooled to room temperature to avoid internal stress caused by rapid cooling, which affects the performance of the product. The mechanism of post-treatment is to use thermal activation to promote further chemical reactions between the active groups in the reactive phosphorus-nitrogen flame retardant and the polyethylene molecular chain, forming a more stable covalent bond network structure, improving the retention of the flame-retardant component in the material and the durability of the flame-retardant effect.

[0078] The specific implementation of the interface reaction kinetics equation set is to establish a mathematical model describing the interaction between silane coupling agent and fiber surface. The interface binding reaction equation is based on reaction kinetics theory, and the input parameters include reaction temperature 60°C, reaction time 90 minutes, silane coupling agent KH550 concentration 3%, wood fiber surface hydroxyl group density 4.2 mmol / g and catalyst concentration 0.1%. The Arrhenius equation is used to describe the variation of reaction rate with temperature, and two key parameters, interface binding rate and reaction completion degree, are output. The diffusion transfer equation is based on Fick's second law, which describes the mass transfer process of silane coupling agent molecules from solution phase to fiber surface. The input parameters include diffusion coefficient 2.1×10 -10 m 2 / s, concentration gradient 0.8 mol / m 3 , mass transfer boundary layer thickness 15 μm, fiber surface area 320 m 2 / g and solution viscosity 1.2×10 -3 Pa·s, and the mass transfer rate and interface concentration distribution are output to guide the adjustment of process parameters. The equation set is solved by finite difference method for numerical calculation, and spatial grid and time step are established. The concentration distribution and reaction conversion rate at each time and position are obtained by iterative calculation.

[0079] The specific implementation of the heat and mass transfer coupling equation set is to establish a mathematical model describing the heat and mass transfer phenomenon in the mold pressing process. The heat conduction equation is based on Fourier's law, and the input parameters include thermal conductivity 0.35 W / (m·K), specific heat capacity 2100 J / (kg·K), density 1200 kg / m 3 , initial temperature 180°C and boundary heat transfer coefficient 45 W / (m 2• K), output temperature field distribution and heat flux density for guiding process control. Mass transfer equation is established based on Fick's law, input parameters include diffusion coefficient 1.8 x 10 -9 m 2 / s, initial concentration distribution 3%, boundary mass transfer coefficient 0.025 m / s and porosity 8%, output concentration field distribution and mass transfer rate for optimizing forming parameters. Phase change heat transfer equation combined with Avrami equation describes the crystallization kinetics process of polyethylene, input parameters include phase change latent heat 293 kJ / kg, phase change temperature 135 °C, crystallinity 65%, cooling rate 5 °C / min and nucleation density 2.5 x 10 12 / m 3 , output crystallization kinetics parameters and microstructure characteristics for evaluating material performance. Equation set is solved numerically by finite element method, geometric model and mesh division are established, boundary conditions and initial conditions are set, field variable distribution at each time is calculated by time stepping method.

[0080] The specific implementation of the multi-objective optimization problem combined with the grey wolf predation optimization algorithm is to establish an intelligent optimization solution framework. First, a multi-objective optimization function model is established, and four optimization variables including the addition amount of reactive phosphorus-nitrogen flame retardant, reaction temperature, reaction time and stirring speed are set, and the variable value ranges are 8%~16%, 160 °C~185 °C, 2~8 minutes and 150~350 rpm respectively. Four objective functions are defined, including a flame retardant performance maximization function, a mechanical property loss minimization function, a processing performance optimization function and a cost control function, and the weights of each function are set to 0.4, 0.3, 0.2 and 0.1 respectively. The grey wolf predation optimization algorithm realizes global optimization by simulating the social behavior and hunting strategy of the grey wolf population. The population size is initialized to 50 individuals, and each individual represents a set of parameter combinations. The grey wolf hierarchy is established, and alpha wolf, beta wolf and delta wolf are selected as leaders according to the fitness value, and the rest of the individuals are selected as omega wolf. The algorithm iteratively updates the population through three behavior stages of surrounding, hunting and attacking, the surrounding coefficient is linearly decreased from 2 to 0 to control the search range, and the random vector balances the exploration and exploitation capabilities. The termination condition of the algorithm is to reach the maximum iteration number of 200 times or the Pareto optimal solution set does not improve significantly for 20 consecutive iterations, and finally the solution with the best comprehensive performance is selected from the Pareto frontier as the optimal reaction condition output.

[0081] It should be noted that the technical scheme of the multi-objective optimization problem combined with the grey wolf hunting optimization algorithm to determine the optimal reaction condition has significant technical effects and principle advantages compared with the traditional single-objective optimization and empirical parameter setting method. The traditional method usually uses a single objective function for optimization or relies on engineering experience to determine the process parameters. This approach has the problems of local optimal trap and improper handling of the mutual constraint relationship between parameters, making it difficult to achieve global optimal balance between flame retardation performance, mechanical performance, processing performance and cost control. The multi-objective optimization problem constructed in the present application takes the addition amount of reaction-type phosphorus-nitrogen flame retardant DOPO-HQ, reaction temperature, reaction time and stirring speed as mutually constrained objective functions. By avoiding the disadvantages of over-optimization of a certain performance index at the expense of other performances in single-objective optimization, the concept of Pareto optimal solution set is used to realize the coordination and global optimization of multiple objective functions.

[0082] The grey wolf hunting optimization algorithm simulates the social hierarchy and cooperative hunting behavior of grey wolf groups, has a strong balance mechanism of global search ability and local development ability, and has better convergence and stability compared with traditional bionic optimization methods such as genetic algorithm and particle swarm optimization. Alpha wolf, beta wolf and delta wolf represent different levels of optimal solutions in the algorithm, and omega wolf gradually converges to the optimal solution by simulating surrounding, hunting and attacking behavior. This multi-level guidance mechanism avoids the problem of algorithm falling into local optimum, and ensures finding the global optimal solution in complex multi-dimensional parameter space. The design of the surrounding coefficient linearly decreasing from 2 to 0 cleverly balances the exploration and development ability of the algorithm, and improves the optimization efficiency and solution accuracy by performing large-scale global search in the early optimization stage and fine local search in the later optimization stage.

[0083] The combination of multi-objective optimization and grey wolf algorithm fundamentally changes the way of determining the process parameters of in-situ polymerization of reaction-type phosphorus-nitrogen flame retardant. By mathematical modeling, the complex chemical reaction process is converted into a quantifiable optimization problem, and the intelligent search ability of the algorithm is used to find the best parameter combination in the multi-dimensional parameter space. This method not only improves the scientificity and accuracy of parameter setting, but also greatly shortens the process development cycle and avoids the waste of resources and time consumption caused by traditional trial-and-error method. The best reaction conditions determined by the optimization algorithm ensure the full reaction and uniform distribution of the chemical bonding reaction between the reaction-type phosphorus-nitrogen flame retardant and the polyethylene molecular chain, improve the fixation effect and dispersion uniformity of the flame retardant in the composite material, and significantly improve the flame retardation efficiency and comprehensive performance stability of the composite material.

[0084] It should be noted that the application adopts a chemical bonding mechanism of silane coupling agent surface modification and in-situ polymerization of reactive flame retardant, which has significant technical advantages compared with the traditional physical blending method. The traditional method only relies on van der Waals force and mechanical entanglement to realize the combination of flame retardant and fiber, and the binding force is weak and phase separation is easy to occur. The application chemically modifies the wood fiber by silane coupling agent KH550, utilizes the condensation reaction of ethoxy in silane molecule and fiber surface hydroxyl to form stable silicon-oxygen covalent bond, and the amino functional group extends outward to form an active interface layer, which provides a chemical reaction site for the subsequent grafting of reactive phosphorus-nitrogen flame retardant DOPO-HQ. In the process of melt blending, the reactive flame retardant is chemically bonded with the amino group of the silane coupling agent and the polyethylene molecular chain at the same time through in-situ polymerization reaction, forming a double chemical connection network of flame retardant-coupling agent-fiber and flame retardant-matrix. This chemical bonding mechanism completely changes the existence state of the flame retardant in the composite material from the molecular level, avoids the problems of flame retardant migration and precipitation caused by traditional physical blending, and realizes the permanent fixation and uniform distribution of the flame retardant.

[0085] It should be noted that the application adopts a precise control mechanism of nano flame retardant synergistic system and multi-objective optimization algorithm, which realizes the precise control of process parameters by constructing a synergistic flame retardant system of nano aluminum oxide and layered double hydroxide composite, and combining with gray wolf hunting optimization algorithm. Compared with the traditional single flame retardant addition and empirical process parameter setting, it has obvious technical advantages. The nano flame retardant synergistic system utilizes the complementarity of different nanoparticles in the flame retardant mechanism, nano aluminum oxide provides high temperature stability and thermal conduction barrier effect, layered double hydroxide releases crystal water and forms a porous carbon layer when heated, and the two synergistically produce double flame retardant effects of physical barrier and chemical inhibition, significantly improving the flame retardant efficiency. The multi-objective optimization algorithm optimizes multiple mutually restrictive process parameters such as reaction temperature, reaction time and stirring speed by simulating the social hierarchy system and cooperative hunting behavior of gray wolf group, realizes the best balance of flame retardant performance, mechanical property, processing performance and cost control, and avoids the local optimization problem caused by traditional empirical parameter setting.

[0086] The synergistic effect of the two key technical ideas has systematic technical advantages over the prior art. The chemical bonding mechanism provides a stable carrier and dispersion basis for the nano flame-retardant synergistic system. The wood fiber surface modified by silane coupling agent has stronger polarity and reactivity, which is conducive to the adsorption and anchoring of nano flame-retardant particles, and avoids the agglomeration of nano particles in the composite material. At the same time, the process parameters precisely controlled by the multi-objective optimization algorithm ensure the full chemical bonding reaction and uniform dispersion of nano particles. The optimal reaction temperature and time obtained by algorithm optimization not only ensure the complete combination of silane coupling agent and fiber surface, but also promote the in-situ polymerization reaction of reactive flame retardant, realizing the complete construction of chemical bonding network. This synergistic mechanism makes the flame retardant not only firmly combined in the fiber and matrix through chemical bonds, but also plays a higher flame-retardant efficiency under the enhancement of nano synergistic system, forming a stable structure, uniform dispersion, and efficient three-dimensional flame-retardant network, which completely solves the performance defect problem caused by poor compatibility of traditional flame retardant and fiber.

[0087] It should be noted that for the determination of the optimal reaction conditions, the existing technology mainly has three limitations in determining the optimal reaction conditions. First, the one-sidedness of the traditional single-objective optimization method, the existing technology usually uses a single objective function for optimization, such as only considering flame retardant performance or only considering mechanical performance indicators. This approach has the problems of local optimal trap and improper handling of the mutual constraint relationship between parameters, making it difficult to achieve global optimal balance between flame retardant performance, mechanical performance, processing performance, and cost control, and prone to over-optimization of a certain performance indicator at the expense of other performances. Second, the blindness of the empirical parameter setting method, traditional process development mainly relies on engineering experience and trial-and-error method to determine process parameters, estimates parameter range based on historical data and empirical formula, and finds suitable process conditions through a large number of experiments and trial-and-error. This approach lacks theoretical guidance and systematicness, has long development cycle and large resource consumption. Third, the limitation of traditional optimization algorithm, the existing genetic algorithm, particle swarm algorithm and other bionic optimization methods have insufficient convergence and stability when dealing with complex multi-dimensional parameter space, and are prone to premature convergence to local optimum, lacking effective global search and local development balance mechanism.

[0088] The combination of multi-objective optimization and grey wolf hunting algorithm in the application has unique technical features, and the combination mode is to organically integrate the mathematical modeling of multi-objective optimization problem and the bionic search mechanism of grey wolf algorithm. Specifically, the multi-objective optimization part constructs the addition amount of reactive phosphorus-nitrogen flame retardant DOPO-HQ, reaction temperature, reaction time and stirring speed as four mutually restricted objective functions, including the maximum flame retardant performance function, the minimum mechanical property loss function, the processing performance optimization function and the cost control function. The multi-objective function fitness value is calculated by the weighted summation method, and the weights are set to 0.4, 0.3, 0.2 and 0.1 respectively, and the Pareto optimal solution set is formed to avoid the one-sidedness of single-objective optimization. The grey wolf algorithm part searches the optimal solution of the multi-objective optimization problem by simulating the social hierarchy system and cooperative hunting behavior of the grey wolf group. The algorithm sorts the population of 50 individuals according to the multi-objective function fitness value to establish the four-level hierarchical structure of alpha wolf, beta wolf, delta wolf and omega wolf, wherein the alpha wolf represents the optimal solution in the current Pareto optimal solution set, the beta wolf and the delta wolf represent the suboptimal solution, and the omega wolf represents other candidate solutions. The deep combination of the two is reflected in the three behavior stages of the algorithm: in the surrounding stage, the distance vector between the omega wolf and the three leader wolves is calculated to update the position, and the distance calculation is based on the comprehensive evaluation of the four objective functions; in the hunting stage, the candidate position of the omega wolf is calculated according to the position information of the three leader wolves in the multi-objective function space, and the search is carried out in the Pareto frontier area through the hunting equation; in the attack stage, when the surrounding coefficient is less than 1, the local search is triggered, and the omega wolf moves to the average position of the three leader wolves in the multi-objective space, realizing the fine search of the Pareto optimal solution. This combination mode makes the algorithm not only maintain the globality and coordination of the multi-objective optimization problem, but also fully utilize the strong search ability and intelligent balance mechanism of the grey wolf algorithm. Through the dynamic adjustment of the surrounding coefficient from 2 to 0, a large range of global search is carried out in the early optimization stage to find the Pareto frontier, and a fine local search is carried out in the later optimization stage to determine the optimal solution, and finally the solution with the best comprehensive performance is selected from the Pareto optimal solution set as the optimal reaction condition output through the multi-attribute decision method.

[0089] Specifically, the principle of the present application is that the key to solving the technical problem of poor compatibility of organic phosphorus flame retardant with wood fiber lies in constructing a multi-level chemical bonding network and an interface compatibility enhancement mechanism. First, the sodium hydroxide pretreatment increases the active site of the hydroxyl group on the surface of the wood fiber, providing sufficient reaction groups for the grafting of silane coupling agent KH550. The ethoxy group in the silane coupling agent molecule reacts with the hydroxyl group on the surface of the wood fiber to form a stable silicon-oxygen bond, and the amino functional group extends outward to form an active interface layer. Second, the nano-aluminum oxide and layered double hydroxide in the nano-flame retardant synergistic system interact with the modified wood fiber surface through van der Waals force and hydrogen bonding, forming a nano-flame retardant protective layer around the fiber, enhancing the interface bonding strength and providing a physical barrier effect. Most importantly, the reactive phosphorus-nitrogen flame retardant DOPO-HQ undergoes in-situ polymerization during the melt blending process, and its phosphorus-oxygen bond can not only chemically react with the amino group of the silane coupling agent, but also form a chemical bond with the polyethylene molecular chain, establishing a three-dimensional cross-linked network structure of flame retardant-fiber-matrix. This chemical bonding mechanism fundamentally changes the weak interaction state between the flame retardant and the fiber through van der Waals force under the traditional physical blending method, and realizes molecular-level compatibility through the formation of covalent bonds. In addition, by precisely controlling the process parameters such as reaction temperature, time and stirring speed through the gray wolf hunting optimization algorithm, the in-situ polymerization reaction is ensured to proceed fully and uniformly, avoiding the problem of poor compatibility caused by incomplete local reaction. The multi-objective optimization strategy balances the requirements of flame retardant performance, mechanical properties and processing performance, so that the chemical bonding network can ensure the flame retardant effect without excessive cross-linking affecting the processability of the material.

[0090] A specific embodiment 1 of the present application is provided below, and the specific implementation of each step in embodiment 1 is described in detail as follows.

[0091] The specific implementation of step S01 is the same as described above and will not be described in detail here.

[0092] The specific implementation of step S02 is to use silane coupling agent KH550 to chemically modify the surface of the wood fiber. The molecular formula of silane coupling agent KH550 is H2N(CH2)3Si(OC2H5)3, and this process involves the calculation of a set of interface reaction kinetics equations. The interface bonding reaction equation is established based on the Arrhenius reaction kinetics theory, and is specifically expressed as follows:

[0093]

[0094] In the formula, α is the interface bonding rate, t is the reaction time, the unit is min, k0 is the pre-exponential factor, the value is 2.3×10 12 , the unit is min -1 , E aEa is the activation energy, the value is 45.2 kJ / mol, R is the gas constant, the value is 8.314 J / (mol·K), T is the reaction temperature, the value is 333 K, C s is the silane coupling agent KH550 concentration, the value is 3%, C OH is the wood fiber surface hydroxyl group density, the value is 4.2 mmol / g, n1, n2, n3 are the reaction orders, the values are 1.2, 0.8, and 1.5, respectively.

[0095] wherein the interface bonding rate a is determined by infrared spectroscopy, including steps 1: preparing KBr tablets from the modified fiber sample, step 2: scanning in the wave number range of 4000-400 cm -1 to obtain an infrared spectrum, and step 3: calculating the interface bonding rate according to the absorbance value of the Si-O-C bond characteristic peak at 1050 cm -1 The activation energy E a is obtained by experiment, including steps 1: performing reaction kinetics experiments at different temperatures, step 2: plotting the relationship between lnk and 1 / T, and step 3: calculating the activation energy value according to the slope of the straight line.

[0096] The diffusion transfer equation is established based on Fick's second law and describes the mass transfer process of silane coupling agent molecules in the solution phase, which is specifically expressed as follows:

[0097]

[0098] In the formula, C is the concentration of silane coupling agent in the solution, the unit is mol / m 3 , D eff is the effective diffusion coefficient, the value is 2.1×10 -10 m 2 / s, R is the radial coordinate, the unit is m, k m is the mass transfer coefficient, the value is 3.5×10 -5 m / s, A s is the specific surface area of the fiber, the value is 320 m 2 / g, V s is the solution volume, the unit is m 3 , C i is the fiber surface interface concentration, the unit is mol / m 3 .

[0099] wherein the effective diffusion coefficient D eff is obtained by calculation, including steps 1: determining the molecular volume of the silane coupling agent molecule and the solvent viscosity, step 2: calculating the molecular diffusion coefficient according to the Stokes-Einstein equation, and step 3: considering the hindering effect of the fiber to obtain the effective diffusion coefficient. The mass transfer coefficient k mThe experimental method is adopted, including steps 1: determining the mass transfer rate under different stirring speeds, step 2: establishing the relationship between the mass transfer rate and the Reynolds number, and step 3: calculating the mass transfer coefficient according to the Sherwood number correlation formula. The numerical value of the concentration gradient is 0.8 mol / m 3 , the numerical value of the mass transfer boundary layer thickness is 15 μm, the numerical value of the solution viscosity is 1.2 × 10 -3 Pa·s, and the numerical value of the catalyst concentration is 0.1%.

[0100] The specific embodiment of step S03 is to prepare a nano flame-retardant synergistic system, wherein the molecular formula of the layered double hydroxide is In the formula, M 2+ and M 3+ are divalent and trivalent metal ions, respectively, is an interlayer anion, x is the molar fraction of the metal ion, the numerical value ranges from 0.2 to 0.4, m H2O is the number of interlayer water molecules, the numerical value ranges from 0.5 to 2.0, and n ion is the valence number of the anion.

[0101] The specific embodiment of step S04 is to adopt a twin-screw extruder for melt blending of the polyethylene matrix and the modified fiber, and the blending parameters are optimized through the output parameters of the interfacial reaction kinetics equation set. The dispersion effect evaluation index of the nano flame-retardant synergistic system is determined according to the interfacial bonding rate α output by the interfacial bonding reaction equation, and when α > 0.85, it is considered that the fiber modification is sufficient, and good dispersion effect can be obtained. The mass transfer rate J m is adjusted according to the blending time, and the mass transfer rate calculation formula is:

[0102] J m = k m (C-C i );

[0103] In the formula, when J m > 2.1 × 10 -6 mol / (m 2 ·s), the blending time is set to 3 minutes, and when J m < 1.5 × 10 -6 mol / (m 2 ·s), the blending time is extended to 5 minutes.

[0104] The specific embodiment of step S05 is to add a reactive phosphorus-nitrogen flame retardant DOPO-HQ with a molecular formula of C 18 H 13 O4P during the melt blending process, and the optimal reaction conditions are determined through a multi-objective optimization problem combined with a grey wolf hunting optimization algorithm. The multi-objective optimization function model is represented as follows:

[0105] minF(x) = [f1(x), f2(x), f3(x), f4(x)] T ;

[0106] where F(x) is a multi-objective function vector, x = [x1, x2, x3, x4] T is a decision variable vector, x1 is the amount of flame retardant additive, with a value range of 8-16%, x2 is the reaction temperature, with a value range of 160-185℃, x3 is the reaction time, with a value range of 2-8 min, and x4 is the stirring speed, with a value range of 150-350 rpm.

[0107] The objective function is specifically represented as follows:

[0108] f1(x) = -w f1 ·OI(x) - w f2 ·UL94(x);

[0109]

[0110] f4(x) = w f7 ·C m (x) + w f8 ·C e (x);

[0111] where f1(x) is a flame retardant performance maximization function, f2(x) is a mechanical property loss minimization function, f3(x) is a processing performance optimization function, and f4(x) is a cost control function, OI(x) is the oxygen index, UL94(x) is the vertical burning grade score, σ t (x) is the tensile strength, σ t0 is the basic tensile strength, with a value of 25 MPa, I s (x) is the impact strength, I s0 is the basic impact strength, with a value of 8 kJ / m 2 , MFI(x) is the melt flow index, MFI0 is the basic melt flow index, with a value of 2.5 g / 10 min, T p (x) is the processing temperature, T p0 is the basic processing temperature, with a value of 180℃, C m (x) is the raw material cost, C e (x) is the energy consumption cost, w f1 to w f8 are weight coefficients, with values of 0.6, 0.4, 0.5, 0.5, 0.6, 0.4, 0.7, and 0.3, respectively.

[0112] The position update formula of the grey wolf hunting optimization algorithm is represented as follows:

[0113]

[0114] wherein, are position vectors of alpha wolf, beta wolf, delta wolf respectively, is current omega wolf position vector, is convergence factor vector, is random vector, are candidate position vectors calculated based on alpha wolf, beta wolf, delta wolf positions respectively, t is current iteration number.

[0115] The convergence factor calculation formula is:

[0116]

[0117] wherein, is linearly decreasing parameter, t max is maximum iteration number, the value is 200, is random vector between 0 and 1.

[0118] The multi-objective function fitness value calculation formula is:

[0119] F fitness = w obj1 ·f1(x) + w obj2 ·f2(x) + w obj3 ·f3(x) + w obj4 ·f4(x);

[0120] wherein, F fitness is comprehensive fitness value, w obj1 , w obj2 , w obj3 , w obj4 are objective function weights, the values are 0.4, 0.3, 0.2, 0.1 respectively.

[0121] The distance vector calculation formula is:

[0122]

[0123] wherein, is distance vector, X i , Y i are the i-th components of two position vectors respectively, n is the dimension of decision variable, the value is 4.

[0124] The specific implementation of step S06 is to prepare the flame-retardant composite material product by using the compression molding process, and the process involves the calculation of heat and mass transfer coupled equations. The heat conduction equation is established based on Fourier's law, and is specifically expressed as follows:

[0125]

[0126] In the formula, ρ is the density, with a value of 1200 kg / m³. 3 c p The specific heat capacity is 2100 J / (kg·K), T heat Q represents the temperature field during heat transfer, measured in K, where λ is the thermal conductivity, with a value of 0.35 W / (m·K). gen This is the internal heat source term, with units of W / m. 3 The boundary heat transfer coefficient is 45 W / (m²). 2 ·K), with an initial temperature of 180℃.

[0127] The mass transfer equation is based on Fick's law and is expressed as follows:

[0128]

[0129] In the formula, C w Moisture concentration, unit: kg / m³ 3 D w The water diffusion coefficient has a value of 1.8 × 10⁻⁶. -9 m 2 / s, S w The rate of water consumption is expressed in kg / (m²). 3 The initial concentration distribution was 3%, the boundary mass transfer coefficient was 0.025 m / s, and the porosity was 8%.

[0130] The phase change heat transfer equation is established by combining the Avramie equation, and is specifically expressed as follows:

[0131]

[0132] In the formula, X c For crystallinity, K(T) mold K(T) is the crystallization rate constant. mold )=K0exp(-E c / RT mold K0 is the pre-exponential factor with a value of 1.2 × 10⁻⁶. 8 s -1 E c The activation energy for crystallization is 125 kJ / mol, n av The Avramiya index has a value of 2.5, T mol d represents the molding temperature in K, the latent heat of phase change is 293 kJ / kg, the phase change temperature is 135℃, the cooling rate is 5℃ / min, and the nucleation density is 2.5 × 10⁻⁶. 12 / m 3 .

[0133] wherein the crystallinity X c Differential scanning calorimetry was used to determine the crystallinity, including the steps of 1: 3-5 mg sample was prepared and placed in an aluminum crucible, 2: heating at a rate of 10 ℃ / min from 30 ℃ to 200 ℃, 3: calculating the crystallinity according to the melting peak area. Crystallization activation energy E c Kissinger method was used to calculate, including the steps of 1: DSC test was carried out at different heating rates, 2: recording the crystallization peak temperature, 3: drawing the versus 1 / T p to obtain the activation energy.

[0134] The specific implementation of step S07 is to post-treat the shaped product to improve the persistence of the flame-retardant effect. The crosslinking reaction kinetics equation in the post-treatment process is expressed as follows:

[0135]

[0136] In the formula, β is the crosslinking reaction conversion rate, k cross is the crosslinking reaction rate constant, the value is 8.5×10 6 s -1 , E cross is the crosslinking reaction activation energy, the value is 78.5 kJ / mol, T treat is the heat treatment temperature, unit: K, C DOPO is the concentration of the reactive phosphorus-nitrogen flame retardant, C PE is the concentration of the polyethylene molecular chain, p1, p2, p3 are the crosslinking reaction orders, the values are 1.0, 0.5, 2.0 respectively. The heat treatment time is determined according to the degree of completion of the crosslinking reaction. When β>0.90, it is considered that the crosslinking is sufficient, and at this time the corresponding heat treatment time is 24 hours.

[0137] In the formula, β is the crosslinking reaction conversion rate, k cross Isothermal reaction kinetics experiment was used to obtain, including the steps of 1: isothermal heat treatment was carried out at different temperatures, 2: crosslinking reaction conversion rate was determined by sampling at regular time, 3: the value of activation energy was obtained according to the Arrhenius equation fitting.

[0138] In this embodiment, it should be noted that the interface bonding reaction equation Based on the Arrhenius reaction kinetics theory, the chemical bonding process of silane coupling agent with the hydroxyl groups on the surface of wood fibers is described. This equation realizes the accurate control of the modification degree of the fiber surface by quantifying the change law of the interface bonding rate with time, and compared with the traditional empirical modification method, it can accurately predict the optimal reaction time and conditions, avoid the destruction of the fiber structure caused by excessive reaction, ensure the formation of stable interfacial compatibility between the modified fiber and the polyethylene matrix, and significantly improve the mechanical properties and the durability of the flame retardant effect of the composite material.

[0139] Diffusion transfer equation Based on the establishment of Fick's second law, the diffusion mass transfer process of silane coupling agent molecules in the solution and the adsorption consumption on the fiber surface are considered. This equation can accurately describe the mass transfer kinetics of coupling agent molecules from the solution bulk to the fiber surface, and by optimizing the mass transfer parameters, it significantly improves the utilization efficiency and distribution uniformity of the coupling agent compared with the traditional static immersion treatment, ensuring sufficient and uniform chemical modification of the fiber surface, and laying a foundation for the effective dispersion of the subsequent nano flame retardant synergistic system.

[0140] Mass transfer rate equation J m = k m (C-C i ) quantifies the mass transfer efficiency of silane coupling agent, and by monitoring the change of mass transfer rate in real time, it can dynamically adjust the blending time and process parameters. Compared with the traditional method of fixed process parameters, the dynamic adjustment mechanism guided by this equation ensures the optimal dispersion state of the modified fiber in the polymer matrix, effectively avoids the fiber agglomeration phenomenon, and improves the uniformity of the microstructure and the stability of the macroscopic performance of the composite material.

[0141] Multi-objective optimization function model minF(x) = [f1(x), f2(x), f3(x), f4(x)] T A comprehensive optimization framework for flame retardant performance, mechanical properties, processing performance and cost control is established, and multiple performance indicators that conflict with each other are unified in the mathematical model. Compared with the traditional single objective optimization or trial and error method, this model can systematically find the best balance point of each performance indicator, avoid the technical defects of sacrificing one for the other, realize the maximum improvement of the comprehensive performance of the composite material, and especially minimize the loss of mechanical properties while ensuring the flame retardant effect.

[0142] Position update formula of gray wolf hunting optimization algorithm The cooperative hunting behavior of gray wolf packs is simulated, and global optimization is achieved through the guidance of alpha wolves, beta wolves, and delta wolves. Compared with traditional gradient descent algorithms or genetic algorithms, this algorithm has stronger global search capability and faster convergence speed, can effectively avoid falling into local optimal solution, ensure finding the real optimal parameter combination of reactive phosphorus-nitrogen flame retardant addition, and significantly improve the performance optimization efficiency of flame-retardant composites.

[0143] Convergence factor calculation formula and The exploration and development balance of the algorithm is controlled, and the smooth transition from global search to local refinement is achieved through the linearly decreasing convergence parameter. This dynamic adjustment mechanism can improve the accuracy of the solution while ensuring the sufficiency of the search range compared with the optimization algorithm with fixed parameters, ensure the accurate positioning of the optimal reaction condition, and avoid the optimization failure problem caused by improper parameter setting.

[0144] Heat conduction equation The evolution of the temperature field distribution in the compression molding process is described, and the fine control of the molding process is achieved by accurately calculating the heat transfer behavior inside the composite material. Compared with the traditional empirical temperature control, this equation can predict and optimize the spatial distribution and temporal evolution of the temperature field, ensure uniform heating of each part of the composite material, avoid performance inhomogeneity caused by local overheating or deficiency, and significantly improve the stability and consistency of product quality.

[0145] Mass transfer equation The migration behavior of water and volatile components during molding is described, and the accurate regulation of the formation of the microstructure of the material is achieved by quantifying the mass transfer process. Compared with the traditional molding process that ignores the influence of mass transfer, the process optimization guided by this equation can effectively control the pore structure and density distribution, reduce internal defects of the product, and improve the reliability of the mechanical properties and flame retardant properties of the composite material.

[0146] Phase change heat transfer equation Based on the Avrami theory, the crystallization kinetics process of the polyethylene matrix is described, and the optimal design of the microstructure is achieved by accurately controlling the crystallization behavior. Compared with the traditional macroscopic cooling control, this equation can predict and regulate the crystallinity, crystal size, and morphology distribution, ensure the optimal dispersion state of the reactive phosphorus-nitrogen flame retardant in the matrix, and significantly improve the efficiency of the flame-retardant component and the comprehensive performance of the material.

[0147] Crosslinking reaction kinetics equation The further crosslinking reaction between the reactive flame retardant and the polymer matrix during the post-processing is described, and the precise control of the persistence of the flame retardant effect is achieved by quantifying the crosslinking degree. Compared with the traditional fixed-time heat treatment, the dynamic monitoring and control mechanism guided by this equation ensures the sufficient progress of the crosslinking reaction, avoids the performance degradation caused by over-treatment, realizes the best balance between the flame retardant performance and the processing performance, and significantly improves the service life and application reliability of the flame-retardant composite material.

[0148] In order to better understand and implement the present application, the following provides an embodiment 2 of a specific application scenario of the present application: a research team undertook the technical task of developing a new type of flame-retardant composite material plate, which needs to be applied in the interior field of high-rise buildings and faces strict flame-retardant performance requirements. Although the traditional inorganic flame-retardant filling method can provide certain flame-retardant effect, it often leads to a significant decrease in the mechanical properties of the material and a deterioration in the processing performance, which is difficult to meet the actual application requirements. The research team decided to use the bio-based composite material flame-retardant performance enhancement method of the present application to solve this technical problem.

[0149] The research team first pretreated the wood fibers according to step S01. Pine wood fibers were selected as the reinforcing phase, and the fiber length was controlled within the range of 2-8 mm, among which the short fibers with an aspect ratio of 5-15 accounted for 30%, the medium fibers with an aspect ratio of 15-40 accounted for 50%, and the long fibers with an aspect ratio of 40-80 accounted for 20%. 500g of wood fibers were immersed in a NaOH solution with a concentration of 8%, and treated at 80℃ for 2 hours to remove the surface lignin and hemicellulose impurities by alkaline hydrolysis. After treatment, the fibers were washed with deionized water until the pH value was 7.0, and then dried in an oven at 105℃ until the moisture content was 2.5%.

[0150] In step S02, the research team used silane coupling agent KH550 to modify the surface of the fibers. The dried wood fibers were mixed with silane coupling agent KH550 at a mass ratio of 100:3, and the coupling agent was diluted with anhydrous ethanol to a concentration of 10%. The modification treatment was carried out at a reaction temperature of 60℃ for 90 minutes, and the mechanical stirring speed was maintained at 120rpm. According to the interface reaction kinetics equation, when the reaction time reached 85 minutes, the interface bonding rate a reached 0.87, meeting the requirements of sufficient modification. Figure 2 The interface bonding reaction kinetics process of silane coupling agent KH550 and the surface hydroxyl groups of wood fibers is shown. The abscissa is the reaction time (minutes), and the ordinate is the interface bonding rate a. The black solid line represents the trend of the interface bonding rate with time, showing a typical exponential growth characteristic, with a faster initial reaction rate and a gradual flattening in the later stage. The gray dashed line in the figure indicates the target bonding rate 0.87, and the dark gray dashed line indicates the optimal reaction time 85 minutes.

[0151] In the preparation process of the nano flame-retardant synergistic system of step S03, the research team mixed nano Al2O3 with layered double hydroxide The mass ratio is 2:1. The particle size distribution of nano Al2O3 is 10-200 nm, of which 10-50 nm ultrafine particles account for 35%, 50-100 nm fine particles account for 45%, and 100-200 nm medium particles account for 20%. After mixing, ultrasonic dispersion treatment is adopted, power 300W, frequency 40kHz, dispersion time 20 minutes, and finally a uniform suspension with D90 value of 185nm is obtained. Figure 3 The distribution of particles in different particle size ranges in the nano flame-retardant synergistic system is shown in the form of a histogram. The abscissa is the particle size range (nanometer), and the ordinate is the distribution percentage. The four columns represent the particle size ranges of 10-50nm, 50-100nm, 100-200nm, and 200-500nm, with the color gradually changing from black to light gray. The chart shows that the particles in the 50-100nm particle size range account for the highest proportion of 45%, the 10-50nm particle size range accounts for 35%, the 100-200nm accounts for 20%, and the 200-500nm particle size range is basically eliminated through optimization control. The specific percentage value is marked at the top of each column, clearly reflecting the optimization effect of particle size distribution after ultrasonic dispersion treatment.

[0152] In step S04, the research team adopts a twin-screw extruder for melt blending, with a ratio of polyethylene 100 parts, modified wood fiber 30 parts, and nano flame-retardant synergistic system 15 parts. The temperature settings of each section of the extruder are shown in Table 1:

[0153] Table 1 Temperature setting table of twin-screw extruder

[0154] Segment name Set temperature Hopper segment 120℃ Plasticizing segment 150℃ Homogenizing segment 165℃ Discharge segment 160℃

[0155] The screw speed is controlled at 150 rpm, and the material residence time is 3 minutes. According to the diffusion transfer equation, the mass transfer rate J m is 2.3×10 -6 mol / (m 2 ·s), which exceeds the critical value of 2.1×10 -6 mol / (m 2 ·s), confirming that the blending time setting is reasonable.

[0156] In step S05, the research team determines the optimal addition conditions of the reactive phosphorus-nitrogen flame retardant DOPO-HQ through a multi-objective optimization problem combined with the grey wolf hunting optimization algorithm. The algorithm initializes a population size of 50 individuals and converges after 180 iterations. The optimal parameter combination is determined as follows: DOPO-HQ addition amount of 12%, reaction temperature of 172°C, reaction time of 4.5 minutes, and stirring speed of 275 rpm. Under these conditions, the flame retardant forms a chemical bond with the polyethylene matrix through in-situ polymerization, significantly improving the durability of the flame retardation effect. Figure 4 The convergence of the fitness values of different levels of grey wolf individuals in the multi-objective optimization process is shown. The horizontal axis represents the number of iterations, and the vertical axis represents the fitness value. The four curves represent the alpha wolf (optimal solution), beta wolf (suboptimal solution), delta wolf (third optimal solution), and population average fitness, with colors ranging from black to light gray. All curves show an upward convergence trend, with the alpha wolf having the highest fitness value and the algorithm converging at the 180th iteration. The grey dashed line indicates the convergence point. This chart visually presents the optimization process and convergence characteristics of the grey wolf hunting optimization algorithm.

[0157] The step S06 molding process uses a heat and mass transfer coupled equation set for process optimization. The mold is preheated to 180°C, and the blended material is evenly laid and pre-pressed at 5 MPa for 30 seconds, then the forming pressure is increased to 15 MPa. According to the temperature field distribution calculated by the heat conduction equation, the internal temperature uniformity of the product is ensured within ±3°C. The mass transfer equation shows that the moisture concentration decreases from 3% to 0.8%, meeting the product quality requirements. The phase change heat transfer equation calculates that the polyethylene crystallinity reaches 68%, and the holding time is set to 5 minutes. After molding, the cooling rate is reduced to below 40°C at 5°C / min, and the product is demolded. Figure 5 The temperature field distribution of the product at different positions in the molding process is shown. The horizontal axis represents the molding time (minutes), and the vertical axis represents the temperature (degrees Celsius). The three solid lines represent the temperature changes at the center, surface, and edge of the product, with colors ranging from black to dark gray. The light gray dashed line represents the mold set temperature of 180°C, and the gray dashed line indicates the holding time of 5 minutes. The chart shows that the temperature of different parts of the product gradually tends to be uniform, reflecting the precise temperature control effect under the guidance of the heat and mass transfer coupled equation set.

[0158] In step S07, the research team optimizes the post-processing of the product. The molded product is placed in a 120°C air drying oven for heat treatment for 24 hours to promote further crosslinking between DOPO-HQ and the polyethylene matrix. According to the crosslinking reaction kinetics equation monitoring, the crosslinking reaction conversion rate β reaches 0.92 after 22 hours, confirming that the crosslinking reaction is fully completed. Figure 6The crosslinking reaction process of the post-processing stage reaction-type phosphorus-nitrogen flame retardant DOPO-HQ and the polyethylene matrix is described. The abscissa is the heat treatment time (hours), and the ordinate is the crosslinking reaction conversion rate β. The black solid line represents the conversion rate growth curve with time, showing an exponential growth characteristic of fast first and slow later. The gray dotted line indicates the target conversion rate 0.92, the dark gray dotted line indicates the standard time 22 hours, and the light gray dotted line indicates the heat treatment time set by the process 24 hours. The graph verifies the accuracy of the crosslinking reaction kinetics equation, providing theoretical support for the determination of the post-processing process time.

[0159] The prepared flame-retardant composite plate was subjected to comprehensive performance testing, and the main results are shown in Table 2:

[0160] Table 2 Performance test results of flame-retardant composite materials

[0161] Performance index Test result Test standard Oxygen index 32.5％ GB / T 2406.2 Vertical combustion grade V-0 grade GB / T 2408 Tensile strength 28.7 MPa GB / T 1040.1 Impact strength 7.6 kJ / m 2 ]] GB / T 1043.1 Melt flow index 2.8 g / 10 min GB / T 3682 Density 1.15 g / cm 3 ]]> GB / T 1033.1

[0162] To verify the effectiveness of the multi-objective optimization algorithm, the research team also tested the comprehensive performance of samples prepared under different process conditions, and the specific data are shown in Table 3:

[0163] Table 3 Performance comparison table of samples prepared under different process conditions

[0164] Sample number DOPO-HQ addition amount Reaction temperature Oxygen index Tensile strength Comprehensive score A1 10％ 165℃ 29.8％ 26.2 MPa 78.5 A2 12％ 172℃ 32.5％ 28.7 MPa 89.6 A3 14％ 180℃ 34.1％ 25.8 MPa 82.3 A4 16％ 185℃ 35.6％ 22.9 MPa 76.7

[0165] Table 3 shows that sample A2 prepared under the process conditions determined by the optimization algorithm has the highest comprehensive score, verifying the effectiveness of the multi-objective optimization method.

[0166] Under the precise control of interface reaction kinetics and heat and mass transfer processes, the research team successfully prepared flame-retardant composites with excellent performance. Through the guidance of the interface bonding reaction equation, the modification efficiency of the silane coupling agent was improved by 15%, ensuring good bonding between the fibers and the matrix. The application of the diffusion transfer equation improved the uniformity of the coupling agent distribution by 12%, reducing the occurrence of local defects. The use of the multi-objective optimization algorithm improved the utilization efficiency of the flame retardant by 18%, while ensuring the flame retardant performance and maximizing the mechanical properties of the material.

[0167] The application of the heat and mass transfer coupled equation set realized the fine control of the molding process, and the internal temperature uniformity of the product was improved by 16%, and the standard deviation of the density distribution was reduced by 14%. The crystallization control under the guidance of the phase change heat transfer equation made the material microstructure more perfect, and the crystallinity was improved by 8%, providing favorable conditions for the uniform dispersion of the flame retardant. The monitoring of the crosslinking reaction kinetics equation ensured the sufficiency of the post-processing process, and the crosslinking density was improved by 13%, significantly improving the durability of the flame retardant effect.

[0168] The traditional means for solving the technical problem of flame-retardant building interior materials mainly adopts the way of filling a large amount of inorganic flame retardant, and realizes the flame-retardant effect through physical dilution and heat decomposition mechanism. Although this method can meet the basic flame-retardant requirements, it has significant defects: the inorganic filler has poor compatibility with the polymer matrix, the interface bonding is weak, which leads to a significant decrease in the mechanical properties of the material; the flame retardant is not uniformly dispersed, and the aggregation phenomenon easily occurs, which affects the stability of the flame-retardant effect; the processing performance is deteriorated, the melt viscosity is increased, and the molding is difficult; the flame retardant is easy to migrate and precipitate, and the long-term use effect is attenuated.

[0169] The present application brings significant progress compared with the traditional means: through the modification of silane coupling agent guided by interface reaction kinetics equation, the interface bonding strength is increased by 19%, effectively solving the compatibility problem of filler and matrix; the flame-retardant efficiency is increased by 17% by using a nano flame-retardant synergistic system and precise dispersion control, while reducing the amount of filler; the application of multi-objective optimization algorithm realizes the best balance of performance, the tensile strength retention rate reaches 87% under the premise of ensuring V-0 level flame-retardant grade, which is increased by 16% compared with the traditional method; through the chemical bonding and crosslinking reaction control of the reactive flame retardant, the durability of the flame-retardant effect is increased by 18%, effectively inhibiting the migration and precipitation of the flame retardant; the molding process optimization guided by the heat and mass transfer coupling equation set makes the product quality stability increased by 15%, and the waste rate is significantly reduced. These technical progresses make the flame-retardant composite material prepared by the present application not only meet the strict flame-retardant performance requirements, but also maintain good mechanical properties and processing performance, providing an effective technical solution for the safe application of building interior materials.

[0170] It should be noted that the variables involved in the present application are explained in detail as shown in Tables 4 and 5.

[0171] Table 4 Variable Explanation Table (First Part)

[0172]

[0173] Table 5 Variable Explanation Table (Second Part)

[0174]

[0175]

[0176] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for enhancing the flame retardant properties of a bio-based composite material, characterized in that, The method comprises the following steps: The wood fibers are treated in a sodium hydroxide solution to remove surface impurities and increase hydroxyl active sites; The dried wood fibers are surface modified with silane coupling agent KH550 to form covalent bonds between the silane coupling agent and the hydroxyl groups on the surface of the wood fibers; a nano flame-retardant synergistic system is prepared by mixing nano aluminum oxide and layered double hydroxide to obtain a nano flame-retardant particle suspension; the polyethylene matrix is melted, and the polyethylene, modified wood fibers and nano flame-retardant synergistic system are blended according to a certain ratio, and then melt-blended using a double-screw extruder; the blending parameters are optimized by an interfacial reaction kinetics equation set; a reactive phosphorus-nitrogen flame retardant DOPO-HQ is added during the melt blending process, and the reactive phosphorus-nitrogen flame retardant DOPO-HQ is chemically bonded to the polyethylene molecular chain through in-situ polymerization; the optimal reaction conditions are determined by combining a multi-objective optimization problem with a grey wolf hunting optimization algorithm; the blended material is formed into a flame-retardant composite product through a mold pressing forming process, and the forming process parameters are optimized by a heat and mass transfer coupling equation set; and the flame-retardant composite product is treated through heat treatment.

2. The method of bio-based composite fire performance enhancement according to claim 1, wherein, The wood fibers are treated in a sodium hydroxide solution to remove surface impurities and increase hydroxyl active sites; 3. The method of bio-based composite fire performance enhancement according to claim 2, wherein, The surface modification treatment is performed by mixing the dried wood fibers with silane coupling agent KH550 at a mass ratio of 100:3, and reacting at 60°C for 90 minutes to form covalent bonds between the silane coupling agent and the hydroxyl groups on the surface of the wood fibers.

4. The method of bio-based composite fire performance enhancement according to claim 3, wherein, The silane coupling agent KH550 is an amino-containing silane compound with a molecular formula of H2N(CH2)3Si(OC2H5)3, which is used to improve the interfacial compatibility between the wood fibers and the polyethylene matrix.

5. The method of bio-based composite fire performance enhancement according to claim 4, wherein, The nano flame-retardant synergistic system is prepared by mixing nano aluminum oxide and layered double hydroxide at a mass ratio of 2:1, and then uniformly dispersing the mixture for 20 minutes to obtain a nano flame-retardant particle suspension.

6. The method of bio-based composite fire performance enhancement according to claim 5, wherein, The layered double hydroxide is an inorganic compound with a layered structure, and its molecular formula is wherein M 2+ and M 3+ are divalent and trivalent metal ions respectively, A n- is an interlayer anion, and has excellent flame-retardant synergistic effect.

7. The method of bio-based composite fire performance enhancement according to claim 6, wherein, The polyethylene matrix is melted at 165°C, and the polyethylene, modified wood fibers and nano flame-retardant synergistic system are blended according to a ratio of 100:30:15, and then melt-blended using a double-screw extruder for 3 minutes.

8. The method of bio-based composite fire performance enhancement according to claim 7, wherein, The reactive phosphorus-nitrogen flame retardant DOPO-HQ is the addition product of 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide and hydroquinone, and the molecular formula is C 18 H 13 O4P, which belongs to the reactive organic phosphorus flame retardant, and is combined with the polymer molecular chain through phosphorus-oxygen bond.

9. The method of bio-based composite fire performance enhancement according to claim 8, wherein, The amount of the reactive phosphorus-nitrogen flame retardant DOPO-HQ added is 12% of the mass of the polyethylene, and the reactive phosphorus-nitrogen flame retardant DOPO-HQ is chemically bonded to the polyethylene molecular chain through in-situ polymerization.

10. The method of bio-based composite fire performance enhancement according to claim 9, wherein, The interfacial reaction kinetics equation set includes an interfacial bonding reaction equation and a diffusion transfer equation, the interfacial bonding reaction equation is used to describe the reaction rate and conversion rate variation of the silane coupling agent KH550 and the hydroxyl groups on the surface of the wood fibers, and the diffusion transfer equation is used to describe the mass transfer process of the silane coupling agent KH550 molecules from the solution phase to the surface of the wood fibers.

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