A method and device for predicting asphalt VOCs emission rate and emission amount under a wide temperature range

Abstract

The application provides a method and device for predicting the emission rate and emission amount of asphalt VOCs in a wide temperature range, and relates to the field of road engineering. The method comprises the following steps: obtaining time-resolved concentration test data of volatile organic compounds of target asphalt under multiple set temperature conditions based on a thermal exposure test; processing and fitting the time-resolved concentration test data to obtain corresponding model parameters of the target asphalt under the multiple set temperature conditions; performing temperature dependence analysis on the model parameters and establishing a functional relationship between the model parameters and the test temperature; constructing an emission rate prediction model applicable to the condition of continuous temperature change based on the functional relationship; and predicting and outputting the emission rate and emission amount of the target asphalt based on the emission rate prediction model. The application solves the problem that the traditional method cannot accurately predict the emission rate and cumulative emission amount of volatile organic compounds in asphalt under the condition of a wide temperature range in the actual working condition that the temperature continuously changes with time during road construction and paving.

Description

Technical Field

[0001] This application relates to the field of road engineering, and in particular to a method and apparatus for predicting the emission rate and amount of asphalt VOCs over a wide temperature range. Background Technology

[0002] Asphalt, a widely used road surface material in transportation infrastructure construction, undergoes a series of processes during construction, including heating, transportation, paving, and compaction. During this process, asphalt is exposed to heat, continuously releasing large amounts of volatile organic compounds (VOCs). These VOCs not only have pungent odors, affecting the construction environment, but can also participate in the formation of ozone and secondary organic aerosols in the atmosphere, causing regional photochemical pollution and posing a potential threat to human health and the ecological environment.

[0003] The goal is to predict the emissions of volatile organic compounds (VOCs) in asphalt using experimental measurements under isothermal conditions and empirical model fitting. However, this method, which only measures within a single or narrow temperature range, cannot reflect the nonlinear changes in VOC release behavior under different temperature gradients. Especially under actual conditions of continuous temperature variation over time during road construction and paving, this method cannot accurately predict the emission rate and cumulative emissions of VOCs from asphalt over a wide temperature range.

[0004] Therefore, there is an urgent need for a method and device for predicting the emission rate and amount of asphalt VOCs over a wide temperature range. Summary of the Invention

[0005] This application provides a method and apparatus for predicting the emission rate and amount of VOCs from asphalt under a wide temperature range, which solves the problem that traditional methods cannot accurately predict the emission rate and cumulative emission amount of volatile organic compounds in asphalt under wide temperature range conditions in actual working conditions where the temperature changes continuously over time during road construction and paving.

[0006] The first aspect of this application provides a method for predicting the emission rate and amount of VOCs from asphalt over a wide temperature range. The method includes: obtaining time-resolved concentration test data of volatile organic compounds in target asphalt under multiple set temperature conditions based on thermal exposure tests; processing and fitting the time-resolved concentration test data to obtain model parameters corresponding to the target asphalt under multiple set temperature conditions; performing temperature dependence analysis on the model parameters and establishing a functional relationship between the model parameters and the test temperature; constructing an emission rate prediction model suitable for continuous temperature variation conditions based on the functional relationship; and predicting and outputting the emission rate and amount of the target asphalt based on the emission rate prediction model.

[0007] Optionally, based on heat exposure tests, time-resolved concentration test data of volatile organic compounds in the target asphalt under multiple set temperature conditions are obtained. Specifically, this includes: heating the target asphalt under multiple set temperature conditions in a closed chamber, and obtaining time-resolved concentration test data of volatile organic compounds in the target asphalt through sampling at multiple time points.

[0008] Optionally, the time-resolved concentration test data is processed and fitted to obtain the model parameters corresponding to the target asphalt under multiple set temperature conditions. Specifically, this includes: calculating the emission rate data of the target asphalt at different time points based on the time-resolved concentration test data; fitting the model parameters based on the emission rate data; and determining the initial emission rate parameters, characteristic time parameters, and shape factors of the target asphalt under multiple set temperature conditions based on the fitted curves; and using the initial emission rate parameters, characteristic time parameters, and shape factors as model parameters.

[0009] Optionally, temperature dependence analysis is performed on the model parameters, and a functional relationship between the model parameters and the test temperature is established. Specifically, this includes constructing a functional relationship between the model parameters and the test temperature, which includes a first functional relationship between the initial emission rate parameter and the test temperature, a second functional relationship between the characteristic time parameter and the test temperature, and a third functional relationship between the shape factor and the test temperature.

[0010] Optionally, an emission rate prediction model applicable to continuous temperature variation conditions is constructed based on functional relationships, specifically including: constructing the emission rate prediction model using the following formula:

[0011] in, Indicates the test temperature. Indicates time, Indicates the target asphalt in Emission rates under time and test temperature conditions, Indicates the initial emission rate parameter. Indicates the characteristic time parameter. α , m , k All are shape factors. , , , , , , , , and All are fitting parameters, among which, α Used to adjust the curvature of the emission rate curve over time. mUsed to characterize the nonlinearity of the emission rate decay process, k Used to characterize the extent to which temperature affects the rate of emission decline; , , , , , , , , and All are fitting parameters, among which, , Initial emission rate parameters Fitting parameters for the functional relationship between temperature and temperature. , Characteristic time parameters Fitting parameters for the functional relationship between temperature and temperature. , The fitting parameters are the functional relationships between the shape parameter α and temperature. , The fitting parameters are the functional relationships between the shape parameter k and temperature.

[0012] Optionally, based on the emission rate prediction model, the emission amount of the target asphalt is predicted and output. Specifically, this includes: recording the initial time point and the target time point, and obtaining the emission rate corresponding to each time point between the initial time point and the target time point based on the emission rate prediction model, with the target time point located after the initial time point; integrating the emission rate corresponding to each time point, and predicting and outputting the emission amount of the target asphalt corresponding to the target time point based on the integration result.

[0013] Optionally, after predicting the emission rate and emission amount of the target asphalt based on the emission rate prediction model, the method further includes: obtaining a first error value between the predicted emission rate and the actual emission rate, and obtaining a second error value between the predicted emission amount and the actual emission amount; determining whether the first error value is greater than the emission rate error threshold and whether the second error value is greater than the emission amount error threshold; if the first error value is greater than the emission rate error threshold and the second error value is greater than the emission amount error threshold, then the model parameters are corrected at least as follows: performing dynamic linear scaling correction on the initial emission rate parameters based on the first error to keep the predicted emission intensity consistent with the actual emission intensity on a short time scale; performing adaptive time constant adjustment on the characteristic time parameters based on the second error value to balance the time decay characteristics of the model with the cumulative emission trend; and performing nonlinear fitting reconstruction on the shape factor based on the joint distribution of the residuals between the first and second errors, and obtaining the globally optimal parameter combination by iteratively minimizing the sum of squared residuals.

[0014] A second aspect of this application provides a device for predicting the emission rate and amount of asphalt VOCs over a wide temperature range. The device includes an acquisition module and a processing module, wherein... The acquisition module is used to acquire time-resolved concentration test data of volatile organic compounds in target asphalt under multiple set temperature conditions based on heat exposure tests; process and fit the time-resolved concentration test data, and acquire the corresponding model parameters of target asphalt under multiple set temperature conditions; perform temperature dependence analysis on model parameters, and establish the functional relationship between model parameters and test temperature.

[0015] The processing module is used to construct an emission rate prediction model applicable to continuous temperature variation conditions based on functional relationships; based on the emission rate prediction model, it predicts and outputs the emission rate and emission amount of the target asphalt.

[0016] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described above.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, which is executed by a processor according to any of the methods described above.

[0018] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Based on thermal exposure tests, time-resolved concentration test data of volatile organic compounds (VOCs) in target asphalt under multiple set temperature conditions are obtained; the time-resolved concentration test data are processed and fitted, and model parameters corresponding to the target asphalt under multiple set temperature conditions are obtained; temperature dependence analysis is performed on the model parameters, and a functional relationship between the model parameters and the test temperature is established; based on the functional relationship, an emission rate prediction model applicable to continuous temperature variation conditions is constructed; based on the emission rate prediction model, the emission rate and emission amount of the target asphalt are predicted and output, thereby realizing continuous, dynamic, and quantitative prediction of the VOC emission behavior of the target asphalt under a wide temperature range. This allows the calculation of emission rate and emission amount to simultaneously reflect the coupling effect of multiple factors such as temperature change, time evolution, and material aging, overcoming the limitation that traditional isothermal empirical models cannot accurately extrapolate, and improving the applicability and accuracy of asphalt VOC emission assessment in real construction and service environments.

[0019] 2. The target asphalt was heated under multiple set temperature conditions in a closed chamber, and time-resolved concentration test data of volatile organic compounds in the target asphalt were obtained by sampling at multiple time points. This yielded a volatile organic compound concentration change sequence covering a wide temperature range and with temporal continuity, providing basic data support for the subsequent establishment of an asphalt volatile organic compound emission rate model, and ensuring the comparability of emission behavior under different temperature conditions and the identifiability of model parameters.

[0020] 3. By introducing a two-layer correction mechanism using a first and second error value, key parameters of the emission rate prediction model are dynamically corrected. First, based on the first error value between the predicted and actual emission rates, dynamic linear scaling correction is applied to the initial emission rate parameters to ensure that the model's emission intensity remains consistent with the actual emission intensity over short timescales, thereby improving instantaneous prediction accuracy. Second, based on the second error value between the predicted and actual emissions, the time constant of the characteristic time parameters is adaptively adjusted to coordinate the model's time decay characteristics with the cumulative emission trend, making the overall emission prediction closer to real changes. Finally, by combining the joint residual distribution of the first and second error values, the shape factor is nonlinearly fitted and reconstructed, and the globally optimal parameter combination is obtained by iteratively minimizing the sum of squared residuals to achieve self-consistent optimization of the model under different temperatures and timescales. The beneficial effect is that this step can simultaneously balance the accuracy of instantaneous emission intensity and cumulative emission trend, achieving adaptive convergence and dynamic correction of the emission model, significantly improving the model's generalization ability and prediction stability under wide temperature ranges and complex thermal history conditions. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of a method for predicting the emission rate and amount of asphalt VOCs under a wide temperature range, provided in an embodiment of this application. Figure 2 This is a schematic diagram of a module for predicting the emission rate and amount of asphalt VOCs under a wide temperature range, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0022] Explanation of reference numerals in the attached figures: 21. Acquisition module; 22. Processing module; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0025] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0026] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0027] Please refer to Figure 1 The diagram illustrates a flowchart of a method for predicting the emission rate and amount of asphalt VOCs under a wide temperature range, provided in an embodiment of this application. The flowchart mainly includes the following steps: S101 to S105.

[0028] Step S101: Based on the heat exposure test, obtain the time-resolved concentration test data of volatile organic compounds in the target asphalt under multiple set temperature conditions.

[0029] Specifically, in existing technologies, studies on the emission of volatile organic compounds (VOCs) from asphalt often employ fixed temperatures or narrow temperature ranges, conducting measurements at specific points only at a single temperature. This fails to reflect the dynamic emission characteristics of asphalt materials during actual construction due to continuous temperature variations, resulting in a lack of extrapolability and continuity in the emission data. Consequently, this limits the applicability of the model in real-world engineering environments. To overcome this limitation, this application introduces a wide-temperature-range experimental design in step S101. Multiple representative temperature gradients are set during the thermal exposure process to cover the temperature range that asphalt may experience throughout the entire process from heating to paving. Time-resolved concentration data are simultaneously collected under different temperature conditions, forming a fundamental dataset that reflects the temperature-dependent VOC emission patterns of asphalt. This provides comprehensive data support for subsequent model fitting and temperature dependence analysis.

[0030] In one possible implementation, step S101 further includes: heating the target asphalt under multiple set temperature conditions in a closed chamber, and obtaining time-resolved concentration test data of volatile organic compounds in the target asphalt by sampling at multiple time points.

[0031] Specifically, the target asphalt sample is placed in a sealed chamber equipped with constant temperature control, flow regulation, and airtightness maintenance functions. Heat exposure tests are conducted under at least three set temperature conditions, preferably 120℃, 140℃, 160℃, and 180℃, to cover the main temperature range of asphalt materials during the heating and cooling process during construction. Under each temperature condition, the airflow velocity within the chamber is kept stable and a constant temperature field is maintained. Gas sampling is performed at multiple time points using a built-in sampling channel, with at least five sampling time points, preferably 5 min, 10 min, 20 min, 30 min, 60 min, 120 min, 240 min, and 480 min. After each sampling, the concentration of volatile organic compounds (VOCs) in the sample is detected using a gas analysis device, obtaining concentration sequence data corresponding to the temperature and time. This sealed chamber heat exposure test allows for accurate recording of the VOCs release process of asphalt at different temperature stages under conditions free from external airflow interference, thereby establishing a concentration data foundation with time resolution and temperature responsiveness. Please refer to Table 1, which shows the time-resolved concentrations of VOCs (unit: mg / m³) under different temperature and time conditions provided in the embodiments of this application. 3 ).

[0032] [Table 1]

[0033] Step S102: Process and fit the time-resolved concentration test data, and obtain the model parameters corresponding to the target asphalt under multiple set temperature conditions.

[0034] Specifically, volatile organic compound (VOC) concentration data at multiple temperatures and time points are converted into corresponding emission rate data, which are then used as model input. An emission rate fitting model is employed to fit curves to the data under different temperature conditions. Through this fitting process, a set of model parameters characterizing the emission characteristics of the target asphalt at various temperatures is obtained, including initial emission rate parameters, characteristic time parameters, and multiple shape factor parameters. This step achieves the transformation from experimental data to a parameterized mathematical model, enabling a quantitative description of the asphalt VOC emission process across time and temperature dimensions, providing core parameter support for subsequent temperature-dependent analysis and emission prediction.

[0035] In one possible implementation, step S102 further includes: calculating the emission rate data of the target asphalt at different time points based on the time-resolved concentration test data; fitting the model parameters based on the emission rate data, and determining the initial emission rate parameters, characteristic time parameters, and shape factor of the target asphalt under multiple set temperature conditions based on the fitted curve; and using the initial emission rate parameters, characteristic time parameters, and shape factor as model parameters.

[0036] Specifically, the experimental data, namely the time-resolved concentration experimental data, is derived from the volatile organic compound (VOC) volume concentration data detected in the closed-chamber heat exposure test in step S101. This concentration data is converted into emission rate data using the principle of mass conservation. Based on the principle of mass conservation, the change in VOC content within the closed chamber can be expressed as:

[0037]

[0038] in, Indicates the volume of a closed chamber; Indicates time VOCs volume concentration at the following levels; Indicates the sampling flow rate of the sampling pump; Indicates the target asphalt in time The VOCs emission rate. Rearranging the above equation, we get:

[0039]

[0040] in, Indicates volume concentration Over time The rate of change, since the actual measured data are discrete sampling points. , Therefore, the first-order forward difference approximation is used to calculate the concentration change rate, given the lower value.

[0041] Substituting the approximate concentration change rate into the rearranged formula above, we can obtain the formula for calculating the VOC emission rate of asphalt in a closed laboratory chamber under heat exposure:

[0042] Then the relevant model parameters can be obtained. The expression is:

[0043] in, The initial emission rate parameter characterizes the VOCs release intensity during the initial stage of heat exposure, expressed in mg / s. The characteristic time parameter is used to reflect the characteristic scale of emission rate changes over time. α , m , k All are shape factors, which are used to describe the nonlinear trend and decay characteristics of emission curves. Please refer to Table 2, which shows the time-resolved emission rates of VOCs (unit: mg / s) under different temperature and time conditions provided in the embodiments of this application.

[0044] [Table 2]

[0045] Please refer to Table 3, which shows the parameter fitting results of the emission rate prediction model provided in the embodiments of this application.

[0046] [Table 3]

[0047] Step S103: Perform temperature dependence analysis on the model parameters and establish the functional relationship between the model parameters and the experimental temperature.

[0048] Specifically, the initial emission rate parameters, characteristic time parameters, and shape factors obtained under different temperature conditions are used as input data, and regression analysis is performed on them with the test temperature to identify the regularity of each parameter's change with temperature. Based on the distribution characteristics of each parameter in the temperature dimension, mathematical forms that can reflect temperature dependence are selected, including exponential functions, power functions, or logarithmic functions, etc., and the optimal function type and corresponding fitting coefficients are determined by nonlinear fitting methods.

[0049] In one possible implementation, step S103 further includes: constructing a functional relationship between model parameters and test temperature, the functional relationship including a first functional relationship between initial emission rate parameters and test temperature, a second functional relationship between characteristic time parameters and test temperature, and a third functional relationship between shape factor and test temperature.

[0050] The initial emission rate parameter, characteristic time parameter, and shape factor in the model all have a functional relationship with the test temperature, which can be characterized by exponential, power, or logarithmic functions. The expression for the first functional relationship between the initial emission rate parameter and the test temperature is as follows:

[0051] in, Indicates the test temperature. , These are the fitting parameters for the temperature-dependent function, and their values ​​are obtained by applying data at different temperatures. The samples were obtained through nonlinear regression, and the function form belongs to one of the exponential expressions allowed in the embodiments. The expression for the second functional relationship between the characteristic time parameter and the experimental temperature is as follows:

[0052]

[0053] in, , The fitting parameters are represented as a temperature-dependent function, and their values ​​are obtained by applying different temperatures. The samples were obtained through power function regression, which is a power function expression allowed in the embodiments. The expression for the third function relationship between the shape factor and the experimental temperature is as follows:

[0054]

[0055] in, , , , , , To fit the parameters to the corresponding temperature-dependent function, respectively, by applying different temperatures... α , m , k The samples were obtained using power functions, linear fractions, and exponential functions through nonlinear regression, all of which are permissible function forms in the embodiments.

[0056] Step S104: Construct an emission rate prediction model applicable to continuous temperature change conditions based on functional relationships.

[0057] Specifically, the established functional relationships between the initial emission rate parameters, characteristic time parameters, and shape factor and the test temperature are used as inputs, and the temperature is... By introducing the emission rate equation as an independent variable, the emission rate function can adjust the model parameters in real time under continuous temperature changes, thereby forming a temperature-responsive emission rate prediction model.

[0058] In the embodiments of this application, the emission rate prediction model is constructed using the following formula:

[0059] in, Indicates the test temperature. Indicates time, Indicates the target asphalt in Emission rates under time and test temperature conditions, This represents the initial emission rate parameter, used to characterize the intensity of pollutant release from the material during the initial stage of thermal exposure. This represents a characteristic time parameter used to describe the characteristic transition timescale in the process of emission rate changing over time; α , m , k All are shape factors, among which, α Used to adjust the curvature of the emission rate curve over time, reflecting the rate characteristics of the transition from a rapid release phase to a slow release phase in the release process; m Used to characterize the nonlinearity of the emission rate decay process, reflecting the gradual characteristics of the release rate change at different time scales; k Used to characterize the effect of temperature on emission rate decay behavior, reflecting the changing characteristics of release kinetics under different temperature conditions. , , , , , , , , and All of these are fitting parameters.

[0060] , Initial emission rate parameters Fitting parameters of the functional relationship between temperature and temperature are used to establish the predictive relationship of initial emission intensity under temperature change conditions; , Characteristic time parameters Fitting parameters for the functional relationship between temperature and pollutant release duration; , The fitting parameter is the functional relationship between the shape parameter α and temperature, used to describe the effect of temperature change on the shape of the time response curve; , The fitting parameter is the functional relationship between the shape parameter m and temperature, used to describe the effect of temperature change on the nonlinear decay characteristics of the release rate; , The fitting parameter is the function of shape parameter k versus temperature, used to describe the effect of temperature change on the sensitivity of the release kinetics process.

[0061] The derivation of the emission rate prediction model is as follows: To satisfy the three properties of "near-constant rate in the early stage, power-law tail in the late stage, and monotonicity and integrability", a structural time frame for emission rates is constructed. Assuming that the decay of emission rates with respect to a dimensionless structural time follows an exponential law, the master rate equation is given as follows:

[0062] in, This represents the attenuation intensity coefficient of the structure over time; the larger the value, the more sensitive it is to the attenuation of the structure over time. This represents a dimensionless, monotonically increasing, and smooth structure-time mapping used to represent physical time. This is transformed into an equivalent process that characterizes aging processes across multiple mechanisms and time scales.

[0063] To achieve early near-platform behavior, it is required that when When a slight deviation from the quasi-constant velocity form occurs, the corresponding target asymptotic form is given as follows:

[0064] in, The characteristic time is used to define the time scale for the transition from platform to decay. This represents the early power-order shape factor, which determines the strength of curvature deviating from the plateau in the early stages; This represents a higher-order minor term.

[0065] To achieve late-stage power-law tail behavior, it is required that when At that time, it will be referred to as "big" from now on. It exhibits power-law decay, and the corresponding objective asymptotic expression is given below:

[0066] in, , This determines the exponent at the tail of the late power law. This is a higher-order correction after the logarithmic terms. To simultaneously satisfy the small... Like Mi, Da Considering the combined requirements of logarithmicity, dimensionlessness, smoothness, and monotonicity, the simplest time mapping structure is selected as follows:

[0067]

[0068] Wherein, the mapping is in At that time, it will be referred to as small hereafter. ,and At the same level, Time and Same order, and strictly monotonically increasing and in It is continuously differentiable at that point.

[0069] Substituting the above structural time mapping into the rate master equation, we obtain the closed-form rate function that satisfies the three properties as follows:

[0070] Among them, when hour, about Strictly monotonically decreasing; integrable when the study interval is finite time; if considering The total amount over the entire time domain, then in The integral converges over time, thus ensuring that the cumulative emissions are limited.

[0071] To verify the rationality of the structure timing selection, a small A reverse expansion was performed to check the correctness of the platform's transition to decay, and a small... Expanded as follows:

[0072] Substituting this into the rate expression and performing a small exponential expansion, we obtain the early approximation as follows:

[0073] in, The above results show that the coefficients of the first-order term are... The initial attenuation slope after controlling the platform; the coefficients of the second-order terms and Related to controlling early curvature and turning speed. Then, for large... A reverse expansion is performed to check the correctness of the power law tail, and the decomposition and asymptotic form are given below:

[0074]

[0075] Based on this, the late power law and the first correction term are obtained as follows:

[0076] The above dominant term gives the power law exponent. , ,and This is the primary correction for the later stages. To give Introduce unobservable internal changes under thermal exposure with an interpretable physical meaning. Define the logarithmic sensitivity of emission intensity to internal variations as a constant. The definition is given as follows:

[0077]

[0078] At the same time, the cumulative rate of internal changes relative to structural time is set to a constant. This refers to the fixed-amplitude internal change increment that occurs per unit of structural time, defined as follows:

[0079] According to the chain rule:

[0080] in, It represents aging sensitivity and describes the impact of unit internal changes on the logarithmic emission rate. This represents the cumulative aging scale, describing the rate of accumulation of internal changes; The intensity of emission reduction caused by the time progression of the unit structure at this moment. Substituting back the rate function, we obtain the final expression with a clear mechanistic mapping as follows:

[0081]

[0082] in, Control the initial intensity, Controlling the time scale, Control the shape and the steepness of the transition. Control the attenuation intensity throughout the entire range; parameter values ​​satisfy... To perform verifiability verification, the rate function can be linearized in the structure-time coordinate system, and... A linear regression test was performed, and the linearized relationship is given below:

[0083]

[0084] If the experimental data is consistent with the above-mentioned structural timeframe, then Compared to The relationship is approximately linear, with a slope of And the intercept is Its coefficient of determination It should be close to 1; in typical asphalt thermal exposure data, Available This supports the linearization assumption.

[0085] Under the internal aging assumption, first fix the structural time frame and The definition of temperature is then incorporated into the factorization characterization of aging sensitivity and aging accumulation scale, so that... It exhibits an interpretable temperature dependence. Assume the emission rate follows an Arrhenius-type law, and that internal unobservable variations exist. This raises the effective energy barrier, thus revealing the coupling relationship between the rate and the energy barrier:

[0086]

[0087] in, This indicates an internal change. absolute temperature is The experimental emission rate function at that time; This represents the pre-exponential factor, used to give the baseline rate amplitude under the barrier-free limit; Indicates an effective potential barrier that evolves with internal changes; Represents the gas constant; Represents absolute temperature. To characterize the linear lifting effect of aging on the potential barrier, we introduce:

[0088]

[0089] in, This indicates aging sensitivity; a higher value indicates a stronger impact of each unit of internal change on the logarithmic rate. Substituting into the above equation and using the chain rule, we get:

[0090]

[0091] in, The linear rate of rise of the potential barrier with respect to internal changes is given; from this, we obtain...

[0092] The above derivation shows that aging reduces the effective energy barrier from Elevated to However, reducing emission rates, and the aging sensitivity is inversely proportional to temperature: the higher the temperature, the lower the aging rate. The lower the sensitivity to changes within a unit, the less likely it is that a disturbance of the same magnitude during aging at high temperatures will cause a significant decrease in the logarithmic rate, which can be explained by physical meaning.

[0093] when When an Arrhenius-type relationship is satisfied, it indicates that regardless of whether the "aging increment" originates from bulk diffusion, desorption from bound sites, chain rearrangement, or initial oxidation, the key controlling step is always a thermally activated event that crosses the energy barrier, reflecting the dominant role of the same microscopic mechanism. This pattern suggests that the relationship between the dominant factor of the aging rate and temperature follows thermal excitation kinetics, and that energy barrier crossing is the main rate-determining step. Based on the aforementioned temperature dependence analysis, the wide-temperature-range emission rate prediction model can be obtained as follows:

[0094]

[0095] When the ambient temperature during road construction Over time When the pattern of temperature change is known, temperature can be considered a function of time. Therefore, the emission rate equation can be reduced to a purely time-based form. Please refer to Table 4, which presents a comparison of the parameters of the wide-temperature-range emission rate prediction model for different types of asphalt samples provided in the embodiments of this application.

[0096] [Table 4]

[0097] Step S105: Based on the emission rate prediction model, predict and output the emission rate and emission amount of the target asphalt.

[0098] Specifically, continuous temperature variation data of the target asphalt under thermal exposure conditions is input into the emission rate prediction model. Combined with time series data, calculations are performed to obtain predicted emission rates at different temperatures and time points, thus generating a dynamic emission rate curve. To further obtain emission results, the emission rate prediction curve is integrated within a set time range. This step includes: recording the initial and target time points, and obtaining the emission rates corresponding to each time point between the initial and target time points based on the emission rate prediction model (the target time point is after the initial time point); integrating the emission rates corresponding to each time point, and predicting the emission amount of the target asphalt at the target time point based on the integration results. This yields the cumulative volatile organic compound (VOC) emissions of the target asphalt within the corresponding time interval. Through this process, the instantaneous emission rate and corresponding cumulative emission amount over continuous time can be output separately, forming a prediction result that can be dynamically updated with time and temperature changes. This step realizes the transformation from a parametric model to an operational emission assessment, providing a foundation for quantitative analysis of VOC emissions under different construction stages and different thermal exposure temperature conditions.

[0099] Please refer to Table 5, which shows the fitting verification results of the emission rate model provided in the embodiments of this application (asphalt: SK-90).

[0100] [Table 5]

[0101] Please refer to Table 6, which shows the fitting verification results of the emission model provided in the embodiments of this application (asphalt: BJ-SBS).

[0102] [Table 6]

[0103] Table 5 shows the coefficient of determination (R²) of the emission rate model under various temperature conditions. 2 All values ​​were above 0.90, indicating a high degree of agreement between the predicted and measured emission rates. The absolute error was within 10. -6 ~10 -5The relative error is generally controlled within 5% at the mg / s level.

[0104] Table 6 shows the coefficient of determination (R²) of the emission rate model under various temperature conditions. 2 All values ​​were above 0.98, indicating that the model has extremely high accuracy in predicting cumulative emissions. The absolute error was within 10. -4 ~10 -3 The relative error is generally controlled within 15%, with the speed in the mg / s range.

[0105] Comprehensive analysis results show that the emission rate and emission quantity prediction method proposed in this invention can accurately reflect the VOCs release behavior of asphalt materials under different thermal exposure temperatures and time conditions, and has good engineering applicability and promotion value.

[0106] In one possible implementation, step S105 further includes: obtaining a first error value between the predicted emission rate and the actual emission rate, and obtaining a second error value between the predicted emission amount and the actual emission amount; determining whether the first error value is greater than an emission rate error threshold and whether the second error value is greater than an emission amount error threshold; if the first error value is greater than the emission rate error threshold and the second error value is greater than the emission amount error threshold, then the model parameters are corrected as follows: the initial emission rate parameters are dynamically linearly scaled based on the first error to keep the predicted emission intensity consistent with the actual emission intensity on a short time scale; the characteristic time parameters are adaptively adjusted based on the second error value to balance the time decay characteristics of the model with the cumulative emission trend; the shape factor is nonlinearly fitted and reconstructed based on the joint distribution of the residuals between the first error and the second error, and the globally optimal parameter combination is obtained by iteratively minimizing the sum of squared residuals.

[0107] Specifically, the first step is to calculate the error scalar and residual sequences, and form a joint objective. The first error in emission rate and the second error in emission amount are defined as follows:

[0108]

[0109]

[0110] in, and For numerical stabilization constants, The normalized root mean square reflects the fitting bias of the rate across the entire time domain. Reflecting the relative deviation of cumulative emissions, Let be the number of effective time points. The joint weighted objective function for rate and emissions is defined as:

[0111]

[0112] in, and The weighting coefficient reflects the proportion of attention paid to instantaneous fitting and cumulative consistency. This objective is used for subsequent nonlinear reconstruction of shape factor and termination criterion of line search.

[0113] The second step is to set the threshold and perform the judgment. A given emission rate error threshold is used. Error threshold of emissions .like and Perform strong coupling correction; if and The current parameters are accepted and the process terminates; for the other two unilateral threshold crossing cases, intensity correction or time-scale correction is performed respectively.

[0114] when and At that time, a strongly coupled correction is performed, which includes three-segment linked updates, and its input is... With the present The output is the updated version. .

[0115]

[0116] in, For the early time window, Control the width of the platform area. This is the scaling intensity coefficient. Subsequently, the time constant of the characteristic time parameter is adaptively adjusted:

[0117]

[0118] in, For learning rate, This is the step size cutoff factor. When... hour, Exponential factors make Increase it to slow down the overall decay. Finally, adjust the set of shape factors. Perform nonlinear fitting and reconstruction:

[0119]

[0120]

[0121] After the fixed update Minimize below:

[0122] Using a damped Gauss-Newton step:

[0123] in , For the residual vector, For Jacobian matrices, The damping coefficient is adjusted using a trust region strategy. Update. , Determined by line search. This three-segment linkage is based on... Execution is performed sequentially.

[0124] when and When, accept parameters and output and record and .

[0125] when and At that time, intensity correction is performed. Platform area scaling correction is then performed.

[0126]

[0127] Then fixed Fine-tuning :

[0128] in , If necessary, without changing small amplitude under constraints Tuning:

[0129]

[0130] in Indicates in Projection step under constraints.

[0131] when and At that time, perform time scale correction. Perform log-step updates of feature times.

[0132]

[0133] like If it still hasn't reached the threshold, then make minor adjustments. :

[0134] in Let be the step size, then the derivative can be derived from... Provided.

[0135] Temperature law parameter consistency update: If different temperature conditions If the shift is systematic, then under a fixed shape mapping, for Perform regularized regression:

[0136] in For the parameterization mapping of the family of temperature-dependent functions, is the regularization coefficient.

[0137] when and Terminate and output Otherwise, proceed to the next iteration. After the above iteration update is completed, if there are still cases where the first error value or the second error value does not meet the preset threshold, the updated model parameters and temperature-dependent parameters are re-input into the emission rate prediction model, and the error calculation and parameter correction loop continues until both the first error value and the second error value meet the corresponding threshold conditions. Finally, the emission rate prediction model after convergence correction and its corresponding model parameter set are output to achieve high-precision prediction output of the emission rate and emission amount of the target asphalt under wide temperature range conditions.

[0138] The relevant parameters for the above process are defined as follows: For the initial emission rate parameters, For characteristic time parameters, For shape factor, For aging sensitivity, For aging cumulative scale, For attenuation intensity, China These are the temperature law fitting coefficients. To update the step size or scale the intensity, For damping, For window and step size hyperparameters, For numerically stable terms, To meet the threshold, As weight, This is the temperature law canonical coefficient. This is the stopping criterion.

[0139] Please refer to Figure 2 This document illustrates a schematic diagram of a device for predicting the emission rate and amount of asphalt VOCs over a wide temperature range, provided in an embodiment of this application. The device includes an acquisition module 21 and a processing module 22. The acquisition module 21 is used to acquire time-resolved concentration test data of volatile organic compounds of the target asphalt under multiple set temperature conditions based on heat exposure test; process and fit the time-resolved concentration test data, and acquire the model parameters corresponding to the target asphalt under multiple set temperature conditions; perform temperature dependence analysis on the model parameters, and establish the functional relationship between the model parameters and the test temperature.

[0140] Processing module 22 is used to construct an emission rate prediction model applicable to continuous temperature change conditions based on functional relationships; based on the emission rate prediction model, it predicts and outputs the emission rate and emission amount of the target asphalt.

[0141] In one possible implementation, the acquisition module 21 is used to acquire time-resolved concentration test data of volatile organic compounds in the target asphalt under multiple set temperature conditions based on the heat exposure test. Specifically, it includes: heating the target asphalt under multiple set temperature conditions in a closed chamber, and acquiring time-resolved concentration test data of volatile organic compounds in the target asphalt through multi-time point sampling.

[0142] In one possible implementation, the acquisition module 21 is used to process and fit the time-resolved concentration test data, and to acquire the model parameters corresponding to the target asphalt under multiple set temperature conditions. Specifically, this includes: calculating the emission rate data of the target asphalt at different time points based on the time-resolved concentration test data; fitting the model parameters based on the emission rate data, and determining the initial emission rate parameters, characteristic time parameters, and shape factors of the target asphalt under multiple set temperature conditions based on the fitted curve; and using the initial emission rate parameters, characteristic time parameters, and shape factors as model parameters.

[0143] In one possible implementation, the acquisition module 21 is used to perform temperature dependence analysis on the model parameters and establish a functional relationship between the model parameters and the test temperature. Specifically, it includes: constructing a functional relationship between the model parameters and the test temperature, which includes a first functional relationship between the initial emission rate parameter and the test temperature, a second functional relationship between the characteristic time parameter and the test temperature, and a third functional relationship between the shape factor and the test temperature.

[0144] In one possible implementation, the processing module 22 is used to construct an emission rate prediction model applicable to continuous temperature variation conditions based on a functional relationship, specifically including: constructing the emission rate prediction model using the following formula:

[0145] in, Indicates the test temperature. Indicates time, Indicates the target asphalt in Emission rates under time and test temperature conditions, Indicates the initial emission rate parameter. Indicates the characteristic time parameter. α , m , k All are shape factors. , , , , , , , , and All are fitting parameters, among which, α Used to adjust the curvature of the emission rate curve over time. m Used to characterize the nonlinearity of the emission rate decay process, k Used to characterize the extent to which temperature affects the rate of emission decline; , , , , , , , , and All are fitting parameters, among which, , The initial emission rate parameter Fitting parameters for the functional relationship between temperature and temperature. , Characteristic time parameters Fitting parameters for the functional relationship between temperature and temperature. , The fitting parameters are the functional relationships between the shape parameter α and temperature. , The fitting parameters are the functional relationships between the shape parameter k and temperature.

[0146] In one possible implementation, the processing module 22 is used to predict and output the emission amount of the target asphalt based on the emission rate prediction model. Specifically, it includes: recording the initial time point and the target time point, and obtaining the emission rate corresponding to each time point between the initial time point and the target time point based on the emission rate prediction model, wherein the target time point is located after the initial time point; integrating the emission rate corresponding to each time point, and predicting and outputting the emission amount of the target asphalt corresponding to the target time point based on the integration result.

[0147] In one possible implementation, after the processing module 22 predicts and outputs the emission rate and emission amount of the target asphalt based on the emission rate prediction model, the method further includes: obtaining a first error value between the predicted emission rate and the actual emission rate, and obtaining a second error value between the predicted emission amount and the actual emission amount; determining whether the first error value is greater than an emission rate error threshold and whether the second error value is greater than an emission amount error threshold; if the first error value is greater than the emission rate error threshold and the second error value is greater than the emission amount error threshold, then the model parameters are corrected as follows: the initial emission rate parameters are dynamically linearly scaled based on the first error to keep the predicted emission intensity consistent with the actual emission intensity on a short time scale; the time constant of the characteristic time parameters is adaptively adjusted based on the second error value to balance the time decay characteristics of the model with the cumulative emission trend; the shape factor is nonlinearly fitted and reconstructed based on the joint distribution of the residuals between the first error and the second error, and the globally optimal parameter combination is obtained by iteratively minimizing the sum of squared residuals.

[0148] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0149] This application also provides an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0150] The communication bus 302 is used to enable communication between these components.

[0151] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0152] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0153] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0154] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for predicting the emission rate and amount of asphalt VOCs over a wide temperature range.

[0155] exist Figure 3In the illustrated electronic device, the user interface 303 is primarily used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call the prediction application stored in the memory 305 for wide-temperature-range asphalt VOCs emission rates and emissions. When executed by one or more processors 301, the electronic device performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0156] This application also provides a computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0157] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0158] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0159] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0161] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0162] The above description is merely an exemplary embodiment disclosed in this application and should not be construed as limiting the scope of this application. Any equivalent changes and modifications made in accordance with the teachings of this application shall still fall within the scope of this application.

[0163] This application is intended to cover any variations, uses, or adaptations disclosed herein that follow the general principles disclosed herein and include common knowledge or customary technical means in the art that are not described in this application.

Claims

1. A method for predicting the emission rate and amount of VOCs from asphalt over a wide temperature range, characterized in that, The method includes: Time-resolved concentration data of volatile organic compounds in target asphalt were obtained based on thermal exposure tests under multiple set temperature conditions. The time-resolved concentration test data are processed and fitted, and the model parameters corresponding to the target asphalt under multiple set temperature conditions are obtained. Temperature dependence analysis was performed on the model parameters, and a functional relationship between the model parameters and the experimental temperature was established. Based on the aforementioned functional relationship, an emission rate prediction model suitable for continuous temperature variation conditions is constructed. Based on the emission rate prediction model, the emission rate and emission amount of the target asphalt are predicted and output.

2. The method according to claim 1, characterized in that, The method of obtaining time-resolved concentration data of volatile organic compounds in target asphalt under multiple set temperature conditions based on heat exposure tests specifically includes: The target asphalt was heated under multiple set temperature conditions in a closed chamber, and time-resolved concentration test data of volatile organic compounds in the target asphalt were obtained by sampling at multiple time points.

3. The method according to claim 1, characterized in that, The process of processing and fitting the time-resolved concentration test data, and obtaining the model parameters corresponding to the target asphalt under multiple set temperature conditions, specifically includes: Calculate the emission rate data of the target asphalt at different time points based on the time-resolved concentration test data; The model parameters are fitted based on the emission rate data, and the initial emission rate parameters, characteristic time parameters, and shape factors of the target asphalt under multiple set temperature conditions are determined based on the fitted curve. The initial emission rate parameter, the characteristic time parameter, and the shape factor are used as the model parameters.

4. The method according to claim 3, characterized in that, The step of performing temperature dependence analysis on the model parameters and establishing the functional relationship between the model parameters and the experimental temperature specifically includes: A functional relationship is constructed between the model parameters and the test temperature. The functional relationship includes a first functional relationship between the initial emission rate parameter and the test temperature, a second functional relationship between the characteristic time parameter and the test temperature, and a third functional relationship between the shape factor and the test temperature.

5. The method according to claim 4, characterized in that, The emission rate prediction model based on the aforementioned functional relationship, applicable to conditions of continuous temperature variation, specifically includes: The emission rate prediction model is constructed using the following formula: ； in, Indicates the test temperature, Indicates time, Indicates that the target asphalt is in The emission rate at the time and the test temperature, Indicates the initial emission rate parameter. This represents the characteristic time parameter. α , m , k All are the shape factors, where, α Used to adjust the curvature of the emission rate curve over time. m Used to characterize the nonlinearity of the emission rate decay process, k Used to characterize the extent to which temperature affects the rate of emission decline; , , , , , , , , and All are fitting parameters, among which, , The initial emission rate parameter Fitting parameters for the functional relationship between temperature and temperature. , Characteristic time parameters Fitting parameters for the functional relationship between temperature and temperature. , The fitting parameters are the functional relationships between the shape parameter α and temperature. , The fitting parameters are the functional relationships between the shape parameter k and temperature.

6. The method according to claim 1, characterized in that, Based on the emission rate prediction model, the emission amount of the target asphalt is predicted and output, specifically including: Record the initial time point and the target time point, and obtain the emission rate corresponding to each time point between the initial time point and the target time point based on the emission rate prediction model, wherein the target time point is located after the initial time point; The emission rate corresponding to each time point is integrated, and the emission amount corresponding to the target asphalt at the target time point is predicted and output based on the integration result.

7. The method according to claim 3, characterized in that, After predicting and outputting the emission rate and emission amount of the target asphalt based on the emission rate prediction model, the method further includes: Obtain a first error value between the predicted emission rate and the actual emission rate, and obtain a second error value between the predicted emissions and the actual emissions; Determine whether the first error value is greater than the emission rate error threshold and whether the second error value is greater than the emission quantity error threshold; If the first error value is greater than the emission rate error threshold, and the second error value is greater than the emission amount error threshold, then the model parameters are corrected by at least the following: Based on the first error, the initial emission rate parameter is dynamically linearly scaled down, and the dynamic linear scaling down is used to keep the predicted emission intensity consistent with the actual emission intensity on a short time scale. Based on the second error value, the time constant of the characteristic time parameter is adaptively adjusted to balance the time decay characteristics of the model with the cumulative emission trend; Based on the joint residual distribution between the first error and the second error, the shape factor is reconstructed by nonlinear fitting, and the globally optimal parameter combination is obtained by iteratively minimizing the sum of squared residuals.

8. A device for predicting the emission rate and amount of asphalt VOCs over a wide temperature range, characterized in that, The device includes an acquisition module and a processing module, wherein, The acquisition module is used to acquire time-resolved concentration test data of volatile organic compounds of the target asphalt under multiple set temperature conditions based on heat exposure tests; process and fit the time-resolved concentration test data, and acquire the model parameters corresponding to the target asphalt under multiple set temperature conditions; perform temperature dependence analysis on the model parameters, and establish a functional relationship between the model parameters and the test temperature. The processing module is used to construct an emission rate prediction model applicable to continuous temperature change conditions based on the functional relationship; and to predict and output the emission rate and emission amount of the target asphalt based on the emission rate prediction model.

9. An electronic device, characterized in that, The device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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