Production process of high-performance recycled asphalt concrete

Abstract

The invention relates to the technical field of concrete production, in particular to a high-performance recycled asphalt concrete production process which comprises the following steps: acquiring content simulation data of old materials with different particle sizes, and obtaining a mixed state regulation and control demand value of each particle size based on the content simulation data; determining an interference coefficient of each contrast particle size, and determining a feedback regulation and control tendency value in the content change process of each particle size based on the interference coefficient; and combining the mixed state regulation demand value and the feedback regulation tendency value to obtain a parameter regulation weight of each particle size, obtaining actual content data of the old material of each particle size, and performing mixed state parameter regulation to obtain a mixed asphalt mixture, thereby obtaining the recycled asphalt concrete. According to the method, the parameter regulation and control weight is determined by analyzing the content simulation data of the old materials with different particle sizes so as to regulate the mixing state parameters, so that the mixing state uniformity of the mixture is improved, and the production quality of the recycled asphalt concrete is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of concrete production, and in particular to a production process of high-performance recycled asphalt concrete. Background Art

[0002] Recycled asphalt concrete refers to an asphalt mixture made by recycling old asphalt pavement materials, crushing, screening, heating, etc., and then remixing with new asphalt, new aggregates and additives in a certain proportion. The application of recycled asphalt concrete aims to reduce resource waste, reduce production costs, and reduce environmental impact. As an environmentally friendly and economical pavement material, recycled asphalt concrete has significant advantages, but it still faces many technical problems in actual production and application.

[0003] The common asphalt concrete production process currently uses a fixed mixing time to mix new and old asphalt. However, in actual production, the particle size and composition of the old asphalt (such as the content of asphalt and aggregate) will affect its heat absorption performance and melting behavior. Since materials of different particle sizes have different thermal conductivity characteristics, larger particles may take longer to heat to reach the desired softening state, while smaller particles are easy to heat and melt. In this case, using a fixed mixing time may result in an uneven mixing state within the mixture. If the maximum mixing time is used directly for mixing, although the mixing speed can be accelerated, it will cause energy waste. Summary of the invention

[0004] In order to solve the technical problem of uneven mixing in the production process of recycled asphalt concrete due to the influence of fixed mixing time, the purpose of the present invention is to provide a high-performance recycled asphalt concrete production process, and the technical solution adopted is as follows: An embodiment of the present invention provides a high-performance recycled asphalt concrete production process, the method comprising the following steps: Acquire simulated content data of several types of old materials with different particle sizes corresponding to old asphalt, wherein the simulated content data is data between the maximum historical content value and the minimum historical content value of old materials with different particle sizes; Obtaining a set of simulated data changes of the content of a target particle size and a set of mixed state values, analyzing the correlation between the two sets, and obtaining a required value for mixed state regulation under changes in the content of the target particle size, wherein the target particle size is any particle size; Determine the interference coefficient of the content simulation data change of each comparative particle size on the mixing state under the target particle size content change, and determine the feedback control tendency value during the target particle size content change based on the interference coefficient, wherein the comparative particle size is a particle size other than the target particle size; The parameter control weight of the target particle size is obtained by combining the mixed state control demand value and the feedback control tendency value; based on the method for obtaining the parameter control weight of the target particle size, the parameter control weights of all particle sizes except the target particle size are obtained; The actual data of the content of each particle size of old asphalt corresponding to the old asphalt is obtained, and the mixing state parameters are adjusted in combination with the parameter control weight of each particle size to obtain the asphalt mixture after mixing, and then the recycled asphalt concrete is obtained.

[0005] Furthermore, a mixed state value set of the target particle size is obtained, including: The simulated content data of the target particle size is changed, and the simulated content data of various comparative particle sizes remain unchanged, to obtain a plurality of first particle size content combinations, wherein the particle size content combinations are composed of simulated content data of different particle sizes; Obtaining a viscosity time series of each first particle size content combination, and performing dimensionality reduction processing on the viscosity time series to obtain a mixing state value of each first particle size content combination during the test; All the mixed state values ​​are grouped together to obtain a mixed state value set of the target particle size.

[0006] Further, the dimension reduction process of the viscosity time series is performed to obtain the mixing state value of each first particle size content combination during the test process, including: For any viscosity time series, the average value of the viscosity time series is calculated to obtain the mean viscosity value, and the average value of all two adjacent viscosity differences is calculated to obtain the average viscosity difference value; The mixing state value during the test is obtained by combining the viscosity mean value and the viscosity difference mean value.

[0007] Further, the combining the viscosity mean value and the viscosity difference mean value to obtain the mixing state value during the test process includes: An inverse proportional analysis is performed on the viscosity mean to obtain a first inverse proportional value; and a product of the first inverse proportional value and the viscosity difference mean value is normalized to obtain a mixing state value of the first particle size content combination during the test.

[0008] Further, the determination of the interference coefficient of the content simulation data change of each comparative particle size on the mixing state under the change of the target particle size content includes: The simulated data of the content of the selected comparative particle size in each first particle size content combination is changed, and the simulated data of the content of other comparative particle sizes and the target particle size remain unchanged, to obtain each second particle size content combination corresponding to each change, wherein the selected comparative particle size is any comparative particle size; Based on the viscosity time series of each second particle size content combination corresponding to any change, the mixing state value of each second particle size content combination is obtained, and then the mixing state control demand value under the target particle size content change after the change is obtained; The simulated data change of the content of the selected comparison particle size is used as the horizontal coordinate, and the mixed state control demand value under the change of the target particle size content after the corresponding change is used as the vertical coordinate to obtain each data point on the coordinate system; The contribution rate of the content simulation data change of the selected comparative particle size to the content simulation data change of the target particle size is analyzed based on each data point on the coordinate system, and the interference coefficient of the content simulation data change of the selected comparative particle size to the mixing state under the change of the target particle size content is determined.

[0009] Further, the analysis of the contribution rate of the content simulation data change of the selected comparative particle size to the content simulation data change of the target particle size based on each data point on the coordinate system, and the determination of the interference coefficient of the content simulation data change of the selected comparative particle size to the mixing state under the change of the target particle size content, includes: Based on the PCA algorithm, several straight lines with different slopes are obtained through the coordinate origin, and then the maximum projection variance of all data points on the coordinate system on the straight line is calculated; the slope of the straight line corresponding to the maximum projection variance is determined, and the first product of the maximum projection variance and the slope is calculated; The first product is normalized to obtain an interference coefficient of a mixing state under a change in the content simulation data of a selected comparative particle size with respect to a change in the content of a target particle size.

[0010] Further, the feedback control tendency value in the process of determining the target particle size content change based on the interference coefficient includes: Based on the mixed state control demand value under the target particle size content change after each content simulation data change of the selected comparison particle size, calculate the average value of all mixed state control demand values; Based on the method of determining the interference coefficient of the mixed state under the change of the content simulation data of the selected comparative particle size, the interference coefficient corresponding to each comparative particle size is obtained; Combining the average value of all the mixed state control demand values ​​corresponding to each comparative particle size and the interference coefficient, a feedback control tendency value during the change of the target particle size content is obtained; Among them, the average value of all mixed state control demand values ​​is positively correlated with the feedback control tendency value, and the interference coefficient is negatively correlated with the feedback control tendency value.

[0011] Furthermore, the average value of all the mixed state control demand values ​​corresponding to each comparative particle size and the interference coefficient are combined to obtain the feedback control tendency value during the change of the target particle size content, including: Performing an inverse proportional analysis on the interference coefficient corresponding to each comparative particle size to obtain a second inverse proportional value of the interference coefficient corresponding to each comparative particle size; The second product of the average value of all the mixed state control demand values ​​corresponding to each comparative particle size and the second inverse proportional value is calculated, and all the second products are accumulated to obtain the feedback control tendency value during the change of the target particle size content.

[0012] Furthermore, the parameter control weight of the target particle size is obtained by combining the mixed state control demand value and the feedback control tendency value, including: The third product of the mixed state regulation demand value and the feedback regulation tendency value is calculated, and the third product is normalized to obtain the parameter regulation weight of the target particle size.

[0013] Furthermore, the actual data of the content of each particle size of old material corresponding to the old asphalt is obtained, and the mixing state parameters are adjusted in combination with the parameter control weight of each particle size, including: Calculating the fourth product of the actual content data of each particle size and the parameter control weight, and then calculating the cumulative value of the fourth products of all particle sizes, so as to normalize the cumulative value to obtain the total parameter control weight; The default parameters of the stirring time in the mixed state are obtained, and the default parameters are weighted by using the total parameter control weight to obtain the adjusted stirring time.

[0014] The present invention has the following beneficial effects: The present invention provides a high-performance recycled asphalt concrete production process, which obtains a set of simulated data changes of the content of the target particle size corresponding to the old asphalt and a set of mixed state values, and determines the mixed state control demand value under the target particle size content change by analyzing the correlation between the two sets, which can feedback the influence of the target particle size content change on the mixed state, overcomes the defect of not considering the relationship between the particle size content change and the mixed state, and is convenient for the subsequent determination of the influence of different particle sizes on the mixed state parameter control, that is, determining the parameter control weight; determining the interference coefficient of the content simulation data change of each comparison particle size on the mixed state under the target particle size content change, and then determining the feedback control tendency value in the process of target particle size content change, which can avoid the different degrees of influence of the old asphalt under different particle sizes on the overall mixing state, resulting in errors in the subsequent control process, and improves the accuracy of the feedback control process; obtaining the actual data of the content of each particle size of old materials corresponding to the old asphalt, combining the parameter control weight of each particle size to adjust the mixing state parameters to obtain a mixed asphalt mixture, and then obtaining recycled asphalt concrete, which improves the production efficiency of the production process and reduces the energy consumption in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 This is a flow chart of a high-performance recycled asphalt concrete production process according to an embodiment of the present invention; Figure 2 This is a flowchart for obtaining a mixed state value set of a target particle size in an embodiment of the present invention; Figure 3 4 is a flowchart for implementing step S3 in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the technical solutions proposed by the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The application scenarios targeted by the present invention may be: As an environmentally friendly and economical pavement material, recycled asphalt concrete has significant advantages, but it still faces many technical problems in actual production and application. For example, due to the difference in particle size of old asphalt, directly using a fixed mixing time may lead to uneven mixing state inside the mixture, which in turn makes the quality of recycled asphalt concrete low. Among them, recycled concrete is usually composed of old asphalt, new asphalt, aggregates, additives and other materials. Old asphalt refers to asphalt recycled from old pavements, and its performance may usually decline due to aging.

[0020] In order to reduce energy consumption in the production process and ensure the uniformity of the internal mixing state of the mixture, thereby improving the quality of the generated recycled asphalt concrete, this embodiment provides a high-performance recycled asphalt concrete production process, such as Figure 1 As shown, the following steps are included: S1, obtaining simulated data of the content of several types of old materials with different particle sizes corresponding to old asphalt.

[0021] The acquisition of simulated content data of several types of old materials of different particle sizes corresponding to old asphalt can be achieved through methods such as on-site sampling, historical data statistics, numerical simulation or image recognition, and the selection of an appropriate method depends on project requirements, time and budget costs. This embodiment does not specifically limit the data acquisition method. In addition, after acquiring the data, this embodiment requires detailed analysis and verification to ensure the accuracy and reliability of the collected content simulation data, so as to provide a scientific basis for the subsequent optimization of the recycled asphalt concrete production process. Among them, the content simulation data is the data between the historical maximum content and the historical minimum content of the old materials of the corresponding particle size.

[0022] Optionally, collect historical data on the recycling and treatment of old asphalt pavement in the past; analyze the historical data to extract the content data of old materials with different particle sizes as content simulation data. Obtaining content simulation data by statistically analyzing historical data can save time and cost to a certain extent, and is suitable for large-scale data analysis, but historical data may not be fully applicable to the current project, and the data quality depends on the accuracy of historical records.

[0023] Preferably, representative samples are collected from the current mixed old asphalt pavement; the samples are crushed and sieved in the laboratory to separate old materials of different particle sizes (such as 0-5mm, 5-10mm, 10-15mm, etc.); then the metal, plastic and other impurities in the old materials are removed by magnetic separation, and the old materials are cleaned when necessary to remove soil and other pollutants, and the treated old materials are dried; finally, the content of old materials of various particle sizes in the total sample is calculated by weighing or volume measurement as content simulation data. The content simulation data obtained through on-site sampling and laboratory analysis is more real and reliable, and can reflect the actual particle size distribution of old materials.

[0024] So far, this embodiment has obtained the simulated data of the content of each particle size of old material corresponding to the old asphalt, that is, obtained a particle size content combination composed of the simulated data of the content of old materials of different particle sizes.

[0025] S2, obtaining a set of simulated data changes of the target particle size content and a set of mixed state values, analyzing the correlation between the two sets, and obtaining a required value for mixed state regulation under changes in the target particle size content.

[0026] Here, the target particle size refers to any one of all particle sizes; the mixed state value set refers to the mixed state of multiple particle size content combinations obtained by changing the target particle size content simulation data, and the mixed state can be obtained by analyzing the viscosity of the simulated mixture corresponding to the particle size content combination; the mixed state control demand value refers to the control demand for related parameters under the mixed state of each particle size content combination obtained by changing the target particle size content simulation data, such as stirring time. The greater the control demand, the higher the possibility of controlling the mixing state parameters under the premise of a larger target particle size content.

[0027] The above step S2 can be implemented through steps S21 to S22 (not shown in the figure): S21, obtaining a set of simulated data changes of the content of the target particle size and a set of mixed state values.

[0028] First, a set of simulated data changes of the content of the target particle size is obtained.

[0029] In this embodiment, the content simulation data of the target particle size is numerically changed between the historical maximum content value and the historical minimum content value of the target particle size to obtain each new content simulation data, and a content simulation data change set of the target particle size is obtained. Among them, the interval between two adjacent, new content simulation data can be set to 0.1mm, and the implementer can set the interval size according to the specific actual situation and experience value; the historical maximum content value and the historical minimum content value refer to the maximum value and minimum value of the target particle size in all previous content data.

[0030] Secondly, obtain the mixed state value set of the target particle size, which can be obtained by Figure 2 The steps S211 to S212 shown implement: S211, changing the content simulation data of the target particle size, and keeping the content simulation data of various comparison particle sizes unchanged, to obtain a plurality of first particle size content combinations.

[0031] In this embodiment, the first particle size content combination is obtained by the control variable method. For the particle size content combination obtained in step S1, the content simulation data of the target particle size in the particle size content combination is continuously replaced by each data in the content simulation data change set of the target particle size, while the content simulation data of other particle sizes except the target particle size is kept unchanged, so that multiple new particle size content combinations can be obtained, and the particle size content combination and the new particle size content combination are collectively referred to as the first particle size content combination. Among them, the particle sizes other than the target particle size are used as the comparison particle sizes of the target particle size.

[0032] S212, obtaining the viscosity time series of each first particle size content combination, and performing dimensionality reduction processing on the viscosity time series to obtain the mixing state value of each first particle size content combination during the test process.

[0033] In the first step, the viscosity time series of each first particle size content combination is obtained.

[0034] The viscosity time series data corresponding to different historical particle size content combinations are obtained through experiments, and the machine learning model is trained to predict the viscosity time series of the first particle size content combination.

[0035] As an example, the specific steps include: 1) Prepare a mixture of different particle size content combinations in the laboratory, use a viscometer to measure the viscosity of the mixture at different time points, and generate viscosity time series data. 2) Clean and normalize the experimental data obtained in step 1), and divide the preprocessed experimental data into a training set and a test set. 3) Select a machine learning algorithm, such as a neural network, support vector machine, or random forest, input the particle size content combination and stirring conditions corresponding to the training set (ensuring that the heating power and stirring rate are consistent) for model training, and output the viscosity time series. 4) Use the test set to evaluate the prediction accuracy of the model, obtain the constructed and trained viscosity prediction model, input each first particle size content combination into the viscosity prediction model, and obtain the viscosity time series of each first particle size content combination.

[0036] It should be noted that the machine learning model based on experimental data has a fast calculation speed and is suitable for large-scale data processing. In addition, the viscosity prediction model can still be used when calculating the viscosity time series of the second particle size content combination, which improves the efficiency of the mixed state value analysis to a certain extent. Among them, the detailed training process of the viscosity prediction model is a prior art and is not within the scope of protection of the present invention, and will not be elaborated here.

[0037] In another example, by using the inclined trough method, sampling and testing are performed at fixed time intervals, and concrete samples are injected into the inclined trough to measure the time and length of concrete flow, thereby calculating and obtaining the viscosity value.

[0038] In the second step, the viscosity time series is processed by dimension reduction to obtain the mixing state value of each first particle size content combination during the test.

[0039] Since the viscosity time series is time series data within a period of time, it is two-dimensional data, which is not conducive to subsequent analysis and processing. Therefore, it is necessary to reduce the dimension of the viscosity time series. Dimensionality reduction can reduce the complexity of the data while retaining its main features and information, and avoid the influence of complex changes in the time series data of the mixed state on the content change law. Among them, common dimensionality reduction methods are dimensionality reduction methods based on feature extraction, dimensionality reduction methods based on model fitting, and dimensionality reduction methods based on machine learning.

[0040] Based on prior knowledge, asphalt will melt within a certain temperature range. When the temperature is higher, its viscosity is lower. When the viscosity of the mixture is lower during the stirring and mixing process, it means that the stirring efficiency is higher, the mixing quality of the mixture is better, and the need for mixing state regulation is smaller. At the same time, the mixing quality of the mixture gradually increases with time, so the greater the decrease in viscosity, the higher the current mixing efficiency is, and the smaller the need for mixing state regulation is.

[0041] Based on the above description of the correlation between viscosity and mixing state, this embodiment proposes a method for dimensionality reduction processing of viscosity time series, including: The average value of the viscosity time series is calculated to obtain the mean viscosity value, and the average value of all two adjacent viscosity differences is calculated to obtain the average viscosity difference value; the viscosity mean value and the viscosity difference average value are combined to obtain the mixing state value during the test.

[0042] Furthermore, an inverse proportional analysis is performed on the viscosity mean to obtain a first inverse proportional value; the product of the first inverse proportional value and the viscosity difference mean value is normalized to obtain a mixing state value of the first particle size content combination during the test.

[0043] As an example, the mixing state value of the i-th first particle size content combination during the test process is The calculation formula can be: ; In the formula, norm represents the linear normalization function, Indicates data The first inverse proportional value obtained by inverse proportional processing, represents the mean viscosity of the viscosity time series of the i-th first particle size content combination, Represents the average viscosity difference of the viscosity time series of the i-th first particle size content combination.

[0044] In the calculation formula of the mixing state value, the viscosity difference is equal to the difference between the previous viscosity data and the next viscosity data in the viscosity time series, which can represent the decrease in viscosity over time. If there are multiple small decreases, it means that the mixing efficiency is low, and the mixing state of the mixture corresponding to the i-th first particle size content combination is poor, and the mixing state value is small; the viscosity mean can represent the overall viscosity of the mixture corresponding to the i-th first particle size content combination during the stirring process. The larger the overall viscosity, the lower the stirring efficiency, the worse the mixing quality of the mixture, the worse the mixing state of the mixture corresponding to the i-th first particle size content combination, and the smaller the mixing state value.

[0045] S22, performing correlation analysis on the content simulation data change set and the mixing state value set to obtain the mixing state control demand value under the target particle size content change.

[0046] First of all, it should be noted that the final stirring quality of the mixture is related to the content of different particle sizes added. Based on the content simulation data change set and the mixing state value set obtained by controlling the variables above, the feedback control tendency of the mixing device due to the change in the content of the target particle size in the mixture can be obtained. The feedback control tendency is the control demand under the mixing state.

[0047] Specifically, the data in the target particle size content simulation data change set are arranged from small to large or from large to small to obtain a new content simulation data change set; for the target particle size content simulation data of the first particle size content combination corresponding to each mixing state value in the mixing state value set, the mixing state values ​​in the mixing state value set are sorted in the same arrangement method as the new content simulation data change set to obtain a new mixing state value set, in order to ensure that two data with the same sequence number in the two new sets correspond to the same first particle size content combination; based on the Pearson correlation coefficient, the correlation between the new content simulation data change set and the new mixing state value set is analyzed to obtain the absolute value of the correlation coefficient, which is used as the mixing state control demand value under the target particle size content change.

[0048] Among them, the mixing state control demand value represents the influence of the target particle size on the mixing state. The closer the mixing state control demand value is to 1, the greater the influence of the change in the content simulation data of the target particle size on the mixing state of the mixture. It further indicates that the higher the feedback control tendency of the target particle size change on the mixing device, that is, the higher the demand for related parameter control, such as the higher the demand for controlling the stirring time. The implementation process of the Pearson correlation coefficient is a prior art and is not within the scope of protection of the present invention. It will not be elaborated here. Of course, the implementer may also adopt other existing correlation analysis methods, and this embodiment does not make specific limitations on this.

[0049] So far, this embodiment has obtained the mixing state control requirement value under the change of target particle size content.

[0050] S3, determining the interference coefficient of the content simulation data change of each comparative particle size on the mixing state under the target particle size content change, and determining the feedback control tendency value during the target particle size content change based on the interference coefficient.

[0051] Here, the interference coefficient refers to the degree of influence of the change of any comparative particle size in the first particle size content combination on the mixing state corresponding to the combination, and the feedback control tendency value refers to the degree of influence of the changes of all comparative particle sizes on the mixing state under the change of the target particle size. The reason is that the changes in the content of different particle sizes in the mixture have different impacts on the mixing state. When adding old asphalt in different proportions, the temperature absorption efficiency between the new and old asphalt in the entire mixing device may change. Therefore, changes in other particle size contents will cause errors in the feedback control of the entire mixing process, so it is necessary to calculate the feedback control tendency value based on the interference coefficient.

[0052] The above step S3 can be Figure 3 The steps S31 to S35 shown implement: S31, changing the content simulation data of the selected comparative particle size in each first particle size content combination, keeping the content simulation data of other comparative particle sizes and target particle size unchanged, and obtaining each second particle size content combination corresponding to each change.

[0053] In this embodiment, the selected comparison particle size is any comparison particle size, and the second particle size content combination is a combination in which the target particle size and any comparison particle size in the combination change relative to the particle size content combination, that is, a combination obtained by changing the content simulation data of the selected comparison particle size in each first particle size content combination, and each change corresponds to multiple different second particle size content combinations.

[0054] S32, obtaining the mixing state value of each second particle size content combination based on the viscosity time series of each second particle size content combination corresponding to any change, and then obtaining the mixing state control demand value under the target particle size content change after the change.

[0055] In this embodiment, referring to the contents recorded in the above step S2, the viscosity time series of each second particle size content combination can be obtained, and then the viscosity time series of the second particle size content combination is subjected to dimensionality reduction processing to obtain the mixing state value of each second particle size content combination, and then the mixing state control demand value under the target particle size content change after the selected comparison particle size content simulation data changes each time is obtained. It should be noted that for processes with different data to be analyzed but the same calculation method, this embodiment does not repeat the description thereof.

[0056] S33, using the simulated data change of the content of the selected comparison particle size as the horizontal coordinate, and using the mixing state control demand value under the target particle size content change after the corresponding change as the vertical coordinate, to obtain each data point on the coordinate system.

[0057] In this embodiment, data points on the coordinates are constructed by the change of the selected comparative particle size and the influence of the target particle size on the mixing state when the selected comparative particle size changes.

[0058] S34, analyzing the contribution rate of the content simulation data change of the selected comparative particle size to the content simulation data change of the target particle size based on each data point on the coordinate system, and determining the interference coefficient of the content simulation data change of the selected comparative particle size to the mixing state under the target particle size content change.

[0059] It should be noted that the change in the content of old materials with different particle sizes will affect the mixing state. In order to analyze the weight of each particle size for parameter adjustment, it is necessary to quantify the degree of interference of each comparative particle size change on the target particle size in the mixing state.

[0060] The above step S34 can be implemented through steps S341 to S342 (not shown in the figure): S341, based on the PCA algorithm idea, obtain several straight lines with different slopes through the coordinate origin, and then calculate the maximum projection variance of all data points in the coordinate system on the straight line; determine the slope of the straight line corresponding to the maximum projection variance, and calculate the first product of the maximum projection variance and the slope.

[0061] S342, normalizing the first product to obtain an interference coefficient of a mixing state under a change in the simulated data of the content of the selected comparison particle size with respect to a change in the content of the target particle size.

[0062] Specifically, based on the idea of ​​PCA algorithm, firstly, several straight lines with different slopes are obtained through the coordinate origin, and the projections of all data points in the coordinate system on each straight line are obtained; then, the projection variance corresponding to each straight line is calculated, and the maximum projection variance is selected from all the projection variances; then, the slope of the straight line corresponding to the maximum projection variance is determined, and the maximum projection variance and the slope are combined to calculate the product of the two as the first product; finally, the first product is normalized to obtain the interference coefficient of the mixed state under the change of the content simulation data of the selected comparison particle size under the change of the target particle size content.

[0063] Among them, PCA projects the original data into a new coordinate system through linear transformation, so that the variance of the projected data on the new coordinate axis is maximized. The first principal component is the direction with the largest variance after data projection, the second principal component is the direction orthogonal to the first principal component and has the second largest variance, and so on.

[0064] As an example, the calculation formula for the interference coefficient of the content simulation data change of the xth comparison particle size on the mixing state under the change of the target particle size content can be: ; In the formula, It represents the interference coefficient of the content simulation data change of the x-th comparison particle size on the mixing state under the change of the target particle size content, x represents the x-th comparison particle size, j represents the target particle size, norm represents the normalization function, Represents the slope of the straight line corresponding to the maximum projection variance, represents the maximum projected variance.

[0065] In the calculation formula of the interference coefficient, the maximum projected variance reflects the degree of influence of the content change of the x-th comparison particle size on the mixing state of the target particle size. The larger the variance, the more significant the influence. The calculation process of the projected variance is the prior art and is not within the protection scope of the present invention, and will not be elaborated on here; the slope reflects the sensitivity of the content change of the x-th comparison particle size to the mixing state of the target particle size. The larger the slope, the more sensitive the target particle size is to the change in the content of the x-th comparison particle size; the interference coefficient combines the degree of influence and sensitivity, and quantifies the interference intensity of the x-th comparison particle size on the target particle size.

[0066] It should be noted that the calculation principle of the interference coefficient is based on the idea of ​​PCA algorithm. By calculating the maximum projection variance and slope of the data points on the straight line, the degree of interference of the content simulation data change of the x-th comparison particle size on the target particle size mixing state is quantified. The core idea is to comprehensively reflect the significance and sensitivity of the impact through the product of the projection variance and the slope, which can provide a basis for optimizing the mixing process.

[0067] S35, determining the feedback control tendency value during the change of the target particle size content based on the interference coefficient.

[0068] The above step S35 can be implemented through steps S351 to S353 (not shown in the figure): S351, calculating the average value of all the mixing state control demand values ​​based on the mixing state control demand value under the target particle size content change after each content simulation data change of the selected comparison particle size.

[0069] S352, based on the method of determining the interference coefficient of the mixing state under the change of the content simulation data of the selected comparative particle size to the change of the target particle size content, obtain the interference coefficient corresponding to each comparative particle size.

[0070] S353, combining the average value and interference coefficient of all mixed state control demand values ​​corresponding to each comparative particle size, to obtain the feedback control tendency value during the change of the target particle size content.

[0071] Here, the average value of all mixed state control demand values ​​is positively correlated with the feedback control tendency value, and the interference coefficient is negatively correlated with the feedback control tendency value. The positive correlation means that the larger the average value, the larger the feedback control tendency value, and the negative correlation means that the larger the interference coefficient, the smaller the feedback control tendency value.

[0072] Specifically, an inverse proportional analysis is performed on the interference coefficient corresponding to each comparative particle size to obtain a second inverse proportional value of the interference coefficient corresponding to each comparative particle size; the average value of all mixed state control demand values ​​corresponding to each comparative particle size and the second product of the second inverse proportional value are calculated, and all the second products are accumulated to obtain the feedback control tendency value during the change of the target particle size content.

[0073] As an example, the calculation formula for the feedback control tendency value during the change of the target particle size content can be: ; In the formula, It indicates the feedback control tendency value during the change of target particle size content, N indicates the total number of comparison particle sizes, It indicates the average value of the required value for the mixed state control under the change of the target particle size content when the content of the x-th comparison particle size changes. It indicates the interference coefficient of the content simulation data change of the x-th comparison particle size on the mixing state under the change of the target particle size content. +0.01 is to avoid the extreme situation that causes the interference coefficient to be zero. Represents the second inverse proportional value of the interference coefficient corresponding to the x-th comparative particle size.

[0074] In the calculation formula of the feedback control tendency value, the smaller the interference coefficient is, the more real the impact of the current comparison particle size content change on the mixing state is. Therefore, the average value of the mixing state control demand value is weighted using a fraction, and each comparison particle size is traversed at the same time to obtain the feedback control tendency value during the target particle size content change process. The larger the feedback control tendency value is, the higher the control tendency of the target particle size content change on the mixing state process.

[0075] So far, this embodiment has obtained the feedback control tendency value during the change of the target particle size content.

[0076] S4, combining the mixed state control demand value and the feedback control tendency value to obtain the parameter control weight of the target particle size; based on the method for obtaining the parameter control weight of the target particle size, obtain the parameter control weights of all particle sizes except the target particle size.

[0077] It should be noted that in the process of mixing the new and old asphalt, the new and old asphalt are mixed with a fixed stirring time. Since the temperature absorption capacity of the old materials with different particle sizes and different contents is affected by the proportion of asphalt and stone in the particles of the old materials themselves, the asphalt of the old materials melts slowly. At this time, using a fixed stirring time will make the mixing state inside the mixture uneven, and directly using the maximum stirring time will lead to energy waste. Therefore, it is necessary to determine the parameter control weight of each particle size in order to adjust the current stirring time.

[0078] Specifically, the third product of the mixed state control demand value and the feedback control tendency value is calculated, and the third product is normalized to obtain the parameter control weight of the target particle size.

[0079] As an example, the calculation formula for the parameter control weight of the target particle size can be: ; In the formula, represents the parameter control weight of the target particle size, norm represents the linear normalization function, Indicates the required value for the mixed state control of the target particle size, It indicates the feedback control tendency value during the change of target particle size content.

[0080] In the calculation formula of the parameter control weight, the factors of the parameter control requirements under the changes of the two target particle sizes are combined to obtain the parameter control weight caused by the influence of the change of the content of the target particle size on the mixing state of the mixture. The larger the parameter control weight, the higher the parameter control requirement of the content of the target particle size. After obtaining the parameter control weight of the target particle size, the parameter control weight of each particle size can be obtained by referring to the method of obtaining the parameter control weight of the target particle size.

[0081] It should be noted that after obtaining the parameter control weights for each particle size, it can be applied to the mixing time adjustment of any recycled asphalt concrete production, which improves the robustness of the mixing time adjustment. It also helps to obtain a concrete production process that is more suitable for different actual situations and has old asphalt crushing, without the need to repeatedly calculate the parameter control weights for different particle sizes.

[0082] At this point, this embodiment obtains the parameter control weights corresponding to each particle size of the old asphalt.

[0083] S5, obtaining actual data of the content of old materials of each particle size corresponding to the old asphalt, adjusting the mixing state parameters in combination with the parameter control weight of each particle size to obtain the asphalt mixture after mixing, and then obtaining the recycled asphalt concrete.

[0084] Specifically, the fourth product of the actual content data of each particle size and the parameter control weight is calculated, and then the cumulative value of the fourth product of all particle sizes is calculated, so as to normalize the cumulative value to obtain the total parameter control weight; the default parameters of the stirring time under the mixed state are obtained, and the default parameters are weighted using the total parameter control weight to obtain the adjusted stirring time.

[0085] As an example, the calculation formula of the adjusted mixing state parameter can be: ; In the formula, represents the adjusted stirring time, U represents the default parameter of the stirring time under the mixed state, and c represents a hyperparameter, whose empirical value can be 0.5. represents the total parameter control weight, norm represents the linear normalization function, J represents the number of particle size types, It represents the parameter control weight of the target particle size, and can also represent the parameter control weight of the j-th particle size. Indicates the actual data of the content of the jth particle size.

[0086] In the calculation formula of the adjusted mixing state parameters, the greater the total parameter control weight, the greater the need for adjusting the stirring time in the current mixing process. When the adjustment demand is greater than 0.5, the stirring time needs to be increased. Conversely, when the adjustment demand is less than 0.5, the stirring time needs to be reduced. By determining the adjusted mixing state parameters, poor final mixing effect or energy waste caused by differences in the content of different particle sizes can be avoided.

[0087] In this embodiment, after obtaining the parameter control weights for each particle size, the parameter control weights are applied to the adjustment of the mixing time. First, the actual data of the content of each particle size of old asphalt corresponding to the actual production process of recycled asphalt concrete is obtained, and the actual data of the content of each particle size of old material and the parameter control weights obtained by analyzing the content changes are integrated to obtain the control weights of the mixing time; then the control weights are used to adjust the default mixing time, and the recycled asphalt concrete production process is continued based on the adjusted mixing time, that is, according to actual needs, the mixed asphalt mixture is added, and the final mixing drum is stirred, thereby obtaining recycled asphalt concrete.

[0088] At this point, this embodiment completes the production of high-performance recycled asphalt concrete.

[0089] The present invention provides a high-performance recycled asphalt concrete production process. By analyzing simulated data on the content of different particle sizes of old asphalt in the asphalt mixture, the feedback adjustment weights of the different particle sizes in the mixing process are determined, and the mixing time is corrected based on the feedback adjustment weights, thereby effectively improving the production efficiency of the production process while reducing energy consumption in the production process.

[0090] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A high-performance recycled asphalt concrete production process, characterized in that: The following steps are involved: Acquire simulated content data of several types of old materials with different particle sizes corresponding to old asphalt, wherein the simulated content data is data between the maximum historical content value and the minimum historical content value of old materials with different particle sizes; Obtaining a set of simulated data changes of the content of a target particle size and a set of mixed state values, analyzing the correlation between the two sets, and obtaining a required value for mixed state regulation under changes in the content of the target particle size, wherein the target particle size is any particle size; Determine the interference coefficient of the content simulation data change of each comparative particle size on the mixing state under the target particle size content change, and determine the feedback control tendency value during the target particle size content change based on the interference coefficient, wherein the comparative particle size is a particle size other than the target particle size; The parameter control weight of the target particle size is obtained by combining the mixed state control demand value and the feedback control tendency value; based on the method for obtaining the parameter control weight of the target particle size, the parameter control weights of all particle sizes except the target particle size are obtained; The actual data of the content of each particle size of old asphalt corresponding to the old asphalt is obtained, and the mixing state parameters are adjusted in combination with the parameter control weight of each particle size to obtain the asphalt mixture after mixing, and then the recycled asphalt concrete is obtained.

2. A high-performance recycled asphalt concrete production process according to claim 1, characterized in that: Get the mixed state value set of the target particle size, including: The simulated content data of the target particle size is changed, and the simulated content data of various comparative particle sizes remain unchanged, to obtain a plurality of first particle size content combinations, wherein the particle size content combinations are composed of simulated content data of different particle sizes; Obtaining a viscosity time series of each first particle size content combination, and performing dimensionality reduction processing on the viscosity time series to obtain a mixing state value of each first particle size content combination during the test; All the mixed state values ​​are grouped together to obtain a mixed state value set of the target particle size.

3. A high-performance recycled asphalt concrete production process according to claim 2, characterized in that: The step of performing dimension reduction processing on the viscosity time series to obtain a mixing state value of each first particle size content combination during the test process includes: For any viscosity time series, the average value of the viscosity time series is calculated to obtain the mean viscosity value, and the average value of all two adjacent viscosity differences is calculated to obtain the average viscosity difference value; The mixing state value during the test is obtained by combining the viscosity mean value and the viscosity difference mean value.

4. A high-performance recycled asphalt concrete production process according to claim 3, characterized in that: The combining the viscosity mean value and the viscosity difference mean value to obtain the mixing state value during the test process includes: An inverse proportional analysis is performed on the viscosity mean to obtain a first inverse proportional value; and a product of the first inverse proportional value and the viscosity difference mean value is normalized to obtain a mixing state value of the first particle size content combination during the test.

5. A high-performance recycled asphalt concrete production process according to claim 3, characterized in that: The method of determining the interference coefficient of the content simulation data change of each comparative particle size on the mixing state under the change of the target particle size content includes: The simulated data of the content of the selected comparative particle size in each first particle size content combination is changed, and the simulated data of the content of other comparative particle sizes and the target particle size remain unchanged, to obtain each second particle size content combination corresponding to each change, wherein the selected comparative particle size is any comparative particle size; Based on the viscosity time series of each second particle size content combination corresponding to any change, the mixing state value of each second particle size content combination is obtained, and then the mixing state control demand value under the target particle size content change after the change is obtained; The simulated data change of the content of the selected comparison particle size is used as the horizontal coordinate, and the mixed state control demand value under the change of the target particle size content after the corresponding change is used as the vertical coordinate to obtain each data point on the coordinate system; The contribution rate of the content simulation data change of the selected comparative particle size to the content simulation data change of the target particle size is analyzed based on each data point on the coordinate system, and the interference coefficient of the content simulation data change of the selected comparative particle size to the mixing state under the target particle size content change is determined.

6. A high-performance recycled asphalt concrete production process according to claim 5, characterized in that: The step of analyzing the contribution rate of the content simulation data change of the selected comparative particle size to the content simulation data change of the target particle size based on each data point on the coordinate system, and determining the interference coefficient of the content simulation data change of the selected comparative particle size to the mixing state under the change of the target particle size content, comprises: Based on the PCA algorithm, several straight lines with different slopes are obtained through the coordinate origin, and then the maximum projection variance of all data points on the coordinate system on the straight line is calculated; the slope of the straight line corresponding to the maximum projection variance is determined, and the first product of the maximum projection variance and the slope is calculated; The first product is normalized to obtain an interference coefficient of a mixing state under a change in the content simulation data of a selected comparative particle size with respect to a change in the content of a target particle size.

7. A high-performance recycled asphalt concrete production process according to claim 6, characterized in that: The method of determining the feedback control tendency value in the process of target particle size content change based on the interference coefficient includes: Based on the mixed state control demand value under the target particle size content change after each content simulation data change of the selected comparison particle size, calculate the average value of all mixed state control demand values; Based on the method of determining the interference coefficient of the mixed state under the change of the content simulation data of the selected comparative particle size, the interference coefficient corresponding to each comparative particle size is obtained; Combining the average value of all the mixed state control demand values ​​corresponding to each comparative particle size and the interference coefficient, a feedback control tendency value during the change of the target particle size content is obtained; Among them, the average value of all mixed state control demand values ​​is positively correlated with the feedback control tendency value, and the interference coefficient is negatively correlated with the feedback control tendency value.

8. A high-performance recycled asphalt concrete production process according to claim 7, characterized in that: The feedback control tendency value during the change of the target particle size content is obtained by combining the average value of all the mixed state control demand values ​​corresponding to each comparative particle size and the interference coefficient, including: Performing an inverse proportional analysis on the interference coefficient corresponding to each comparative particle size to obtain a second inverse proportional value of the interference coefficient corresponding to each comparative particle size; The second product of the average value of all the mixed state control demand values ​​corresponding to each comparative particle size and the second inverse proportional value is calculated, and all the second products are accumulated to obtain the feedback control tendency value during the change of the target particle size content.

9. A high-performance recycled asphalt concrete production process according to claim 1, characterized in that: The parameter control weight of the target particle size is obtained by combining the mixed state control demand value and the feedback control tendency value, including: The third product of the mixed state regulation demand value and the feedback regulation tendency value is calculated, and the third product is normalized to obtain the parameter regulation weight of the target particle size.

10. A high-performance recycled asphalt concrete production process according to claim 1, characterized in that: The method of obtaining the actual data of the content of each particle size of old material corresponding to the old asphalt and adjusting the mixing state parameters in combination with the parameter control weight of each particle size includes: Calculating the fourth product of the actual content data of each particle size and the parameter control weight, and then calculating the cumulative value of the fourth products of all particle sizes, so as to normalize the cumulative value to obtain the total parameter control weight; The default parameters of the stirring time in the mixed state are obtained, and the default parameters are weighted by using the total parameter control weight to obtain the adjusted stirring time.

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