A high-performance recycled asphalt concrete production process

Abstract

The present invention relates to the technical field of concrete production, and specifically relates to a high-performance recycled asphalt concrete production process, including: obtaining content simulation data of old materials with different particle sizes, and obtaining the regulation demand value of the mixing state of each particle size based on the content simulation data; determining the interference coefficient of each comparison particle size, and determining the feedback regulation tendency value during the change process of the content of each particle size based on the interference coefficient; combining the regulation demand value of the mixing state and the feedback regulation tendency value to obtain the parameter regulation weight of each particle size, obtaining the actual content data of old materials of each particle size to adjust the mixing state parameters to obtain an asphalt mixture at the end of mixing, and further obtaining recycled asphalt concrete. By analyzing the content simulation data of old materials with different particle sizes, the present invention determines the parameter regulation weight to adjust the mixing state parameters, improves the uniformity of the mixing state of the mixture, and further improves the production quality of recycled asphalt concrete.

Description

Technical Field

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

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

[0003] Currently, common asphalt concrete production processes usually mix old and new asphalt based on a fixed mixing time. However, in actual production, the particle size and composition of old asphalt (such as the content of asphalt and aggregates) affect its heat absorption performance and melting behavior. Since materials with different particle sizes have different heat conduction characteristics, larger particles may require a longer time to heat up to the required softened state, while smaller particles are easier to heat and melt. In this case, using a fixed mixing time may result in an uneven mixing state inside the mixture. If the maximum mixing time is directly used for mixing treatment, although the mixing speed can be increased, 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 affected by the fixed mixing time, the purpose of the present invention is to provide a production process for high-performance recycled asphalt concrete, and the specific technical solutions adopted are as follows:

[0005] An embodiment of the present invention provides a production process for high-performance recycled asphalt concrete, and the method includes the following steps:

[0006] Obtain the content simulation data of several kinds of particle size old materials corresponding to old asphalt, and the content simulation data is the data between the historical content maximum value and the historical content minimum value of the corresponding particle size old materials;

[0007] Obtain the change set of content simulation data and the mixing state value set of the target particle size, analyze the correlation between the two sets, and obtain the mixing state regulation demand value under the change of the target particle size content, where the target particle size is any one of the particle sizes;

[0008] Determine the interference coefficient of the change in the content simulation data of each comparison particle size on the mixing state under the change of the target particle size content, and determine the feedback regulation tendency value during the change of the target particle size content based on the interference coefficient, where the comparison particle size is other particle sizes except the target particle size;

[0009] Combine the mixed state regulation demand value and the feedback regulation tendency value to obtain the parameter regulation weight of the target particle size; based on the acquisition method of the parameter regulation weight of the target particle size, obtain the parameter regulation weights of all particle sizes other than the target particle size;

[0010] Obtain the actual data of the content of each particle size of the old material corresponding to the old asphalt, and combine the parameter regulation weights of each particle size to adjust the mixed state parameters to obtain the asphalt mixture at the end of mixing, and then obtain the recycled asphalt concrete.

[0011] Further, obtain the set of mixed state values of the target particle size, including:

[0012] Vary the simulated data of the content of the target particle size while keeping the simulated data of the content of various comparison particle sizes unchanged to obtain a number of first particle size content combinations, where the particle size content combination is composed of the simulated data of the content of different particle sizes;

[0013] Obtain the viscosity time series of each first particle size content combination, and perform dimensionality reduction processing on the viscosity time series to obtain the mixed state value of each first particle size content combination during the test;

[0014] Combine all the mixed state values to form a set to obtain the set of mixed state values of the target particle size.

[0015] Further, the step of performing dimensionality reduction processing on the viscosity time series to obtain the mixed state value of each first particle size content combination during the test includes:

[0016] For any viscosity time series, calculate the average value of the viscosity time series to obtain the viscosity mean value, and calculate the average value of all adjacent two viscosity differences to obtain the average viscosity difference value;

[0017] Combine the viscosity mean value and the average viscosity difference value to obtain the mixed state value during the test.

[0018] Further, the step of combining the viscosity mean value and the average viscosity difference value to obtain the mixed state value during the test includes:

[0019] Perform inverse proportion analysis on the viscosity mean value to obtain the first inverse proportion value; perform normalization processing on the product of the first inverse proportion value and the average viscosity difference value to obtain the mixed state value of the first particle size content combination during the test.

[0020] Further, the step of determining the interference coefficient of the change in the simulated data of the content of each comparison particle size on the mixed state under the change in the content of the target particle size includes:

[0021] Vary the simulated content of the selected comparison particle size in each combination of the first particle size contents, while keeping the simulated contents of other comparison particle sizes and the target particle size unchanged, to obtain each combination of the second particle size contents corresponding to each change, where the selected comparison particle size is any one of the comparison particle sizes;

[0022] Based on the viscosity time series of each combination of the second particle size contents corresponding to any change, obtain the mixing state values of each combination of the second particle size contents, and further obtain the mixing state regulation demand value under the change of the target particle size content after this change;

[0023] Take the change of the simulated content of the selected comparison particle size as the abscissa, and take the mixing state regulation demand value under the change of the target particle size content after the corresponding change as the ordinate to obtain each data point on the coordinate system;

[0024] Based on each data point on the coordinate system, analyze the contribution rate of the change of the simulated content of the selected comparison particle size to the change of the simulated content of the target particle size, and determine the interference coefficient of the change of the simulated content of the selected comparison particle size to the mixing state under the change of the target particle size content.

[0025] Further, the analyzing the contribution rate of the change of the simulated content of the selected comparison particle size to the change of the simulated content of the target particle size based on each data point on the coordinate system, and determining the interference coefficient of the change of the simulated content of the selected comparison particle size to the mixing state under the change of the target particle size content includes:

[0026] Based on the idea of the PCA algorithm, obtain several straight lines with different slopes passing through the origin of the coordinate, and then calculate the maximum projection variance of all data points on 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;

[0027] Perform normalization processing on the first product to obtain the interference coefficient of the change of the simulated content of the selected comparison particle size to the mixing state under the change of the target particle size content.

[0028] Further, the determining the feedback regulation tendency value during the change of the target particle size content based on the interference coefficient includes:

[0029] Based on the mixing state regulation demand value under the change of the target particle size content after each change of the simulated content of the selected comparison particle size, calculate the average value of all mixing state regulation demand values;

[0030] Based on the determination method of the interference coefficient of the change of the simulated content of the selected comparison particle size to the mixing state under the change of the target particle size content, obtain the interference coefficient corresponding to each comparison particle size;

[0031] Combining the average value of all the mixing state regulation demand values corresponding to each comparison particle size and the interference coefficient, a feedback regulation tendency value during the change process of the target particle size content is obtained;

[0032] Among them, the average value of all the mixing state regulation demand values is positively correlated with the feedback regulation tendency value, and the interference coefficient is negatively correlated with the feedback regulation tendency value.

[0033] Further, the combining the average value of all the mixing state regulation demand values corresponding to each comparison particle size and the interference coefficient to obtain the feedback regulation tendency value during the change process of the target particle size content includes:

[0034] Performing an inverse proportion analysis on the interference coefficient corresponding to each comparison particle size to obtain a second inverse proportion value of the interference coefficient corresponding to each comparison particle size;

[0035] Calculating a second product of the average value of all the mixing state regulation demand values corresponding to each comparison particle size and the second inverse proportion value, and accumulating and calculating all the second products to obtain the feedback regulation tendency value during the change process of the target particle size content.

[0036] Further, the combining the mixing state regulation demand value and the feedback regulation tendency value to obtain the parameter regulation weight of the target particle size includes:

[0037] Calculating a third product of the mixing state regulation demand value and the feedback regulation tendency value, and performing a normalization process on the third product to obtain the parameter regulation weight of the target particle size.

[0038] Further, the obtaining the actual content data of each particle size old material corresponding to the old asphalt and combining the parameter regulation weights of each particle size to adjust the mixing state parameters includes:

[0039] Calculating a fourth product of the actual content data of each particle size and the parameter regulation weight, further calculating the accumulated value of the fourth products of all particle sizes, and thus performing a normalization process on the accumulated value to obtain the total parameter regulation weight;

[0040] Obtaining the default parameter of the stirring time in the mixing state, and using the total parameter regulation weight to perform a weighting process on the default parameter to obtain the adjusted stirring time.

[0041] The present invention has the following beneficial effects:

[0042] The present invention provides a production process for high-performance recycled asphalt concrete, 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 mixing state values. By analyzing the correlation between the two sets, the regulation requirement value of the mixing state under the change of the target particle size content is determined, which can reflect the influence of the change of the target particle size content on the mixing state, overcomes the defect that the existing technology does not consider the relationship between the particle size content change and the mixing state, and is convenient for subsequently determining the influence of different particle sizes on the regulation of the mixing state parameters, that is, determining the parameter regulation weight; determining the interference coefficient of the change of the simulated data of the content of each comparison particle size on the mixing state under the change of the target particle size content, and then determining the feedback regulation tendency value during the change of the target particle size content, which can avoid the different degrees of influence of the old asphalt under different particle sizes on the overall mixing state and cause errors in the subsequent regulation process, and improves the accuracy of the feedback regulation process; obtaining the actual data of the content of each particle size of the old material corresponding to the old asphalt, and adjusting the mixing state parameters in combination with the parameter regulation weight of each particle size to obtain the asphalt mixture at the end of mixing, and then obtaining the recycled asphalt concrete, which improves the production efficiency of the production process and reduces the energy consumption during the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a flowchart for implementing a production process for high-performance recycled asphalt concrete according to an embodiment of the present invention;

[0045] Figure 2 It is a flowchart for implementing the process of obtaining the set of mixing state values of the target particle size in the embodiment of the present invention;

[0046] Figure 3 It is a flowchart for implementing step S3 in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific implementation manners, structures, features and effects of the technical solutions proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0049] The application scenarios targeted by the present invention can be:

[0050] As an environmentally friendly and economical pavement material, recycled asphalt concrete has significant advantages, but still faces many technical problems in actual production and application. For example, affected by the particle size difference of old asphalt, directly adopting a fixed mixing time may lead to uneven mixing state inside the mixture, thereby resulting in low production quality of recycled asphalt concrete. Among them, recycled concrete is usually composed of materials such as old asphalt, new asphalt, aggregates, additives, etc. Old asphalt refers to the asphalt recovered from old pavements, and its performance may usually decline due to aging.

[0051] In order to reduce energy consumption during the production process and ensure the uniformity of the mixing state inside the mixture, thereby improving the production quality of recycled asphalt concrete, this embodiment provides a high-performance recycled asphalt concrete production process, as Figure 1 shown, including the following steps:

[0052] S1, obtaining content simulation data of several kinds of old materials with different particle sizes corresponding to old asphalt.

[0053] Regarding obtaining content simulation data of several kinds of old materials with different particle sizes corresponding to old asphalt, it can be achieved by methods such as on-site sampling, historical data statistics, numerical simulation, or image recognition. The choice of a suitable method depends on project requirements, time, and budget costs. This embodiment does not make specific limitations on the data acquisition method. Additionally, after obtaining the data in this embodiment, detailed analysis and verification are required to ensure the accuracy and reliability of the collected content simulation data, so as to provide a scientific basis for optimizing the recycled asphalt concrete production process in the subsequent steps. Among them, the content simulation data is the data between the historical content maximum value and the historical content minimum value of the old materials corresponding to the particle size.

[0054] Optionally, collect historical data on the recycling and treatment of old asphalt pavements in the past; analyze the historical data to extract the content data of old materials with different particle sizes as the content simulation data. Obtaining content simulation data through historical data statistics can save time and costs to a certain extent and is suitable for large-scale data analysis. However, the historical data may not be fully applicable to the current project, and the data quality depends on the accuracy of historical records.

[0055] 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 impurities such as metals and plastics in the old materials are removed by magnetic separation, and the old materials are washed if necessary to remove dirt and other pollutants, and the treated old materials are dried; finally, by weighing or volume measurement, the content of old materials of various particle sizes in the total sample is calculated 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 the old materials.

[0056] So far, this embodiment has obtained the content simulation data of old materials of each particle size corresponding to the old asphalt, that is, a particle size content combination composed of content simulation data of old materials of different particle sizes is obtained.

[0057] S2. Obtain a set of changes in content simulation data of the target particle size and a set of mixing state values, analyze the correlation between the two sets, and obtain the mixing state regulation demand value under the change of the target particle size content.

[0058] Here, the target particle size refers to any one of all particle sizes; the set of mixing state values refers to the mixing states of multiple particle size content combinations obtained from the change of the content simulation data of the target particle size, and the mixing state can be obtained by analyzing the viscosity of the simulated mixture corresponding to the particle size content combination; the mixing state regulation demand value refers to the regulation demand for relevant parameters under the mixing states of each particle size content combination obtained from the change of the content simulation data of the target particle size, such as stirring time. The greater the regulation demand, the higher the possibility of regulating the mixing state parameters on the premise of the greater content of the target particle size.

[0059] The above step S2 can be implemented through steps S21 to S22 (not shown in the figure):

[0060] S21. Obtain a set of changes in content simulation data of the target particle size and a set of mixing state values.

[0061] First, obtain a set of changes in content simulation data of the target particle size.

[0062] In this embodiment, the content simulation data of the target particle size is numerically changed between the historical content maximum value and the historical content minimum value of the target particle size to obtain each new content simulation data, and a set of changes in content simulation data 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 specific actual situations and empirical values; the historical content maximum value and the historical content minimum value refer to the maximum value and the minimum value of the target particle size in all previous content data.

[0063] Secondly, obtain a set of mixing state values for the target particle size, which can be achieved through Figure 2 the steps S211 to S212 shown in:

[0064] S211, vary the content simulation data of the target particle size while keeping the content simulation data of various comparison particle sizes unchanged, to obtain a number of first particle size content combinations.

[0065] In this embodiment, the first particle size content combinations are obtained by the method of controlling variables. For the particle size content combinations obtained in step S1, each data in the set of variation of the content simulation data of the target particle size is continuously used to replace the content simulation data of the target particle size in the particle size content combination, while keeping the content simulation data of other particle sizes except the target particle size unchanged, and multiple new particle size content combinations can be obtained. The particle size content combination and the new particle size content combinations are collectively referred to as the first particle size content combinations. Among them, other particle sizes except the target particle size are used as the comparison particle sizes of the target particle size.

[0066] S212, obtain the viscosity time series of each first particle size content combination, and perform dimensionality reduction processing on the viscosity time series to obtain the mixing state value of each first particle size content combination during the test.

[0067] The first step is to obtain the viscosity time series of each first particle size content combination.

[0068] Obtain the viscosity time series data corresponding to different historical particle size content combinations through experiments, train a machine learning model, and predict the viscosity time series of the first particle size content combinations.

[0069] As an example, the specific steps include:

[0070] 1) Prepare mixtures with different particle size content combinations in the laboratory, use a viscometer to measure the viscosity values of the mixtures 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, etc., input the particle size content combinations corresponding to the training set and the stirring conditions (under the condition of ensuring the same heating power and stirring rate) 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, and input each first particle size content combination into the viscosity prediction model to obtain the viscosity time series of each first particle size content combination.

[0071] It should be noted that the machine learning model based on experimental data has a fast calculation speed, is suitable for large-scale data processing, and the viscosity prediction model can still be used when calculating the viscosity time series of the second particle size content combination later, 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 prior art and not within the scope of protection of the present invention, so it will not be elaborated in detail here.

[0072] In another example, by the inclined trough method, sampling detection is carried out at fixed time intervals, and the concrete sample is injected into the inclined trough, and the flow time and length of the concrete are measured, so as to calculate and obtain the viscosity value.

[0073] In the second step, dimensionality reduction processing is performed on the viscosity time series to obtain the mixed state value of each first particle size content combination during the test process.

[0074] Since the viscosity time series is time series data within a period of time and is two-dimensional data, which is not conducive to subsequent analysis and processing, it is necessary to perform dimensionality reduction processing on the viscosity time series. Dimensionality reduction processing can reduce the complexity of the data, while retaining its main features and information, and avoiding the influence of the complex changes of the time series data of the mixed state on the content change law. Among them, common dimensionality reduction methods include dimensionality reduction methods based on feature extraction, dimensionality reduction methods based on model fitting, and dimensionality reduction methods based on machine learning, etc.

[0075] Based on prior knowledge, asphalt will melt within a certain temperature range. When the temperature is higher, its viscosity is smaller. And when the viscosity of the mixture is smaller 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 mixed state regulation is smaller; at the same time, the mixing quality of the mixture gradually increases with time. Therefore, the greater the decrease in viscosity, the higher the current mixing efficiency and the smaller the need for mixed state regulation.

[0076] Based on the above description of the correlation between viscosity and mixed state, this embodiment proposes a method for performing dimensionality reduction processing on the viscosity time series, including:

[0077] Calculate the average value of the viscosity time series to obtain the viscosity mean value, and calculate the average value of the differences between all adjacent viscosities to obtain the average viscosity difference value; combine the viscosity mean value and the average viscosity difference value to obtain the mixed state value during the test process.

[0078] Further, perform inverse proportion analysis on the viscosity mean value to obtain the first inverse proportion value; normalize the product of the first inverse proportion value and the average viscosity difference value to obtain the mixed state value of the first particle size content combination during the test process.

[0079] As an example, the mixed state value of the i-th first particle size content combination during the test process The calculation formula can be:

[0080] ; where norm represents the linear normalization function, represents the first inverse proportional value obtained by performing inverse proportional processing on the data ; represents the viscosity mean 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.

[0081] In the calculation formula of the mixing state value, the viscosity difference is equal to the difference between the previous viscosity data and the subsequent viscosity data in the viscosity time series, which can represent the rate of decrease of viscosity over time. If there are multiple small rates of decrease, it indicates low mixing efficiency, 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 greater the overall viscosity, the lower the stirring efficiency, the worse the mixing quality of the mixture, and 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.

[0082] S22. Perform a correlation analysis on the content simulation data change set and the mixing state value set to obtain the mixing state regulation demand value under the change of the target particle size content.

[0083] First of all, it should be noted that the quality of the final stirring 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 variables above, the feedback regulation tendency of the change in the content of the target particle size in the mixture on the mixing device can be obtained, and the feedback regulation tendency is the regulation demand under the mixing state.

[0084] Specifically, for the data in the content simulation data change set of the target particle size, arrange them in ascending or descending order to obtain a new content simulation data change set; for the content simulation data of the target particle size of the first particle size content combination corresponding to each mixing state value in the mixing state value set, sort the mixing state values in the mixing state value set in the same arrangement method as the new content simulation data change set to obtain a new mixing state value set, which is to ensure that the two data with the same serial number in the two new sets correspond to the same first particle size content combination; based on the Pearson correlation coefficient, analyze the correlation between the new content simulation data change set and the new mixing state value set to obtain the absolute value of the correlation coefficient as the mixing state regulation demand value under the change of the target particle size content.

[0085] Among them, the mixing state regulation demand value represents the influence of the target particle size on the mixing state. The closer the mixing state regulation demand value is to 1, the greater the influence of the change in the simulated content data of the target particle size on the mixing state of the mixture, which further indicates that the feedback regulation tendency of the change in the target particle size on the mixing device is higher, that is, the demand for regulating relevant parameters is higher, such as the demand for regulating the stirring time; the implementation process of the Pearson correlation coefficient is a prior art and not within the protection scope of the present invention, so it will not be elaborated in detail here. Of course, the implementer can also adopt other existing correlation analysis methods, and this embodiment does not make specific limitations on this.

[0086] So far, this embodiment has obtained the mixing state regulation demand value under the change of the target particle size content.

[0087] S3. Determine the interference coefficient of the change in the simulated content data of each comparison particle size on the mixing state under the change of the target particle size content, and determine the feedback regulation tendency value during the change of the target particle size content based on the interference coefficient.

[0088] Here, the interference coefficient refers to the influence degree of the change of any comparison particle size in the first particle size content combination on the corresponding mixing state of the combination, and the feedback regulation tendency value refers to the comprehensive influence degree of the changes of all comparison particle sizes on the mixing state under the change of the target particle size. The reason is that the influence amplitudes of the changes in the contents of different particle sizes in the mixture on the mixing state are different. When adding different proportions of old asphalt, it may cause changes in the temperature absorption efficiency between the new and old asphalt in the entire mixing device. Therefore, the changes in the contents of other particle sizes will cause errors in the feedback regulation of the entire mixing process. Therefore, it is necessary to calculate the feedback regulation tendency value based on the interference coefficient.

[0089] The above step S3 can be implemented through Figure 3 the steps S31 to S35 shown as follows:

[0090] S31. Make the simulated content data of the selected comparison particle size in each first particle size content combination change, and keep the simulated content data of other comparison particle sizes and the target particle size unchanged, so as to obtain each second particle size content combination corresponding to each change.

[0091] In this embodiment, the selected comparison particle size is any one of the comparison particle sizes, and the second particle size content combination is a combination in which both the target particle size and any one of the comparison particle sizes in the combination change relative to the particle size content combination, that is, the combination obtained by changing the simulated content 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.

[0092] S32. Based on the viscosity time series of each second particle size content combination corresponding to any change, obtain the mixing state value of each second particle size content combination, and further obtain the mixing state regulation demand value under the target particle size content change after this change.

[0093] In this embodiment, referring to the content described in step S2 above, the viscosity time series of each second particle size content combination can be obtained. Then, dimensionality reduction processing is performed on the viscosity time series of the second particle size content combination to obtain the mixing state value of each second particle size content combination, and further obtain the mixing state regulation demand value under the target particle size content change after each change in the simulation data of the content of the selected comparison particle size. It should be noted that for processes with different data to be analyzed but the same calculation method, this embodiment will not repeat the description.

[0094] S33. Take the change in the simulated content data of the selected comparison particle size as the abscissa, and take the mixing state regulation demand value under the target particle size content change after the corresponding change as the ordinate to obtain each data point on the coordinate system.

[0095] In this embodiment, data points on the coordinate are constructed based on the change situation of the selected comparison particle size and the influence of the target particle size on the mixing state when the selected comparison particle size changes.

[0096] S34. Analyze the contribution rate of the change in the simulated content data of the selected comparison particle size to the change in the simulated content data of the target particle size based on each data point on the coordinate system, and determine the interference coefficient of the change in the simulated content data of the selected comparison particle size on the mixing state under the target particle size content change.

[0097] It should be noted that the change in the content of the 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 interference degree of each comparison particle size change on the target particle size in the mixing state.

[0098] The above step S34 can be implemented through steps S341 to S342 (not shown in the figure):

[0099] S341. Based on the PCA algorithm idea, obtain several straight lines with different slopes passing through the origin of the coordinate, and then calculate the maximum projection variance of all data points on 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.

[0100] S342. Perform normalization processing on the first product to obtain the interference coefficient of the change in the simulated content data of the selected comparison particle size on the mixing state under the target particle size content change.

[0101] Specifically, based on the idea of the PCA algorithm, first obtain several straight lines with different slopes passing through the origin of coordinates, and obtain the projections of all data points on the coordinate system on each straight line; then, calculate the projection variance corresponding to each straight line, and select the maximum projection variance from all the projection variances; then, determine the slope of the straight line corresponding to the maximum projection variance, and fuse the maximum projection variance and the slope to calculate their product as the first product; finally, perform a normalization process on the first product to obtain the interference coefficient of the change in the content simulation data of the selected comparison particle size on the mixing state under the change in the content of the target particle size.

[0102] Among them, PCA projects the original data into a new coordinate system through a linear transformation, so that the variance of the projected data on the new coordinate axes is maximized. The first principal component is the direction with the largest variance after the data projection, and the second principal component is the direction orthogonal to the first principal component and with the second largest variance, and so on.

[0103] As an example, the calculation formula for the interference coefficient of the change in the content simulation data of the x-th comparison particle size on the mixing state under the change in the content of the target particle size can be:

[0104] ; In the formula, represents the interference coefficient of the change in the content simulation data of the x-th comparison particle size on the mixing state under the change in the content of the target particle size, 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 projection variance.

[0105] In the calculation formula of the interference coefficient, the maximum projection variance reflects the degree of influence of the change in the content 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 projection variance is a prior art and is not within the scope of protection of the present invention, so it will not be elaborated in detail here; the slope reflects the sensitivity of the change in the content 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.

[0106] It should be noted that the calculation principle of the interference coefficient is based on the idea of the PCA algorithm. By calculating the maximum projection variance and slope of the data points on the straight line, the interference degree of the change in the content simulation data of the x-th comparison particle size on the mixing state of the target particle size is quantified. Its core idea is to comprehensively reflect the significance and sensitivity of the influence through the product of the projection variance and the slope, which can provide a basis for optimizing the mixing process.

[0107] S35. Determine the feedback regulation tendency value during the change process of the target particle size content based on the interference coefficient.

[0108] The above step S35 can be implemented through steps S351 to S353 (not shown in the figure):

[0109] S351. Calculate the average value of all mixing state regulation demand values based on the mixing state regulation demand value under the change of the target particle size content after each change in the simulation data of the content of the selected comparison particle size.

[0110] S352. Obtain the interference coefficient corresponding to each comparison particle size based on the determination method of the interference coefficient of the mixing state under the change of the target particle size content with the change of the simulation data of the content of the selected comparison particle size.

[0111] S353. Combine the average value of all mixing state regulation demand values corresponding to each comparison particle size and the interference coefficient to obtain the feedback regulation tendency value during the change process of the target particle size content.

[0112] Here, the average value of all mixing state regulation demand values is positively correlated with the feedback regulation tendency value, and the interference coefficient is negatively correlated with the feedback regulation tendency value. Positive correlation means that the larger the average value, the larger the feedback regulation tendency value, while negative correlation means that the larger the interference coefficient, the smaller the feedback regulation tendency value.

[0113] Specifically, perform an inverse proportion analysis on the interference coefficient corresponding to each comparison particle size to obtain the second inverse proportion value of the interference coefficient corresponding to each comparison particle size; calculate the second product of the average value of all mixing state regulation demand values corresponding to each comparison particle size and the second inverse proportion value, and accumulate and calculate all the second products to obtain the feedback regulation tendency value during the change process of the target particle size content.

[0114] As an example, the calculation formula for the feedback regulation tendency value during the change process of the target particle size content can be:

[0115] ; where, represents the feedback regulation tendency value during the change process of the target particle size content, N represents the total number of comparison particle sizes, represents the average value of the mixing state regulation demand values under the change of the target particle size content when the content of the x-th comparison particle size changes, represents the interference coefficient of the change of the simulation data of the content 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 extreme situations resulting in the interference coefficient being zero, represents the second inverse proportion value of the interference coefficient corresponding to the x-th comparison particle size.

[0116] In the calculation formula of the feedback regulation tendency value, the smaller the interference coefficient is, the more real the influence of the content change of the current comparison particle size on the mixing state is. Therefore, a fraction is used to weight the average value of the mixing state regulation demand value, and each comparison particle size is traversed, thereby obtaining the feedback regulation tendency value during the change process of the target particle size content. The greater the feedback regulation tendency value is, the higher the regulation tendency of the target particle size content change on the mixing state process is.

[0117] So far, this embodiment has obtained the feedback regulation tendency value during the change process of the target particle size content.

[0118] S4. Combine the mixing state regulation demand value and the feedback regulation tendency value to obtain the parameter regulation weight of the target particle size; based on the acquisition method of the parameter regulation weight of the target particle size, obtain the parameter regulation weights of all particle sizes other than the target particle size.

[0119] It should be noted that during the mixing process of the new and old asphalt, by mixing the new and old asphalt with a fixed stirring time, since the temperature absorption capacity of the old material with different particle sizes and different contents is affected by the proportion of asphalt and stone in the particles of the old material itself, the asphalt melting speed of the old material is slow. 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 cause energy consumption waste; therefore, it is necessary to determine the parameter regulation weight of each particle size in order to adjust the current stirring time.

[0120] Specifically, calculate the third product of the mixing state regulation demand value and the feedback regulation tendency value, and perform normalization processing on the third product to obtain the parameter regulation weight of the target particle size.

[0121] As an example, the calculation formula of the parameter regulation weight of the target particle size can be:

[0122] ; in the formula, represents the parameter regulation weight of the target particle size, norm represents the linear normalization function, represents the mixing state regulation demand value of the target particle size, represents the feedback regulation tendency value during the change process of the target particle size content.

[0123] In the calculation formula of the parameter regulation weight, by combining the factors of the parameter regulation demand under two target particle size changes, the parameter regulation weight caused by the influence of the content change of the target particle size on the mixing state of the mixture can be obtained. The greater the parameter regulation weight is, the higher the parameter regulation demand for the content size of the target particle size is. After obtaining the parameter regulation weight of the target particle size, referring to the acquisition method of the parameter regulation weight of the target particle size, the parameter regulation weights of each particle size can be obtained.

[0124] It should be noted that after obtaining the parameter regulation weights for each particle size, they can be applied to the mixing time adjustment in the production of any recycled asphalt concrete, improving the robustness of the mixing time adjustment and also helping to obtain a concrete production process that is more suitable for different actual situations and has old asphalt crushing, without the need to recalculate the parameter regulation weights for different particle sizes.

[0125] So far, this embodiment has obtained the parameter regulation weights for each particle size corresponding to the old asphalt.

[0126] S5. Obtain the actual data of the content of each particle size of the old material corresponding to the old asphalt, and combine the parameter regulation weights of each particle size to adjust the mixing state parameters to obtain the asphalt mixture at the end of mixing, and then obtain the recycled asphalt concrete.

[0127] Specifically, calculate the fourth product of the actual content data of each particle size and the parameter regulation weight, and then calculate the cumulative value of the fourth products of all particle sizes, so as to perform normalization processing on the cumulative value to obtain the total parameter regulation weight; obtain the default parameter of the mixing time in the mixing state, and use the total parameter regulation weight to perform weighted processing on the default parameter to obtain the adjusted mixing time.

[0128] As an example, the calculation formula for the adjusted mixing state parameters can be:

[0129] ; In the formula, represents the adjusted mixing time, U represents the default parameter of the mixing time in the mixing state, c represents the hyperparameter, and its empirical value can be 0.5, represents the total parameter regulation weight, norm represents the linear normalization function, J represents the number of particle size types, represents the parameter regulation weight of the target particle size, and can also represent the parameter regulation weight of the j-th particle size, represents the actual content data of the j-th particle size.

[0130] In the calculation formula for the adjusted mixing state parameters, the larger the total parameter regulation weight, the greater the need for adjusting the mixing time in the current mixing process. When the adjustment requirement is greater than 0.5, the mixing time needs to be increased. On the contrary, when the adjustment requirement is less than 0.5, the mixing time needs to be decreased; by determining the adjusted mixing state parameters, it is possible to avoid poor final mixing effects or energy waste caused by differences in the content of different particle sizes.

[0131] In this embodiment, after obtaining the parameter regulation weights for each particle size, the parameter regulation weights are applied to the adjustment of the stirring time. First, obtain the actual data of the content of old materials with each particle size corresponding to the old asphalt in the actual production process of recycled asphalt concrete. Then, fuse the actual data of the content of old materials with each particle size and the parameter regulation weights obtained from the analysis of the content change to obtain the regulation weights for the stirring time. Furthermore, use the regulation weights to adjust the default stirring time, and continue the production process of recycled asphalt concrete based on the adjusted stirring time, that is, according to the actual requirements, add materials to the asphalt mixture after mixing is completed, and perform final stirring in the mixing barrel, thereby obtaining recycled asphalt concrete.

[0132] So far, this embodiment has completed the production of high-performance recycled asphalt concrete.

[0133] The present invention provides a production process for high-performance recycled asphalt concrete. By analyzing the simulated data of the content of old asphalt with different particle sizes in the asphalt mixture, the feedback regulation weights for different particle sizes during the mixing and stirring process are judged, and the stirring time is corrected based on the feedback regulation weights, effectively improving the production efficiency of the production process and reducing the energy consumption during the production process.

[0134] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and should all be included within 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 mixed state control requirement value under a change in the content of the target particle size, wherein the target particle size is any particle size, and the mixed state control requirement value indicates the influence of the target particle size on the mixed state, and the closer the mixed state control requirement value is to 1, the greater the influence of the simulated data change of the content of the target particle size on the mixed state of the mixture; 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 in the process of 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, 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 target particle size change; 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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