An asphalt reverse regulation method based on chemical composition-multistage structure-rheological property multi-parameter coupling

Abstract

This invention discloses a method for reverse regulation of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties. The method acquires multi-dimensional data such as chemical composition parameters (component content and elemental content), multi-level structural characteristics (molecular packing, micelles, and colloidal structure), and rheological property indices (zero-shear viscosity, low-temperature complex modulus, temperature sensitivity, and crossover frequency) of asphalt. Based on this, statistical screening and correlation analysis methods are used to establish the multi-parameter coupling relationship between the chemical composition, multi-level structure, and rheological properties of asphalt, identify the main controlling factors, construct their regulation paths, and establish a reverse regulation framework that infers the structure and composition from the target rheological properties. This invention complements traditional empirical trial-and-error methods and design methods based on single performance indicators, enabling systematic analysis and comprehensive regulation of asphalt material properties from a holistic perspective of multi-parameter coupling, thus improving the scientific rigor and reliability of on-demand material design.

Description

Technical Field

[0001] This invention belongs to the field of asphalt material design technology, and relates to a method for regulating the performance of asphalt materials, specifically a reverse regulation method for asphalt based on the coupling of multiple parameters such as chemical composition, multi-level structure and rheological properties. Background Technology

[0002] Asphalt binders are widely used in road engineering due to their excellent viscoelasticity, cohesion, and durability. Their rheological properties are influenced by the interaction and coupling effects of their complex chemical composition and multi-level structure (including molecular packing structure, nano-aggregate structure, and microphase distribution structure), resulting in a complex nonlinear and time-dependent mechanical response under external stimuli such as temperature, shear, and aging.

[0003] Current research and development paradigms for asphalt materials still primarily rely on experience-based trial-and-error methods and simplified evaluation indicators, neglecting the complex coupling relationship between asphalt chemical composition, multi-level structure, and rheological properties. Existing composition-structure-property relationship models established based on experimental parameters such as composition, polarity, and functional groups often suffer from fragmentation, narrow applicability, and difficulty in unified generalization, leading to difficulties in accurately predicting and directionally improving material performance under complex service conditions. Therefore, there is an urgent need to construct a more accurate and universally applicable method for regulating the performance of asphalt materials, starting from the coupling mechanism of chemical composition, multi-level structure, and rheological properties. Summary of the Invention

[0004] To address the problems of existing asphalt material design relying on experience, fragmented composition-structure-performance relationships, and insufficient design accuracy and versatility, this invention provides a reverse control method for asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties. This method achieves efficient and precise asphalt formulation optimization by analyzing and controlling the coupling relationship between asphalt's chemical composition, molecular structure, and rheological properties.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A method for reverse control of asphalt based on multi-parameter coupling of chemical composition, hierarchical structure, and rheological properties includes the following steps:

[0007] Step 1: Separate the saturated components (Sa), aromatic components (Ar), resins (Re), and asphaltenes (As) in asphalt and determine their contents; determine the contents of carbon (C), hydrogen (H), nitrogen (N), and sulfur (S) in asphalt, and calculate the oxygen (O) content;

[0008] Step 2: Conduct a wide-angle X-ray scattering (WAXS) test and two small-angle X-ray scattering (SAXS) tests on the asphalt;

[0009] Step 3: Remove background and noise from one WAXS and two SAXS curves; perform multi-peak fitting on the WAXS curve based on the Lorentz function, quantify the structural information reflected by each peak, and calculate the molecular packing distance based on the fitting parameters of each peak. Molecular packing size and the relative content of this structure Based on the analysis of the SAXS curve using Porod and Guinier's laws, and by applying the Beaucage unified model for curve fitting, the micelle structure parameters of asphalt (zero-angle scattering intensity) were obtained. Radius of gyration and fractal dimension ) and colloidal structure parameters (fractal dimension) );

[0010] Step 4: Conduct steady-state flow scanning tests on asphalt using a dynamic shear rheometer to establish the steady-state shear viscosity curve of asphalt and extract the zero-shear viscosity of asphalt. As an indicator for evaluating high-temperature performance, the complex modulus of asphalt was established by conducting dynamic frequency scanning tests on asphalt using a dynamic shear rheometer and combining the time-temperature equivalence principle. Storage modulus and loss modulus Master curve; extract 1.6×10 -1 ~2.8×10 7 Slope of the complex modulus of asphalt over a wide frequency band of rad / s As an indicator for evaluating temperature sensitivity, a wide frequency range of 2.8 × 10⁻⁶ was extracted. 7 Complex modulus at rad / s As an indicator for evaluating low-temperature performance, the wide frequency at the intersection of storage modulus and loss modulus is extracted as the crossover frequency. , as an indicator for evaluating the dominance of elasticity;

[0011] Step 5: Perform statistical distribution analysis on the parameters obtained from the experimental methods in Steps 1 to 4, and remove extreme outliers; use the coefficient of variation (CV) screening method to remove parameters with low discrimination between samples (CV < 0.05); use the correlation screening method to remove highly correlated parameters, and finally perform subsequent correlation analysis on the remaining parameters;

[0012] Step 6: Based on the Spearman rank correlation coefficient method, establish correlation heatmaps between chemical composition parameters and multi-level structure parameters, and between multi-level structure parameters and rheological property parameters; determine the parameter with the strongest correlation to the target parameter as the main control factor, and distinguish its promoting or inhibiting effect according to its correlation direction, thereby classifying the main control factor into main control promoting factor or main control inhibiting factor, and the remaining parameters with correlation are determined as auxiliary factors.

[0013] Step 7: The path formed by sequentially connecting the main control factors is called the main control regulation path, which represents the most important performance regulation mechanism; the path formed by the participation of auxiliary factors is called the auxiliary regulation path, which performs coordinated optimization regulation based on the regulation of the main control path.

[0014] Compared with the prior art, the present invention has the following advantages:

[0015] 1. Based on the multi-parameter coupling relationship between asphalt chemical composition, multi-level structure and rheological properties, by identifying the main controlling factors and their regulation paths, a reverse regulation framework is constructed to infer the structure and composition from the target rheological properties, so that the design of asphalt materials can be transformed from empirical trial and error to on-demand regulation.

[0016] 2. This invention complements traditional trial-and-error methods and design methods based on single performance indicators. It can systematically analyze and comprehensively control the performance of asphalt materials from a holistic perspective of multi-parameter coupling, thereby improving the scientificity and reliability of on-demand material design.

[0017] 3. This invention, through the combined WAXS and SAXS tests, systematically obtains the molecular packing, micelle, and colloidal multi-level structural characteristics of asphalt, achieving continuous characterization from the molecular scale to the nanoscale, and providing a reliable basis for the mechanism analysis and on-demand control of asphalt rheological properties. Attached Figure Description

[0018] Figure 1 The WAXS and SAXS scattering intensity curves of asphalt are shown. (a) Structural features characterized by one WAXS curve and two SAXS curves, (b) WAXS curve of asphalt sample, and (c) SAXS curve of asphalt sample.

[0019] Figure 2 The figures show the steady-state shear viscosity curve and the master curve of dynamic shear modulus of asphalt. (a) Steady-state shear viscosity curve, (b) Master curve of dynamic shear modulus.

[0020] Figure 3 A correlation heatmap for correlation filtering.

[0021] Figure 4 This is a heatmap showing the correlation between asphalt composition, structure, and performance parameters.

[0022] Figure 5 This is a schematic diagram of the asphalt composition-structure-performance regulation path. Detailed Implementation

[0023] The technical solution of the present invention will be further described below with reference to the accompanying drawings, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention that do not depart from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention.

[0024] This invention provides a method for reverse regulation of asphalt based on multi-parameter coupling of chemical composition, hierarchical structure, and rheological properties. The method acquires multi-dimensional data on asphalt's chemical composition parameters (component content and elemental content), hierarchical structural characteristics (molecular packing, micelles, and colloidal structure), and rheological performance indices (zero-shear viscosity, low-temperature complex modulus, temperature sensitivity, and crossover frequency). Based on this, statistical screening and correlation analysis methods are used to establish the multi-parameter coupling relationship between the chemical composition, hierarchical structure, and rheological properties of asphalt, identify the main controlling factors, construct their regulation paths, and establish a reverse regulation framework that infers structure and composition from target rheological properties. This method can improve the efficiency and controllability of asphalt material development, reduce reliance on trial and error, and has good pertinence and applicability. Figure 5 As shown, the specific steps are as follows:

[0025] Step 1: The saturated fraction (Sa), aromatic fraction (Ar), resin (Re), and asphaltenes (As) in asphalt were separated and their contents determined using the Corbett method based on column chromatography. The contents of carbon (C), hydrogen (H), nitrogen (N), and sulfur (S) in asphalt were determined using an elemental analyzer based on the combustion method, and the oxygen (O) content was calculated using the difference method.

[0026] Step 2: A single-segment WAXS and two-segment SAXS tests were conducted on asphalt using the two-dimensional (grazing incidence) small-angle / wide-angle X-ray scattering beamline (2D(GI) SAXS / WAXS) Xenocs xeus3 at the Nanovacuum Interconnection Experimental Station of the Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences. For the WAXS test, the distance between the sample and the detector was set to 50 mm, and the data acquisition time was 150 s. For the two-segment SAXS tests, the distances between the sample and the detector were set to 800 mm and 4600 mm, respectively, and the data acquisition time was 300 s.

[0027] Step 3: After removing background and noise from one WAXS and two SAXS curves, connect them to form a continuous curve (e.g., Figure 1 (as shown in (a)). Multi-peak fitting of the WAXS curve based on the Lorentz function was performed to quantitatively analyze the structural information reflected by each peak (e.g., ...). Figure 1 (as shown in (b)). The Lorentz function is:

[0028]

[0029] In the formula, Baseline value (mm) -1 ), Peak area (Å) -1 mm -1 ), Half-width at half maximum (Å) -1 ), Peak position (Å) -1 ).

[0030] The molecular packing distance can be calculated based on the fitting parameters of each peak mentioned above. ), molecular packing size ( ), and the relative content of this structure ( ):

[0031]

[0032]

[0033]

[0034] In the formula, Peak position (Å) -1 ), Half-width at half maximum (Å) -1 ), For the first Peak area (Å) -1 mm -1 ), This represents the total peak area.

[0035] Of the five characteristic peaks of the WAXS curve, ≈2.19~2.22 Å, corresponding to the locally ordered structure inside the molecule; ≈3.58~3.63Å, corresponding to the close stacking of layers between asphaltene molecules, similar to the layered stacking of graphene sheets; ≈4.11~4.33 Å, corresponding to parallel packing between molecules; ≈4.81~5.14 Å, corresponding to the intermolecular packing angle; ≈8.63~9.03Å, corresponding to the lateral packing of asphaltene molecules.

[0036] Analysis of SAXS curves based on Porod and Guiniier's laws (e.g.) Figure 1 (c) is shown), and the Beaucage unified model is used for curve fitting to obtain the micelle structure parameters of asphalt (zero-angle scattering intensity). Radius of gyration and fractal dimension ) and colloidal structure parameters (fractal dimension) ):

[0037]

[0038] In the formula, , Zero-angle scattering intensities (mm) for colloids and micelles, respectively. -1 ), , Å represents the radius of gyration of the colloid and micelle, respectively. , These are the Porod region scaling factors for colloids and micelles, respectively. , are the fractal dimensions of colloids and micelles, respectively.

[0039] Step 4: Conduct steady-state flow sweep tests on asphalt using a dynamic shear rheometer (temperature 60℃, shear rate 0.001~1000 s⁻¹). - ¹), establish the steady-state shear viscosity curve of asphalt, and extract the zero-shear viscosity of asphalt ( )(like Figure 2 (a) shows). Dynamic frequency sweep tests were conducted on asphalt using a dynamic shear rheometer (temperatures 0℃, 10℃, 20℃, 30℃, 40℃, 50℃, 60℃ and 70℃, angular frequency 100~0.01 rad / s). The complex modulus of asphalt was established based on the time-temperature equivalence principle. Storage modulus ( ) and loss modulus ( The master curve. For SHRP samples with only dynamic shear test data, the complex viscosity master curve is obtained through the modulus-viscosity conversion relationship, and then fitted to obtain... .use As a high-temperature performance evaluation indicator, the modulus-viscosity conversion relationship is as follows:

[0040]

[0041]

[0042]

[0043] In the formula: Storage modulus (Pa). The loss modulus is (Pa). The viscosity is the complex viscosity (Pa·s). The viscosity is the dynamic viscosity (Pa·s). The viscosity is the loss viscosity (Pa·s).

[0044] like Figure 2 As shown in (b), the wide frequency range (1.6 × 10) is extracted. -1 ~2.8×10 7 The slope of the complex modulus of asphalt (rad / s) (This is used as an indicator to evaluate temperature sensitivity.) A wide frequency range of 2.8 × 10⁻⁶ was extracted. 7 Complex modulus at rad / s ( ), used as an indicator for evaluating low-temperature performance. The wide frequency range at the intersection of storage modulus and loss modulus is extracted as the crossover frequency ( ). ( ), used as an indicator to evaluate the dominance of elasticity.

[0045] Step 5: Perform statistical distribution analysis on the parameters obtained by the above experimental methods, and remove extreme outliers; use the coefficient of variation (CV) screening method to remove parameters with low discrimination between samples (CV < 0.05); use correlation screening methods (such as... Figure 3 As shown in the figure, highly correlated parameters are removed, and the remaining parameters are then subjected to subsequent correlation analysis. The formula for calculating CV is:

[0046]

[0047] In the formula: Let the standard deviation of the parameter be . This represents the average value of the parameters.

[0048] Step Six: Based on the Spearman rank correlation coefficient method, establish correlation heatmaps between chemical composition parameters and multi-level structural parameters, and between multi-level structural parameters and rheological performance parameters (e.g., Figure 4 As shown in the figure, the parameter with the strongest correlation to the target parameter is identified as the principal controlling factor, and its promoting or inhibiting effect is distinguished according to the direction of its correlation. Thus, the principal controlling factor is classified as a principal promoting factor or a principal inhibiting factor. The remaining parameters with correlation are identified as auxiliary factors. The formula for calculating the Spearman rank correlation coefficient is:

[0049]

[0050] In the formula: The difference in rank between paired values. This represents the number of samples.

[0051] Step Seven: As Figure 5As shown, the path formed by sequentially connecting the main control factors is called the main control regulation path, representing the most important performance regulation mechanism; the path formed by the participation of auxiliary factors is called the auxiliary regulation path, which can be used for synergistic optimization regulation based on the main control path. In the process of regulating asphalt performance, directional regulation can be implemented first based on the main control path, supplemented by fine-tuning through auxiliary paths to improve the regulation effect.

Claims

1. A method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties, characterized in that... The method includes the following steps: Step 1: Separate the saturated components, aromatic components, resins and asphaltenes in the asphalt and determine their contents; determine the contents of carbon, hydrogen, nitrogen and sulfur in the asphalt, and calculate the oxygen content; Step 2: Conduct a wide-angle X-ray scattering (WAXS) test and two small-angle X-ray scattering (SAXS) tests on the asphalt. Step 3: Remove background and noise from one WAXS and two SAXS curves; perform multi-peak fitting on the WAXS curve based on the Lorentz function, quantify the structural information reflected by each peak, and calculate the molecular packing distance based on the fitting parameters of each peak. Molecular packing size and the relative content of this structure ; SAXS curves were analyzed based on Porod and Guinier's laws, and curve fitting was performed using the Beaucage unified model to obtain the micelle structure parameters and colloidal structure parameters of asphalt. Step 4: Conduct steady-state flow scanning tests on the asphalt to establish its steady-state shear viscosity curve and extract its zero-shear viscosity. As an indicator for evaluating high-temperature performance, dynamic frequency scanning tests were conducted on asphalt, and the complex modulus of asphalt was established based on the time-temperature equivalence principle. Storage modulus and loss modulus Master curve; extract 1.6×10 -1 ~2.8×10 7 Slope of the complex modulus of asphalt over a wide frequency band of rad / s As an indicator for evaluating temperature sensitivity, a wide frequency range of 2.8 × 10⁻⁶ was extracted. 7 Complex modulus at rad / s As an indicator for evaluating low-temperature performance, the wide frequency range at the intersection of storage modulus and loss modulus is extracted as the crossover frequency. , as an indicator for evaluating the dominance of elasticity; Step 5: Perform statistical distribution analysis on the parameters obtained from the experimental methods in Steps 1 to 4, and remove extreme outliers; use the coefficient of variation (CV) screening method to remove parameters with low discrimination between samples; use the correlation screening method to remove highly correlated parameters, and finally perform subsequent correlation analysis on the remaining parameters. Step 6: Based on the Spearman rank correlation coefficient method, establish correlation heatmaps between chemical composition parameters and multi-level structural parameters, and between multi-level structural parameters and rheological performance parameters, respectively. The parameter with the strongest correlation to the target parameter is identified as the main control factor, and its promoting or inhibiting effects are distinguished according to the direction of its correlation. Thus, the main control factor is divided into main control promoting factor or main control inhibiting factor, and the remaining parameters with correlation are identified as auxiliary factors. Step 7: The path formed by sequentially connecting the main control factors is called the main control regulation path, which represents the most important performance regulation mechanism; the path formed by the participation of auxiliary factors is called the auxiliary regulation path, which performs coordinated optimization regulation based on the regulation of the main control path.

2. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1, characterized in that... In step two, for the WAXS test, the distance between the sample and the detector is set to 50 mm and the data acquisition time is 150 s; for the two SAXS tests, the distance between the sample and the detector is set to 800 mm and 4600 mm respectively, and the data acquisition time is 300 s.

3. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1, characterized in that... In step three, the Lorentz function is: In the formula, Baseline value, Peak area Half the peak width, This is the peak position.

4. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1, characterized in that... In step three, the molecular packing distance Molecular packing size and relative content The calculation formula is: In the formula, Half the peak width, Peak position, For the first Peak area of ​​the peak This represents the total peak area.

5. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1, characterized in that... In step three, the micelle structure parameters include zero-angle scattering intensity. Radius of gyration and fractal dimension Colloidal structure parameters include fractal dimension. The calculation formula is: In the formula, , These represent the zero-angle scattering intensities of colloids and micelles, respectively. , These are the radii of gyration for the colloid and the micelle, respectively. , These are the Porod region scaling factors for colloids and micelles, respectively. , are the fractal dimensions of colloids and micelles, respectively.

6. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1, characterized in that... In step four, the flow scanning test temperature is 60℃, and the shear rate is 0.001~1000s. - ¹; The temperature for the frequency scanning test was 0℃, 10℃, 20℃, 30℃, 40℃, 50℃, 60℃ and 70℃, and the angular frequency was 100~0.01 rad / s.

7. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1, characterized in that... In step four, the modulus-viscosity conversion relationship is as follows: In the formula: To store modulus, For loss modulus, For complex viscosity, For dynamic viscosity, This is for loss viscosity.

8. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1, characterized in that... In step five, the criterion for low discrimination is CV < 0.

05.

9. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1 or 8, characterized in that... The formula for calculating CV is: In the formula: Let the standard deviation of the parameter be . This represents the average value of the parameters.

10. The method for reverse control of asphalt based on multi-parameter coupling of chemical composition, multi-level structure, and rheological properties according to claim 1, characterized in that... In step six, the formula for calculating the Spearman rank correlation coefficient is as follows: In the formula: The difference in rank between paired values. This represents the number of samples.

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