Redispersible latex powder based on styrene-acrylate copolymer and preparation method thereof

Abstract

The invention relates to the technical field of information management, in particular to redispersible latex powder based on styrene-acrylate copolymer and a preparation method of the redispersible latex powder. The latex powder is prepared by preparing a polymer emulsion through a core-shell polymerization process and then carrying out spray drying and composite surface modification treatment. In the preparation method, an adaptive fuzzy clustering algorithm is introduced to optimize a polymerization process, spray drying parameters are dynamically optimized by adopting Kalman filtering, and a fractional order differential equation performance prediction model, a Markov chain environmental adaptability evaluation model and a Q-learning intelligent early warning decision system are established. The latex powder has excellent water resistance, bonding strength and environmental adaptability, the energy consumption in the production process is low, the product quality is stable, and the latex powder can be widely applied to the field of building materials.

Description

Technical Field

[0001] This invention belongs to the field of latex powder preparation technology, specifically a redispersible latex powder based on styrene-acrylate copolymer and its preparation method. Background Technology

[0002] Redispersible latex powder, as an important building additive, improves the performance of materials such as dry-mixed mortar by redispersing in water to form a latex film. Currently, redispersible latex powder based on styrene-acrylate copolymers is widely used in the market, but the following technical problems still exist in its preparation and application:

[0003] Insufficient water resistance: Traditional styrene-acrylic redispersible latex powder exhibits poor water resistance in its latex film after contact with water, easily leading to swelling, decreased strength, and other problems. This results in significant performance degradation of building materials using this latex powder in humid environments. This is mainly due to the unreasonable distribution of hydrophilic groups in the polymer molecular chain and imperfect surface treatment processes of the latex powder.

[0004] Limited bond strength: Existing products have insufficient bond strength with substrates (such as concrete and ceramic tiles), especially on low-strength substrates or complex surfaces, where bond failure is common. This is closely related to the particle size distribution, surface energy, and film-forming properties of the polymer particles.

[0005] Spray drying process has high energy consumption and unstable product quality: The control of traditional spray drying process parameters (such as air inlet temperature, feed rate, atomization pressure) relies heavily on experience and lacks a precise dynamic adjustment mechanism, resulting in high energy consumption in the production process and uneven particle size distribution of latex powder, with large batch-to-batch quality fluctuations.

[0006] Poor environmental adaptability: Existing latex powders have insufficient performance stability when facing complex environmental conditions such as temperature changes and humidity fluctuations. For example, their workability deteriorates in low-temperature environments, and they are prone to clumping in high-temperature and high-humidity environments, which limits their application range.

[0007] Traditional technologies typically employ single surface modifiers or simple adjustments to the polymerization formulation to improve performance, but this approach has significant limitations. For example, while adding excessive hydrophobic agents can improve water resistance, it severely impacts the redispersibility and workability of the latex powder; and simply optimizing the polymerization process has limited effect on improving bond strength. In the spray drying stage, the lack of intelligent parameter optimization algorithms prevents dynamic adjustment of process parameters based on real-time production data, leading to energy waste and unstable product quality. Furthermore, existing technologies do not adequately consider the environmental impact and lack an effective environmental adaptability assessment and optimization system. Summary of the Invention

[0008] This invention aims to provide a redispersible latex powder, which is prepared by core-shell polymerization of styrene and acrylate as the main monomers to obtain a polymer emulsion, and then spray-dried and surface modified. The polymer emulsion formulation of the latex powder includes the following components in parts by weight: 30-40 parts styrene, 25-35 parts butyl acrylate, 10-20 parts methyl methacrylate, 2-5 parts methacrylic acid, 1-3 parts silane coupling agent, 0.5-1 part initiator, 1-2 parts emulsifier, and 80-100 parts deionized water.

[0009] Furthermore, the polymer emulsion has a core-shell structure, with the core layer mainly composed of styrene and methyl methacrylate, and the shell layer mainly composed of butyl acrylate and methacrylic acid, and is grafted with a silane coupling agent.

[0010] Furthermore, the surface modification treatment employs a composite surface modifier, which is composed of polyvinyl alcohol, calcium stearate, and nano-silica in a weight ratio of 5:2:1, and the amount added is 1-3% of the weight of the latex powder.

[0011] Furthermore, the method for preparing the redispersible latex powder includes the following steps:

[0012] Preparation of core-shell polymer emulsion: Styrene, methyl methacrylate, deionized water, emulsifier and initiator are added to a reactor and polymerized to prepare the core layer polymer; then a mixture of butyl acrylate, methacrylic acid, silane coupling agent and remaining deionized water is added dropwise, and polymerization continues to prepare the shell layer, thus obtaining a core-shell structured polymer emulsion;

[0013] Spray drying: A dynamic optimization method based on Kalman filtering is used to optimize and control the inlet air temperature, outlet air temperature, and feed rate in real time during the spray drying process. The state estimation formula of the Kalman filter is as follows: Surface modification: Add a composite surface modifier to the spray-dried latex powder and mix at high speed for 5-10 minutes.

[0014] Furthermore, during the spray drying process, the inlet air temperature is controlled at 180-220℃, the outlet air temperature is controlled at 80-95℃, and the feeding speed is controlled at 50-80L / h.

[0015] Furthermore, during the core-shell polymerization process, an adaptive fuzzy clustering algorithm is used to optimize the reaction temperature, stirring rate, and feeding sequence. The membership degree calculation formula for the fuzzy clustering is as follows:

[0016] Furthermore, the performance prediction method for the redispersible latex powder employs a fractional differential equation model for performance prediction, wherein the fractional differential equation is: Let x(t) be the fractional derivative order, A and B be the system matrices, x(t) be the state variable, and u(t) be the input variable.

[0017] Furthermore, the environmental adaptability assessment method for redispersible latex powder employs a Markov chain model for dynamic evaluation. It establishes a state transition probability matrix to assess the performance stability of the latex powder under changing environmental factors in real time. Where nij is the number of times a state transitions from state i to state j, and m is the number of states.

[0018] Furthermore, the intelligent early warning decision-making method for redispersible latex powder employs the Q-learning algorithm to construct an intelligent early warning decision-making system, wherein the update formula of the Q-learning algorithm is:

[0019] Q(s,a)=Q(s,a)+α[r+γmax a′ Q(s′,a′)-Q(s,a)];

[0020] The state estimation formula for the Kalman filter is as follows: in

[0021] This is the optimal estimate of the spray drying system state at time k.

[0022] This is a one-step prediction based on the state at time k-1;

[0023] K k The Kalman gain matrix;

[0024] z k This represents the sensor's measured data at time k.

[0025] H is the observation matrix. The membership degree calculation formula for the fuzzy clustering is:

[0026] in

[0027] μ ij : The membership degree of sample i to cluster j is used to characterize the degree of matching between the real-time collected data samples such as viscosity, temperature, and conversion rate during the polymerization reaction and the ideal process parameters cluster. The value range is [0, 1].

[0028] c: Cluster number, corresponding to 3-5 types of process states such as "reaction stability zone, transition zone, and abnormal zone" in the core-shell polymerization process;

[0029] d ij The distance between sample i and cluster center j, calculated using Euclidean or Mahalanobis distance, reflects the deviation between the actual reaction data and the ideal process state.

[0030] d ik : The distance between sample i and cluster center k;

[0031] m: Fuzziness index, with a value of 1.5-2.5, is used to control the degree of fuzzification of membership and adjust the sensitivity of dynamic adjustment of polymerization process parameters.

[0032] The fractional differential equation is:

[0033] Caputo fractional derivative operator

[0034] α is the order of the fractional derivative, ranging from 0.6 to 0.9.

[0035] x(t) is the state vector, containing performance parameters such as bond strength, water absorption rate, and elastic modulus.

[0036] A is the system matrix, obtained through training with over 1000 sets of maintenance experimental data.

[0037] B is the input matrix, which relates the impact of environmental variables on performance.

[0038] u(t) is the input vector, containing environmental parameters such as curing temperature, humidity, and time. The state transition probability matrix...

[0039] Q(s,a): The value function of performing action α in state s, which quantifies the matching benefits of "storage environment-application strategy" and guides parameter optimization;

[0040] α: Learning rate, ranging from 0.1 to 0.3, controls the step size of the policy update;

[0041] r: Reward value, calculated based on the performance indicators of latex powder and its environmental adaptability, with positive incentives for good strategies and negative penalties for bad strategies;

[0042] γ: Discount factor, ranging from 0.9 to 0.95, balancing current and future returns;

[0043] s: Status, including storage temperature and humidity, construction environment, and remaining shelf life of the latex powder;

[0044] a: Action, corresponding to decision options such as storage strategy and application strategy;

[0045] s': The next state after performing action α;

[0046] a′: The set of optional actions under state s′.

[0047] Beneficial effects

[0048] Significantly Improved Water Resistance: Through core-shell structure design and composite surface modification, the water absorption rate of the redispersed latex powder is reduced, and the tensile strength retention rate after immersion in water for 7 days exceeds 85%. Significantly Increased Bond Strength: The tensile bond strength with concrete substrates reaches 1.2-1.5 MPa, which is 1.5-2 times that of traditional products, and the bonding performance on low-strength substrates (such as aerated concrete) is equally excellent. Reduced Production Energy Consumption and Stable Product Quality: Energy consumption in the spray drying process is reduced by 15-20%, the particle size distribution uniformity of the latex powder is improved by 30%, and the performance fluctuation between batches is less than 5%, meeting the application requirements of high-end building materials. Enhanced Environmental Adaptability: The latex powder exhibits good performance stability within a temperature range of -10℃ to 40℃ and humidity conditions of 30-90%, with significantly improved low-temperature workability and anti-caking ability, broadening its application scenarios. Attached Figure Description

[0049] Figure 1 System operation principle flowchart. Detailed Implementation

[0050] Example 1: Preparation of styrene-acrylic redispersible latex powder

[0051] The core-shell emulsion was prepared by weighing out 35 parts St, 30 parts BA, 15 parts MMA, 3 parts MAA, 2 parts KH-570, 0.8 parts ammonium persulfate, 1.5 parts sodium dodecyl sulfate, and 90 parts deionized water by weight. The components included styrene (St), butyl acrylate (BA), methyl methacrylate (MMA), monomethyl methacrylate (MAA), and silane coupling agent (KH-570).

[0052] First, St, MMA and some deionized water are added to the reactor and polymerization is initiated at 80°C to prepare the core polymer.

[0053] Then, a mixture of BA, MAA and KH-570 was added dropwise, along with the remaining deionized water and initiator. The reaction temperature was controlled at 75-85℃, and the reaction time was 4-5 hours to obtain a core-shell polymer emulsion.

[0054] Spray drying parameter optimization

[0055] The drying parameters are dynamically adjusted using a Kalman filter algorithm. The initial inlet air temperature is set to 200℃, and the inlet air temperature, outlet air temperature, and feed flow rate data are collected in real time by sensors.

[0056] The state estimate is calculated using the Kalman filter formula, and the heating power and feed pump speed are automatically adjusted to keep the inlet air temperature stable at 200±5℃, the outlet air temperature controlled at 85±3℃, and the feed rate maintained at 65L / h.

[0057] For surface modification treatment, weigh the composite modifier according to the weight ratio of PVA: calcium stearate: nano silica = 5:2:1, and add it to the spray-dried latex powder. The amount added is 2% of the weight of the latex powder.

[0058] Mix at 1200 rpm for 8 minutes in a high-speed mixer to obtain the finished redispersible latex powder.

[0059] A polymer emulsion with a "hard core, soft shell" structure was prepared by core-shell polymerization using styrene (St), butyl acrylate (BA), and methyl methacrylate (MMA) as the main monomers, and introducing the functional monomer methacrylic acid (MAA) and a silane coupling agent (KH-570). The core layer, mainly composed of St and MMA, provides a rigid framework; the shell layer, mainly composed of BA and MAA, is grafted with a silane coupling agent to improve water resistance and adhesion.

[0060] An adaptive fuzzy clustering algorithm is used to optimize reaction temperature, stirring rate, and feeding sequence in core-shell polymerization. This algorithm collects real-time data on viscosity, temperature, and conversion rate during the polymerization reaction and utilizes a fuzzy clustering formula: Where uij is the membership degree of sample i to cluster j, dij is the distance between sample i and cluster center j, c is the number of clusters, and m is the fuzzy index. By dynamically adjusting the reaction parameters, polymer particles form a uniform core-shell structure, with particle size distribution controlled within the range of 0.1-1 μm and polydispersity index (PDI) less than 0.2.

[0061] A spray drying parameter optimization model based on an improved weighted Kalman filter is constructed, which integrates real-time sensor data such as inlet air temperature, feed flow rate, and atomization pressure, and uses the following formula for state estimation: By dynamically adjusting the drying parameters using this model, the inlet air temperature is controlled at 180-220℃, the outlet air temperature at 80-95℃, and the feeding speed at 50-80L / h, thereby stabilizing the moisture content of the latex powder at 0.5-1.5%, improving the particle size distribution uniformity by more than 30%, and reducing energy consumption by 15-20%.

[0062] The state estimation formula for the Kalman filter is as follows: in

[0063] This is the optimal estimate of the spray drying system state at time k.

[0064] This is a one-step prediction based on the state at time k-1;

[0065] K k The Kalman gain matrix;

[0066] z kThis represents the sensor's measured data at time k.

[0067] H is the observation matrix. The membership degree calculation formula for the fuzzy clustering is: ,in

[0068] μ ij : The membership degree of sample i to cluster j is used to characterize the degree of matching between the real-time collected data samples such as viscosity, temperature, and conversion rate during the polymerization reaction and the ideal process parameters cluster. The value range is [0, 1].

[0069] c: Cluster number, corresponding to 3-5 types of process states such as "reaction stability zone, transition zone, and abnormal zone" in the core-shell polymerization process;

[0070] d ij The distance between sample i and cluster center j, calculated using Euclidean or Mahalanobis distance, reflects the deviation between the actual reaction data and the ideal process state.

[0071] d ik : The distance between sample i and cluster center k;

[0072] m: Fuzziness index, with a value of 1.5-2.5, is used to control the degree of fuzzification of membership and adjust the sensitivity of dynamic adjustment of polymerization process parameters.

[0073] The fractional differential equation is:

[0074] Caputo fractional derivative operator

[0075] α is the order of the fractional derivative, ranging from 0.6 to 0.9.

[0076] x(t) is the state vector, which includes performance parameters such as bond strength, water absorption rate, and elastic modulus.

[0077] A is the system matrix, obtained through training with over 1000 sets of maintenance experimental data.

[0078] B is the input matrix, which relates the impact of environmental variables on performance.

[0079] u(t) is the input vector, which contains environmental parameters such as curing temperature, humidity, and time.

[0080] The state transition probability matrix

[0081] P ij The probability of transitioning from state i to state j

[0082] n ij The number of samples that transition from performance state i to state j under the influence of environmental factors.

[0083] m represents the total number of performance states, corresponding to five categories: "Excellent", "Good", "Medium", "Poor", and "Failed".

[0084] Q(s, α): The value function of performing action a in state s, which quantifies the matching benefit of "storage environment-application strategy" and guides parameter optimization;

[0085] α: Learning rate, ranging from 0.1 to 0.3, controls the step size of the policy update;

[0086] r: Reward value, calculated based on the performance indicators of latex powder and its environmental adaptability, with positive incentives for good strategies and negative penalties for bad strategies;

[0087] γ: Discount factor, ranging from 0.9 to 0.95, balancing current and future returns;

[0088] s: Status, including storage temperature and humidity, construction environment, and remaining shelf life of the latex powder;

[0089] a: Action, corresponding to decision options such as storage strategy and application strategy;

[0090] s′: The next state after performing action α;

[0091] a′: The set of optional actions under state s′.

[0092] Example 2: Performance Comparison of Different Formulations

[0093] To verify the technical effects of the present invention, the following comparative experiment was designed:

[0094]

[0095]

[0096] Experimental results show that the latex powder of the present invention is superior to traditional products and the control group in terms of water resistance, bonding strength and energy consumption, verifying the synergistic effect of each invention point.

[0097] Example 3: Environmental Adaptability Test

[0098] The performance of the latex powder of the present invention and the conventional styrene-acrylic emulsion were respectively tested under the following environmental conditions:

[0099] Performance tests were conducted on the redispersed liquid and hardened mortar at a low-temperature environment (-10℃):

[0100] Preparation of redispersible liquid: The dispersion was prepared by mixing latex powder and water in a ratio of 1:4 and stirred at room temperature (23±2℃) for 10±2 minutes; Curing process: After the dispersion was cast into shape, it was first cured in a standard environment (23±2℃, 50±5%RH) for 72 hours, and then transferred to a -10℃ environment for freezing for 24 hours.

[0101] Performance indicators: The redispersed liquid has good workability after curing (no condensation, and workability meets the requirements of GB / T 23449), and the compressive strength retention rate of hardened mortar is ≥90% (strength retention rate = strength after low temperature curing / standard curing strength × 100%).

[0102] Comparative testing: Under the same process, traditional styrene-acrylic latex powder exhibits significant coagulation in the dispersion (operability loss rate ≥40%), and the hardened mortar strength retention rate is ≤60%. The redispersible latex powder is prepared through core-shell polymerization (30-40 parts styrene, 25-35 parts butyl acrylate, etc., by weight), spray drying (inlet air temperature 180-220℃), and surface modification (polyvinyl alcohol / calcium stearate compound). Through a test process of 72 hours of standard curing + 24 hours of freezing, its applicability in low-temperature building construction scenarios (such as winter exterior wall insulation mortar construction) is verified, ensuring that the early strength of the mortar reaches the standard rate of ≥95% at -10℃. High humidity environment (90% humidity, 30℃): After 30 days of storage, the latex powder of this invention shows no clumping and good redispersibility, while the clumping rate of traditional products reaches 30%, and the redispersibility decreases significantly.

[0103] Temperature cycling (-10℃ to 40℃, once a day for a total of 10 times): The performance fluctuation of the latex powder of this invention is less than 5%, while the performance fluctuation of traditional products exceeds 15%.

[0104] Test results show that the latex powder of the present invention has excellent environmental adaptability and can meet the needs of building applications under different climatic conditions.

[0105] Example 4

[0106] Styrene-acrylic redispersible latex powder modified with nano-titanium dioxide and its preparation method

[0107] I. Preparation Process and Formulation Innovation

[0108] Core-shell emulsion formulation optimization (parts by weight)

[0109] Core layer: 38 parts styrene, 18 parts methyl methacrylate, 0.7 parts initiator, 1.2 parts emulsifier, 40 parts deionized water; Shell layer: 32 parts butyl acrylate, 4 parts methacrylic acid, 2.5 parts silane coupling agent (KH-560), 3 parts nano-titanium dioxide (average particle size 20nm), 50 parts deionized water. Innovation: The introduction of nano-TiO2 to replace part of the silane coupling agent utilizes its surface hydroxyl groups to form hydrogen bonds with the polymer chains. Simultaneously, the nanoparticles fill the pores of the latex film, enhancing water resistance and UV aging resistance.

[0110] Polymerization process: Microemulsion polymerization and fuzzy clustering are combined. The microemulsion polymerization process is used to form nanoscale microemulsions with monomers and emulsifiers (sodium dodecylbenzenesulfonate). The polymerization temperature is controlled at 70-75℃ and the stirring rate is 400-600rpm.

[0111] By utilizing an adaptive fuzzy clustering algorithm, the feeding rate is adjusted in real time based on the conductivity and particle size distribution of the reaction system, ensuring that the average particle size of the polymer particles is controlled within 80-120 nm and the PDI < 0.15. Innovations: Compared to traditional emulsion polymerization, microemulsion polymerization can obtain polymer particles with smaller diameters, improving film density; the fuzzy clustering algorithm further optimizes particle size uniformity, enhancing bonding strength.

[0112] Spray drying parameter optimization

[0113] A Kalman filter algorithm is used to dynamically control the inlet air temperature at 190-210℃ and the outlet air temperature at 85-90℃. 0.5% polyvinylpyrrolidone (PVP) is added to the feed liquid as a drying aid to reduce surface tension and prevent the agglomeration of nano-TiO2. Innovation: The synergistic effect of PVP and nano-TiO2 reduces particle aggregation during the drying process, ensuring uniform distribution of the modifier.

[0114] Surface modification treatment composite modifier formulation: 4 parts polyvinyl alcohol (PVA-1788), 2 parts calcium stearate, 1 part nano silica, and 0.5 parts silane coupling agent (KH-792), added at 2.5% of the latex powder weight, and mixed in a double cone mixer at 800 rpm for 12 minutes. Innovation: The introduction of KH-792 silane coupling agent allows its amino groups to form hydrogen bonds with the hydroxyl groups of PVA, and simultaneously react with the hydroxyl groups on the surface of nanoparticles, enhancing the interfacial bonding between the modifier and the latex powder, and improving redispersibility and water resistance.

[0115] II. Performance Testing and Efficiency Analysis

[0116] Nano-TiO2 is uniformly dispersed within the polymer shell, acting as a "physical barrier" to hinder water molecule penetration. Furthermore, its surface hydroxyl groups form hydrogen bonds with the carboxyl groups of methacrylic acid, enhancing the membrane's density. Under UV irradiation, the photocatalytic effect of nano-TiO2 decomposes adsorbed organic matter, delaying membrane aging and degradation. Smaller polymer particle sizes (80-120 nm) result in denser particle packing during film formation, reducing porosity and increasing the contact area with the substrate, significantly improving adhesion strength. An adaptive fuzzy clustering algorithm, through real-time control of reaction parameters, prevents nanoparticle aggregation, ensuring the uniformity of the modification effect. The amino group of KH-792 silane coupling agent forms hydrogen bonds with the hydroxyl group of PVA. Simultaneously, its alkoxy groups hydrolyze and condense with the hydroxyl groups on the surface of nano-SiO2, forming an "organic-inorganic" hybrid interface layer on the surface of the latex powder particles. This improves redispersibility (due to the hydrophilicity of PVA) and enhances water resistance (due to the hydrophobic layer of calcium stearate), while also strengthening the binding force between the modifier and the latex powder, reducing agglomeration during storage. Unlike traditional single silane modification or simple blending of nanomaterials, this embodiment embeds nano-TiO2 into the polymer shell through core-shell polymerization and combines it with interface optimization using surface modifiers, achieving an integrated "structure-performance" design and solving the problems of nanomaterial agglomeration and weak interfacial bonding. The introduction of microemulsion polymerization technology controls particle size from the polymerization source, and combined with dynamic optimization using an adaptive fuzzy clustering algorithm, improves particle size uniformity by more than 40%, breaking through the limitation of wide particle size distribution in traditional emulsion polymerization and laying the foundation for high performance. Multi-dimensional environmental adaptability optimization: Through the anti-UV aging of nano TiO2, the low-temperature dispersibility design of composite modifiers, and the intelligent early warning of storage environment by the Q-learning system (such as automatic prompt for sealed storage in high humidity environment), the performance of latex powder is stable in the range of -15℃ to 45℃ and humidity of 30-95%, which expands the application scenarios by more than 50% compared with traditional products.

Claims

1. A method for preparing redispersible latex powder, characterized in that, Includes the following steps: Preparation of core-shell polymer emulsion: Styrene, methyl methacrylate, deionized water, emulsifier and initiator are added to a reactor and polymerized to prepare the core layer polymer; then a mixture of butyl acrylate, methacrylic acid, silane coupling agent and remaining deionized water is added dropwise, and polymerization continues to prepare the shell layer, thus obtaining a core-shell structured polymer emulsion; Spray drying: A dynamic optimization method based on Kalman filtering is used to optimize and control the inlet air temperature, outlet air temperature, and feed rate in real time during the spray drying process. The state estimation formula of the Kalman filter is as follows: in This is the optimal estimate of the spray drying system state at time k. This is a one-step prediction based on the state at time k-1; K k The Kalman gain matrix; z k This represents the sensor's measured data at time k. H is the observation matrix.

2. The preparation method according to claim 1, characterized in that, The polymer emulsion is prepared by core-shell polymerization using styrene and acrylate as the main monomers, and then spray-dried and surface modified. The polymer emulsion formulation of the latex powder includes the following components in parts by weight: 30-40 parts styrene, 25-35 parts butyl acrylate, 10-20 parts methyl methacrylate, 2-5 parts methacrylic acid, 1-3 parts silane coupling agent, 0.5-1 part initiator, 1-2 parts emulsifier, and 80-100 parts deionized water.

3. The redispersible latex powder according to claim 1, characterized in that, The polymer emulsion has a core-shell structure, with the core layer mainly composed of styrene and methyl methacrylate, and the shell layer mainly composed of butyl acrylate and methacrylic acid, and is grafted with a silane coupling agent.

4. The redispersible latex powder according to claim 1, characterized in that, The surface modification treatment uses a composite surface modifier, which is composed of polyvinyl alcohol, calcium stearate and nano silica in a weight ratio of 5:2:1, and the amount added is 1-3% of the weight of the latex powder. During the spray drying process, the inlet air temperature is controlled at 180-220℃, the outlet air temperature is controlled at 80-95℃, and the feeding speed is controlled at 50-80L / h.

5. The preparation method according to claim 4, characterized in that, During the core-shell polymerization process, an adaptive fuzzy clustering algorithm is used to optimize the reaction temperature, stirring rate, and feeding sequence. The membership degree calculation formula for the fuzzy clustering is as follows: in μ ij : The membership degree of sample i to cluster j is used to characterize the degree of matching between the real-time collected data samples such as viscosity, temperature, and conversion rate during the polymerization reaction and the ideal process parameters cluster. The value range is [0, 1]. c: Cluster number, corresponding to 3-5 types of process states such as "reaction stability zone, transition zone, and abnormal zone" in the core-shell polymerization process; d ij The distance between sample i and cluster center j, calculated using Euclidean or Mahalanobis distance, reflects the deviation between the actual reaction data and the ideal process state. d ik : The distance between sample i and cluster center k; m: Fuzziness index, with a value of 1.5-2.5, is used to control the degree of fuzzification of membership and adjust the sensitivity of dynamic adjustment of polymerization process parameters.

6. A method for predicting the performance of redispersible latex powder according to any one of claims 1-3, characterized in that, Performance prediction is performed using a fractional differential equation model, wherein the fractional differential equation is: Caputo fractional derivative operator α is the order of the fractional derivative, ranging from 0.6 to 0.

9. x(t) is the state vector, which includes performance parameters such as bond strength, water absorption rate, and elastic modulus. A is the system matrix, obtained through training with over 1000 sets of maintenance experimental data. B is the input matrix, which relates the impact of environmental variables on performance. u(t) is the input vector, which contains environmental parameters such as curing temperature, humidity, and time.

7. A method for assessing the environmental adaptability of redispersible latex powder according to any one of claims 1-3, characterized in that, A Markov chain model is used for dynamic evaluation. The performance stability of latex powder under changing environmental factors is assessed in real time by establishing a state transition probability matrix. P ii The probability of transitioning from state i to state j n ii The number of samples that transition from performance state i to state j under the influence of environmental factors. m represents the total number of performance states, corresponding to five categories: "Excellent, Good, Average, Poor, and Failed".

8. A smart early warning decision-making method based on the redispersible latex powder according to any one of claims 1-3, characterized in that, An intelligent early warning decision-making system is constructed using the Q-learning algorithm. The update formula of the Q-learning algorithm is: Q(s, a) = Q(s, a) + α[r + γmax] a′ Q(s′,a′)-Q(s,a)], Q(s, a): The value function of performing action α in state s, which quantifies the matching benefit of "storage environment-application strategy" and guides parameter optimization; α: Learning rate, ranging from 0.1 to 0.3, controls the step size of the policy update; r: Reward value, calculated based on the performance indicators of latex powder and its environmental adaptability, with positive incentives for good strategies and negative penalties for bad strategies; γ: Discount factor, ranging from 0.9 to 0.95, balancing current and future returns; s: Status, including storage temperature and humidity, construction environment, and remaining shelf life of the latex powder; a: Action, corresponding to decision options such as storage strategy and application strategy; s′: The next state after performing action α; a′: The set of optional actions under state s′.

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