Highly heat-conductive low-smoke halogen-free flame-retardant cable material modified based on graphene and preparation method thereof
By modifying graphene and optimizing the component ratio and preparation parameters using multiple algorithms, the problem of balancing thermal conductivity and flame retardancy in low-smoke halogen-free flame-retardant cable materials has been solved. This has achieved a synergistic improvement in the high thermal conductivity, low smoke halogen-free properties, and flame retardancy of the cable material, as well as the stability of product performance. It is suitable for power transmission, rail transit, new energy, and construction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANGZHOU KEJIA NEW MATERIALS CO LTD
- Filing Date
- 2026-02-12
- Publication Date
- 2026-06-05
AI Technical Summary
Existing low-smoke halogen-free flame-retardant cable materials suffer from poor thermal conductivity, low flame-retardant efficiency, and poor mechanical properties. Furthermore, the rough control of component ratios and preparation parameters leads to unstable product performance, making it difficult to meet the requirements of high-end equipment.
A graphene-modified thermally conductive and flame-retardant system was adopted. Various algorithms were used to optimize the composition ratio and preparation parameters, including multiple linear regression, response surface optimization, particle packing density, and flame retardant efficiency. By combining phosphorus-based and inorganic flame retardants, a continuous thermally conductive path was constructed to improve thermal conductivity and flame retardant performance. Furthermore, a BP neural network was introduced to optimize extrusion parameters to ensure product stability.
It achieves a synergistic improvement in high thermal conductivity, low smoke and halogen-free properties, and flame retardant performance. The thermal conductivity of the cable material is improved by more than 50%, the limiting oxygen index is ≥32%, the smoke density level is ≤45, the vertical combustion reaches UL94 V-0 level, the product performance stability is improved, the batch-to-batch difference is ≤3%, and the production cost is reduced.
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Figure CN122145908A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of cable material technology, specifically relating to a graphene-modified high thermal conductivity, low smoke, halogen-free, and flame-retardant cable material and its preparation method, and particularly to a cable material and its preparation process that achieves synergistic improvement in high thermal conductivity, low smoke, halogen-free, and flame-retardant performance through multi-algorithm optimization of component ratios and preparation parameters. Background Technology
[0002] With the rapid development of power transmission, rail transportation, and new energy, the performance requirements for cable materials are increasing. They not only need excellent electrical insulation properties but also must meet environmental and safety requirements such as high thermal conductivity, low smoke halogen-free properties, and flame retardancy. Low smoke halogen-free flame-retardant cable materials, because they do not produce toxic or harmful gases or large amounts of smoke during combustion, pose less harm to the environment and human health, and have become the mainstream development direction for cable materials. However, existing low smoke halogen-free flame-retardant cable materials generally suffer from poor thermal conductivity, low flame-retardant efficiency, and unsatisfactory mechanical properties, making it difficult to meet the needs of high-end equipment.
[0003] Graphene, a two-dimensional nanomaterial with ultra-high thermal conductivity, is widely used in the thermal conductivity modification of polymer materials. Introducing graphene into cable materials can effectively improve their thermal conductivity, accelerate heat dissipation during cable operation, and extend cable life. However, graphene has drawbacks such as high surface energy and easy aggregation, resulting in uneven dispersion in the matrix resin and difficulty in fully exerting its thermal conductivity. At the same time, graphene has poor compatibility with matrix resins and flame retardants, which can easily lead to a decrease in the mechanical properties of cable materials. Furthermore, single-graphene modification is difficult to achieve synergistic optimization of thermal conductivity and flame retardancy.
[0004] Currently, existing research on graphene-modified cable materials largely relies on empirical methods to determine component ratios and preparation parameters. This leads to problems such as unreasonable proportions and crude parameter control, resulting in unstable overall performance of the cable materials and difficulty in achieving a balance between thermal conductivity, flame retardancy, and processing performance. Furthermore, the lack of systematic algorithmic support in existing technologies prevents precise optimization of the compounding ratios of each component and the preparation process parameters, resulting in limited performance improvements, high production costs, and restrictions on their industrial application.
[0005] For example, existing patent CN113956563A discloses a high thermal conductivity, low smoke, halogen-free flame-retardant polyolefin cable material and its preparation method. It uses a composite of modified silica and boron nitride as a thermally conductive and flame-retardant filler. While this can improve the thermal conductivity and flame-retardant properties of the cable material to some extent, it does not use graphene as a core thermally conductive modifier, resulting in limited improvement in thermal conductivity. Furthermore, it does not incorporate algorithms to optimize the component ratio and preparation parameters, leading to poor product performance stability and difficulty in meeting the high thermal conductivity requirements of high-end applications. Simultaneously, existing technologies have limited applications of algorithms for predicting cable material performance and optimizing parameters, making precise control of component ratios and preparation processes impossible, resulting in significant performance differences between batches of products.
[0006] Therefore, developing a cable material and its preparation method that utilizes graphene modification, combines multi-algorithm optimization of component ratios and preparation parameters, and achieves synergistic improvement in high thermal conductivity, low smoke and halogen-free properties, as well as efficient and controllable preparation process and stable performance, has become an urgent technical problem to be solved in this field. Summary of the Invention
[0007] The purpose of this invention is to overcome the shortcomings of existing technologies, such as difficulty in achieving both thermal conductivity and flame retardancy in graphene-modified cable materials, coarse control of component ratio and preparation parameters, and unstable product performance. This invention provides a graphene-modified high thermal conductivity, low smoke, halogen-free flame retardant cable material and its preparation method. By introducing multiple algorithms to optimize component ratio and preparation parameters, the invention solves the problems of graphene agglomeration and poor compatibility, and achieves synergistic optimization of the cable material's thermal conductivity, flame retardancy, processing, and mechanical properties.
[0008] To achieve the above objectives, the present invention adopts the following technical solution:
[0009] A graphene-modified high thermal conductivity, low smoke, halogen-free flame-retardant cable material comprises a matrix resin, a graphene-modified thermally conductive and flame-retardant system, a synergistic flame retardant agent, and processing aids. The components are formulated in proportions by weight, and the optimal proportions are determined using a component optimization algorithm. The graphene-modified thermally conductive and flame-retardant system uses graphene as its core, which, after surface modification, is compounded with thermally conductive fillers to achieve a synergistic improvement in both thermal conductivity and flame-retardant properties. The cable material satisfies the following condition: thermal conductivity ≥ It has a limiting oxygen index of ≥32%, a smoke density rating (SDR) of ≤45, and achieves UL94 V-0 rating for vertical combustion.
[0010] The cable material of this invention uses graphene as the core thermally conductive modifier. Surface modification treatment improves its dispersibility and compatibility, and combined with thermally conductive fillers, it constructs a continuous thermally conductive pathway, significantly enhancing the thermal conductivity of the cable material. Simultaneously, a phosphorus-based and inorganic flame retardant compound is used as a synergistic flame retardant system, working in conjunction with the graphene-modified thermally conductive flame retardant system to enhance flame retardant performance, achieving a synergistic effect of low smoke, halogen-free, flame retardant, and high thermal conductivity. Furthermore, this invention introduces multiple component optimization algorithms to replace traditional empirical methods, enabling precise control of the component ratios, ensuring stable product performance, and solving the performance defects caused by unreasonable ratios in existing technologies.
[0011] Furthermore, the matrix resin is at least one of polyolefin resins, including polyethylene, ethylene-vinyl acetate copolymer, and ethylene-octene copolymer elastomer. The matrix resin accounts for 30-60 parts by mass of the total mass of the cable material, and its proportion is determined by a multiple linear regression optimization algorithm. The algorithm expression is as follows: ,in The overall processing performance of the matrix resin is scored. These represent the mass percentages of the three resins. For regression coefficients, Let be the error term, and let be the optimization objective. ≥85 points (out of 100).
[0012] Polyolefin resins possess excellent processing performance, electrical insulation properties, and mechanical properties, making them the preferred matrix resins for cable materials. Polyethylene exhibits superior electrical insulation and chemical resistance, while ethylene-vinyl acetate copolymer (EVA) offers good flexibility and compatibility. Ethylene-octene copolymer elastomer enhances the impact resistance of cable materials. The combination of these three resins optimizes the overall performance of the matrix resin. A multiple linear regression optimization algorithm precisely determines the optimal ratio by fitting the linear relationship between the resin proportions and processing performance, ensuring the matrix resin possesses good thermal stability, flowability, and compatibility, thus providing a foundation for the uniform mixing of subsequent components. The comprehensive processing performance score Y1 primarily evaluates melt flow rate, thermal decomposition temperature, and tensile strength, with weights of 0.4, 0.3, and 0.3 respectively, for a maximum score of 100. The comprehensive score is calculated after normalization.
[0013] Furthermore, the graphene-modified thermally conductive and flame-retardant system includes modified graphene and thermally conductive filler. The modified graphene is obtained by surface modification of raw graphene with a silane coupling agent. During the modification process, a response surface methodology (RSM) algorithm is used to determine the modification parameters. The RSM algorithm uses modification temperature, modification time, and coupling agent dosage as independent variables, and graphene dispersion uniformity as the response value to establish a quadratic regression model. ,in For dispersion uniformity, A represents the modification temperature, B represents the modification time, and C represents the amount of coupling agent. The regression coefficients indicate that the optimized dispersion uniformity is ≥90%.
[0014] The surface of raw graphene is inert and prone to aggregation. Surface modification with silane coupling agents introduces active groups into the graphene surface, improving its compatibility with the matrix resin and thermally conductive fillers, and reducing aggregation. Response surface methodology (RSM) optimization, compared to single-factor experiments, comprehensively considers the impact of various modification parameters and their interactions on dispersion uniformity. By establishing a quadratic regression model, it accurately predicts the optimal combination of modification parameters, ensuring uniform dispersion of modified graphene in the system and fully utilizing its ultra-high thermal conductivity. Dispersion uniformity is a key factor in this process. The distribution of graphene in the matrix resin was observed by scanning electron microscopy (SEM), and the dispersion area ratio was calculated by image analysis, which is the dispersion uniformity. The preferred silane coupling agent is one of KH-550 and KH-560. The amount of coupling agent is 5%-15% of the graphene mass, the modification temperature is 80-120℃, and the modification time is 1-3h.
[0015] Meanwhile, referring to the research results of graphene modification in the prior art, the modification parameters optimized by the response surface optimization algorithm of this invention can make the grafting rate of active groups on the graphene surface ≥85%, further improving its compatibility with the matrix resin, avoiding the problem of decreased thermal conductivity caused by agglomeration. Compared with unmodified graphene, the dispersion uniformity of modified graphene is improved by more than 30%, which can effectively construct a continuous thermal conduction path.
[0016] Furthermore, the thermally conductive filler is a blend of at least two of boron nitride, alumina, and silicon dioxide, with a mass ratio of the thermally conductive filler to modified graphene of (5-15):1. The blending ratio is optimized using a particle packing density algorithm, which calculates the packing density of fillers with different particle sizes. ,in For the first Volume fraction of the filler For the first The density of the filler was optimized to a bulk density ≥1.6 g / cm³ to construct a continuous heat conduction path.
[0017] Boron nitride, alumina, and silicon dioxide are all high thermal conductivity inorganic fillers. Boron nitride possesses excellent thermal conductivity and insulation properties, alumina has a high thermal conductivity and low cost, and silicon dioxide can improve the processing performance of cable materials. Combining multiple fillers can achieve complementary advantages. Particle packing density algorithms optimize the mixing ratio of different particle sizes and types of fillers to increase the packing density, reduce internal voids, and construct continuous thermal conductivity pathways, thereby improving the overall thermal conductivity of cable materials. For example, using a mixture of large-particle-size (5-10μm) boron nitride and small-particle-size (1-3μm) alumina, combined with modified graphene, can achieve close packing of the fillers, improving thermal conductivity by more than 40% compared to single fillers.
[0018] In addition, the particle size distribution of the thermally conductive filler was detected by a laser particle size analyzer, and the particle size range was optimized using a normal distribution model to ensure uniform particle size distribution and further improve the packing density. After optimization, the particle size distribution range of the filler is ≤5μm, and the packing density reaches [value missing]. This ensures high thermal conductivity.
[0019] Furthermore, the synergistic flame retardant is a compound system of phosphorus-based flame retardant and inorganic flame retardant. The phosphorus-based flame retardant is at least one of red phosphorus masterbatch and ammonium polyphosphate, and the inorganic flame retardant is at least one of magnesium hydroxide and aluminum hydroxide. The mass fraction of the synergistic flame retardant accounts for 20-40 parts of the total mass of the cable material, and its compounding ratio is determined by a flame retardant efficiency optimization algorithm. The algorithm expression is as follows: Where η is the flame retardant efficiency. This refers to the mass parts of phosphorus-based flame retardants. This refers to the mass parts of the inorganic flame retardant. The coefficient indicates that the optimized flame retardant efficiency is ≥88%.
[0020] Phosphorus-based flame retardants achieve their flame-retardant effect through a dual action of gas-phase and condensed-phase flame retardancy, decomposing at high temperatures to produce... Free radicals, capturing the combustion chain reaction and It inhibits the combustion reaction and generates a char layer to prevent the transfer of heat and oxygen; the inorganic flame retardant decomposes and absorbs heat at high temperatures, releasing water vapor to dilute the combustible gas, thus playing a role in cooling and suppressing smoke. The combination of the two can produce a synergistic flame retardant effect, improve flame retardant efficiency, reduce the amount of flame retardant used, and avoid the decline in mechanical properties caused by excessive flame retardant.
[0021] The flame retardant efficiency optimization algorithm quantifies the impact of the compounding ratio of phosphorus-based and inorganic flame retardants on flame retardant efficiency, avoiding performance defects caused by excessive use of a single flame retardant, while reducing production costs. The flame retardant efficiency η is comprehensively evaluated by the limiting oxygen index and vertical flaming rating; the higher the limiting oxygen index and the higher the flaming rating, the higher the flame retardant efficiency. These are the fitting coefficients, obtained by fitting experimental data. In addition, microencapsulated red phosphorus masterbatch is preferred to avoid red phosphorus oxidation and improve its stability. Ammonium polyphosphate is preferred to be type II ammonium polyphosphate with a degree of polymerization ≥1000, which has a better flame retardant effect. Magnesium hydroxide and aluminum hydroxide are preferred to be nano-sized with a particle size of 50-100nm, which has better dispersibility and a more significant smoke suppression effect.
[0022] Furthermore, the processing aids include antioxidants, lubricants, and compatibilizers, with each component accounting for 1-5 parts by mass of the total cable material. The weighting of each processing aid is determined using the analytic hierarchy process (AHP). A hierarchical structure is constructed, consisting of a target layer (processing performance), a criterion layer (thermal stability, flowability, compatibility), and a scheme layer (each processing aid), and weight vectors are calculated. ,in This ensures that there is no obvious agglomeration during processing and that extrusion is smooth.
[0023] Antioxidants are used to improve the thermal stability of cable materials and prevent thermal oxidative degradation during processing and use; a combination of antioxidants 1010 and 168 is preferred. Lubricants are used to improve the flowability of materials and reduce frictional resistance during processing; stearic acid or calcium stearate is preferred. Compatibilizers are used to further improve the compatibility of each component and reduce agglomeration; maleic anhydride-grafted polyolefin compatibilizers are preferred. The analytic hierarchy process (AHP) can systematically consider the influence of each processing aid on different processing performance indicators, scientifically determine the proportion and weight of each aid, avoid processing defects caused by excessive or insufficient amounts of a single aid, and ensure smooth mixing and extrusion processes and uniform product particles.
[0024] The specific implementation steps of the Analytic Hierarchy Process (AHP) are as follows: 1) Construct a hierarchical structure: the target layer is processing performance, the criterion layer is thermal stability, flowability, and compatibility, and the solution layer is antioxidants, lubricants, and compatibility agents; 2) Construct a judgment matrix by comparing the importance of each criterion layer and solution layer pairwise to determine the judgment matrix; 3) Perform a consistency test by calculating the consistency index CI and the consistency ratio CR. When CR < 0.1, the judgment matrix meets the consistency requirements; 4) Calculate the weight vector to obtain the proportion weight of each processing aid, where the antioxidant weight is... Lubricant weight Compatibilizer weight .
[0025] This invention also provides a method for preparing graphene-modified high thermal conductivity, low smoke, halogen-free flame-retardant cable material, comprising the following steps:
[0026] S1: Raw material pretreatment, drying the matrix resin to a moisture content ≤0.1%, and mixing the modified graphene with the thermally conductive filler uniformly to obtain a graphene-modified thermally conductive and flame-retardant system;
[0027] Raw material pretreatment removes moisture from the matrix resin, preventing air bubbles from forming during processing and affecting the performance of the cable material. Pre-mixing modified graphene with the thermally conductive filler ensures uniform dispersion, laying the foundation for constructing a continuous thermal conductivity pathway. The matrix resin is dried using a forced-air drying oven at 80-100℃ for 2-4 hours. The modified graphene and thermally conductive filler are mixed using a high-speed mixer at 1000-1500 r / min for 10-20 minutes. A small amount of lubricant can be added during mixing to further improve dispersibility.
[0028] S2: Mixing and compounding. The pretreated matrix resin, graphene-modified thermally conductive and flame-retardant system, synergistic flame retardant, and processing aids are added to the mixing equipment according to the specified ratio. A temperature gradient mixing algorithm is used to control the mixing parameters, with the mixing temperature set according to... Gradient heating, where The mixing time is determined by a viscosity feedback algorithm: when the viscosity of the mixing system reaches... Stop mixing at that time;
[0029] The temperature gradient mixing algorithm avoids resin degradation and flame retardant decomposition caused by instantaneous high temperatures, while ensuring gradual melting and uniform mixing of all components, thus improving the mixing effect. The viscosity feedback algorithm accurately determines the mixing endpoint by monitoring the viscosity changes of the mixing system in real time, avoiding uneven component dispersion due to insufficient mixing or decreased mechanical properties due to overmixing. An internal mixer is preferred for mixing equipment, with a rotor speed of 50-80 r / min and a temperature gradient heating rate of [missing information]. This ensures that the materials melt gradually and that all components are fully mixed.
[0030] The specific implementation process of the viscosity feedback algorithm is as follows: Viscosity data of the mixing system is collected in real time using an online viscometer, once every 10 seconds. A viscosity-time change curve is established, and the first derivative method is used to determine the viscosity stability point. When the viscosity first derivative of three consecutive collections reaches a stable value... When the target viscosity is reached, mixing is stopped. At this point, the components of the mixing system are evenly dispersed and in a good melt state, which provides a guarantee for subsequent extrusion granulation.
[0031] S3: Extrusion granulation. The mixed material is fed into the extruder. The BP neural network algorithm is used to optimize the extrusion parameters. The extrusion temperature, screw speed and feeding speed are used as input parameters and the particle size uniformity is used as output parameter. The BP neural network model is trained. After optimization, the particle size uniformity is ≥92%. After extrusion, the material is air-cooled and pelletized to obtain the finished cable material.
[0032] The BP neural network algorithm possesses strong nonlinear fitting capabilities, accurately establishing the mapping relationship between extrusion parameters and particle size uniformity. By training the model and optimizing extrusion parameters, it ensures uniform product particle size and stable performance. A twin-screw extruder is preferred, with extrusion temperature matched to mixing temperature. The screw speed is 100-200 r / min, the feeding speed is 20-50 kg / h, the air cooling temperature is 20-30℃, and the pellet length is 2-3 mm to ensure uniform pellet size.
[0033] The specific training process of the BP neural network model is as follows: 1) Collect sample data and select different extrusion temperatures ( 1) Input samples: screw speed (100-200 r / min) and feeding speed (20-50 kg / h) were used as input samples, and the corresponding particle size uniformity was used as output samples. A total of 50-80 sets of sample data were collected. 2) Data preprocessing: the input and output samples were normalized to map the data to the [0,1] interval to avoid the influence of data dimensions. 3) Network structure construction: the number of neurons in the input layer was 3, the number of neurons in the hidden layer was 10-15, and the number of neurons in the output layer was 1. The activation function was the Sigmoid function. 4) Model training: the gradient descent method was used to optimize the network weights and thresholds. The learning rate was set to 0.01-0.05, and the number of training times was 1000-2000. Training was stopped when the model prediction error was ≤3%. 5) Parameter optimization: the trained model was used to adjust the extrusion parameters in real time to ensure that the particle size uniformity was ≥92%.
[0034] Furthermore, referring to existing extrusion granulation technologies, the extrusion parameters optimized by the BP neural network algorithm in this invention can achieve a particle size variation coefficient of ≤5% and a batch-to-batch performance difference of ≤3%, which greatly improves the stability of product performance and solves the problem of uneven particle size caused by the rough control of extrusion parameters in the existing technology.
[0035] S4: Performance testing. A multi-index comprehensive evaluation algorithm is used to test the thermal conductivity, flame retardancy, and mechanical properties of the finished cable material. When all indicators meet the preset standards, it is a qualified product.
[0036] The multi-index comprehensive evaluation algorithm can comprehensively and objectively evaluate the overall performance of cable materials, avoiding the limitations of single-index evaluation and ensuring that products meet usage requirements. Specific performance testing indicators include: thermal conductivity (thermal conductivity ≥ 100%). Flame retardant properties (limiting oxygen index ≥32%, smoke density ≤45, vertical burning UL94 V-0 rating), mechanical properties (tensile strength ≥12MPa, elongation at break ≥300%), electrical insulation properties (volume resistivity ≥ ).
[0037] The multi-index comprehensive evaluation algorithm uses the entropy weight method to determine the weight of each performance index. The specific steps are as follows: 1) Collect performance test data of n groups of finished cable materials and construct... Matrix (m is the number of performance indicators); 2) Data normalization processing to eliminate the influence of indicator dimensions. For positive indicators (thermal conductivity, limiting oxygen index, tensile strength, etc.), adopt... For negative indicators (smoke density level), the following is adopted: 3) Calculate the information entropy of each indicator. Where k = 1 / lnn, 4) Calculate the indicator weights Among them, thermal conductivity is the most important factor. Flame retardant performance weight Mechanical property weight Electrical insulation performance weight 5) Calculate the comprehensive evaluation score. When Y ≥ 85 points, the product is deemed qualified.
[0038] Compared with the prior art, the present invention has the following beneficial effects:
[0039] This invention uses graphene as the core thermally conductive modifier. After surface modification with a silane coupling agent, it is combined with thermally conductive fillers to construct a continuous thermally conductive pathway, significantly improving the thermal conductivity of cable materials, achieving a thermal conductivity of up to [missing value]. Compared to existing low-smoke halogen-free flame-retardant cable materials, the thermal conductivity is improved by more than 50%, which can effectively accelerate the dissipation of heat during cable operation and extend the service life of the cable. At the same time, the use of phosphorus-based and inorganic flame retardants as a synergistic flame retardant system, in conjunction with the graphene-modified thermally conductive and flame-retardant system, achieves improved low-smoke halogen-free and flame-retardant performance, with a limiting oxygen index ≥32%, smoke density level ≤45, and vertical burning reaching UL94 V-0 level, meeting environmental and safety requirements.
[0040] This invention introduces various algorithms to optimize component ratios and preparation parameters, including multiple linear regression optimization algorithm, response surface optimization algorithm, particle packing density algorithm, flame retardant efficiency optimization algorithm, analytic hierarchy process, temperature gradient mixing algorithm, viscosity feedback algorithm, BP neural network algorithm, and multi-index comprehensive evaluation algorithm, etc., to replace traditional empirical methods. This enables precise control of component ratios and preparation processes, solves the problems of graphene agglomeration, poor compatibility, and uneven component dispersion, ensures stable overall performance of cable materials, with batch-to-batch performance differences ≤3%, while reducing production costs and improving industrial production efficiency.
[0041] The cable material of this invention uses polyolefin resin as the matrix and is optimized with processing aids, exhibiting excellent processing and mechanical properties. Its tensile strength is ≥12MPa, and its elongation at break is ≥300%. During processing, there is no significant agglomeration, extrusion is smooth, and large-scale extrusion granulation can be achieved. Simultaneously, the cable material possesses excellent electrical insulation properties, with a volume resistivity ≥ It can be widely used in fields such as power transmission, rail transit, new energy, and construction, and has a wide range of applications.
[0042] The preparation method of this invention is simple, efficient and controllable. Through raw material pretreatment, gradient mixing, precise extrusion granulation and comprehensive testing of multiple indicators, the product quality is ensured to be stable. No toxic or harmful gases are generated during the preparation process, which meets environmental protection requirements. At the same time, by optimizing the preparation parameters through algorithms, energy consumption and raw material waste can be reduced, and production costs can be lowered. Compared with existing preparation methods, the production cost is reduced by 10%-15%, which has good economic and social benefits. Detailed Implementation
[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0044] Example 1
[0045] A graphene-modified high thermal conductivity, low smoke, halogen-free flame-retardant cable material, by weight, comprises: 45 parts of matrix resin (25 parts of polyethylene, 15 parts of ethylene-vinyl acetate copolymer, and 5 parts of ethylene-octene copolymer elastomer, the proportions of which were determined by a multiple linear regression optimization algorithm). 25 parts of a graphene-modified thermally conductive and flame-retardant system (3 parts modified graphene, 12 parts boron nitride, 10 parts alumina, with a thermally conductive filler to modified graphene mass ratio of 7.3:1 and particle packing density of 10 parts). ), 25 parts of synergistic flame retardant (8 parts of red phosphorus masterbatch, 7 parts of ammonium polyphosphate, and 10 parts of magnesium hydroxide, the proportion was determined by the flame retardant efficiency optimization algorithm, η=90%), and 3 parts of processing aid (0.8 parts of antioxidant 1010, 1.2 parts of stearic acid, and 1 part of maleic anhydride grafted polyethylene, the proportion weight W=(0.3,0.4,0.3) was determined by the analytic hierarchy process).
[0046] The preparation of modified graphene involved determining the modification parameters using response surface methodology. The dosage of silane coupling agent KH-550 was 10% of the graphene mass, the modification temperature was 100℃, and the modification time was 2 hours. The optimized dispersion uniformity was 92%. The quadratic regression model was used. ;in: denoted as graphene dispersion uniformity, A as modification temperature, B as modification time, C as the amount of silane coupling agent KH-550, 85.2 as a constant term, and 2.1, 1.8, and 2.5 as the coefficients of the first-order terms of A, B, and C, respectively. The coefficients of the interaction terms AB, AC, and BC are respectively. The coefficient of the quadratic term, For model fit ( The closer to 1, the better the model fit.
[0047] The method for preparing this cable material includes the following steps:
[0048] S1: Raw material pretreatment: Place the matrix resin in a forced-air drying oven and dry at 85℃ for 3 hours until the moisture content is 0.08%; Add the modified graphene, boron nitride, and alumina to a high-speed mixer and mix at 1200r / min for 15 minutes to obtain a graphene-modified thermally conductive and flame-retardant system.
[0049] S2: Mixing and compounding. The pretreated matrix resin, graphene-modified thermally conductive and flame-retardant system, synergistic flame retardant, and processing aids are added to an internal mixer. A temperature gradient mixing algorithm is used, with temperatures increasing in a gradient of 115℃→135℃→155℃ at a rate of 12℃ / min and a rotor speed of 65 r / min. Viscosity is monitored in real-time using a viscosity feedback algorithm. When the viscosity reaches... Stop mixing when the time is up; mixing time is 18 minutes.
[0050] S3: Extrusion granulation. The mixed material is fed into a twin-screw extruder. The extrusion parameters are optimized using a BP neural network algorithm. The extrusion temperature is 160℃, the screw speed is 150r / min, and the feeding speed is 35kg / h. After optimization, the particle size uniformity is 93%, and the prediction error of the BP neural network model is 2.5%. After extrusion, the material is air-cooled at 25℃ and granulated (2.5mm in length) to obtain the finished cable material.
[0051] S4: Performance testing, employing a multi-index comprehensive evaluation algorithm, collecting 10 sets of sample data, and calculating the weight of each index. The overall evaluation score was 89 points, indicating that the product is qualified.
[0052] Performance test results of finished cable material: thermal conductivity Limiting oxygen index 34%, smoke density rating 42, vertical burning UL94 V-0 rating, tensile strength 13.2 MPa, elongation at break 320%, volume resistivity All meet the preset standards.
[0053] Example 2
[0054] A graphene-modified high thermal conductivity, low smoke, halogen-free flame-retardant cable material, by weight, comprises: 35 parts of matrix resin (18 parts of polyethylene, 10 parts of ethylene-vinyl acetate copolymer, and 7 parts of ethylene-octene copolymer elastomer, the proportions of which were determined by a multiple linear regression optimization algorithm). 30 parts of a graphene-modified thermally conductive and flame-retardant system (4 parts modified graphene, 15 parts boron nitride, 11 parts silicon dioxide, with a thermally conductive filler to modified graphene mass ratio of 6.5:1 and a particle packing density of 1000 kJ / kg). ), 30 parts of synergistic flame retardant (10 parts of red phosphorus masterbatch, 12 parts of aluminum hydroxide, and 8 parts of magnesium hydroxide, the proportion was determined by the flame retardant efficiency optimization algorithm, η=89%), and 4 parts of processing aids (1 part of antioxidant 1010, 0.5 parts of antioxidant 168, 1.5 parts of calcium stearate, and 1 part of maleic anhydride grafted polypropylene, the proportion weight W=(0.35,0.4,0.25) was determined by the analytic hierarchy process.
[0055] The preparation of modified graphene involved determining the modification parameters using response surface methodology. The dosage of silane coupling agent KH-560 was 12% of the graphene mass, the modification temperature was 110℃, and the modification time was 1.5 h. The optimized dispersion uniformity was 93%. The quadratic regression model was as follows:
[0056] , ;
[0057] in: denoted as graphene dispersion uniformity, A as modification temperature, B as modification time, C as the amount of silane coupling agent KH-560, 86.5 as a constant term, and 2.3, 1.9, and 2.7 as the coefficients of the first-order terms of A, B, and C, respectively. The coefficients of the interaction terms AB, AC, and BC are respectively. They are respectively The coefficients of the quadratic term, R², represent the model fit.
[0058] The method for preparing this cable material includes the following steps:
[0059] S1: Raw material pretreatment: The matrix resin is placed in a forced-air drying oven and dried at 90℃ for 2.5h, with a moisture content of 0.07%; Modified graphene, boron nitride, and silicon dioxide are added to a high-speed mixer and mixed at 1300r / min for 12min to obtain a graphene-modified thermally conductive and flame-retardant system.
[0060] S2: Mixing and compounding. The pretreated matrix resin, graphene-modified thermally conductive and flame-retardant system, synergistic flame retardant, and processing aids are added to an internal mixer. A temperature gradient mixing algorithm is used, with temperatures increasing in a gradient of 112℃→132℃→152℃ at a rate of 10℃ / min and a rotor speed of 70 r / min. Viscosity is monitored in real-time using a viscosity feedback algorithm. When the viscosity reaches... Stop mixing when the time is up; mixing time is 16 minutes.
[0061] S3: Extrusion granulation. The mixed material is fed into a twin-screw extruder. The extrusion parameters are optimized using a BP neural network algorithm. The extrusion temperature is 155℃, the screw speed is 160r / min, and the feeding speed is 30kg / h. After optimization, the particle size uniformity is 94%, and the prediction error of the BP neural network model is 2.2%. After extrusion, the material is air-cooled at 28℃ and granulated (2.2mm in length) to obtain the finished cable material.
[0062] S4: Performance testing, employing a multi-index comprehensive evaluation algorithm, collecting 10 sets of sample data, and calculating the weight of each index. The overall evaluation score was 90 points, indicating that the product is qualified.
[0063] Performance test results of finished cable material: thermal conductivity Limiting oxygen index 35%, smoke density rating 40, vertical burning UL94 V-0 rating, tensile strength 13.8 MPa, elongation at break 330%, volume resistivity All meet the preset standards.
[0064] Example 3
[0065] A graphene-modified high thermal conductivity, low smoke, halogen-free flame-retardant cable material, by weight, comprises: 55 parts of matrix resin (30 parts of polyethylene, 18 parts of ethylene-vinyl acetate copolymer, and 7 parts of ethylene-octene copolymer elastomer, the proportions of which were determined by a multiple linear regression optimization algorithm). ), 20 parts of a graphene-modified thermally conductive and flame-retardant system (2 parts modified graphene, 8 parts alumina, 10 parts silicon dioxide, with a thermally conductive filler to modified graphene mass ratio of 9:1, and a particle packing density of 100%. ), 22 parts of synergistic flame retardant (10 parts of ammonium polyphosphate and 12 parts of magnesium hydroxide, the ratio was determined by the flame retardant efficiency optimization algorithm, η=88%), and 2 parts of processing aid (0.6 parts of antioxidant 168, 0.8 parts of stearic acid, and 0.6 parts of maleic anhydride grafted polyethylene, the ratio weight W=(0.25,0.45,0.3) was determined by the analytic hierarchy process).
[0066] The preparation of modified graphene involved determining the modification parameters using response surface methodology. The dosage of silane coupling agent KH-550 was 8% of the graphene mass. The modification temperature was 90℃, and the modification time was 2.5 h. The optimized dispersion uniformity was 91%. The quadratic regression model was... , ;in: denoted as graphene dispersion uniformity, A as modification temperature, B as modification time, C as the amount of silane coupling agent KH-550, 84.8 as a constant term, and 2.0, 1.7, and 2.4 as the coefficients of the first-order terms of A, B, and C, respectively. The coefficients of the interaction terms AB, AC, and BC are respectively. The coefficient of the quadratic term, This represents the goodness of fit of the model.
[0067] The method for preparing this cable material includes the following steps:
[0068] S1: Raw material pretreatment: Place the matrix resin in a forced-air drying oven and dry at 80℃ for 4 hours until the moisture content is 0.09%; Add the modified graphene, alumina, and silicon dioxide to a high-speed mixer and mix at 1100r / min for 18 minutes to obtain the graphene-modified thermally conductive and flame-retardant system.
[0069] S2: Mixing and compounding. The pretreated matrix resin, graphene-modified thermally conductive and flame-retardant system, synergistic flame retardant, and processing aids are added to an internal mixer. A temperature gradient mixing algorithm is used, with temperatures increasing in a gradient of 118℃→138℃→158℃ at a rate of 15℃ / min and a rotor speed of 60 r / min. Viscosity is monitored in real-time using a viscosity feedback algorithm. When the viscosity reaches... Stop mixing when the time is up; mixing time is 20 minutes.
[0070] S3: Extrusion granulation. The mixed material is fed into a twin-screw extruder. The extrusion parameters are optimized using a BP neural network algorithm. The extrusion temperature is 165℃, the screw speed is 140r / min, and the feeding speed is 40kg / h. After optimization, the particle size uniformity is 92%, and the prediction error of the BP neural network model is 2.8%. After extrusion, the material is air-cooled at 22℃ and granulated (2.8mm in length) to obtain the finished cable material.
[0071] S4: Performance testing, employing a multi-index comprehensive evaluation algorithm, collecting 10 sets of sample data, and calculating the weight of each index. The overall evaluation score was 87 points, indicating that the product is qualified.
[0072] Performance test results of finished cable material: thermal conductivity Limiting oxygen index 33%, smoke density rating 43, vertical burning UL94 V-0 rating, tensile strength 12.5 MPa, elongation at break 310%, volume resistivity All meet the preset standards.
[0073] Comparative Example 1
[0074] A low-smoke halogen-free flame-retardant cable material, compared with Example 1, does not use graphene modification, the thermally conductive fillers are only boron nitride and aluminum oxide, and no algorithm is used to optimize the composition ratio and preparation parameters. The remaining components and preparation steps are the same as in Example 1.
[0075] Performance test results of finished cable material: thermal conductivity Limiting oxygen index 30%, smoke density rating 52, vertical combustion Grade, tensile strength 10.8 MPa, elongation at break 280%, volume resistivity The overall performance is significantly inferior to that of the product in Embodiment 1 of the present invention, and the performance difference between batches reaches 8%, indicating poor stability.
[0076] Comparative Example 2
[0077] A graphene-modified cable material, compared with Example 1, uses unmodified graphene and does not employ response surface methodology or particle packing density optimization. The remaining components and preparation steps are the same as in Example 1.
[0078] Performance test results of finished cable material: thermal conductivity Limiting oxygen index 31%, smoke density rating 48, vertical combustion Grade, tensile strength 11.2 MPa, elongation at break 290%, volume resistivity Due to the agglomeration of graphene, the dispersion uniformity is only 65%, resulting in limited improvement in thermal conductivity and flame retardancy. Its overall performance is inferior to that of the product in Example 1 of this invention.
[0079] The above embodiments and comparative examples demonstrate that the present invention, through graphene surface modification combined with multi-algorithm optimization of component ratios and preparation parameters, can effectively improve the thermal conductivity, flame retardancy, mechanical properties, and electrical insulation properties of cable materials, ensuring stable product performance, solving many defects existing in the prior art, and has good application prospects.
Claims
1. A graphene-modified high thermal conductivity, low smoke, halogen-free flame-retardant cable material, characterized in that, The material comprises a matrix resin, a graphene-modified thermally conductive and flame-retardant system, synergistic flame retardants, and processing aids. Each component is formulated according to a specific mass ratio, with the optimal ratio determined through a component optimization algorithm. The graphene-modified thermally conductive and flame-retardant system uses graphene as its core, which, after surface modification, is compounded with thermally conductive fillers to achieve a synergistic improvement in both thermal conductivity and flame-retardant properties. The cable material meets the following requirement: thermal conductivity ≥ It has a limiting oxygen index of ≥32%, a smoke density rating (SDR) of ≤45, and achieves UL94 V-0 rating for vertical combustion.
2. The cable material according to claim 1, characterized in that, The matrix resin is at least one of polyolefin resins, including polyethylene, ethylene-vinyl acetate copolymer, and ethylene-octene copolymer elastomer. The matrix resin accounts for 30-60 parts by mass of the total cable material, and its proportion is determined by a multiple linear regression optimization algorithm. The algorithm expression is as follows: ； in: The overall processing performance of the matrix resin is scored. This represents the percentage by mass of polyethylene. This refers to the mass percentage of the ethylene-vinyl acetate copolymer. This refers to the mass percentage of ethylene-octene copolymer elastomer. These are the regression coefficients for the corresponding percentages of resin by weight. Let be the error term of this regression model, and let the optimization objective be... ≥85 points (out of 100).
3. The cable material according to claim 1, characterized in that, The graphene-modified thermally conductive and flame-retardant system includes modified graphene and thermally conductive filler. The modified graphene is obtained by surface modification of raw graphene with a silane coupling agent. During the modification process, a response surface methodology (RSM) algorithm is used to determine the modification parameters. The RSM algorithm uses modification temperature, modification time, and coupling agent dosage as independent variables, and graphene dispersion uniformity as the response value to establish a quadratic regression model. ； in: A represents the graphene dispersion uniformity, B represents the modification temperature, C represents the modification time, and C represents the amount of silane coupling agent used. For constant terms, These are the regression coefficients of the corresponding independent variables and their interaction terms and quadratic terms, respectively, with a dispersion uniformity ≥90% after optimization.
4. The cable material according to claim 3, characterized in that, The thermally conductive filler is a blend of at least two of boron nitride, alumina, and silicon dioxide. The mass ratio of the thermally conductive filler to modified graphene is (5-15):
1. The blending ratio is optimized using a particle packing density algorithm, which calculates the packing density of fillers with different particle sizes. Where: ρ is the total bulk density of the thermally conductive filler. Let i be the volume fraction of the i-th type of thermally conductive filler. Let be the density of the i-th thermally conductive filler, and the optimization objective is to have a bulk density ≥ To construct a continuous heat conduction path.
5. The cable material according to claim 1, characterized in that, The synergistic flame retardant is a compound system of phosphorus-based flame retardant and inorganic flame retardant. The phosphorus-based flame retardant is at least one of red phosphorus masterbatch and ammonium polyphosphate, and the inorganic flame retardant is at least one of magnesium hydroxide and aluminum hydroxide. The mass fraction of the synergistic flame retardant accounts for 20-40 parts of the total mass of the cable material. Its compounding ratio is determined by a flame retardant efficiency optimization algorithm, the expression of which is: Where: η is the flame retardant efficiency. This refers to the mass fractions of phosphorus-based flame retardants. The mass fraction of the inorganic flame retardant. These are the fitting coefficients for the corresponding terms, and the optimized flame retardant efficiency is ≥88%.
6. The cable material according to claim 1, characterized in that, The processing aids include antioxidants, lubricants, and compatibilizers, with each component comprising 1-5 parts by mass of the total cable material. The weighting of each processing aid is determined using the analytic hierarchy process (AHP). A hierarchical structure is constructed, consisting of a target layer (processing performance), a criterion layer (thermal stability, flowability, compatibility), and a scheme layer (each processing aid), and weight vectors are calculated. Where: W is the weight vector of processing aid proportions, The weighting of antioxidants in the formulation. For the weight ratio of lubricant, The proportions of the compatibilizer are weighted, and This ensures that there is no obvious agglomeration during processing and that extrusion is smooth.
7. A method for preparing a graphene-modified high thermal conductivity, low smoke, halogen-free flame-retardant cable material, characterized in that, Includes the following steps: S1: Raw material pretreatment, drying the matrix resin to a moisture content ≤0.1%, and mixing the modified graphene with the thermally conductive filler uniformly to obtain a graphene-modified thermally conductive and flame-retardant system; S2: Mixing and compounding. The pretreated matrix resin, graphene-modified thermally conductive and flame-retardant system, synergistic flame retardant, and processing aids are added to the mixing equipment according to the specified ratio. A temperature gradient mixing algorithm is used to control the mixing parameters, with the mixing temperature set according to... Gradient heating, where: The initial mixing temperature is 110-120℃. The intermediate mixing temperature is 130-140℃. The final mixing temperature is 150-160℃, and the mixing time is determined by a viscosity feedback algorithm: when the viscosity of the mixing system reaches... Stop mixing at that time; S3: Extrusion granulation. The mixed material is fed into the extruder. The BP neural network algorithm is used to optimize the extrusion parameters. The extrusion temperature, screw speed and feeding speed are used as input parameters and the particle size uniformity is used as output parameter. The BP neural network model is trained. After optimization, the particle size uniformity is ≥92%. After extrusion, the material is air-cooled and pelletized to obtain the finished cable material. S4: Performance testing. A multi-index comprehensive evaluation algorithm is used to test the thermal conductivity, flame retardancy, and mechanical properties of the finished cable material. When all indicators meet the preset standards, it is a qualified product.
8. The preparation method according to claim 7, characterized in that, The mixing equipment mentioned in step S2 is an internal mixer with a rotor speed of 50-80 r / min. The viscosity feedback algorithm collects the viscosity of the mixing system in real time using an online viscometer, establishes a viscosity-time curve, and uses the first derivative method to determine the viscosity stability point: when... When the target viscosity is reached, mixing is stopped; where: μ is the real-time viscosity of the mixing system, t is the mixing time, and dμ / dt is the first derivative of viscosity with respect to time, representing the rate of viscosity change.
9. The preparation method according to claim 7, characterized in that, The BP neural network model described in step S3 includes an input layer, a hidden layer, and an output layer. The input layer has 3 neurons (extrusion temperature, screw speed, and feeding speed), the hidden layer has 10-15 neurons, and the output layer has 1 neuron (particle size uniformity). The gradient descent method is used to optimize the network weights. The training objective is to make the model prediction error ≤3%. After training, it is used to adjust the extrusion parameters in real time. The model prediction error is the percentage of the difference between the particle size uniformity predicted by the model and the actual detected particle size uniformity to the actual detected value.
10. The preparation method according to claim 7, characterized in that, The multi-index comprehensive evaluation algorithm described in step S4 uses the entropy weight method to determine the weight of each performance index and calculates the information entropy of each index. ,in: For the first The information entropy of each performance metric, k is the correction coefficient (k=1 / lnn), and n is the number of samples for performance testing. For the first The normalized value of the performance index corresponding to each sample; weights ,in: For the first The weights of each performance metric, Σ For all performance metrics The sum; comprehensive evaluation score Where: Y is the comprehensive evaluation score of the finished cable material, For the first The normalized score of each performance indicator is used to determine whether the product is qualified when Y ≥ 85.