High-toughness amino modified vinyl ester resin and preparation method thereof

Abstract

The invention provides high-toughness amino modified vinyl ester resin and a preparation method thereof. The resin comprises a vinyl ester resin matrix, an amino modified component, a cross-linking agent, a catalyst, a solvent, a coupling agent and an antioxidant. The heat stability, the mechanical property and the anti-aging capacity of the resin are improved, and the resin adapts to severe environment conditions. Through combination of the neural network and the automatic control system, efficient optimization of the resin preparation process is realized, generation of by-products is reduced, and the yield is improved. The real-time monitoring and adjustment of the reaction process are realized, the reaction conditions and the resin quality are optimized, and the automation and the intelligence of the production process are brought. The utilization rate of the raw materials is optimized, energy consumption and waste generation are reduced, and the environment-friendly requirement is met. The resin is excellent in performance under the environment of high temperature, high corrosion and high mechanical load, has a wide application prospect, and is particularly suitable for the industrial field needing high heat resistance and long-term stability.

Description

Technical Field

[0001] The present invention relates to the technical field of vinyl ester resins, and specifically to a highly tough amino-modified vinyl ester resin and a preparation method thereof. Background Art

[0002] Vinyl resin is a resin with excellent corrosion resistance and has replaced bisphenol A epoxy resin in many fields abroad. The low density, good corrosion resistance, easy processability and good thermal shock resistance of vinyl ester resin make the resin show good impact resistance and cracking resistance macroscopically. The hydroxyl groups in the molecular chain have good wettability to glass fiber, aramid fiber, ultra-high molecular weight vinyl fiber, etc., and the adhesion ability to organic fibers is also outstanding. Therefore, vinyl ester resin is a kind of resin that has been widely applied, promoted and studied after epoxy resin.

[0003] However, in the use of traditional vinyl ester resin at high temperature, high stress and long-term exposure to harsh environments, its heat resistance, toughness and anti-aging performance still have certain deficiencies. Therefore, improving the toughness and heat resistance of vinyl ester resin has become an important direction in the current research of resin materials.

[0004] In order to improve the heat resistance, toughness and anti-aging properties of vinyl ester resin, researchers have explored various modification methods. Among them, the amino modification method is widely used in the modification of resins because it can react with the carbonyl group in the resin matrix through amino groups to form an amino acid ester structure with higher heat resistance and toughness. This structure can effectively enhance the thermal stability and impact resistance of the resin.

[0005] However, during the amino modification process, due to the complexity of the reaction and the high dependence on reaction conditions, controlling the generation of by-products during the reaction and ensuring the final mechanical properties of the resin are still a challenge. In addition, the cross-linking reaction process of vinyl ester resin is also a key factor affecting its final performance. Factors such as the addition amount of cross-linking agent, the concentration of catalyst, reaction temperature and time directly affect the cross-linking degree of the resin and the final mechanical properties. Traditional cross-linking reactions usually rely on manual experience for control, which easily leads to unstable reaction conditions and the generation of by-products, thus affecting the quality of the resin. Summary of the Invention

[0006] The purpose of the present invention is to provide a preparation method of a highly tough amino-modified vinyl ester resin. By introducing a neural network model for real-time monitoring and optimization of the reaction process, the reaction conditions can be precisely controlled, the generation of by-products can be reduced, and the heat resistance, toughness and final mechanical properties of the resin can be improved. Through this method, not only can the performance of the resin be improved, but also the efficiency and controllability of the production process can be enhanced, providing technical support for the industrial production of high-performance resin materials.

[0007] To achieve the above object, the present invention provides the following technical solution: A highly tough amino-modified vinyl ester resin, the resin comprising a vinyl ester resin matrix, an amino modification component, a crosslinking agent, a catalyst, a solvent, a coupling agent and an antioxidant, wherein:

[0008] The mass of the vinyl ester resin matrix accounts for 60%-80% of the total mass;

[0009] The mass of the amino modification component accounts for 10%-25% of the total mass;

[0010] The mass of the crosslinking agent accounts for 1%-5% of the total mass;

[0011] The mass of the catalyst accounts for 0.5%-2% of the total mass;

[0012] The mass of the solvent accounts for an appropriate amount of the total mass to ensure that the viscosity of the resin is between 500-700 mPa·s;

[0013] The mass of the coupling agent accounts for 0.5%-2% of the total mass;

[0014] The mass of the antioxidant accounts for 0.2%-0.5% of the total mass.

[0015] Further, in the present invention, the vinyl ester resin matrix is selected as styrene-modified epoxy vinyl ester resin. Styrene modification can enhance the molecular structure of the resin through copolymerization reaction, improve the crosslinking degree of the resin, and thereby increase its heat resistance and mechanical strength. The aromatic ring of styrene can form a stable molecular structure and enhance the thermal stability of the resin. Styrene reacts with the unsaturated groups in epoxy vinyl ester through free radical polymerization to form a highly crosslinked network structure. The aromatic ring of styrene provides additional rigidity during the crosslinking process, effectively improving the temperature resistance and chemical stability of the resin.

[0016] The amino modification component is selected as amino silane-epichlorohydrin acrylate complex; amino silane can react with the carbonyl group in the vinyl ester resin to form an amino acid ester structure, enhance the crosslinking network of the resin, and improve the thermal stability, mechanical strength, chemical corrosion resistance, etc. of the resin. Epichlorohydrin acrylate improves the toughness of the resin by introducing a crosslinking structure, enabling it to maintain good physical properties under high temperature and harsh environments. The synergistic effect of amino silane and epoxy groups can provide a more uniform crosslinking structure.

[0017] The amino group in amino silane reacts with the carbonyl group in the resin to form an amino acid ester structure, making the connection between resin molecules more compact, thereby improving the heat resistance, antioxidant property and toughness of the resin. The epoxy group of epichlorohydrin acrylate further enhances the structural stability of the resin through crosslinking reaction.

[0018] The crosslinking agent selected is diphenyl peroxide; compared with traditional benzoyl peroxide, diphenyl peroxide has a higher decomposition temperature, can provide stable free radicals at a higher temperature, and promote the crosslinking reaction. Through the introduction of diphenyl peroxide, the crosslinking reaction can proceed more efficiently, increasing the crosslinking density of the resin, thereby enhancing the heat resistance, mechanical properties, and chemical stability of the resin.

[0019] Diphenyl peroxide decomposes at high temperature to generate free radicals, which react with the double bonds in the resin to promote the formation of a crosslinked network. This crosslinked network makes the molecular structure of the resin more stable, thereby improving the heat resistance and mechanical properties of the resin.

[0020] The catalyst selected is trichlorosilane fluoride; the amino-modified component reacts with the carbonyl group in the vinyl ester resin matrix to form an amino acid ester structure to improve the heat resistance and toughness of the resin. Trichlorosilane fluoride, as a catalyst, can accelerate the crosslinking reaction through the strong catalytic effect of its fluoride. Compared with traditional catalysts, trichlorosilane fluoride has a more prominent catalytic effect in a high-temperature environment, can effectively reduce the occurrence of side reactions, and improve the stability of the resin.

[0021] The fluoride part of trichlorosilane fluoride can promote the crosslinking reaction between resin molecules by providing activation energy and accelerate the crosslinking rate. At the same time, its strong acidity helps to optimize the reaction path of the resin, improve the selectivity of the reaction, and reduce unnecessary by-products.

[0022] The solvent selected is thiophene; thiophene solvents can not only effectively dissolve the resin components, but also improve the molecular structure of the resin through a weak chemical reaction with the resin, making the resin have better stability during the crosslinking process. This type of solvent has stronger solubility in a high-temperature environment, thereby optimizing the reaction conditions of the resin. Thiophene solvents react with the resin through their molecules with unsaturated structures, enhancing the solubility of the resin and promoting the complete progress of the crosslinking reaction. Its aromatic ring structure is beneficial to the dispersion of the resin matrix at high temperature, making the compatibility of the resin with the crosslinking agent and catalyst better.

[0023] The coupling agent selected is fluorosilane coupling agent; the fluorosilane coupling agent has strong chemical stability, can form a strong chemical bond between the resin and the filler, and improve the water resistance, antioxidant property, and mechanical strength of the resin. Its fluorination effect makes the resin remain stable in high-temperature and high-humidity environments. The fluorosilane coupling agent combines with the silicon-oxygen bond in the resin molecule through its fluorine atoms to form a stable chemical bond, enhancing the water resistance, heat resistance, and anti-aging property of the resin, thereby improving the long-term stability of the resin.

[0024] The antioxidant selected is tris(2,4-dichlorophenyl) phosphate; this antioxidant can effectively scavenge free radicals in the resin, prevent oxidative degradation, and has a remarkable effect especially in high-temperature environments. Compared with common antioxidants, tris(2,4-dichlorophenyl) phosphate can provide stronger protection during the aging process of the resin, thereby extending the service life of the resin. Tris(2,4-dichlorophenyl) phosphate reacts with the oxidation free radicals in the resin through its phosphate group, inhibits the occurrence of oxidation reactions, and maintains the stability of the resin during the aging process of the resin, reducing the impact of oxidation on the resin properties.

[0025] The combination of styrene modification, diphenyl peroxide and trifluorochlorosilane in the high-toughness amino-modified vinyl ester resin enables the resin to maintain stable mechanical properties and chemical stability at higher temperatures, enhancing the thermal stability and high-temperature resistance of the resin. The synergistic effect of amino silane and epichlorohydrin acrylate enhances the cross-linking reaction of the resin, thereby improving the strength, heat resistance and anti-aging performance of the resin and increasing the cross-linking density of the resin. The fluorinated silicon coupling agent can effectively improve the bonding force between the resin and the filler and optimize the mechanical properties of the resin. Optimize the antioxidant property of the resin: Antioxidants such as tris(2,4-dichlorophenyl) phosphate effectively delay the oxidation process of the resin, improve its durability, and enhance the compatibility between the resin and the filler. These technical advantages provide broad application prospects for this resin, especially in environments with high temperature, high corrosion and high mechanical load, showing superior performance and a long service life.

[0026] A preparation method of the high-toughness amino-modified vinyl ester resin according to the above, comprising the following steps:

[0027] Step 1, mix the vinyl ester resin matrix and the amino-modified component according to a mass ratio, the amino-modified component is an amino silane compound or an amino acid compound, control the temperature at 60-80 °C during mixing, and the reaction time is 3-4 hours to ensure the complete progress of the amino-modification reaction;

[0028] Step 2, after the amino-modification reaction is completed, add a cross-linking agent, a catalyst, a solvent, a catalyst and an antioxidant, stir evenly, and adjust the amount of the solvent to ensure that the viscosity of the resin is between 500-700 mPa·s;

[0029] Step 3, heat the mixture to 70-90 °C and maintain it for 2-3 hours to complete the cross-linking reaction. When adding the cross-linking agent and the catalyst, control the reaction time to be 2-3 hours, and monitor the cross-linking degree of the resin. Use a neural network to monitor and adjust the catalyst concentration, cross-linking agent concentration and temperature during the cross-linking reaction process in real time, optimize the reaction rate, and minimize the generation of by-products;

[0030] Step 4, after the cross-linking reaction is completed, add an inhibitor to ensure the stability of the resin during further curing. Pour the resin solution into a mold and cure it at 100 - 120 °C for 4 hours, followed by post-curing at room temperature for 6 - 12 hours to ensure that the resin is fully cured and has ideal mechanical properties.

[0031] Further, in the present invention, the steps of using a neural network to monitor and adjust the cross-linking reaction process in Step 3 are as follows:

[0032] Step 3.1, data collection and initialization. Monitor the temperature inside the reactor in real time through a temperature sensor, monitor the inflow rate and concentration of the catalyst and cross-linking agent through a flow meter and a mass meter, monitor the viscosity change data of the resin through a viscosity sensor, and monitor the generation of by-products through a gas analyzer.

[0033] Step 3.2, use the data from Step 3.1 to train a multi-variable neural network model. The neural network will predict the reaction rate r through the input reaction conditions 2 and further optimize the concentrations of the catalyst and cross-linking agent and the temperature control during the reaction process.

[0034] The neural network model design includes an input layer, a hidden layer, and an output layer. Input layer: Input the real-time temperature of the reaction, catalyst concentration, cross-linking agent concentration, reaction time, and reactant concentration information. Hidden layer: Use multiple hidden layers to capture the complex non-linear relationships between reaction conditions, and adjust the depth and width of the model to improve accuracy. Output layer: Predict the cross-linking reaction rate r 2 and output the predicted value of the by-product generation amount.

[0035] Step 3.3, input the real-time data into the neural network. The neural network calculates the optimal reaction rate and by-product prediction based on historical data and current conditions. The neural network predicts the reaction rate r according to the current reaction conditions 2 and the by-product generation amount, and compares them with the ideal values. When the neural network detects that the reaction rate is too fast or the by-product generation amount is too high, it will send an adjustment signal to adjust the reaction conditions such as temperature, catalyst concentration, and cross-linking agent concentration.

[0036] Step 3.4, after each reaction stage is completed, the neural network evaluates the quality of the resin and the generation of by-products. The neural network gradually optimizes the reaction conditions through a feedback mechanism and adjusts the reaction model.

[0037] As the reaction continues, the neural network continues to collect new data and train, improving the accuracy and response speed of the model. After each adjustment, the neural network optimizes the catalyst concentration, cross-linking agent concentration, and temperature settings based on the new data and reaction results.

[0038] Step 3.5: The neural network evaluates the generation trend of by-products through real-time monitoring and makes optimization adjustments. If the by-products exceed the expected range, the neural network prompts the reaction system to adjust the temperature, catalyst concentration, or cross-linking agent concentration. When the generation of by-products reaches the minimum value, the neural network locks the current optimization conditions and continuously maintains the optimal reaction state.

[0039] Evaluate the matching degree between the final product and the target performance, compare the mechanical properties and by-product content of the resin under different experimental conditions, and ensure that the optimized preparation method can provide higher-quality resin.

[0040] Furthermore, in the present invention, in step 3.1, during the data acquisition stage, the temperature T of the reaction is obtained. i , catalyst concentration C cat，i , cross-linking agent concentration C cross，i , reaction time t i , resin viscosity η i , by-product concentration P i The data is represented as a single data.

[0041] D is the entire experimental data set; i is the experiment number; T i , C cat，i , C cross，i , t i are the input data of the experimental conditions; η i is the resin viscosity; P i is the by-product concentration. The data is used as the input features of the neural network to establish the relationship between the reaction conditions and the reaction results during the training process.

[0042] Furthermore, in the present invention, in step 3.2, the neural network model is trained with historical data to predict the reaction rate and by-product generation under different reaction conditions. The neural network model is trained by the following formula:

[0043] y pred = f(W 1 ·x + b 1 , W 2 ·σ(W 1 ·x + b 1 ) + b 2 ,..., W n ·σ(W n-1 ·σ(W n-2 ·x + b n-2 ) + b n-1 ) + b n );

[0044] x is the input feature vector, including temperature, catalyst concentration, cross-linking agent concentration, and time; W iand b i are the weight matrix and bias vector for each layer; σ is the activation function used to introduce non - linear features; y pred is the predicted output of the model, representing the by - product concentration;

[0045] The neural network updates the weights and biases according to the difference between the predicted value and the actual value through the backpropagation algorithm. The loss function usually uses mean squared error or cross - entropy to measure:

[0046] L is the loss function; y true，i is the true value, the by - product concentration measured in the experiment; y pred，i is the value predicted by the neural network; n is the number of samples. Minimizing the loss function enables the neural network to learn the optimal weights W i and bias b i , in order to accurately predict the by - product concentration.

[0047] Furthermore, in the present invention, in step 3.3, the data during the reaction process is monitored in real - time, and the reaction conditions are adjusted according to the prediction feedback of the neural network. During the real - time monitoring process, the neural network inputs the real - time data, x current =(T current , C cat，current , C cross，current , t current ) into the trained neural network model to predict the current by - product concentration y pred :

[0048] y pred =f(W 1 ·x current +b 1 , W 2 ·σ(W 1 ·x current +b 1 )+b 2 ,...);

[0049] According to the prediction result y pred , the system will judge in real - time whether the reaction conditions need to be adjusted. If y pred exceeds the predetermined threshold, the system will adjust the catalyst concentration, cross - linker concentration or temperature.

[0050] Furthermore, in the present invention, in step 3.3, the optimization suggestions output by the neural network are connected to the automatic control system to automatically adjust the catalyst flow rate, cross - linker flow rate and temperature. The automatic control system includes valve control, heating equipment control, and solvent flow rate regulation. By precisely adjusting the reaction conditions, an optimal reaction environment is achieved.

[0051] Further, in the present invention, after each reaction cycle in step 3.4, new experimental data is fed back as new input to the neural network to further optimize the model, and the neural network is trained again to update the weights W i and the bias b i :

[0052]

[0053] are the gradients of the loss function with respect to the weights and the bias, which are used to update the network parameters. After each optimization, the neural network will be able to better predict the reaction rate and by-product generation and adjust the reaction conditions.

[0054] Further, in the present invention, in step 3.5, by optimizing the reaction conditions, the generation of by-products is reduced and the final performance of the resin is ensured. The optimized reaction conditions can be described by the following objective function:

[0055]

[0056] R yield is the yield of the resin, which reflects the reaction efficiency; P byproduct is the concentration of the by-products, indicating the generation of by-products; λ 1 and λ 2 are the weight coefficients, which are used to balance the relationship between the yield and the generation of by-products. This objective function aims to maximize the resin yield and minimize the generation of by-products, and optimize the overall reaction process by adjusting the reaction conditions. The optimization result evaluation can be carried out in the following ways:

[0057] Product quality evaluation: Evaluate according to the mechanical properties of the resin;

[0058] By-product concentration: Monitor the generation of by-products to ensure that its concentration is lower than the predetermined threshold;

[0059] By optimizing the objective function and adjusting the reaction conditions in real time, the neural network can continuously improve the reaction process to ensure the high performance and low by-product generation of the final resin.

[0060] The vinyl ester resin matrix reacts with amino-modified components (such as amino-silane compounds or amino acid compounds). This reaction mainly forms covalent bonds through chemical cross-linking between amino groups and vinyl groups, thereby improving the mechanical properties and heat resistance of the resin. After the amino-modification reaction, a cross-linking agent (such as peroxide) and a catalyst are added to carry out the cross-linking reaction. The purpose of the cross-linking reaction is to connect the segments between resin molecules through chemical bonds, enhancing the mechanical strength, thermal stability, and anti-aging performance of the resin. The rate of the cross-linking reaction is closely related to the reaction temperature, catalyst concentration, and cross-linking agent concentration. Excessive temperature may lead to the formation of by-products, while too low temperature may result in incomplete reaction or too long reaction time.

[0061] The working principle of the neural network can map these complex reaction processes into mathematical formulas, capturing the non-linear relationships between reaction conditions such as temperature, concentration, time, and reaction results (such as resin properties and by-product generation amounts). Specifically, the neural network model optimizes the reaction process in the following ways:

[0062] Non-linear modeling: During the preparation process of the resin, the effects of factors such as temperature, catalyst concentration, and cross-linking agent concentration on the reaction rate and by-product generation are non-linear, and traditional linear models cannot effectively capture this relationship. The neural network can capture these complex relationships through a multi-layer network structure (non-linear transformation of the hidden layer).

[0063] Optimizing reaction conditions: Through the loss function L, the neural network can minimize the generation of by-products and adverse reactions, while maximizing the resin yield and quality. In the multi-dimensional reaction space, the neural network will adjust the reaction conditions according to real-time feedback data to achieve the best reaction effect.

[0064] Real-time adjustment and feedback: The neural network can monitor key parameters during the reaction process in real time, such as temperature and concentration, and adjust the reaction conditions in a timely manner according to the predicted results (such as the predicted by-product concentration). Through an automatic control system (such as a temperature control device, a flow control valve, etc.), the system can adjust the reaction temperature, catalyst concentration, and cross-linking agent concentration according to the suggestions of the neural network to ensure the best performance of the resin and the lowest by-product generation.

[0065] Among them, the loss function L is the core in the learning process of the neural network. It reflects the gap between the model prediction and the actual result. By optimizing the loss function, the neural network can adjust the reaction conditions, thereby reducing the generation of by-products and improving the reaction efficiency. The objective function F optIt is the ultimate goal of optimizing the reaction process by the neural network. It comprehensively considers the balance between resin yield and by-product generation, and realizes the optimal reaction effect by dynamically adjusting the reaction conditions. During the reaction process, the neural network can monitor the progress of the reaction in real time and predict the reaction rate and by-product generation. If the concentration of by-products is too high, the neural network will adjust the reaction conditions by controlling the temperature, catalyst, and cross-linking agent concentration to reduce the generation of by-products. Through this intelligent feedback mechanism, the reaction process can be continuously optimized in each cycle.

[0066] Therefore, by combining the neural network with the resin preparation process, efficient optimization of reaction conditions can be achieved, minimizing by-product generation and improving the quality of the resin. The role of the neural network in this is multi-faceted. It can accurately predict the reaction results through non-linear modeling and can also adjust the reaction conditions in real time to cope with changes during the reaction process. By optimizing each link in the reaction process, ultimately higher reaction efficiency, lower by-product generation, and better-quality resin products can be achieved.

[0067] Advantageous effects. The technical solution of this application has the following technical effects:

[0068] The present invention improves the thermal stability, mechanical properties, and anti-aging ability of the resin, making it suitable for harsh environmental conditions. Through the combination of the neural network and the automatic control system, efficient optimization of the resin preparation process is achieved, reducing by-product generation and increasing the yield. Real-time monitoring and adjustment of the reaction process are realized, optimizing the reaction conditions and the quality of the resin, bringing automation and intelligence to the production process. The utilization rate of raw materials is optimized, reducing energy consumption and waste generation, meeting environmental protection requirements. The present invention enables the resin to perform excellently in environments with high temperature, high corrosion, and high mechanical load, having a wide range of application prospects, especially suitable for industrial fields requiring high heat resistance and long-term stability.

[0069] It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be regarded as part of the inventive subject matter of the present disclosure as long as such concepts do not contradict each other.

[0070] The foregoing and other aspects, embodiments, and features of the teachings of the present invention can be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as the features and / or advantageous effects of exemplary embodiments, will be apparent from the following description or will be learned through practice of the specific embodiments according to the teachings of the present invention. Description of the Drawings

[0071] The accompanying drawings are not intended to be drawn to scale. In the accompanying drawings, each identical or nearly identical component shown in the various figures may be denoted by the same reference numeral. For the sake of clarity, not every component is labeled in each figure. Now, embodiments of various aspects of the present invention will be described by way of example and with reference to the accompanying drawings, wherein:

[0072] Figure 1 It is a comparison chart of the tensile strength, flexural strength, and impact strength of Examples 1, 2, 3 and Comparative Examples 1, 2, 3.

[0073] Figure 2 It is a comparison chart of the thermal decomposition temperature of Examples 1, 2, 3 and Comparative Examples 1, 2, 3.

[0074] Figure 3 It is a comparison chart of the retention rate of tensile strength after aging of Examples 1, 2, 3 and Comparative Examples 1, 2, 3.

[0075] Figure 4 It is a comparison chart of the yields of Examples 4, 5 and Comparative Examples 4, 5.

[0076] Figure 5 It is a comparison chart of the by-product concentrations of Examples 4, 5 and Comparative Examples 4, 5.

[0077] Figure 6 It is a comparison chart of the tensile strength, flexural strength, and impact strength of Examples 4, 5 and Comparative Examples 4, 5.

[0078] Figure 7 It is a comparison chart of the thermal decomposition temperature of Examples 4, 5 and Comparative Examples 4, 5. Detailed Description of the Invention

[0079] In order to better understand the technical content of the present invention, specific examples are given below and described in conjunction with the accompanying drawings. In the present disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the present disclosure do not necessarily define all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those concepts and embodiments described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed in the present invention are not limited to any embodiment. In addition, some aspects of the present invention can be used alone, or in any suitable combination with other aspects of the present invention.

[0080] The advantages of the high-toughness amino-modified vinyl ester resin in terms of thermal stability, mechanical properties, and anti-aging ability are verified through the following examples. The following are the designs of the examples and comparative examples:

[0081] Table 1 shows the resin formulations (mass percentages)

[0082]

[0083] Component description:

[0084] Vinyl ester resin matrix: Styrene-modified epoxy vinyl ester resin;

[0085] Amino-modified component: Amino-silane-epichlorohydrin acrylate complex;

[0086] Crosslinking agent: Diphenyl peroxide;

[0087] Catalyst: Trichlorosilane;

[0088] Solvent: Thiophene, the dosage is adjusted according to the actual situation to make the resin viscosity between 500 - 700 mPa·s;

[0089] Coupling agent: Fluorosilane coupling agent;

[0090] Antioxidant: Tris(2,4-dichlorophenyl) phosphate.

[0091] Hardware equipment is as follows:

[0092] Reaction vessel: Provide the space and environment required for the reaction.

[0093] Sensor system:

[0094] The temperature sensor uses a PT100 thermocouple or an infrared thermometer, the flowmeter uses a mass flowmeter or an electromagnetic flowmeter, the viscosity sensor uses a rotational or in-line viscometer, the gas analyzer uses a GC-MS on-line monitor, and there is also a pressure sensor. Real-time monitor the temperature (T), catalyst / crosslinking agent flow rate (C cat , C cross ), resin viscosity (η), by-product concentration (P) and other parameters in the reactor. Transmit the analog signal to the central control system.

[0095] Central processing unit:

[0096] An industrial computer can use Siemens SIMATIC IPC, a programmable logic controller PLC can use Rockwell ControlLogix, and an edge computing module uses NVIDIA Jetson. After receiving the sensor data, run a pre-trained multivariate neural network model to predict the reaction rate r 2 and the by-product generation amount. Generate control instructions according to the prediction results and optimize the reaction conditions through the PID algorithm or fuzzy logic.

[0097] Automatic control system:

[0098] The temperature control device uses an electric heating mantle and a circulating water cooling system. The flow control valve uses a proportional valve or an electric control valve. The stirrer is driven by a variable frequency motor, and the feeding pump uses a metering pump or a peristaltic pump. It receives the control signal from the central processing unit and dynamically adjusts the reaction conditions. By means of PID regulation of the heating power or the cooling water flow rate, the temperature error is maintained at ≤ ±1°C. The flow valve adjusts the addition rate of the catalyst / crosslinking agent according to the set ratio (error < 0.5%). When the neural network detects a decrease in the reaction rate, the feeding pump is started to supplement the crosslinking agent, and at the same time, the stirring speed is increased (200 - 500 rpm) to ensure uniform mixing.

[0099] The closed-loop control process is as follows. Step 1: The sensor collects real-time data (temperature, flow rate, viscosity, etc.). Step 2: The data is transmitted to the central processing unit via an industrial network. Step 3: The neural network model predicts the reaction trend (such as the risk of by-product generation). Step 4: Control instructions (such as cooling down, reducing the flow rate) are generated and sent to the actuator. Step 5: The actuator adjusts the reaction conditions to form a dynamic closed-loop feedback.

[0100] The hardware system realizes the full-process intelligent control of resin preparation through the closed-loop process of sensor collection → model prediction → execution control → data feedback. The combination of the non-linear modeling ability of the neural network and the real-time response of industrial hardware significantly improves the yield (95 - 97% vs. 90 - 93% of the traditional method) and product consistency (the by-product concentration is reduced by 60%), and is applicable to the production of special resins with high-precision requirements.

[0101] The preparation method of Example 1 is as follows. Step 1: The vinyl ester resin matrix and the amino-modified component are mixed according to the mass ratio. During the mixing, the temperature is controlled at 60 - 80°C, and the reaction time is 3 - 4 hours to ensure the complete progress of the amino-modification reaction;

[0102] Step 2: After the amino-modification reaction is completed, a crosslinking agent, a catalyst, a solvent, a catalyst, and an antioxidant are added and stirred evenly. The amount of the solvent is adjusted to ensure that the viscosity of the resin is between 500 - 700 mPa·s;

[0103] Step 3: The mixture is heated to 70 - 90°C and maintained for 2 - 3 hours to complete the crosslinking reaction. When adding the crosslinking agent and the catalyst, the reaction time is controlled at 2 - 3 hours, and the crosslinking degree of the resin is monitored. The neural network is used to monitor and adjust the catalyst concentration, crosslinking agent concentration, and temperature during the crosslinking reaction process in real time to optimize the reaction rate and minimize the generation of by-products;

[0104] Step 4: After the crosslinking reaction is completed, an inhibitor is added to ensure the stability of the resin during further curing. The resin solution is poured into a mold and cured at 100 - 120°C for 4 hours, and then post-cured at room temperature for 6 - 12 hours to ensure that the resin is completely cured and has ideal mechanical properties.

[0105] For other examples and comparative examples, the corresponding components can be adjusted.

[0106] Table 2: Performance test results

[0107]

[0108] Thermal decomposition temperature: refers to the temperature at which the resin loses 5% of its weight.

[0109] Aging conditions: 85°C, 85% relative humidity, ultraviolet light irradiation for 500 hours.

[0110] Retention rate of tensile strength after aging: (tensile strength after aging / initial tensile strength) x 100%.

[0111] Such as Figure 1-3 , through comparison, it can be seen that the influence of the amino-modified component is as follows. Comparing Examples 1, 2, and 3 with Comparative Example 1, it can be seen that adding the amino-modified component can significantly improve the thermal stability, mechanical properties, and anti-aging ability of the resin. This is because the amino-modified component can react with the vinyl ester resin to form a denser cross-linked network, improving the strength and toughness of the resin.

[0112] The influence of the cross-linking agent is as follows. Comparing Example 1 with Comparative Example 2, it can be seen that using a suitable cross-linking agent can increase the cross-linking density of the resin, thereby improving its thermal stability and mechanical properties. The influence of the catalyst is as follows. Comparing Example 1 with Comparative Example 3, it can be seen that using a catalyst can accelerate the cross-linking reaction and improve the performance of the resin. The influence of the ratio is as follows: Comparing Examples 1, 2, and 3, it can be seen that different component ratios have a certain influence on the performance of the resin, and it needs to be based on actual applications.

[0113] Verify the optimization effect of the neural network and the automatic control system in the resin preparation process through the following examples. The following are the designs of the examples and comparative examples.

[0114] Table 3: Resin formula (mass percentage):

[0115]

[0116] Component description:

[0117] Vinyl ester resin matrix: styrene-modified epoxy vinyl ester resin;

[0118] Amino-modified component: amino silane-epichlorohydrin acrylate complex;

[0119] Cross-linking agent: diphenyl peroxide;

[0120] Catalyst: trifluorochlorosilane;

[0121] Solvent: thiophene, the dosage is adjusted according to the actual situation to make the resin viscosity between 500 - 700 mPa·s;

[0122] Coupling agent: fluorosilane coupling agent;

[0123] Antioxidant: tris(2,4-dichlorophenyl) phosphate.

[0124] The preparation method is carried out as in Example 1, and the main difference lies in the control method of Step 3: Examples 4 and 5: Use neural network and automatic control system to conduct real-time monitoring and adjustment according to Steps 3.1 - 3.5. Example 5 uses optimized neural network parameters to improve the control accuracy. Comparative Examples 4 and 5: Do not use neural network control, and according to the traditional empirical control method, keep the temperature, catalyst concentration and crosslinking agent concentration unchanged. Comparative Example 5 adopts a fixed temperature curve and the addition rates of catalyst and crosslinking agent, but these parameters are empirical values optimized based on multiple experiments. The following table is obtained through tests

[0125] Table 4: Performance test results

[0126] Performance Index Example 4 Example 5 Comparative Example 4 Comparative Example 5 Yield (%) 95 97 90 93 By-product Concentration (ppm) 50 30 150 80 Tensile Strength (MPa) 68 72 60 65 Flexural Strength (MPa) 115 125 100 110 <![CDATA[Impact strength (kJ / m 2 )]]> 16 19 12 14 Thermal Decomposition Temperature (TGA, °C) 355 365 340 350

[0127] Yield: It refers to the ratio of the mass of the final resin to the theoretical output.

[0128] By-product concentration: Measured by methods such as GC-MS, and the unit is parts per million (ppm).

[0129] Thermal decomposition temperature: It refers to the temperature when the resin loses 5% of its weight.

[0130] As Figure 4-7 , by comparing Examples 4 and 5 with Comparative Examples 4 and 5, it can be seen that using neural network control can significantly increase the yield of the resin, reduce the by-product concentration, and improve the mechanical properties and thermal stability. This shows that the neural network can effectively optimize the reaction conditions, improve the reaction efficiency and product quality. By comparing Example 5 with Example 4, it can be seen that by optimizing the parameters of the neural network, the control accuracy can be further improved and better performance can be obtained. Although Comparative Example 5 adopts optimized empirical parameters, its performance is still inferior to the neural network control group. This shows that the traditional empirical control method is difficult to cope with complex non-linear reaction processes, while the neural network can better adapt to the changes in the reaction process through real-time monitoring and adjustment.

[0131] Through the above examples and comparative examples, it can be found that:

[0132] 1. Mechanism of action of the amino-modified component (Examples 1, 2, 3 vs. Comparative Example 1)

[0133] Mechanism: The core role of the amino-modified component (amino-silane-epichlorohydrin acrylate complex) lies in its dual reaction activity.

[0134] Amino functional group: The amino group (NH 2 ) can undergo ring-opening or addition reactions with epoxy groups or unsaturated double bonds in the vinyl ester resin matrix. This introduces nitrogen elements into the resin matrix, forming chemical bonds, thereby enhancing the crosslinking density and intermolecular forces of the resin.

[0135] Epoxy / chloropropenyl ester functional groups: These groups can react with other active sites (such as hydroxyl groups, carboxyl groups, etc.) in the resin matrix and further participate in the construction of the crosslinking network.

[0136] The introduction of the amino-modified component makes the crosslinking network of the resin more dense and uniform. The more compact crosslinking network can effectively restrict the movement of resin molecular chains, thereby improving the rigidity, strength, and heat resistance of the resin. The polar amino groups introduced by the amino-modified component increase the hydrogen bond force between resin molecules. Stronger intermolecular forces contribute to improving the cohesion of the resin, thereby enhancing its mechanical properties and anti-aging ability. The amino-modified component can improve the interfacial compatibility between the resin matrix and other components (such as fillers, fibers, etc.), thereby improving the overall performance of the composite material.

[0137] Experimental data evidence, thermal decomposition temperature (TGA): The thermal decomposition temperatures of Examples 1, 2, and 3 are all higher than that of Comparative Example 1. This indicates that the amino-modified component improves the thermal stability of the resin, making it more difficult to decompose at high temperatures. Tensile strength, flexural strength, impact strength: The mechanical properties of Examples 1, 2, and 3 are all better than those of Comparative Example 1. This indicates that the amino-modified component improves the strength and toughness of the resin.

[0138] Tensile strength retention rate after aging: The tensile strength retention rates of Examples 1, 2, and 3 after aging are all higher than that of Comparative Example 1. This indicates that the amino-modified component improves the anti-aging ability of the resin, enabling it to maintain better mechanical properties in harsh environments.

[0139] 2. Influence mechanism of the crosslinking agent (Example 1 vs Comparative Example 2)

[0140] Mechanism: The crosslinking agent (diphenyl peroxide) decomposes to generate free radicals under heating conditions, initiating free radical polymerization reactions of unsaturated double bonds in the vinyl ester resin matrix to form a three-dimensional crosslinking network. The dosage of the crosslinking agent directly affects the density of the crosslinking network. Appropriately increasing the dosage of the crosslinking agent can increase the crosslinking density of the resin, thereby improving its rigidity, strength, and heat resistance. Excessive crosslinking density: Excessive crosslinking density will cause the resin to become brittle, reducing its toughness and impact strength. Excessive crosslinking agent may not react completely and remain in the resin, affecting its performance and stability.

[0141] Experimental data confirm that the thermal decomposition temperature, tensile strength, flexural strength, and impact strength of Comparative Example 2 (low crosslinking agent) are all lower than those of Example 1. This indicates that insufficient crosslinking agent dosage will lead to a decline in the performance of the resin.

[0142] 3. Influence mechanism of the catalyst (Example 1 vs. Comparative Example 3)

[0143] Mechanism: The catalyst (trifluorochlorosilane) can accelerate the decomposition of the crosslinking agent, promote the generation of free radicals, and thus accelerate the crosslinking reaction of vinyl ester resin. The catalyst can shorten the reaction time and improve production efficiency. The catalyst can reduce the temperature required for the crosslinking reaction, thereby reducing the occurrence of side reactions and improving the quality of the resin.

[0144] Experimental data confirm that the thermal decomposition temperature, tensile strength, flexural strength, and impact strength of Comparative Example 3 (without catalyst) are all lower than those of Example 1. This indicates that the absence of the catalyst will lead to a decline in the performance of the resin.

[0145] 4. Optimization mechanism of neural network and automatic control system (Examples 4, 5 vs. Comparative Examples 4, 5)

[0146] Mechanism: The neural network can learn and predict complex non-linear reaction processes, and automatically adjust parameters such as catalyst concentration, crosslinking agent concentration, and temperature according to the real-time monitored data to optimize the reaction rate, minimize the generation of by-products, and obtain the best resin performance. The neural network can monitor and adjust the reaction conditions in real time to keep them in the best state all the time, thereby improving the reaction efficiency and product quality. The neural network can optimize the reaction conditions, reduce the occurrence of side reactions, thereby reducing the by-product concentration and improving the purity of the resin. The neural network can optimize the reaction conditions to obtain the best resin performance, such as higher strength, toughness, and heat resistance.

[0147] Experimental data confirm that Yield: The yields of Examples 4 and 5 are both higher than those of Comparative Examples 4 and 5. By-product concentration: The by-product concentrations of Examples 4 and 5 are both lower than those of Comparative Examples 4 and 5. Tensile strength, flexural strength, impact strength: The mechanical properties of Examples 4 and 5 are better than those of Comparative Examples 4 and 5. Thermal decomposition temperature (TGA): The thermal decomposition temperatures of Examples 4 and 5 are both higher than those of Comparative Examples 4 and 5.

[0148] From the above analysis, it can be seen that the advantages of this high-toughness amino-modified vinyl ester resin are as follows:

[0149] Amino-modified component: Enhance the crosslinking density, increase the intermolecular force, and improve the interfacial compatibility.

[0150] Crosslinking agent: Increase the crosslinking density (the dosage needs to be controlled).

[0151] Catalyst: Accelerate the crosslinking reaction and reduce the reaction temperature.

[0152] Neural networks and automatic control systems: precisely control reaction conditions, reduce by-product generation, and improve product performance. These advantages work together to make the resin have excellent thermal stability, mechanical properties, and anti-aging ability.

[0153] Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Those of ordinary skill in the technical field to which the present invention pertains can make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the scope defined in the claims.

Claims

1. A high-toughness amino-modified vinyl ester resin, characterized in that: The resin comprises a vinyl ester resin matrix, an amino-modified component, a cross-linking agent, a catalyst, a solvent, a coupling agent and an antioxidant, wherein: The mass of the vinyl ester resin matrix accounts for 60%-80% of the total mass; The mass of the amino-modified component accounts for 10%-25% of the total mass; The mass of the cross-linking agent accounts for 1%-5% of the total mass; The mass of the catalyst accounts for 0.5%-2% of the total mass; The mass of the solvent accounts for an appropriate amount of the total mass to ensure that the viscosity of the resin is between 500-700mPa·s; The mass of the coupling agent accounts for 0.5%-2% of the total mass; The weight of the antioxidant accounts for 0.2%-0.5% of the total weight.

2. The high-toughness amino-modified vinyl ester resin according to claim 1, characterized in that: The vinyl ester resin matrix is ​​selected from styrene-modified epoxy vinyl ester resin; The amino-modified component is an aminosilane-epoxidized allyl chloride complex; The cross-linking agent is diphenyl peroxide; The catalyst is trifluorochlorosilane; The solvent is thiophene; The coupling agent is a silicon fluoride coupling agent; The antioxidant is tris(2,4-dichlorophenyl)phosphate; The amino-modified component reacts with the carbonyl group in the vinyl ester resin matrix to form an amino acid ester structure to improve the heat resistance and toughness of the resin.

3. A method for preparing the high-toughness amino-modified vinyl ester resin according to claim 2, characterized in that: The steps include: Step 1, mixing a vinyl ester resin matrix and an amino-modified component according to a mass ratio, wherein the amino-modified component is an aminosilane compound or an amino acid compound, and the temperature during mixing is controlled at 60-80° C. and the reaction time is 3-4 hours to ensure that the amino-modified reaction is completely carried out; Step 2, after the amino modification reaction is completed, add a crosslinking agent, a catalyst, a solvent, a catalyst and an antioxidant, stir evenly, and adjust the amount of the solvent to ensure that the viscosity of the resin is between 500-700 mPa·s; Step 3, heating the mixture to 70-90°C and maintaining it for 2-3 hours to complete the cross-linking reaction. When adding the cross-linking agent and catalyst, the reaction time is controlled to be 2-3 hours, and the cross-linking degree of the resin is monitored. The catalyst concentration, cross-linking agent concentration and temperature during the cross-linking reaction are monitored and adjusted in real time using a neural network to optimize the reaction rate and minimize the generation of by-products; Step 4: After the cross-linking reaction is completed, an inhibitor is added to ensure the stability of the resin during the further curing process. The resin solution is poured into the mold and cured at 100-120°C for 4 hours, followed by post-curing at room temperature for 6-12 hours to ensure that the resin is fully cured and has ideal mechanical properties.

4. The method for preparing the high-toughness amino-modified vinyl ester resin according to claim 3, characterized in that: The steps of using a neural network to monitor and adjust the cross-linking reaction process in step 3 are as follows: Step 3.1, data acquisition and initialization, real-time monitoring of the temperature in the reactor by a temperature sensor, monitoring of the inflow and concentration of the catalyst and the cross-linking agent by a flow meter and a mass meter, monitoring of the viscosity change data of the resin by a viscosity sensor, and monitoring of the generation of by-products by a gas analyzer; Step 3.2, using the data from step 3.1, training a multivariate neural network model, the neural network will predict the reaction rate r2 through the input reaction conditions, and further optimize the concentration of the catalyst and cross-linking agent and the temperature control during the reaction process; The neural network model design includes input layer, hidden layer and output layer. The input layer: inputs the real-time temperature, catalyst concentration, cross-linking agent concentration, reaction time, and reactant concentration information of the reaction; the hidden layer: uses multiple hidden layers to capture the complex nonlinear relationship between reaction conditions, and adjusts the depth and width of the model to improve accuracy; Output layer: predict the cross-linking reaction rate r2 and output the estimated amount of by-products generated; Step 3.3, input the real-time data into the neural network. The neural network calculates the optimal reaction rate and by-product prediction based on historical data and current conditions. The neural network predicts the reaction rate r2 and the amount of by-product generated according to the current reaction conditions and compares them with the ideal values. When the neural network detects that the reaction rate is too fast or the amount of by-product generated is too high, it will send an adjustment signal to adjust the temperature, catalyst concentration, and cross-linking agent concentration reaction conditions; Step 3.4, after each reaction stage, the neural network evaluates the quality of the resin and the generation of by-products. The neural network gradually optimizes the reaction conditions through feedback mechanism and adjustment of the reaction model; As the reaction continues, the neural network continues to collect new data and train to improve the accuracy and response speed of the model. After each adjustment, the neural network optimizes the catalyst concentration, cross-linker concentration and temperature settings based on the new data and reaction results. Step 3.5, the neural network evaluates the generation trend of by-products through real-time monitoring and makes optimization adjustments. If the by-products exceed the expected range, the neural network prompts the reaction system to adjust the temperature, catalyst concentration or cross-linking agent concentration. When the by-product generation reaches the minimum value, the neural network will lock the current optimization conditions and continue to maintain the optimal reaction state. Evaluate the matching degree between the final product and the target performance, compare the mechanical properties and by-product content of the resin under different experimental conditions, and ensure that the optimized preparation method can provide higher quality resin.

5. The method for preparing the high-toughness amino-modified vinyl ester resin according to claim 4, characterized in that: In step 3.1, during the data collection phase, the temperature T of the reaction is obtained. i , catalyst concentration C cat，i , cross-linking agent concentration C cross，i , reaction time t i , resin viscosity η i , by-product concentration P i Data, represented as a data, D is the entire experimental data set; i is the experimental number; Ti, C cat，i , C cross，i , t i is the input data of the experimental conditions; η i is the resin viscosity; P i is the byproduct concentration. The data is used as the input feature of the neural network to establish the relationship between the reaction conditions and the reaction results during the training process.

6. The method for preparing the high-toughness amino-modified vinyl ester resin according to claim 4, characterized in that: In step 3.2, the neural network model is trained by historical data to predict the reaction rate and by-product generation under different reaction conditions. The neural network model is trained by the following formula: y pred =f(W1·x+b1,W2·σ(W1·x+b1)+b2,...,W n ·σ(W n-1 ·σ(W n-2 ·x+b n-2 )+b n-1 )+b n ); x is the input feature vector, including temperature, catalyst concentration, crosslinker concentration, and time; W i and b i is the weight matrix and bias vector of each layer; σ is the activation function used to introduce nonlinear features; t pred is the predicted output of the model, representing the byproduct concentration; The neural network updates weights and biases based on the difference between the predicted value and the actual value through the back-propagation algorithm. The loss function is usually measured using mean square error or cross entropy: L is the loss function; y true，i is the true value, the concentration of the byproduct measured in the experiment; y pred，i is the value predicted by the neural network; n is the number of samples, minimizing the loss function so that the neural network can learn the best weight W i and bias b i , in order to accurately predict the by-product concentration.

7. The method for preparing the high-toughness amino-modified vinyl ester resin according to claim 4, characterized in that: In the step 3.3, the data of the reaction process is monitored in real time, and the reaction conditions are adjusted according to the prediction feedback of the neural network. The neural network converts the real-time data, x current =(T current , C cat，current , C cross，current , t current ) is input into the trained neural network model to predict the current byproduct concentration y pred : y pred =f(W1·x current +b1,W2·o(W1·x current +b1)+b2,....); According to the prediction result y pred , the system will determine in real time whether the reaction conditions need to be adjusted. If y pred Beyond a predefined threshold, the system adjusts the catalyst concentration, crosslinker concentration, or temperature.

8. The method for preparing the high-toughness amino-modified vinyl ester resin according to claim 4, characterized in that: In step 3.3, the optimization suggestions output by the neural network are connected to an automatic control system to automatically adjust the catalyst flow, cross-linking agent flow and temperature. The automatic control system includes valve control, heating equipment control, and solvent flow control. The optimal reaction environment is achieved by precisely adjusting the reaction conditions.

9. The method for preparing the high-toughness amino-modified vinyl ester resin according to claim 4, characterized in that: In step 3.4, after each reaction cycle, the new experimental data will be fed back to the neural network as new input to further optimize the model. The neural network will be trained again and the weight W will be updated. i and bias b i : It is the gradient of the loss function with respect to weights and biases, which is used to update the network parameters. After each optimization, the neural network will be able to better predict the reaction rate and by-product generation, and adjust the reaction conditions.

10. The method for preparing the high-toughness amino-modified vinyl ester resin according to claim 4, characterized in that: In step 3.5, the reaction conditions are optimized to reduce the generation of by-products and ensure the final performance of the resin. The optimized reaction conditions can be described by the following objective function: R yield is the yield of the resin, reflecting the reaction efficiency; P byproduct is the concentration of by-products, indicating the generation of by-products; λ1 and λ2 are weight coefficients used to balance the relationship between yield and by-product generation. The objective function aims to maximize the resin yield and minimize the generation of by-products. The overall reaction process is optimized by adjusting the reaction conditions. The optimization result evaluation can be carried out in the following ways: Product quality assessment: Assessment based on the mechanical properties of the resin; Byproduct concentration: monitor the byproduct generation to ensure that its concentration is below the predetermined threshold; By optimizing the objective function and adjusting the reaction conditions in real time, the neural network is able to continuously improve the reaction process to ensure high performance and low by-product formation of the final resin.

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