Method and device for calculating hot aggregate component, and asphalt station

Abstract

The application discloses a hot aggregate component calculation method and device and an asphalt station, and can improve the calculation accuracy of the hot aggregate component. The hot aggregate in the hot aggregate bin represents the aggregate formed after processing of the cold aggregate in the cold aggregate bin; wherein the hot aggregate component calculation method comprises: establishing a balance equation based on the cumulative feeding amount of the target hot aggregate bin, the cumulative consumption amount of the target hot aggregate bin and the real-time stock level value of the target hot aggregate bin; based on the correlation between the target cold aggregate belt motor frequency converter working frequency and the cumulative feeding amount, the cumulative feeding amount is replaced by a mathematical expression generated according to the target cold aggregate belt motor frequency converter working frequency and the target coefficient in the balance equation; the target coefficient is calculated according to the target cold aggregate belt motor frequency converter working frequency, the cumulative consumption amount of the target hot aggregate bin and the real-time stock level value of the target hot aggregate bin; and the proportion of the cold aggregate component in the target hot aggregate bin is calculated based on the target coefficient.

Description

Technical Field

[0001] This application relates to the field of asphalt mixture production technology, specifically to a method, apparatus, and asphalt plant for calculating hot aggregate composition. Background Technology

[0002] In the asphalt mixture production field, asphalt plant, as the core equipment, plays a decisive role in the quality of the final asphalt mixture through precise control of its aggregate conveying process. Typically, the aggregate conveying process at an asphalt plant involves five closely linked and crucial steps: cold aggregate transport, heating and drying, induced draft dust collection, hot aggregate lifting, and vibrating screening. However, a certain proportion of raw materials is often lost during the heating and drying, induced draft dust collection, and hot aggregate lifting steps. Furthermore, due to the inherent mixing rate of the vibrating screen, the vibrating screening process also affects the actual composition of the hot aggregate. Currently, the industry commonly uses cold screening to determine the hot aggregate composition. This method involves feeding each cold aggregate bin individually with the burner and induced draft fan off, then accurately measuring the weight of the material in each hot aggregate bin to obtain the cold aggregate gradation data, which is then used as a reference for the hot aggregate composition. However, this method has significant limitations. In actual production, due to raw material losses in processes such as heating and drying, dust collection and hot material lifting, as well as the mixed screening effect of vibrating screen, the composition data obtained by cold screening mode deviates significantly from the composition of hot aggregate in actual production. Furthermore, the composition of actual hot aggregate is also affected by the fluctuation of cold material composition during feeding, which further increases the difficulty of hot aggregate composition determination. Summary of the Invention

[0003] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method, apparatus, and asphalt plant for calculating hot aggregate composition, which can improve the accuracy of hot aggregate composition calculation.

[0004] According to a first aspect of this application, a method for calculating the composition of hot aggregate is provided, wherein the hot aggregate in the hot silo represents aggregate formed after processing cold aggregate from the cold silo; wherein the method for calculating the composition of hot aggregate includes: establishing a balance equation based on the cumulative feed rate of the target hot silo, the cumulative consumption rate of the target hot silo, and the real-time material level of the target hot silo; based on the correlation between the operating frequency of the inverter of the target cold aggregate belt motor and the cumulative feed rate, replacing the cumulative feed rate in the balance equation with a mathematical expression generated based on the operating frequency of the inverter of the target cold aggregate belt motor and a target coefficient; wherein the target coefficient is used to characterize the feed rate coefficient of the hot silo; calculating the target coefficient based on the operating frequency of the inverter of the target cold aggregate belt motor, the cumulative consumption rate of the target hot silo, and the real-time material level of the target hot silo; and calculating the proportion of cold aggregate components in the target hot silo based on the target coefficient; wherein the proportion of cold aggregate components characterizes the original proportion of the hot aggregate components.

[0005] As one possible implementation, a balance equation is established based on the cumulative feed rate of the target hot material silo, the cumulative consumption of the target hot material silo, and the real-time material level of the target hot material silo. This equation includes: setting the average feed rate of the target hot material silo; and establishing a first equation for the cumulative feed rate of the target hot material silo, the average feed rate, and the feed duration based on the correlation between the cumulative feed rate of the target hot material silo and the average feed rate. The product of the feed duration and the average feed rate is equal to the cumulative feed rate of the target hot material silo, and the product of the feed duration and the average feed rate represents the cumulative feed rate of the target hot material silo.

[0006] As one possible implementation, based on the correlation between the operating frequency of the target cold material belt motor inverter and the cumulative feed amount, the cumulative feed amount in the balance equation is replaced with a mathematical expression generated based on the operating frequency of the target cold material belt motor inverter and a target coefficient. This includes: obtaining the time value for transporting the hot aggregate components from the cold material bin to the target hot material bin; the correlation between the operating frequency of the target cold material belt motor inverter and the average feed speed, replacing the average feed speed and feed time in the first equation with a mathematical expression jointly generated based on the operating frequency of the target cold material belt motor inverter, the target coefficient, and the time value; wherein, the mathematical expression is used to characterize the cumulative feed amount of the target hot material bin; based on the balance equation, the mathematical expression, and the first equation, the cumulative feed amount is replaced with a mathematical expression jointly generated based on the operating frequency of the target cold material belt motor inverter, the target coefficient, and the time value.

[0007] As one possible implementation, the target coefficient is calculated based on the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material silo, and the real-time material level of the target hot material silo. This includes: calculating the cumulative feed amount of the target hot material silo based on the cumulative consumption of the target hot material silo and the real-time material level of the target hot material silo; and calculating the target coefficient based on the mathematical expression and the cumulative feed amount of the target hot material silo.

[0008] As one possible implementation, based on the balance equation, mathematical expression, and the first equation, the cumulative feed amount is replaced with a mathematical expression generated jointly based on the operating frequency of the target cold material belt motor inverter, the target coefficient, and the time value. This includes: continuously integrating the operating frequency of the target cold material belt motor inverter based on the time value, and simultaneously accumulating the target coefficient of the cold material bin corresponding to the target hot material bin to generate the mathematical expression.

[0009] As one possible implementation, the target coefficient is calculated based on the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material bin, and the real-time material level of the target hot material bin. This includes: inputting the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material bin, and the real-time material level of the target hot material bin into a preset optimization model to obtain the target coefficient output by the preset optimization model.

[0010] As one possible implementation, before calculating the proportion of cold aggregate in the target hot aggregate bin based on the target coefficient, the method for calculating the hot aggregate composition further includes: obtaining preset optimization parameters; weighting the calculated target coefficient based on the preset optimization parameters to obtain an optimized target coefficient; wherein, calculating the proportion of cold aggregate in the target hot aggregate bin based on the target coefficient includes: calculating the proportion of cold aggregate in the target hot aggregate bin based on the optimized target coefficient.

[0011] As one possible implementation, each target coefficient corresponds to a cold material bin, wherein, based on the target coefficient, the proportion of cold material components in the target hot material bin is calculated, including: calculating the sum of the target coefficients of the cold material bins corresponding to the target hot material bin; and using the proportion of the target coefficient of the target cold material bin in the sum of the target coefficients as the proportion of cold material components transported from the target cold material bin to the target hot material bin.

[0012] According to a second aspect of this application, a device for calculating the composition of hot aggregate is provided, wherein the hot aggregate in the hot aggregate bin represents aggregate formed after processing cold aggregate from the cold aggregate bin; wherein the device for calculating the composition of hot aggregate includes: an establishment module for establishing a balance equation based on the cumulative feed amount of the target hot aggregate bin, the cumulative consumption amount of the target hot aggregate bin, and the real-time material level value of the target hot aggregate bin; a replacement module for replacing the cumulative feed amount in the balance equation with a mathematical expression generated based on the operating frequency of the target cold aggregate belt motor inverter and a target coefficient, based on the correlation between the operating frequency of the target cold aggregate belt motor inverter and the cumulative feed amount; wherein the target coefficient is used to characterize the feed rate coefficient of the hot aggregate bin; a first calculation module for calculating the target coefficient based on the operating frequency of the target cold aggregate belt motor inverter, the cumulative consumption amount of the target hot aggregate bin, and the real-time material level value of the target hot aggregate bin; and a second calculation module for calculating the proportion of cold aggregate components in the target hot aggregate bin based on the target coefficient; wherein the proportion of cold aggregate components characterizes the original proportion of hot aggregate components.

[0013] According to a third aspect of this application, an asphalt plant is provided, comprising: at least one cold aggregate bin and at least one hot aggregate bin; wherein the cold aggregate bin transports hot aggregate components to the hot aggregate bin via a cold aggregate conveyor belt; and a hot aggregate component calculation device as described in the second aspect or any implementation thereof, the hot aggregate component calculation device being used to calculate the proportion of hot aggregate components transported from each cold aggregate bin to the hot aggregate bin.

[0014] The hot aggregate composition calculation device and asphalt plant provided in this application first establish a balance equation between hot aggregate bin consumption and feed. Then, using known information, the unknown cumulative feed amount is solved in reverse, and the composition calculation is updated in real-time as the consumption changes. Next, a target coefficient associated with the cumulative feed amount of the target hot aggregate bin is introduced, taking into account losses from heating, drying, and dust collection, and replacing the position of the cumulative feed amount in the balance equation. Finally, by solving for the target coefficient, and using it as the key coefficient for component ratio calculation, gradation deviations caused by the production process can be reduced, improving mix proportion stability. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a flowchart illustrating a method for calculating the composition of hot aggregates provided in an exemplary embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the structure of a calculation device for hot aggregate composition provided in an exemplary embodiment of this application.

[0018] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0019] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0020] In the field of asphalt mixture production, asphalt plants, as core equipment, play a decisive role in the quality of the final asphalt mixture through precise control of their aggregate conveying process. An asphalt plant includes at least one cold aggregate bin and at least one hot aggregate bin. The cold aggregate bin transports hot aggregate components to the hot aggregate bin via a cold aggregate conveyor belt. For example, some small or simple asphalt plants may use a configuration of one cold aggregate bin for every one hot aggregate bin. In some small road maintenance projects, the asphalt plant only needs to produce one or a few fixed-gradation asphalt mixtures. One cold aggregate bin stores cold aggregate of a specific particle size or characteristic, which, after heating and screening, enters the corresponding hot aggregate bin to meet production needs. Most medium and large asphalt plants use a configuration of multiple cold aggregate bins for multiple hot aggregate bins. Typically, there are about 3-8 cold aggregate bins and 4-6 hot aggregate bins. This is because producing asphalt mixtures with different gradations requires various aggregates of different particle sizes and characteristics. Multiple cold material bins can store cold materials of different particle sizes separately. Through a precise metering and conveying system, various cold materials are transported to the heating and drying equipment in a certain proportion. After processing, they enter different hot material bins for subsequent precise proportioning and mixing.

[0021] Typically, the aggregate conveying process at an asphalt plant involves five closely linked and crucial steps: cold aggregate transport, heating and drying, dust collection and ventilation, hot aggregate lifting, and vibrating screening. Aggregates in cold aggregate bins are fed onto conveyor belts at a set speed and quantity by feeders (such as vibrating feeders or belt feeders). The feeder speed can be controlled by adjusting the motor frequency to precisely regulate the amount of cold aggregate conveyed. The conveyor belts transport the cold aggregate from the cold aggregate bins to the heating and drying equipment (such as drum dryers). To ensure the stability and uniformity of cold aggregate transport, multiple feeder and conveyor systems corresponding to different cold aggregate bins are usually set up, and these systems need to be precisely coordinated to ensure that cold aggregates of different particle sizes enter the heating equipment in a predetermined proportion. After heating and drying, the hot aggregate is lifted from the heating equipment to a certain height by a hot aggregate elevator (such as a bucket elevator or chain bucket elevator), and then distributed to different hot aggregate bins via chutes or pipelines. During the distribution process, vibrating screens are typically used to screen the hot aggregates, and the aggregates are then transported to the corresponding hot aggregate bins according to their different particle sizes.

[0022] Raw material loss is a significant concern during aggregate conveying. Specifically, in the heating and drying stage, aggregates undergo physical changes at high temperatures, and some may be lost due to uneven heating or breakage. During the induced draft dust collection process, to remove dust and impurities from the aggregates, some fine aggregates are carried away by the airflow, resulting in raw material loss. In the hot aggregate lifting stage, due to the operating characteristics of the lifting equipment and the collision and friction between aggregates, a certain proportion of raw materials are also lost. Furthermore, the vibrating screening process has a significant impact on the actual composition of the hot aggregates. When screening aggregates, the vibrating screen has a certain degree of mixing, making it difficult to achieve completely precise separation of aggregates of different particle sizes. This leads to a deviation between the composition of the hot aggregates after vibrating screening and the theoretically designed composition.

[0023] Currently, the industry commonly uses cold screening to determine the composition of hot aggregates. This method involves feeding each cold aggregate bin individually with the burner and induced draft fan off, then precisely weighing the material in each hot aggregate bin to obtain cold aggregate composition gradation data, which is used as a reference for the hot aggregate composition. However, this method has significant limitations. In actual production, due to raw material losses during heating and drying, induced draft dust collection, and hot aggregate lifting, as well as the mixing effect of vibrating screens, the composition data obtained through cold screening deviates significantly from the actual hot aggregate composition, failing to accurately reflect the true situation. Furthermore, the actual composition of hot aggregates is also affected by fluctuations in the cold aggregate composition during feeding. Due to uncertainties in the source, storage conditions, and feeding process of cold aggregates, the cold aggregate composition often exhibits a certain range of fluctuation, further increasing the difficulty of determining the hot aggregate composition.

[0024] Given the above, in actual production, the determination of hot aggregate composition currently relies mainly on operator experience for estimation. This requires operators to possess high professional skills and rich practical experience, enabling them to roughly judge the composition of hot aggregate based on various phenomena and parameters during the production process, thereby controlling the production and feeding of asphalt plants. However, this experience-based approach lacks scientific rigor and accuracy, making it difficult to guarantee the stability and consistency of asphalt mixture quality, and also bringing certain difficulties to the production management of asphalt plants.

[0025] Therefore, in order to solve the problem of large deviations in the calculated component values, this application proposes a method, device and asphalt plant for calculating hot aggregate components. Considering the losses from heating, drying and dust collection, it introduces the frequency of the cold aggregate belt motor and the feeding speed coefficient of the hot aggregate bin, which is closer to the actual production. Based on the balance equation, it considers the scenario of large fluctuations in cold aggregate specifications. The calculated component values ​​will be dynamically updated with the production process. Therefore, the calculated component results are accurate and can improve the stability of the mix proportion.

[0026] To reduce the impact of aggregate transportation on the proportion of hot aggregates, this application provides a method for calculating the composition of hot aggregates. Figure 1 This is a schematic flowchart illustrating a method for calculating the composition of hot aggregates provided in an exemplary embodiment of this application. Figure 1 For example, firstly, based on the cumulative feed rate of the target hot material silo, the cumulative consumption of the target hot material silo, and the real-time material level of the target hot material silo, a balance equation is established (see...). Figure 1(S110). In the balance equation, the cumulative consumption and real-time material level of the target hot material bin are known information. Based on the balance equation, the cumulative feed rate of the target hot material bin can be dynamically calculated using the cumulative consumption and real-time material level. Next, based on the relationship between the operating frequency of the target cold material belt motor inverter and the cumulative feed rate, the cumulative feed rate is replaced in the balance equation with a mathematical expression generated based on the operating frequency of the target cold material belt motor inverter and the target coefficient (see S110). Figure 1 S120); where the target coefficient is used to characterize the feeding speed coefficient of the hot material silo, and the target coefficient is an unknown. Due to the influence of the production process, the cumulative feeding amount may change with the initial cold material ratio. However, if the cumulative feeding amount is converted into motor frequency and target coefficient, the fluctuations in the production process can be taken into account, thereby improving the accuracy and stability of the calculated ratio. Then, based on the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material silo, and the real-time material level value of the target hot material silo, the target coefficient is calculated (see S120). Figure 1 (S130). Since the operating frequency of the target cold material belt motor inverter can be directly acquired, and the cumulative feed rate can be calculated from the cumulative consumption and real-time material level, the target coefficient can be dynamically calculated using the balance equation, making the target coefficient accurate and flexible. Finally, based on the target coefficient, the proportion of cold material components in the target hot material bin is calculated (see S130). Figure 1 S140); where the proportion of cold aggregate represents the original proportion of hot aggregate. The cold aggregate will be processed into hot aggregate, and the processing will cause fluctuations in the cost of cold aggregate. Therefore, by using a target coefficient to calculate the proportion of cold aggregate, the impact of the production process on the proportion can be reduced, ultimately providing an accurate cold aggregate composition ratio for the hot aggregate. Using the target coefficient as the benchmark parameter for the proportion, the calculated component ratio will be dynamically updated with the production process, resulting in higher accuracy.

[0027] The following text combines Figure 1 The calculation method for hot aggregate composition provided in the embodiments of this application will be described in more detail.

[0028] In S110, a balance equation is established based on the cumulative feed amount of the target hot material bin, the cumulative consumption amount of the target hot material bin, and the real-time material level value of the target hot material bin.

[0029] In some embodiments, it is assumed that there are c cold material bins and h hot material bins, and the target hot material bin can be any one of the h hot material bins. Based on the cumulative feed rate of the target hot material bin, the cumulative consumption rate of the target hot material bin, and the real-time material level value of the target hot material bin, the balance equation is established firstly as follows:

[0030] PM = S (Formula 1)

[0031] In Formula 1, P represents the cumulative feed amount of the target hot material bin, M represents the cumulative consumption of the target hot material bin, and S represents the material level difference of the hot material bin.

[0032] Next, for the j-th hot material bin (which can be considered the target hot material bin), from time t0 to t... k The equilibrium equation at time t is:

[0033] [p j (t k )―p j (t0)]―[m j (t k )―m j [(t0)]=[s j (t k )―s j Formula 2; (t0)]

[0034] In Formula 2, p j (t k ) indicates that the j-th hot material bin is at t k Feed rate at any given time, p j (t0) represents the feed rate of the j-th hot feed bin at time t0, m j (t k ) indicates that the j-th hot material bin is at t k The amount consumed in a given moment, m j (t0) represents the consumption of the j-th hot material bin at time t0, s j (t k ) indicates that the j-th hot material bin is at t k Real-time material level value at any given moment, s j (t0) represents the real-time material level value of the j-th hot material bin at time t0.

[0035] Among them, the j-th hot material bin reaches t0 at time t1. k The consumption of hot material in the j-th hot material bin at time t0 can be read from the production consumption report. k The level difference in the hot material silo at any given time can be read in real time using a silo level gauge, which is not limited to radar level gauges or rotary paddle level gauges. It is understood that the cumulative consumption of the hot material silo at any given time can be read in the production consumption report, and the real-time level value of the hot material silo at any given time can also be read in real time using a silo level gauge.

[0036] In S120, based on the relationship between the operating frequency of the target cold material belt motor inverter and the cumulative feed amount, the cumulative feed amount is replaced in the balance equation with a mathematical expression generated based on the operating frequency of the target cold material belt motor inverter and the target coefficient.

[0037] In some embodiments, the operating frequency of the target cold material belt motor inverter is the control frequency output by the inverter to the motor. The inverter changes the motor speed by adjusting the output frequency, thereby controlling the belt speed. The operating frequency of the target cold material belt motor inverter can be directly acquired from the equipment. The relationship between the operating frequency of the target cold material belt motor inverter and the belt speed is as follows: increasing the operating frequency of the target cold material belt motor inverter → increasing the motor speed → increasing the belt speed → increasing the amount of cold material conveyed from the hot material bin per unit time (increasing the cumulative feed rate). Therefore, the operating frequency of the cold material belt motor inverter is a key means of adjusting the feed rate, but it also needs to be optimized in conjunction with other parameters.

[0038] Based on the relationship between the operating frequency of the cold material belt motor inverter and the feeding speed, and the relationship between the feeding speed and the cumulative feeding amount, in order to establish the correlation between the operating frequency of the target cold material belt motor inverter and the cumulative feeding amount, firstly, as a possible implementation method, the average feeding speed of the target hot material bin is preset; based on the correlation between the cumulative feeding amount and the average feeding speed of the target hot material bin, the first equation for the cumulative feeding amount, average feeding speed, and feeding time of the target hot material bin is established; wherein, the product of the feeding time and the average feeding speed is equal to the cumulative feeding amount of the target hot material bin, and the product of the feeding time and the average feeding speed represents the cumulative feeding amount of the target hot material bin.

[0039] For example, suppose the j-th hot material bin arrives at time t0. k The average feed rate at time t is v j Then the first equation, that is, the j-th hot material bin at time t0 to t... k The cumulative feed amount at each moment is:

[0040] p j (t k )―p j (t0)=v j (t k Formula 3 (t0);

[0041] In formula three, p j (t k )―p j (t0) can be calculated using formula two, p j (t k ) indicates that the j-th hot material bin is at t k Feed rate at any given time, p j (t0) represents the feed rate of the j-th hot material bin at time t0, v j This indicates that the j-th hot material bin reaches t0 at time t0. k Average feed rate at time t k Indicates t kTime, t0 represents time t0. Therefore, Formula 3 can express the time from time t0 to t... k The cumulative feed rate of the hot material silo is calculated at all times, and the cumulative feed rate is converted into a formula that multiplies the feed rate by the time.

[0042] In Formula 3, it is assumed that from time t0 to t... k The average feeding speed at any given time is assumed to be uniform, but this speed will vary during actual production. Therefore, to account for these variations and improve proportioning accuracy, in some embodiments, the time value for transporting the hot aggregate components from the cold aggregate bin to the target hot aggregate bin is obtained. The correlation between the operating frequency of the target cold aggregate belt motor inverter and the average feeding speed is established by replacing the average feeding speed and feeding time in the first equation with a mathematical expression generated jointly based on the operating frequency of the target cold aggregate belt motor inverter, the target coefficient, and the time value. This mathematical expression characterizes the cumulative feeding amount to the target hot aggregate bin. Based on the balance equation, the mathematical expression, and the first equation, the cumulative feeding amount is replaced with a mathematical expression generated jointly based on the operating frequency of the target cold aggregate belt motor inverter, the target coefficient, and the time value.

[0043] As one possible implementation, since it takes a certain amount of time for the material to be transported from the cold silo to the hot silo, let's assume this time is n seconds (which can be determined based on the actual time it takes for the material to be transported from the cold silo to the hot silo). Simultaneously, a target coefficient is introduced, which can be understood as the cold silo belt motor frequency minus the hot silo feed speed coefficient. Furthermore, it is assumed that from time t0 to t... k At time t, the target coefficient remains unchanged, and the operating frequency of the inverter for the i-th cold material belt motor is xi(t). Based on the mathematical expression and the first equation, we can obtain:

[0044]

[0045] In Formula 4, To generate a mathematical expression based on the target cold material belt motor inverter's operating frequency, target coefficients, and time values, the operating frequency of the target cold material belt motor inverter is continuously integrated based on the time values, while the target coefficients of the cold material bins corresponding to the target hot material bins are accumulated. j (t k )―p j (t0)=v j (t k —t0) is the first equation. In formula four, p j (t k )―p j (t0) can be calculated using formula two, p j (t k ) indicates that the j-th hot material bin is at t kFeed rate at any given time, p j (t0) represents the feed rate of the j-th hot material bin at time t0, v j This indicates that the j-th hot material bin reaches t0 at time t0. k Average feed rate at time t k Indicates t k Time, t0 represents time t0, c represents the number of cold material bins, i represents the i-th cold material bin, which can be understood as the target cold material bin, j represents the j-th hot material bin, which can be understood as the target hot material bin, a ij The target coefficient is represented by n, and the time value is represented by x. i (t) represents the operating frequency of the i-th cold material belt motor inverter. Since cold materials are transported between the cold material bin and the hot material bin via a cold material belt, each cold material bin corresponds to the operating frequency of a cold material belt motor inverter.

[0046] In some embodiments, due to x i (t) Calculating the integral using t might be too time-consuming. Therefore, the division value of t can be set to 1 second, and the trapezoidal rule can be used to approximate the continuous integral. For example:

[0047]

[0048] In Formula 5, p j (t k )―p j (t0) can be calculated using formula two, p j (t k ) indicates that the j-th hot material bin is at t k Feed rate at any given time, p j (t0) represents the feed amount of the j-th hot material bin at time t0, a ij Let c represent the target coefficient, c represent the number of cold material bins, i represent the i-th cold material bin, j represent the j-th hot material bin, and t represent the target coefficient. k Indicates t k Time, t0 represents time t0, n represents the time value, x i (t) represents the operating frequency of the inverter for the i-th cold material belt motor.

[0049] In S130, the target coefficient is calculated based on the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material bin, and the real-time material level of the target hot material bin.

[0050] In some embodiments, based on Formula 2, the cumulative feed rate of the target hot material silo can be calculated according to the cumulative consumption and real-time material level of the target hot material silo. Therefore, based on Formulas 2, 4, and 5, the target coefficient can be calculated according to the mathematical expression and the cumulative feed rate of the target hot material silo, which leads to Formula 6:

[0051]

[0052] In Formula Six, x i (t) represents the operating frequency of the inverter for the i-th cold material belt motor, which can be directly acquired from the equipment. j (t k ) indicates that the j-th hot material bin is at t k The amount consumed in a given moment, m j (t0) represents the consumption of the j-th hot material bin at time t0, s j (t k ) indicates that the j-th hot material bin is at t k Real-time material level value at any given moment, s j (t0) represents the real-time material level value of the j-th hot material bin at time t0, m j (t k ), m j (t0), s j (t k ) and s j (t0) can also be obtained from the equipment, where c represents the number of cold storage bins, i represents the i-th cold storage bin, and n represents the time value. Therefore, there is only one unknown in Formula 6, namely a. ij .

[0053] In actual production, Formula Six produces the following set of equations:

[0054]

[0055] In the system of equations, h represents the hot silo number and c represents the cold silo number. If there are h hot silos and c cold silos, then solving this system of equations requires c*h equations if the elimination method is used. However, the consumption and material level values ​​in the production process are often intermittent cumulative values. At several different time points, there may be periods with no change in consumption or material level, ultimately leading to a situation where there are not enough equilibrium equations to solve the problem.

[0056] To address situations where the number of equilibrium equations is insufficient for a solution, the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material silo, and the real-time material level of the target hot material silo are input into a preset optimization model to obtain the target coefficients output by the preset optimization model. For example, a least squares algorithm can be introduced and combined with a quadratic programming optimization model for solution. The objective function of the optimization model can be set as follows:

[0057]

[0058] In the objective function, p j(k) represents the cumulative feed amount of the j-th hot material bin at time k, m j (k) represents the cumulative consumption of the j-th hot material bin at time k, S j (k) represents the difference in hot material level of the j-th hot material bin at time k, and h represents the number of hot material bins.

[0059] The constraints for optimizing the model can be set as follows:

[0060] ai j ≥0, i=1,…,c,j=1,…,h

[0061] Among them, a ij Let represent the target coefficient. There are c cold material bins and h hot material bins. Let i represent the i-th cold material bin and j represent the j-th hot material bin.

[0062] The decision variables are:

[0063] a ij i = 1, ..., c, j = 1, ..., h

[0064] In other words, a ij This can correspond to one cold material bin and one hot material bin, a ij It can represent the target coefficient for transporting materials from the i-th cold silo to the j-th hot silo.

[0065] Because in the above calculations, the equilibrium equations assume that from time t0 to t... k within time a ij It remains unchanged, but in actual production, a ij Fluctuations may occur due to significant variations in the specifications of the cold material, as well as losses from heating, drying, dust collection, and vibrating sieving. Therefore, in some embodiments, to reduce computational fluctuations caused by these losses, a sliding window dynamic optimization model is introduced to dynamically optimize and solve the equilibrium equations. For example, preset optimization parameters are obtained; the calculated target coefficients are weighted based on the preset optimization parameters to obtain optimized target coefficients; and based on the optimized target coefficients, the proportion of cold material components in the target hot material bin is calculated.

[0066] As one possible implementation, an optimization step size parameter f (set according to empirical values, for example, 0.1; setting it to 0.1 allows for small-step iterations and prevents sudden changes in the target coefficient) can be introduced as a preset optimization parameter. Each time the target coefficient is calculated by sliding window, a new target coefficient is obtained by weighting, ensuring that the coefficient can change dynamically and preventing abnormal sudden changes in the coefficient.

[0067] For example:

[0068] a′ ij = (1―f)×a ij +f*a′ ij Formula 7;

[0069] a″ ij = (1―f)×a′ ij +f*a″ ij Formula 8;

[0070] In Formula 7, a ij This can be considered the first time (or a') ij The target coefficient, a′, was obtained from the previous calculation. ij For the second time (or a) ij The target coefficient, a′, obtained in the last calculation ij It needs to undergo its own iteration, combining historical data 'a' from the sliding window. ij After weighted optimization, a new a′ is obtained. ij , with a′ before the equals sign ij Immediately replace a′ with the output of the optimized model ij This allows for adaptation to dynamic changes and prevents abrupt changes in coefficients. In other words, the model can be dynamically optimized using a sliding window, progressively optimizing the target coefficients. Subsequently, in Formula 8, a′ ij It can also be used as a″ ij Historical data, combined with preset optimization parameters f, for a″ ij Perform weighted optimization.

[0071] After the target coefficient is calculated, in S140, the proportion of cold material components in the target hot material bin is calculated based on the target coefficient.

[0072] For example, calculate the sum of the target coefficients of the cold silos corresponding to the target hot silos; use the proportion of the target coefficient of the target cold silos to the sum of the target coefficients as the proportion of the cold material component transported from the target cold silos to the target hot silos.

[0073] As one possible implementation, the proportion of cold material components in each hot material bin can be obtained through the following conversion method:

[0074]

[0075] …

[0076]

[0077] Where c represents the total number of cold material bins, y1 represents the proportion of cold material from cold material bin 1 contained in heat bin 1, and a 11 This indicates the target coefficient for cold material bin No. 1 corresponding to hot material bin No. 1. This represents the sum of the target coefficients of the cold material bins corresponding to hot material bin #1, j=1 represents hot material bin #1, h represents the number of hot material bins, y2 represents the proportion of cold material from cold material bin #2 contained in hot material bin #1, and a 21This represents the target coefficient for cold material bin No. 2 corresponding to hot material bin No. 1, y c This indicates the proportion of cold feed from cold feed bin c contained in heat bin 1. c1 This represents the target coefficient for cold material bin C corresponding to hot material bin 1. In addition, when j takes other values, the proportion of cold material components in other hot material bins corresponding to cold material bins can also be calculated. In other words, calculating the component proportion based on the target coefficient takes into account losses from heating and drying, induced draft dust collection, and scenarios with large fluctuations in cold material specifications. This results in more accurate component values. Furthermore, considering scenarios with large fluctuations in cold material specifications, the calculated component proportions are dynamically updated with the production process. Based on the balance equation, the component calculation is rapidly updated following changes in the level gauge and consumption, achieving efficient and accurate calculations.

[0078] Figure 2 This is a schematic diagram of the structure of a calculation device for hot aggregate composition provided in an exemplary embodiment of this application, as shown below. Figure 2 As shown, the hot aggregate composition calculation device 2 includes: a setup module 21, which establishes a balance equation based on the cumulative feed amount of the target hot aggregate bin, the cumulative consumption amount of the target hot aggregate bin, and the real-time material level value of the target hot aggregate bin; a replacement module 22, which, based on the correlation between the operating frequency of the target cold aggregate belt motor inverter and the cumulative feed amount, replaces the cumulative feed amount in the balance equation with a mathematical expression generated according to the operating frequency of the target cold aggregate belt motor inverter and a target coefficient; wherein, the target coefficient is used to characterize the feed speed coefficient of the hot aggregate bin; a first calculation module 23, which calculates the target coefficient based on the operating frequency of the target cold aggregate belt motor inverter, the cumulative consumption amount of the target hot aggregate bin, and the real-time material level value of the target hot aggregate bin; and a second calculation module 24, which calculates the proportion of cold aggregate components in the target hot aggregate bin based on the target coefficient; wherein, the proportion of cold aggregate components characterizes the original proportion of the hot aggregate composition.

[0079] As one possible implementation, module 21 can be configured to: preset the average feeding speed of the target hot material bin; based on the correlation between the cumulative feeding amount of the target hot material bin and the average feeding speed, establish a first equation for the cumulative feeding amount of the target hot material bin, the average feeding speed, and the feeding time; wherein, the product of the feeding time and the average feeding speed is equal to the cumulative feeding amount of the target hot material bin, and the product of the feeding time and the average feeding speed represents the cumulative feeding amount of the target hot material bin.

[0080] As one possible implementation, the replacement module 22 can be configured to: obtain the time value of transporting the hot aggregate components from the cold aggregate bin to the target hot aggregate bin; the correlation between the operating frequency of the target cold aggregate belt motor inverter and the average feeding speed, replacing the average feeding speed and feeding time in the first equation with a mathematical expression jointly generated based on the operating frequency of the target cold aggregate belt motor inverter, the target coefficient, and the time value; wherein, the mathematical expression is used to characterize the cumulative feeding amount of the target hot aggregate bin; based on the balance equation, the mathematical expression, and the first equation, replacing the cumulative feeding amount with a mathematical expression jointly generated based on the operating frequency of the target cold aggregate belt motor inverter, the target coefficient, and the time value.

[0081] As one possible implementation, the first calculation module 23 can be configured to: calculate the cumulative feed amount of the target hot material silo based on the cumulative consumption of the target hot material silo and the real-time material level value of the target hot material silo; and calculate the target coefficient based on the mathematical expression and the cumulative feed amount of the target hot material silo.

[0082] As one possible implementation, the replacement module 22 can be configured to: continuously integrate the operating frequency of the target cold material belt motor inverter based on the time value, and simultaneously accumulate the target coefficients of the cold material bin corresponding to the target hot material bin to generate a mathematical expression.

[0083] As one possible implementation, the first calculation module 23 can be configured to input the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material bin, and the real-time material level value of the target hot material bin into a preset optimization model to obtain the target coefficient output by the preset optimization model.

[0084] As one possible implementation, the hot aggregate composition calculation device 2 can be configured to: acquire preset optimization parameters; weight the calculated target coefficients based on the preset optimization parameters to obtain optimized target coefficients; wherein, the second calculation module 24 can be configured to: calculate the proportion of cold aggregate components in the target hot aggregate bin based on the optimized target coefficients.

[0085] As one possible implementation, each target coefficient corresponds to a cold material bin, wherein the second calculation module 24 can be configured to: calculate the sum of the target coefficients of the cold material bins corresponding to the target hot material bins; and use the proportion of the target coefficients of the target cold material bins in the sum of the target coefficients as the proportion of the cold material components transported from the target cold material bins to the target hot material bins.

[0086] An electronic device includes: a processor; a memory for storing processor-executable instructions; and a processor for executing the method for calculating the composition of hot aggregates as described in the embodiments provided in this application.

[0087] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0088] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0089] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0090] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0091] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods for calculating the hot aggregate composition of the various embodiments of this application described above, and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0092] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0093] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0094] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0095] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0096] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0097] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0098] A computer-readable storage medium storing a computer program for executing the method for calculating the composition of hot aggregates as described in the embodiments provided in this application.

[0099] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0100] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for calculating the composition of hot aggregate, characterized in that, The characterization of hot aggregate in hot silos is based on the aggregate formed after processing cold aggregate in cold silos; The calculation methods for hot aggregate components include: A balance equation is established based on the cumulative feed amount of the target hot material bin, the cumulative consumption amount of the target hot material bin, and the real-time material level of the target hot material bin. Based on the relationship between the target cold material belt motor inverter operating frequency and the cumulative feed amount, in the balance equation, the cumulative feed amount is replaced with a mathematical expression generated based on the target cold material belt motor inverter operating frequency and the target coefficient; wherein, the target coefficient is used to characterize the hot material bin feed rate coefficient; The target coefficient is calculated based on the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material bin, and the real-time material level of the target hot material bin. Based on the target coefficient, the proportion of cold aggregate in the target hot aggregate bin is calculated; wherein, the proportion of cold aggregate represents the original proportion of hot aggregate.

2. The method for calculating the composition of hot aggregate according to claim 1, characterized in that, Based on the cumulative feed rate of the target hot material silo, the cumulative consumption of the target hot material silo, and the real-time material level of the target hot material silo, a balance equation is established, including: The average feed rate of the preset target hot material bin; Based on the correlation between the cumulative feed amount of the target hot material bin and the average feed rate, a first equation is established for the cumulative feed amount of the target hot material bin, the average feed rate, and the feed duration; wherein, the product of the feed duration and the average feed rate is equal to the cumulative feed amount of the target hot material bin, and the product of the feed duration and the average feed rate represents the cumulative feed amount of the target hot material bin.

3. The method for calculating the composition of hot aggregate according to claim 2, characterized in that, Based on the correlation between the operating frequency of the target cold material belt motor inverter and the cumulative feed amount, in the balance equation, the cumulative feed amount is replaced with a mathematical expression generated based on the operating frequency of the target cold material belt motor inverter and the target coefficient, including: Obtain the time value for transporting hot aggregate components from the cold silo to the target hot silo; The relationship between the operating frequency of the target cold material belt motor inverter and the average feeding speed is defined by replacing the average feeding speed and feeding time in the first equation with a mathematical expression generated based on the operating frequency of the target cold material belt motor inverter, the target coefficient, and the time value; wherein, the mathematical expression is used to characterize the cumulative feeding amount of the target hot material bin. Based on the balance equation, mathematical expression, and the first equation, the cumulative feed amount is replaced with a mathematical expression generated jointly by the target cold material belt motor inverter operating frequency, target coefficient, and time value.

4. The method for calculating the composition of hot aggregate according to claim 3, characterized in that, The target coefficient is calculated based on the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material silo, and the real-time material level of the target hot material silo, including: Calculate the cumulative feed amount of the target hot material bin based on the cumulative consumption of the target hot material bin and the real-time material level of the target hot material bin; The target coefficient is calculated based on the mathematical expression and the cumulative feed amount of the target hot material bin.

5. The method for calculating the composition of hot aggregate according to claim 3, characterized in that, Based on the aforementioned balance equation, mathematical expression, and the first equation, the cumulative feed amount is replaced with a mathematical expression generated jointly by the target cold material belt motor inverter operating frequency, target coefficient, and time value, including: Based on the time value, the operating frequency of the target cold material belt motor inverter is continuously integrated, and the target coefficients of the cold material bins corresponding to the target hot material bins are accumulated to generate a mathematical expression.

6. The method for calculating the composition of hot aggregate according to claim 1, characterized in that, The target coefficient is calculated based on the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material silo, and the real-time material level of the target hot material silo, including: The target cold material belt motor inverter operating frequency, the target hot material bin cumulative consumption, and the target hot material bin real-time material level value are input into the preset optimization model to obtain the target coefficient output by the preset optimization model.

7. The method for calculating the composition of hot aggregate according to claim 1, characterized in that, Before calculating the proportion of cold aggregate in the target hot aggregate bin based on the target coefficient, the method for calculating the hot aggregate composition further includes: Obtain preset optimization parameters; The calculated target coefficients are weighted based on the preset optimization parameters to obtain optimized target coefficients. The calculation of the proportion of cold material components in the target hot material bin, based on the target coefficient, includes: Based on the optimized target coefficient, the proportion of cold material components in the target hot material bin is calculated.

8. The method for calculating the composition of hot aggregate according to claim 1, characterized in that, Each target coefficient corresponds to a cold material bin, wherein, based on the target coefficient, the proportion of cold material components in the target hot material bin is calculated, including: Calculate the sum of the target coefficients for the cold silos corresponding to the target hot silos; The proportion of the target coefficient in the target cold material bin to the sum of the target coefficients is taken as the proportion of the cold material component transported from the target cold material bin to the target hot material bin.

9. A device for calculating the composition of hot aggregates, characterized in that, The hot aggregate characterization in the hot aggregate bin is based on the aggregate formed after processing the cold aggregate in the cold aggregate bin; wherein, the calculation device for the hot aggregate composition includes: A module is established to create a balance equation based on the cumulative feed amount of the target hot material bin, the cumulative consumption amount of the target hot material bin, and the real-time material level value of the target hot material bin. The replacement module, based on the correlation between the target cold material belt motor inverter operating frequency and the cumulative feed amount, replaces the cumulative feed amount in the balance equation with a mathematical expression generated according to the target cold material belt motor inverter operating frequency and the target coefficient; wherein, the target coefficient is used to characterize the hot material bin feed speed coefficient; The first calculation module calculates the target coefficient based on the operating frequency of the target cold material belt motor inverter, the cumulative consumption of the target hot material bin, and the real-time material level of the target hot material bin. The second calculation module calculates the proportion of cold aggregate components in the target hot aggregate bin based on the target coefficient; wherein the proportion of cold aggregate components represents the original proportion of hot aggregate components.

10. An asphalt station, characterized in that, include: At least one cold silo and at least one hot silo; wherein the cold silo transports the hot aggregate components to the hot silo via a cold material conveyor belt. ； The hot aggregate composition calculation device as described in claim 9 is used to calculate the proportion of hot aggregate composition transported from each cold silo to the hot silo.

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