A warm mix flame retardant composite asphalt modifier and methods of making and using the same

Abstract

The present application relates to the technical field of asphalt modifier, and particularly relates to a warm-mixing flame-retardant composite asphalt modifier and a preparation and use method thereof, which comprises the following steps: mixing a flame-retardant component and an interface modifier in a double-loop gas lifting reactor, heating to 80-120 DEG C, and reacting for 1-3 hours; then adding a warm-mixing component and an auxiliary agent, stirring and compounding at 140-160 DEG C for 0.5-2 hours to obtain a mixture; the mixture is granulated by a water ring cutting granulator to obtain the warm-mixing flame-retardant composite asphalt modifier; wherein the temperature of each section of the water ring cutting granulator is determined according to material physical property parameters, equipment operation parameters, quality targets and environmental data; the temperature of each section of the granulator is dynamically controlled based on the material physical properties, equipment state, quality targets and environmental data, so that the modified agent particle morphology is uniform and the performance is stable, and problems such as caking, adhesion or flame-retardant component failure caused by improper temperature are avoided.

Description

Technical Field

[0001] This invention relates to the technical field of asphalt modifiers, and in particular to a warm-mix flame-retardant composite asphalt modifier and its preparation and application methods. Background Technology

[0002] In recent years, the road construction industry has placed higher demands on the performance and production process of asphalt modifiers. Against this backdrop, warm mix and flame retardant technologies have developed rapidly. In the field of warm mix asphalt modifiers, their ability to reduce the mixing and compaction temperature of asphalt mixtures effectively reduces energy consumption and harmful gas emissions. Flame retardant asphalt modifiers, by adding compounds containing specific flame retardant elements, improve the fire resistance of asphalt and are widely used in tunnels, bridges, and other scenarios with high fire protection requirements. In terms of granulation processes, automated temperature control systems have improved granulation quality to a certain extent.

[0003] Despite some progress in existing technologies, significant shortcomings remain. On the one hand, existing warm-mix and flame-retardant asphalt modifiers have limited functionality and cannot simultaneously meet the dual requirements of warm-mix and flame retardancy, thus restricting their application in complex scenarios. On the other hand, existing granulation temperature control systems adjust the temperature based solely on preset parameters, failing to fully consider the impact of raw material properties and environmental changes on granulation quality, resulting in unstable product quality and consequently affecting the performance of asphalt mixtures.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the present invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a warm-mix flame-retardant composite asphalt modifier and its preparation and application method. Based on the physical properties of raw materials, equipment status, quality targets, and environmental data, the temperature of each section of the granulator is dynamically adjusted to ensure uniform particle morphology and stable performance of the modifier, avoiding problems such as clumping, adhesion, or failure of flame-retardant components due to improper temperature.

[0006] The first objective of this invention is to provide a method for preparing a warm-mix flame-retardant composite asphalt modifier. Based on the physical properties of raw materials, equipment status, quality targets, and environmental data, the temperature of each section of the granulator is dynamically controlled to ensure that the modifier particles have uniform morphology and stable performance, and to avoid problems such as agglomeration, adhesion, or failure of flame-retardant components caused by improper temperature.

[0007] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0008] A method for preparing a warm-mix flame-retardant composite asphalt modifier, comprising:

[0009] The flame retardant component and the interface modifier are mixed in a dual-circulation airlift reactor and heated to 80-120℃ for 1-3 hours.

[0010] Then add the warm mixing components and auxiliary agents, and stir and compound at 140-160℃ for 0.5-2 hours to obtain the mixture;

[0011] The mixture is granulated by a water ring granulator to obtain a warm-mix flame-retardant composite asphalt modifier; the temperature of each section of the water ring granulator is determined by the raw material properties, equipment operating parameters, quality targets and environmental data.

[0012] As a preferred embodiment of the present invention, by weight, the warm-mixing component is 30-70 parts, the flame retardant component is 10-40 parts, the interface modifier is 5-20 parts, and the auxiliary agent is 0.5-5 parts.

[0013] As a preferred embodiment of the present invention, the warm-stirring component is at least one of the following three: pyrolysis wax generated from the catalytic cracking of waste polyolefins, polyurethane prepolymer, zwitterionic surfactant, and alkyl glycoside. The cracking temperature range is 200-400℃, and the carbon chain length of the pyrolysis wax is C20-C50.

[0014] As a preferred embodiment of the present invention, the flame retardant component is at least one of phosphorus-based flame retardants, nitrogen-based flame retardants, and inorganic flame retardants.

[0015] As a preferred embodiment of the present invention, the interface modifier is a carbon nanotube-montmorillonite hybrid material or a thermoplastic elastomer, wherein the carbon nanotubes are generated in situ on the surface of montmorillonite through the thermal decomposition of waste polyolefins, and the mass ratio of carbon nanotubes is 5-15% of the total mass of the montmorillonite hybrid material.

[0016] As a preferred embodiment of the present invention, the auxiliary agent is at least one of a metallocene catalyst, a Ziegler-Natta catalyst, and an antioxidant.

[0017] As a preferred embodiment of the present invention, the method for determining the temperature of each section of the water ring granulator includes:

[0018] Collect historical raw material physical properties, equipment operating parameters, quality targets and environmental data, label the corresponding historical granulation temperature and measured quality data for each stage, form a dataset, and divide it into training set, validation set and test set;

[0019] Build a deep learning model and set constraints;

[0020] Construct a multi-task loss function, which includes the error between the predicted and actual temperature values ​​for each segment, the error between the predicted and actual mass values, and a penalty term for violating constraints.

[0021] The model is trained using a training set, and the loss function is minimized through an optimization algorithm.

[0022] Use the validation set to evaluate the model's performance, and adjust the model's hyperparameters based on the validation set results;

[0023] The trained model is then evaluated using a test set, and the model is optimized based on the evaluation results.

[0024] The optimized model is deployed to receive raw material physical property parameters, equipment operating parameters, quality targets and environmental data in real time, and output temperature setpoints and quality prediction values ​​for each segment.

[0025] As a preferred embodiment of the present invention, the constraints include at least the range of granulation temperatures for each stage, equipment operating parameters, and predicted quality values.

[0026] The second objective of this invention is to provide a warm-mix flame-retardant composite asphalt modifier prepared by the above method, which scientifically combines flame retardant and warm-mix components, thereby reducing the asphalt mixing temperature and enhancing flame retardant performance. Furthermore, it utilizes an interface modifier to improve component compatibility. Additionally, the water ring granulator, based on multi-parameter intelligent temperature control technology, can precisely regulate the temperature of each stage, ensuring uniform particle morphology and stable performance of the modifier.

[0027] The third objective of this invention is to provide a method for using a warm-mix flame-retardant composite asphalt modifier, wherein the dosage of the warm-mix flame-retardant composite asphalt modifier is 2-5% of the asphalt mass.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: 1) The dual-circulation airlift reactor greatly improves the mass transfer efficiency, avoids flame retardant agglomeration, and creates good conditions for the uniform mixing of each component. At the same time, with the help of auxiliary agents, the components are promoted to blend together and form a stable physicochemical combination. The raw material components work together to not only broaden the construction temperature window and reduce mixing energy consumption, but also construct a cross-scale flame retardant system, endow asphalt with good workability and long-term service performance, and solve many problems existing in traditional mechanical mixing.

[0029] 2) This invention innovatively constructs a multi-dimensional parameter-driven intelligent temperature control mechanism, which combines a deep learning model and a multi-task loss function to deeply couple raw material properties, equipment operation, quality targets and environmental data. This mechanism breaks through the limitations of the traditional temperature control mode, realizes adaptive adjustment of granulation temperature, significantly improves the consistency of modifier product quality and process applicability, effectively avoids a series of problems caused by improper temperature, and provides precise and controllable technical support for stable production.

[0030] 3) Warm-mix asphalt, flame retardants, interface modifiers, and additives are compounded in specific proportions, resulting in significant synergistic effects among the components. The warm-mix components reduce the viscosity of asphalt during construction, ensuring the workability and later strength of the mixture at low temperatures. The flame retardant components impart excellent fire resistance to the asphalt through condensed phase isolation and gas phase dilution. The interface modifiers build a bridge between the components and the asphalt, enhancing the compatibility of the system. The additives promote the physicochemical bonding between the components. The components work together to comprehensively optimize the performance of the asphalt, avoiding the drawbacks of single-component modification, achieving complementary advantages among the components, significantly improving the overall quality of the asphalt, and meeting diverse engineering application needs. Attached Figure Description

[0031] Figure 1 This is a schematic flowchart of a method for preparing a warm-mix flame-retardant composite asphalt modifier according to the present invention. Detailed Implementation

[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0033] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0035] Example

[0036] Reference Figure 1 This embodiment provides a method for preparing a warm-mix flame-retardant composite asphalt modifier, comprising:

[0037] S1 involves mixing the flame-retardant component and the interface modifier in a dual-circulation airlift reactor, heating it to 80-120℃, and reacting for 1-3 hours. The flame-retardant component and interface modifier are then added to the dual-circulation airlift reactor, and the reactor's heating system is activated. The temperature is monitored and adjusted to 80-120℃ in real time using a temperature sensor. Simultaneously, inert gas is introduced to create a gas-liquid circulation, ensuring full contact between the two components within the reactor. During mixing, the circulation speed is maintained by a stirring rate controller to ensure uniform dispersion of the flame-retardant component in the interface modifier. The reaction time is determined based on the component dispersion. The dual-circulation airlift reactor utilizes gas lifting force to achieve material circulation, improving mass transfer efficiency by over 30% compared to traditional stirred reactors. This avoids localized agglomeration of the flame-retardant component, laying a foundation for uniform mixing in subsequent warm-stirred compounding.

[0038] S2 is then added to the warm-stirring component and auxiliary agent, and stirred and compounded at 140-160℃ for 0.5-2 hours to obtain a mixture; the warm-stirring component and auxiliary agent are added to the dual-circulation airlift reactor, and the temperature is raised to 140-160℃ by the heating system. The mechanical stirring device is started and the stirring speed is controlled at 150-250 rpm to fully integrate the warm-stirring component with the previous reaction product. During this process, the auxiliary agent plays a dispersing and stabilizing role, promoting the formation of stable physicochemical bonds between the components. The reaction time is adjusted according to the change in material viscosity, and finally a uniform semi-solid mixture is formed.

[0039] The S3 mixture is granulated by a water ring granulator to obtain a warm-mix flame-retardant composite asphalt modifier; the temperature of each section of the water ring granulator is determined by the raw material properties, equipment operating parameters, quality targets and environmental data.

[0040] The physical properties of raw materials include melting point, viscosity, thermal stability, moisture content, particle size distribution, and temperature at which they enter the water ring granulator. These parameters reflect the physical state and flow characteristics of the raw materials at different temperatures and are the basis for determining the granulation temperature.

[0041] The operating parameters of the equipment include the screw speed, feed rate, cutter speed, die diameter and cooling water pressure of the water ring granulator. The screw speed affects the shear heating of the material, the feed rate determines the heat load per unit time, the cutter speed and die diameter together affect the temperature requirements during pellet forming, and the cooling water pressure is related to the temperature drop rate.

[0042] The quality objectives include the particle size accuracy, surface finish, impact strength and melt flow index of the modifier of this invention. Different quality indicators are sensitive to temperature to different degrees. For example, when a high surface finish is required, it is necessary to avoid material stringing caused by too low a temperature or particle sticking caused by too high a temperature.

[0043] Environmental data includes ambient temperature, relative humidity, and atmospheric pressure. Ambient temperature affects the heat dissipation efficiency of the equipment (e.g., in winter, the temperature of the heating section needs to be increased to compensate for heat loss), humidity affects the hygroscopicity of raw materials (at high humidity, auxiliary agents are prone to caking, so the temperature of the drying section needs to be increased), and air pressure affects the boiling point of water (at high altitudes, the temperature of the cooling section needs to be adjusted to avoid boiling).

[0044] This invention deeply couples the temperatures of each section of the water ring granulator with raw material properties, equipment operating parameters, quality targets, and environmental data to form a multi-dimensional parameter-driven intelligent temperature control mechanism: raw material properties define feasible temperature ranges to ensure component stability; equipment parameters quantify heat load to achieve dynamic temperature compensation; quality targets are used to deduce key temperature control nodes to ensure granule forming quality; and environmental data corrects external interference in real time to maintain process stability. This integrated mechanism breaks through the traditional coarse control mode of preset temperature, enabling the granulation temperature to be adaptively adjusted according to raw material characteristics, equipment status, quality requirements, and environmental changes. It fundamentally avoids problems such as particle adhesion, uneven particle size, and component failure caused by improper temperature, significantly improving the consistency of modifier product quality and process applicability, and providing precise and controllable technical support for the stable production of high-performance asphalt modifiers.

[0045] In some embodiments of the present invention, by weight, the warm-mixing component is 30-70 parts, the flame retardant component is 10-40 parts, the interface modifier is 5-20 parts, and the auxiliary agent is 0.5-5 parts.

[0046] By using the above proportions, each component can achieve synergistic effects while fulfilling its own function, significantly improving the overall performance of the modifier. 30-70 parts of warm mix components ensure that cracked wax and other components reduce the viscosity of asphalt during construction, broaden the construction temperature window, and impart good workability and later strength to the mixture; 10-40 parts of flame retardant ensure the effective construction of a flame retardant system that isolates the condensed phase and dilutes the gas phase, giving the material reliable fire resistance; 5-20 parts of interface modifier, with the help of carbon nanotube-montmorillonite hybrid materials or thermoplastic elastomers, enhance the compatibility of each component with asphalt; 0.5-5 parts of auxiliary agents, such as metallocene catalysts, promote the physicochemical reactions between the components and ensure uniform dispersion. This ratio avoids performance imbalance caused by too much or too little of a certain component, and controls costs through reasonable dosage, achieving an optimal balance between performance and cost.

[0047] In some embodiments of the present invention, the warm-mix component is at least one of the following: pyrolysis wax generated from the catalytic cracking of waste polyolefins, polyurethane prepolymer, zwitterionic surfactant, and alkyl glycoside. The cracking temperature range is 200-400℃, and the carbon chain length of the pyrolysis wax is C20-C50. More specifically, the pyrolysis wax, as the core viscosity-reducing component, with its 200-400℃ cracking process and C20-C50 carbon chain length, causes the wax molecules to be in a semi-molten state within the warm-mix construction temperature range of 120-160℃. By incorporating the asphalt colloidal structure, it reduces the high-temperature viscosity and decreases the mechanical energy consumption during mixing. The long-chain structure forms a flexible bridge during the cooling process, maintaining the plasticity of the mixture and avoiding difficulties in aggregate adhesion at low temperatures.

[0048] When polyurethane prepolymer, zwitterionic surfactant, alkyl glycoside and pyrolysis wax are compounded, the surface active ingredients (zwitterionic surfactant and alkyl glycoside) reduce the interfacial tension between asphalt and aggregate, enabling the asphalt to spread quickly and coat the aggregate at lower temperatures, reducing the problem of uneven dispersion caused by insufficient temperature; the polyurethane prepolymer forms hydrogen bonds or physical crosslinks with the polar groups in the asphalt, compensating for the weakening of the bonding strength that may be caused by the decrease in temperature during the warm mixing process, and preventing defects such as peeling and loosening after the mixture is formed.

[0049] The "physical viscosity reduction" of pyrolysis wax and the "interface optimization" of surfactants together broaden the construction temperature window (allowing the mixing temperature to be 10-20℃ lower than that of traditional modified asphalt), while the "structural reinforcement" of polyurethane prepolymer avoids the road performance degradation that may be caused by a single viscosity-reducing component, forming a chain effect of "reducing mixing energy consumption - ensuring construction uniformity - maintaining later strength". This combination not only utilizes the low-cost resource utilization advantage of waste polyolefin pyrolysis wax, but also makes up for the performance shortcomings of single wax-based warm mix agents through functional additives. While saving energy and protecting the environment, it ensures that the modified asphalt still has excellent workability and long-term service performance under warm mix conditions.

[0050] In some embodiments of the present invention, the flame retardant component is at least one of phosphorus-based flame retardants, nitrogen-based flame retardants, and inorganic flame retardants;

[0051] In this invention, flame-retardant components (phosphorus-based, nitrogen-based, and inorganic flame retardants) and warm-mix components (pyrolysis wax, polyurethane prepolymer, surfactants, etc.) form a synergistic effect through multiple mechanisms: the surfactants in the warm-mix components enhance the dispersibility of flame retardants in asphalt due to their amphiphilic structure, preventing agglomeration and improving flame-retardant efficiency; pyrolysis wax, as a carbon source, synergistically promotes char layer formation with phosphorus-based flame retardants; polyurethane prepolymer and nitrogen-based flame retardants construct an elastic network to stabilize the gas-phase flame-retardant gas, forming a cross-scale flame-retardant system of "condensed phase barrier - gas phase dilution".

[0052] In some embodiments of the present invention, the interface modifier is a carbon nanotube-montmorillonite hybrid material or a thermoplastic elastomer, wherein the carbon nanotubes are generated in situ on the surface of montmorillonite through the thermal decomposition of waste polyolefins, and the mass ratio of carbon nanotubes is 5-15% of the total mass of the montmorillonite hybrid material; more specifically, montmorillonite (sodium-based, particle size ≤100nm) is dispersed in the decomposition reaction system of waste polyolefins (such as waste polyethylene), and the decomposition temperature is controlled within 200-400℃. During the catalytic decomposition process of waste polyolefins, the carbon atom clusters generated by chain breakage are deposited on the active sites on the surface of montmorillonite, and carbon nanotubes are grown in situ. By adjusting the decomposition time, the mass ratio of carbon nanotubes is controlled within the range of 5-15%, forming a black uniformly dispersed hybrid material;

[0053] In-situ generated carbon nanotubes are tightly anchored on the surface of montmorillonite. Their high aspect ratio (1000-10000) and the lamellar structure of montmorillonite (1nm thick) form a "nanoscale reinforcing network". In the dual-circulation reactor, the gas-liquid circulation allows the hybrid material to come into full contact with the flame retardant. The surface defect sites of the carbon nanotubes adsorb flame retardant molecules, while the montmorillonite lamellars fix the flame retardant through interlayer van der Waals forces, avoiding agglomeration caused by polarity differences in traditional mechanical mixing. At the same time, the oleophilic surface (carbon nanotubes) and hydrophilic edge (montmorillonite hydroxyl groups) of the hybrid material form an "amphiphilic interface" in the asphalt, promoting the compatibility of warm-mix components (such as pyrolysis wax) with the asphalt matrix.

[0054] In some embodiments of the present invention, the auxiliary agent is at least one of a metallocene catalyst, a Ziegler-Natta catalyst, and an antioxidant;

[0055] Additives help promote the integration of components, enabling warm-mix components, flame-retardant components and interface modifiers to combine better, and improving the overall uniformity and stability of the modifier.

[0056] In some embodiments of the present invention, the method for determining the temperature of each section of the water ring granulator includes:

[0057] Historical raw material properties, equipment operating parameters, quality targets, and environmental data are collected, and corresponding historical granulation temperatures and measured quality data for each stage are labeled to form a dataset. More specifically, multiple sensors are installed in the workshop to continuously collect historical raw material properties, such as using a melting point meter to monitor the melting point of pyrolysis wax and a rotational viscometer to measure the viscosity of the mixture; equipment operating parameters, such as screw speed and feed rate, are collected using built-in sensors; quality targets are clearly defined, such as determining the particle size accuracy based on product standards; environmental data is collected using temperature and humidity sensors and barometers; and corresponding historical granulation temperatures for each stage, as well as measured quality data obtained through quality testing equipment, such as particle hardness and impact strength, are recorded.

[0058] The data was divided into training, validation, and test sets. More specifically, the collected data was divided into training, validation, and test sets according to a ratio of 70%, 20%, and 10%, respectively, and the data was normalized to ensure that the data were within a reasonable numerical range.

[0059] To build a deep learning model, specifically, a Long Short-Term Memory (LSTM) network model is built using the Keras framework in Python. The model contains one input layer, which takes the processed multi-dimensional data as input; three hidden layers, each containing 64 LSTM units, are set to fully learn the time series features in the data; and a fully connected layer is added as the output layer to output the temperature setpoint and quality prediction values ​​for each segment.

[0060] By setting constraints and combining them with model training, the model is guided to find the optimal solution under multiple constraints. The model output not only has theoretical value but also fits the actual production scenario.

[0061] Construct a multi-task loss function, which includes the error between the predicted and actual temperature values ​​for each segment, the error between the predicted and actual mass values, and a penalty term for violating constraints.

[0062] More specifically, the multi-task loss function is:

[0063] L=αL t +βL q +γL c ;

[0064] Where L represents the total loss function value, L t This is the error function between the predicted and actual temperature values ​​for each segment, used to measure the accuracy of temperature prediction. The calculation formula is:

[0065]

[0066] Where n is the number of samples, This is the predicted temperature value for the i-th sample. It is the actual temperature value of the i-th sample;

[0067] L q It is the error function between the predicted and actual quality values, used to evaluate the accuracy of quality prediction. The calculation formula is:

[0068]

[0069] Where m is the number of quality samples. It is the predicted quality value of the j-th sample. It is the actual quality value of the j-th sample;

[0070] Lc This is a penalty term for violating constraints, used to ensure that the model output meets the set constraints, denoted as C. k (k = 1, 2, ..., p, where p is the number of constraints), when a certain constraint C k When violated, L c Represented as:

[0071]

[0072] Where, λ k It corresponds to constraint C. k The penalty coefficient, I(C) k ) is an indicator function, when C k If violated, I(Ck) = 1; otherwise, I(Ck) = 0.

[0073] α, β, and γ are weighting coefficients used to balance the losses of the three parts. By adjusting their values, the importance of different parts can be emphasized according to the needs of the specific problem. Such a multi-task loss function can comprehensively consider temperature prediction error, quality prediction error, and the satisfaction of constraints, enabling the model to optimize multiple objectives simultaneously during training, thereby improving the model's performance and generalization ability, and better adapting to the prediction requirements of temperature and product quality in each section of the water ring cutting granulator in the preparation of warm-mix flame-retardant composite asphalt modifiers.

[0074] The model is trained using a training set, and the loss function is minimized by an optimization algorithm. More specifically, the model is trained using a training set, the Adam (Adaptive Moments Estimation) optimization algorithm is selected, the initial learning rate is set to 0.001, and during the training process, the learning rate is decayed by 10% after every 50 training rounds by adjusting the learning rate scheduler until the loss function converges and stabilizes.

[0075] The model's performance is evaluated using a validation set, and the model's hyperparameters are adjusted based on the validation set results; more specifically, hyperparameters include the number of hidden layer neurons, the number of training epochs, batch size, etc.

[0076] The trained model is then evaluated using a test set, and the model is optimized based on the evaluation results.

[0077] The optimized model is deployed to receive raw material properties, equipment operating parameters, quality targets, and environmental data in real time, and outputs temperature setpoints and quality predictions for each segment. More specifically, the optimized model is exported as an H5 file and integrated into the control system of the water ring granulator. The control system collects raw material properties, equipment operating parameters, quality targets, and environmental data in real time, standardizes the data, and inputs it into the model. The model quickly calculates and outputs temperature setpoints and quality predictions for each segment, achieving precise control of the granulation process.

[0078] The above method achieves data-driven precise granulation temperature control by constructing an intelligent temperature control system. First, sensors collect real-time data on raw material properties, equipment operation, quality targets, and the environment, providing multi-dimensional input for model training. Then, an LSTM deep learning model is built, combined with a multi-task loss function, to simultaneously optimize temperature prediction error, quality prediction accuracy, and constraint satisfaction. This allows the model to capture data temporal characteristics while balancing multiple constraints in production. The introduction of constraints ensures safe equipment operation and product quality compliance, avoiding the blindness and lag of traditional experience-based control. This method is deeply integrated with the preparation process, allowing the temperature of each section of the water ring granulator to be dynamically adjusted according to raw material characteristics, environmental changes, and quality requirements. This significantly improves the uniformity and stability of modifier particles, effectively solving problems such as particle adhesion and uneven particle size caused by improper temperature. It provides an efficient, intelligent, and reliable temperature control solution for industrial production, ensuring the consistency of modifier performance and the controllability of the production process from the source.

[0079] In some embodiments of the present invention, the constraints include at least the range of granulation temperatures for each stage, equipment operating parameters, and predicted quality values;

[0080] The above constraints, combined with the multi-task loss function, form a closed-loop control logic of "temperature-equipment-quality": temperature constraints ensure process feasibility, equipment constraints guarantee production reliability, and quality constraints lock in product objectives. The synergy of these three forces the model to simultaneously meet production safety, equipment lifespan, and quality standards when searching for the optimal solution, avoiding practical application conflicts caused by single-index optimization in traditional methods. This multi-dimensional constraint mechanism not only improves the engineering practicality of the model output, but also deeply integrates data-driven intelligent control with actual production rules by transforming production experience into calculable constraints, achieving seamless integration between theoretical prediction and engineering practice, and ultimately significantly improving the stability of the granulation process and the consistency of product quality.

[0081] The method of using the above-mentioned warm-mix flame-retardant composite asphalt modifier of the present invention involves adding the warm-mix flame-retardant composite asphalt modifier at a dosage of 2-5% of the asphalt mass. Adding the warm-mix flame-retardant composite asphalt modifier at 2-5% of the asphalt mass can effectively optimize asphalt performance while controlling costs. This dosage range allows the modifier to be fully dispersed in the asphalt, significantly improving the warm-mix and flame-retardant properties of the asphalt, reducing construction temperature, reducing energy consumption, and enhancing fire resistance. If the dosage is less than 2%, the modification effect is not significant; if it is higher than 5%, it not only wastes materials and increases costs, but may also affect the asphalt road performance due to system imbalance. A dosage of 2-5% can balance performance improvement and economic cost, maximizing benefits.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for preparing a warm-mix flame-retardant composite asphalt modifier, characterized in that, include: The flame retardant component and the interface modifier are mixed in a dual-circulation airlift reactor and heated to 80-120℃ for 1-3 hours. Then add the warm mixing components and auxiliary agents, and stir and compound at 140-160℃ for 0.5-2 hours to obtain the mixture; The mixture is granulated by a water ring granulator to obtain the warm-mix flame-retardant composite asphalt modifier; wherein, the temperature of each section of the water ring granulator is determined by the raw material physical property parameters, equipment operating parameters, quality targets and environmental data; By weight, the warm-mix component is 30-70 parts, the flame retardant component is 10-40 parts, the interface modifier is 5-20 parts, and the auxiliary agent is 0.5-5 parts. The warm-stirring component is at least one of the following: pyrolysis wax generated from the catalytic cracking of waste polyolefins, polyurethane prepolymer, zwitterionic surfactant, and alkyl glycoside. The cracking temperature range is 200-400℃, and the carbon chain length of the pyrolysis wax is C20-C50. The interface modifier is a carbon nanotube-montmorillonite hybrid material or a thermoplastic elastomer; The auxiliary agent is at least one of a metallocene catalyst, a Ziegler-Natta catalyst, and an antioxidant. The method for determining the temperature of each section of the water ring granulator includes: Collect historical raw material physical properties, equipment operating parameters, quality targets and environmental data, label the corresponding historical granulation temperature and measured quality data for each stage, form a dataset, and divide it into training set, validation set and test set; Build a deep learning model and set constraints; Construct a multi-task loss function, which includes the error between the predicted and actual temperature values ​​for each segment, the error between the predicted and actual mass values, and a penalty term for violating constraints. The model is trained using a training set, and the loss function is minimized through an optimization algorithm. Use the validation set to evaluate the model's performance, and adjust the model's hyperparameters based on the validation set results; The trained model is then evaluated using a test set, and the model is optimized based on the evaluation results. The optimized model is deployed to receive raw material physical property parameters, equipment operating parameters, quality targets and environmental data in real time, and output temperature setpoints for each segment.

2. The preparation method of the warm-mix flame-retardant composite asphalt modifier as described in claim 1, characterized in that, The flame retardant component is at least one of phosphorus-based flame retardants, nitrogen-based flame retardants, and inorganic flame retardants.

3. The preparation method of the warm-mix flame-retardant composite asphalt modifier as described in claim 1, characterized in that, The constraints include at least the range of granulation temperatures for each stage, equipment operating parameters, and predicted quality values.

4. A warm-mix flame-retardant composite asphalt modifier prepared by the preparation method of the warm-mix flame-retardant composite asphalt modifier according to any one of claims 1-3.

5. A method of using the warm-mix flame-retardant composite asphalt modifier as described in claim 4, characterized in that, The dosage of the warm-mix flame-retardant composite asphalt modifier is 2-5% of the asphalt mass.

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