A rotary steel belt granulator production process automatic control method and system

Abstract

The present application relates to the technical field of automatic control of rotary steel belt granulator, and particularly relates to a kind of rotary steel belt granulator production process automatic control method and system, the method comprises the following steps: according to the preset disturbance parameter, the flow and / or temperature of cooling water supplied to steel belt are adjusted to form controlled disturbance;Obtain the instantaneous temperature response information of polymer droplets on steel belt under the action of controlled disturbance;Analyze the instantaneous temperature response information to obtain the instantaneous thermal response characteristics of polymer droplets;Compare the instantaneous thermal response characteristics with the preset reference thermal response characteristics to obtain the characteristic difference;According to the characteristic difference, the heat transfer efficiency change between the steel belt and the cooling water is judged;According to the heat transfer efficiency change, the running speed of the steel belt and / or the parameters of the cooling water are adjusted.The present application realizes the automation and fine control of the granulation process, effectively avoids the production problems caused by the decrease of heat conduction efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of automated control of rotary steel strip granulators, specifically to an automated control method and system for the production process of a rotary steel strip granulator. Background Technology

[0002] In modern industrial production, rotary steel belt granulators are key equipment for efficiently converting molten materials into uniform solid particles. Their operating efficiency and product quality largely depend on the cooling and solidification process of the material on the steel belt. Existing automated control systems typically rely on monitoring macroscopic production parameters such as the inlet temperature of the molten material, the flow rate of the distributor, the speed of the steel belt, and the volume and temperature of the cooling water spray to adjust the cooling intensity.

[0003] However, during prolonged continuous operation of the equipment, minerals in the cooling water may gradually deposit on the back of the steel strip, forming scale. This scale formation is a hidden and gradual process that significantly increases the thermal resistance between the steel strip and the cooling water, thus reducing cooling efficiency. Because the thermal conductivity of scale is much lower than that of stainless steel, even a thin layer of scale is sufficient to significantly increase the thermal resistance from the molten polymer on the upper surface of the steel strip to the cooling water below, forming an invisible "insulating layer" that hinders effective heat conduction. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned shortcomings by proposing an automated control method and system for the production process of a rotary steel strip granulator.

[0005] The present invention adopts the following technical solution:

[0006] An automated control method for the production process of a rotary steel strip granulator, the method comprising the following steps:

[0007] According to preset disturbance parameters, the flow rate and / or temperature of the cooling water supplied to the steel strip are adjusted to form a controlled disturbance;

[0008] Acquire the instantaneous temperature response information of polymer droplets on a steel strip under controlled perturbation;

[0009] By analyzing the instantaneous temperature response information, the instantaneous thermal response characteristics of the polymer droplets are obtained;

[0010] The instantaneous thermal response characteristics are compared with the preset reference thermal response characteristics to obtain the characteristic differences;

[0011] Based on the differences in characteristics, determine the changes in heat transfer efficiency between the steel strip and the cooling water;

[0012] Adjust the running speed of the steel strip and / or the parameters of the cooling water according to the changes in heat transfer efficiency, wherein the parameters of the cooling water include at least one of cooling water flow rate and temperature.

[0013] This technical solution enables the real-time and accurate sensing of changes in heat transfer efficiency between the steel strip and cooling water by introducing controlled disturbances and analyzing the instantaneous thermal response of polymer droplets. This allows for automated and precise control of the granulation process, effectively avoiding production problems caused by decreased heat conduction efficiency.

[0014] This application also discloses an automated control system for the production process of a rotary steel strip granulator, which applies the above-mentioned automated control method for the production process of a rotary steel strip granulator. The system includes:

[0015] The disturbance application module adjusts the flow rate and / or temperature of the cooling water supplied to the steel strip according to preset disturbance parameters to form a controlled disturbance.

[0016] The acquisition module acquires the instantaneous temperature response information of polymer droplets on the steel strip under controlled perturbation.

[0017] The analysis module analyzes the instantaneous temperature response information to obtain the instantaneous thermal response characteristics of the polymer droplets;

[0018] The comparison module compares the instantaneous thermal response characteristics with the preset reference thermal response characteristics to obtain the characteristic differences;

[0019] The judgment module determines the change in heat transfer efficiency between the steel strip and the cooling water based on the differences in characteristics.

[0020] The adjustment module adjusts the running speed of the steel strip and / or the parameters of the cooling water according to the changes in heat transfer efficiency, wherein the parameters of the cooling water include at least one of cooling water flow rate and temperature.

[0021] This technical solution provides a system for implementing the aforementioned automated control method. Through modular design, the functional units work together to achieve automated and intelligent control of the rotary steel strip granulator production process, effectively improving production efficiency and product quality.

[0022] This application effectively solves the problems of lagging process control and inability to prevent intervention in the prior art, and significantly improves the production efficiency, product quality and operational stability of rotary steel strip granulators.

[0023] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0024] Figure 1 This is a flowchart of the automated control method for the production process of the rotary steel strip granulator of the present invention;

[0025] Figure 2 This is a schematic diagram of the automated control system for the rotary steel strip granulator of the present invention. Detailed Implementation

[0026] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0027] This embodiment provides an automated control method and system for the production process of a rotary steel strip granulator, combined with... Figure 1 and Figure 2 As shown.

[0028] refer to Figure 1 An automated control method for the production process of a rotary steel strip granulator, the method comprising the following steps:

[0029] According to preset disturbance parameters, the flow rate and / or temperature of the cooling water supplied to the steel strip are adjusted to form a controlled disturbance;

[0030] Acquire the instantaneous temperature response information of polymer droplets on a steel strip under controlled perturbation;

[0031] By analyzing the instantaneous temperature response information, the instantaneous thermal response characteristics of the polymer droplets are obtained;

[0032] The instantaneous thermal response characteristics are compared with the preset reference thermal response characteristics to obtain the characteristic differences;

[0033] Based on the differences in characteristics, determine the changes in heat transfer efficiency between the steel strip and the cooling water;

[0034] Adjust the running speed of the steel strip and / or the parameters of the cooling water according to the changes in heat transfer efficiency, wherein the parameters of the cooling water include at least one of cooling water flow rate and temperature.

[0035] The "rotary steel strip granulator" is a common industrial piece of equipment. Its core component is a continuously running steel strip. Molten polymer is evenly distributed on the steel strip and cooled and solidified by cooling water below the strip, ultimately forming a granular product. "Controlled disturbance" refers to purposeful and controllable minor adjustments to the flow rate and / or temperature of the cooling water during the production process. This adjustment is not intended to change the overall cooling effect, but rather to create a perceptible change in heat transfer at the interface between the steel strip and the cooling water, allowing for subsequent monitoring of the polymer droplet response. "Instantaneous temperature response information" refers to detailed data on the temperature change of the polymer droplets on the steel strip over time under controlled disturbance. This data reflects the sensitivity and response speed of the polymer droplets to changes in cooling conditions. "Instantaneous thermal response characteristics" are indicators extracted from the instantaneous temperature response information that quantify the cooling behavior of the polymer droplets, such as the rate of temperature change, the magnitude of temperature change, response time, and recovery time. These characteristics are key indicators for evaluating heat transfer efficiency. "Reference thermal response characteristics" are pre-established thermal response characteristics exhibited by polymer droplets under ideal or specific production conditions. They serve as a benchmark for comparison with real-time acquired instantaneous thermal response characteristics to identify anomalies. "Characteristic difference" refers to the deviation between the instantaneous thermal response characteristics and the reference thermal response characteristics; the magnitude and direction of this difference indicate the trend of heat transfer efficiency changes. "Cooling water parameters" mainly include the flow rate and temperature of the cooling water; these parameters are direct factors affecting cooling efficiency and are the primary means used in this application to adjust the cooling intensity.

[0036] Specifically, this method first adjusts the flow rate and / or temperature of the cooling water supplied to the steel strip according to preset disturbance parameters to create a controlled disturbance. For example, this can be achieved in the following ways: One approach is to control the frequency converter of the cooling water pump to periodically fluctuate the cooling water flow rate within a small range of normal operation for a short period (e.g., a few seconds), for example, a sinusoidal waveform fluctuation with an amplitude of ±10 L / min based on 500 L / min. Another approach is to adjust the cooling water temperature control valve to periodically fluctuate the cooling water temperature within a small range of normal operation for a short period, for example, a square waveform fluctuation with an amplitude of ±0.5℃ based on 25℃. Alternatively, both the cooling water flow rate and temperature can be adjusted simultaneously; for example, the temperature fluctuates accordingly while the flow rate fluctuates.

[0037] Next, the instantaneous temperature response information of the polymer droplets on the steel strip under controlled perturbation is obtained. For example, this can be achieved in the following ways: One approach is to install a high-precision infrared thermal imager above the steel strip to scan the surface temperature distribution of the polymer droplets on the steel strip in real time, and record the temperature change curve of the polymer droplets in a specific area over time under controlled perturbation at a high frame rate (e.g., 100 frames / second). Another approach is to embed a miniature thermocouple array on the back side of the steel strip below the cooling water spray area, and indirectly infer the thermal response of the polymer droplets by measuring the temperature change on the back side of the steel strip.

[0038] Subsequently, the instantaneous temperature response information is analyzed to obtain the instantaneous thermal response characteristics of the polymer droplets. For example, this can be achieved in the following ways: One approach is to differentiate the acquired temperature-time curve, calculate the rate of temperature change, and identify the time required for the temperature to reach its maximum change amplitude (response time) and the time required to recover to a steady state from the maximum change amplitude (recovery time). Simultaneously, the peak and trough values ​​of the temperature change are recorded, and the amplitude of the temperature change is calculated. Another approach is to utilize signal processing techniques such as Fourier transform to analyze the frequency components and phase lag of the temperature response information, thereby extracting characteristic parameters related to heat transfer efficiency.

[0039] Then, the instantaneous thermal response characteristics are compared with preset reference thermal response characteristics to obtain the feature differences. For example, this can be achieved in the following ways: One approach is to compare the real-time acquired instantaneous thermal response characteristics (e.g., average temperature change rate, average response time, etc.) with preset reference thermal response characteristics obtained under clean steel strip conditions one by one, calculating the absolute difference or percentage deviation between them. Another approach is to map the instantaneous thermal response characteristics and reference thermal response characteristics into a multi-dimensional feature space, and quantify the feature differences by calculating their Euclidean or Mahalanobis distance in the feature space.

[0040] Next, based on the characteristic differences, the change in heat transfer efficiency between the steel strip and the cooling water is determined. For example, this can be achieved in the following ways: One approach is to set a series of thresholds; when the characteristic differences (e.g., the instantaneous temperature change rate is lower than a reference value, or the instantaneous recovery time is higher than a reference value) exceed these thresholds, it is determined that the heat transfer efficiency has decreased. Another approach is to use a fuzzy logic reasoning system, taking multiple characteristic differences as input and combining them with a pre-set fuzzy rule base, to output the degree of change in heat transfer efficiency (e.g., slight decrease, moderate decrease, severe decrease).

[0041] Finally, based on changes in heat transfer efficiency, the operating speed of the steel belt and / or the parameters of the cooling water are adjusted. The cooling water parameters include at least one of cooling water flow rate and temperature. For example, this can be achieved in two ways: One approach is that when a decrease in heat transfer efficiency is detected, the system automatically reduces the operating speed of the steel belt to prolong the cooling time of the polymer droplets on the steel belt, compensating for the loss of heat transfer efficiency. Another approach is that when a decrease in heat transfer efficiency is detected, the system automatically increases the cooling water flow rate and / or decreases the cooling water temperature to enhance cooling intensity and counteract the impact of the decreased heat transfer efficiency.

[0042] The automated control method for the production process of the rotary steel strip granulator proposed in this application can accurately assess the heat transfer efficiency between the steel strip and cooling water in real time by introducing controlled disturbances and monitoring the instantaneous temperature response of polymer droplets. This allows for preventative adjustments in the early stages of problems, effectively avoiding production accidents and product scrap caused by decreased heat transfer efficiency.

[0043] Specifically, this method first generates a perceptible change in heat transfer at the interface between the steel strip and the cooling water by subjecting a pre-defined controlled perturbation to the flow rate and / or temperature of the cooling water supplied to the steel strip. This perturbation is small and controllable, and will not significantly affect the normal production process. Subsequently, the system acquires the instantaneous temperature response information of polymer droplets on the steel strip under the controlled perturbation. This information contains the sensitivity and response speed of the polymer droplets to changes in cooling conditions, and is key data for evaluating heat transfer efficiency. Next, the instantaneous temperature response information is analyzed to extract the instantaneous thermal response characteristics of the polymer droplets, such as the rate of temperature change, the magnitude of temperature change, the response time, and the recovery time. These characteristics quantify the cooling behavior of the polymer droplets.

[0044] Then, the real-time acquired instantaneous thermal response characteristics are compared with preset reference thermal response characteristics to obtain the characteristic differences. The reference thermal response characteristics are a benchmark established under ideal or specific production conditions. By comparing them with the real-time characteristics, anomalies in heat transfer efficiency can be detected. Based on the characteristic differences, the system can determine the changes in heat transfer efficiency between the steel strip and cooling water, such as whether there is an increase in thermal resistance due to scale accumulation. Finally, based on the judgment results, the system automatically adjusts the running speed of the steel strip and / or the parameters of the cooling water (including cooling water flow rate and temperature) to compensate for the decrease in heat transfer efficiency. For example, when a decrease in heat transfer efficiency is detected, the running speed of the steel strip can be reduced to extend the cooling time, or the cooling water flow rate can be increased and the cooling water temperature can be reduced to enhance the cooling intensity.

[0045] Through the aforementioned collaborative efforts, the method of this application enables real-time monitoring and adaptive adjustment of heat transfer efficiency during the production process of a steel strip granulator. Compared with existing technologies, the core innovation of this application lies in its departure from solely relying on the monitoring of macroscopic production parameters. Instead, it directly senses changes in thermal resistance at the interface between the steel strip and cooling water by introducing controlled disturbances and analyzing the instantaneous thermal response of microscopic polymer droplets. Traditional methods struggle to detect scale formation in its early stages, often only revealing the problem when the product exhibits a "hard exterior, soft interior" appearance or even becomes unusable, resulting in significant losses. In contrast, the method of this application can provide early warnings and make compensatory adjustments based on minute changes in the instantaneous thermal response characteristics of polymer droplets during the initial stages of scale accumulation, effectively preventing production accidents and significantly improving product quality and production efficiency. This feedback control mechanism based on microscopic thermal response makes the automated control of the granulation process more refined and intelligent, representing a significant technological advancement.

[0046] This application further proposes the following steps for analyzing the instantaneous temperature response information to obtain the instantaneous thermal response characteristics of polymer droplets:

[0047] The instantaneous temperature response information of multiple adjacent polymer droplets under controlled perturbation is extracted to obtain temperature response features, which include the temperature change rate, temperature change amplitude, response time and recovery time of each polymer droplet.

[0048] Based on the temperature change rate, temperature change amplitude, response time, and recovery time corresponding to each polymer droplet, calculate the average value and standard deviation of the temperature change rate, temperature change amplitude, response time, and recovery time corresponding to each polymer droplet.

[0049] When any standard deviation exceeds the preset range relative to the corresponding average, abnormal droplet data is removed. Abnormal droplet data refers to droplet data whose calculated value is greater than the preset first threshold. The calculated value is the absolute value of the difference between the corresponding temperature response characteristic and the corresponding average value.

[0050] For the remaining droplet data after removing abnormal droplet data, the average values ​​of temperature change rate, temperature change amplitude, response time, and recovery time for each polymer droplet are recalculated, and the recalculated average values ​​are used as the instantaneous thermal response characteristics of the polymer droplets.

[0051] Specifically, the instantaneous temperature response information of multiple adjacent polymer droplets under controlled perturbation is feature-extracted to obtain temperature response features, which include the temperature change rate, temperature change amplitude, response time, and recovery time for each polymer droplet. Multiple adjacent polymer droplets refer to a group of droplets spatially close to each other on the steel strip. The purpose is to obtain more representative thermal response information by collecting data from multiple droplets in a local area, thereby reducing the impact of randomness or local anomalies of individual droplets on the overall judgment. The temperature change rate refers to how quickly the temperature of the polymer droplet changes over time under controlled perturbation; the temperature change amplitude refers to the difference between the droplet temperature from its initial state to the maximum or minimum temperature reached after the perturbation; the response time is the time required for the droplet temperature to begin to change significantly from the applied perturbation; and the recovery time is the time required for the droplet temperature to recover from the perturbed state to or near its initial stable state. These features together constitute a complete dynamic response profile of the polymer droplets under thermal perturbation, which can comprehensively reflect the heat transfer efficiency between the droplets and the steel strip.

[0052] Furthermore, based on the temperature change rate, temperature change amplitude, response time, and recovery time corresponding to each polymer droplet, the average and standard deviation of these parameters are calculated for each polymer droplet. The average value characterizes the central tendency of adjacent droplets in each temperature response characteristic, reflecting the overall thermal response behavior of this local region. The standard deviation measures the dispersion of the droplets in each characteristic, i.e., the consistency or fluctuation of the thermal response behavior among the droplets.

[0053] Based on this, when any standard deviation exceeds a preset range relative to the corresponding average, abnormal droplet data is removed. Abnormal droplet data is defined as droplet data whose calculated value exceeds a preset first threshold. The calculated value is the absolute value of the difference between the corresponding temperature response characteristic and the corresponding average. This step aims to identify and exclude abnormal data points caused by measurement errors, local impurities, or random factors, ensuring the accuracy of subsequent analysis. The preset range and the first threshold can be set based on actual production experience and statistical data analysis results to balance the sensitivity and false positive rate of anomaly removal.

[0054] Subsequently, the average values ​​of temperature change rate, temperature change amplitude, response time, and recovery time for each polymer droplet were recalculated on the remaining droplet data after removing outlier data. These recalculated average values ​​were then used as the instantaneous thermal response characteristics of the polymer droplets. By removing outliers and recalculating the average values, more stable and accurate instantaneous thermal response characteristics can be obtained, which can more realistically reflect the current heat transfer state between the steel strip and the cooling water.

[0055] This application's solution effectively addresses the problem of inaccurate instantaneous thermal response characteristics caused by the randomness or local anomalies of a single droplet in traditional methods by refining the instantaneous temperature response information of multiple adjacent polymer droplets. Specifically, by collecting data from multiple adjacent droplets, more statistically significant samples can be obtained, thereby reducing the impact of errors from a single measurement point. Simultaneously, the extraction of multi-dimensional features such as temperature change rate, temperature change amplitude, response time, and recovery time provides a more comprehensive and in-depth description of the droplet thermal response. More importantly, the calculation of the mean and standard deviation of these features is introduced, and abnormal droplet data is removed based on the standard deviation. This mechanism effectively identifies and excludes abnormal data points that deviate from the overall trend, thus avoiding interference from abnormal data in the calculation of instantaneous thermal response characteristics. Finally, by recalculating the mean of the cleaned data, the obtained instantaneous thermal response characteristics are ensured to be stable and representative, laying a solid foundation for accurately judging the changes in heat transfer efficiency between the steel strip and cooling water.

[0056] Through the above technical solution, this application can significantly improve the accuracy and reliability of the instantaneous thermal response characteristics of polymer droplets. This solution effectively avoids interference from local noise, measurement errors, or abnormal behavior of individual droplets on the extraction of thermal response characteristics, making the obtained characteristic data more stable and representative. Therefore, it can more accurately reflect the actual heat transfer state between the steel strip and cooling water, providing a more reliable basis for the automated control of the rotary steel strip granulator production process, thereby improving the control precision and product quality stability of the entire granulation process.

[0057] In some preferred embodiments, it is assumed that in a local area of ​​the steel strip, the instantaneous temperature response curves of five adjacent polymer droplets under controlled perturbation are continuously acquired using an infrared thermal imager or other temperature sensors. For each droplet's temperature response curve, four temperature response features are extracted: temperature change rate, temperature change amplitude, response time, and recovery time. For example, the temperature change rate of droplet 1 is X1, amplitude is Y1, response time is Z1, and recovery time is W1; for droplet 2, it is X2, Y2, Z2, W2, and so on up to droplet 5. Subsequently, the average value and standard deviation of each feature of these five droplets are calculated. For example, the average value X_avg and standard deviation X_std of the temperature change rates of the five droplets are calculated. If the absolute difference |X3-X_avg| between the temperature change rate X3 and X_avg of droplet 3 is found to be much greater than a preset first threshold, and X_std exceeds a preset range relative to X_avg, then droplet 3 is determined to be an abnormal droplet, and its data will be discarded. After removing the data from droplet 3, the average temperature change rate is recalculated for the remaining droplets 1, 2, 4, and 5. The same process is applied to the temperature change amplitude, response time, and recovery time. Finally, the average of these four recalculated characteristics is used as the instantaneous thermal response characteristic of the polymer droplets in that local region, for subsequent heat transfer efficiency assessment. In this way, even the presence of individual abnormal droplets will not significantly affect the overall thermal response characteristic assessment, thus ensuring the robustness of the control system.

[0058] This application refines the definition of multiple adjacent polymer droplets. Multiple adjacent polymer droplets are adjacent polymer droplets along the width direction of the steel strip.

[0059] In this context, multiple adjacent polymer droplets refer to a group of polymer droplets distributed along the width of the steel strip. In a rotary steel strip granulator, the polymer droplets cool and solidify on the steel strip, and this cooling process is influenced by the cooling water beneath the steel strip. Since the distribution of the cooling water along the width of the steel strip may be uneven, or the thermal conductivity of the steel strip itself may differ along its width, acquiring and analyzing the instantaneous temperature response information of adjacent polymer droplets along the width of the steel strip can more comprehensively reflect the variation in heat transfer efficiency between the steel strip and the cooling water in different regions. By extracting and statistically analyzing the temperature responses of these droplets, the differences in heat transfer along the width can be effectively captured, providing a more accurate data basis for subsequent efficiency assessment and parameter adjustment.

[0060] This application's solution, by limiting multiple adjacent polymer droplets to adjacent polymer droplets along the width direction of the steel strip, enables more effective capture of the heat transfer uniformity of the steel strip in the width direction when acquiring instantaneous temperature response information. In actual production, factors such as the accumulation of scale on the back of the steel strip and the uneven distribution of cooling water often lead to local differences in the heat transfer efficiency of the steel strip in the width direction. By extracting and analyzing the temperature response characteristics of adjacent droplets in the width direction, these local differences can be identified and quantified in a timely manner, thus providing more refined and targeted data support for subsequent anomaly judgment and parameter adjustment. This selection method helps to improve the sensitivity and accuracy of changes in heat transfer efficiency between the steel strip and cooling water.

[0061] The above technical solution can more accurately reflect the uniformity of heat transfer in the width direction of the steel strip, thereby improving the accuracy of judging changes in heat transfer efficiency between the steel strip and cooling water. Especially when there may be localized scale accumulation or uneven cooling water distribution on the back of the steel strip, this solution can promptly detect and quantify these local anomalies, avoiding misjudgments or delayed judgments caused by overall average values ​​masking local problems. This provides a more reliable and refined data foundation for the automated control of the rotary steel strip granulator production process, helping to improve the quality stability and production efficiency of the granulated products.

[0062] This application further proposes steps for determining the change in heat transfer efficiency between the steel strip and cooling water based on characteristic differences, including:

[0063] The instantaneous thermal response characteristics of multiple adjacent polymer droplets are statistically analyzed to obtain the current average thermal response characteristics and dispersion.

[0064] The current average thermal response characteristics and dispersion are compared with a preset material batch reference set to determine the characteristic differences of the current average thermal response characteristics and dispersion relative to the material batch reference set, wherein the material batch reference set includes the average thermal response characteristics and dispersion of different material batches in the clean steel strip state;

[0065] When the current average thermal response characteristics fall within the preset matching range of the average thermal response characteristics of a certain material batch in the material batch reference set, and the degree of dispersion is within the degree of dispersion of that material batch in the clean steel strip state, it is determined that the heat transfer efficiency between the steel strip and the cooling water has not changed abnormally, and the characteristic difference is caused by the fluctuation of the material's own properties.

[0066] When the average temperature change rate in the current average thermal response characteristics is lower than the corresponding rate reference value and / or the average recovery time is higher than the corresponding time reference value, and the dispersion exceeds the dispersion range of the material batch in the clean steel strip state, it is determined that there is scale accumulation on the back of the steel strip, and the scale accumulation causes a decrease in the heat transfer efficiency between the steel strip and the cooling water. The rate reference value and the time reference value are obtained from the material batch reference set.

[0067] Specifically, after obtaining the instantaneous thermal response characteristics of multiple adjacent polymer droplets, statistical analysis of these characteristics is required to obtain the average thermal response characteristics and dispersion under the current production state. The average thermal response characteristics can be understood as the average values ​​of the temperature change rate, temperature change amplitude, response time, and recovery time of these droplets, reflecting the overall thermal response trend. The dispersion can be understood as the standard deviation or coefficient of variation of these characteristics, reflecting the uniformity or consistency of the thermal response among different droplets.

[0068] Furthermore, to accurately determine the causes of changes in heat transfer efficiency, the currently obtained average thermal response characteristics and dispersion are compared with a pre-established reference set of material batches. This reference set of material batches comprises the average thermal response characteristics and dispersion of different material batches under clean conditions (i.e., free of scale and dirt) obtained through experiments or historical data accumulation. By comparing with this reference set, a benchmark can be established to assess the degree of deviation from the current production status.

[0069] When the current average thermal response characteristics fall within the preset matching range of the average thermal response characteristics of a certain material batch in the material batch reference set, and the degree of dispersion is also within the dispersion range of that material batch in the clean steel strip state, this indicates that the heat transfer efficiency between the steel strip and the cooling water has not changed abnormally. At this time, the observed characteristic differences are mainly considered to be caused by minor fluctuations in the properties of the material itself, such as subtle differences in molecular weight distribution and additive content between material batches.

[0070] However, when the average temperature change rate in the current average thermal response characteristics is lower than the corresponding rate reference value and / or the average recovery time is higher than the corresponding time reference value, and the dispersion exceeds the dispersion range of the material batch in the clean steel strip state, this usually indicates the presence of scale accumulation on the back of the steel strip. Scale formation creates an insulating layer between the back of the steel strip and the cooling water, hindering effective heat transfer and resulting in a decreased cooling rate (lower average temperature change rate) and prolonged cooling time (increased recovery time) for the polymer droplets. Furthermore, scale accumulation is often uneven, leading to differences in heat transfer efficiency across different areas of the steel strip, thus significantly increasing the dispersion of the instantaneous thermal response characteristics of the polymer droplets. The rate reference value and time reference value are baseline values ​​determined based on data from the corresponding material batch in the material batch reference set in the clean steel strip state.

[0071] This application's solution effectively addresses the limitations of traditional methods in distinguishing the causes of changes in heat transfer efficiency by introducing statistical analysis of the instantaneous thermal response characteristics of multiple adjacent polymer droplets and combining this with a material batch reference set for multi-dimensional comparison. Specifically, when scale accumulates on the back of the steel strip, the scale layer increases thermal resistance, leading to a decrease in the steel strip's cooling capacity for the polymer droplets. This results in a slower rate of average temperature change for the polymer droplets and a longer time required for them to recover to a stable temperature. More importantly, scale accumulation is usually uneven, causing different thermal resistances in different regions of the steel strip. This leads to greater differences in the cooling behavior of adjacent polymer droplets, meaning the dispersion of instantaneous thermal response characteristics increases significantly.

[0072] By comparing the average thermal response characteristics and dispersion under the current production conditions with a pre-defined material batch reference set, a comprehensive diagnostic model can be established. The material batch reference set provides baseline thermal response characteristics for different materials under ideal (clean steel strip) conditions, including their average value and normal fluctuation range. When the average thermal response characteristics deviate from the baseline value (e.g., a decrease in the average temperature change rate or an increase in recovery time), and the dispersion also exceeds the normal range, this dual anomaly strongly indicates the possibility of scale accumulation on the back of the steel strip. In contrast, if only the average thermal response characteristics fluctuate slightly while the dispersion remains normal, it is more likely to be judged as a normal fluctuation in the material's inherent properties. Therefore, this solution can accurately diagnose changes in heat transfer efficiency from multiple dimensions, avoiding misjudgments that may result from judging by a single indicator.

[0073] Through the above technical solution, this application can accurately identify the causes of changes in heat transfer efficiency during the production process of a rotary steel strip granulator. Specifically, this solution can effectively distinguish whether the change in heat transfer efficiency is caused by fluctuations in the material's own properties or by scale accumulation on the back of the steel strip. This precise diagnostic capability allows the control system to adopt more targeted adjustment strategies based on the actual problem type. For example, for changes caused by fluctuations in material properties, parameter fine-tuning can be performed; while for scale accumulation, a timely scale cleaning warning can be issued to avoid serious consequences such as decreased granulation quality, increased energy consumption, or even shutdown for maintenance due to scale accumulation. This significantly improves the intelligence level of automated control and the stability and reliability of the production process.

[0074] In some preferred embodiments, a specific example is given below. Suppose that when producing a certain polymer granules, the system first establishes a reference set of material batches in a clean steel belt state using historical data and experiments. This set includes the material's average thermal response characteristics (e.g., average temperature change rate X, average recovery time Y) and its corresponding dispersion range (e.g., standard deviation between Z1 and Z2).

[0075] In actual production, the system continuously acquires the instantaneous temperature response information of multiple adjacent polymer droplets on the steel strip and calculates the current average thermal response characteristics and dispersion.

[0076] Specifically, if the system detects that the average temperature change rate in the current average thermal response characteristics is X', the average recovery time is Y', and the dispersion is Z'.

[0077] Scenario 1: If X' falls within the preset matching range of X, Y' falls within the preset matching range of Y, and Z' is also within the range of Z1 to Z2, then the system determines that the heat transfer efficiency between the steel strip and the cooling water has not changed abnormally, and the currently observed characteristic differences are considered to be normal fluctuations in the material's own properties. In this case, the control system may only make minor parameter adjustments to optimize the granulation effect.

[0078] Scenario 2: If X' is significantly lower than X (e.g., X' < 0.9X), Y' is significantly higher than Y (e.g., Y' > 1.1Y), and Z' significantly exceeds the range of Z1 to Z2 (e.g., Z' > 1.5Z2), the system determines that scale has accumulated on the back of the steel strip, and that this scale accumulation is causing a decrease in the heat transfer efficiency between the steel strip and the cooling water. In this case, the control system will not only attempt to compensate by adjusting the steel strip running speed and / or cooling water parameters, but will also issue a scale cleaning warning when necessary, prompting operators to perform maintenance, based on subsequent judgment results.

[0079] This example clearly demonstrates how this application can achieve accurate diagnosis of the causes of changes in heat transfer efficiency through multi-dimensional feature comparison, thereby providing a reliable basis for subsequent automated control and maintenance decisions.

[0080] This application further proposes steps for scale removal early warning to issue warnings to operators under specific conditions, thereby enabling timely cleaning measures to be taken.

[0081] When it is determined that scale accumulation exists on the back of the steel strip, and that the scale accumulation reduces the heat transfer efficiency between the steel strip and the cooling water, the method further includes a scale removal early warning step, which includes:

[0082] When the dispersion continues to exceed the dispersion range of the corresponding material batch in the clean steel belt state, a scale cleaning warning will be issued;

[0083] And / or, when the running speed of the steel strip and / or the parameters of the cooling water reach the preset maximum compensation limit, and the current average thermal response characteristics have not yet recovered to the preset matching range of the average thermal response characteristics of the corresponding material batch in the state of clean steel strip, a scale cleaning warning will be issued.

[0084] Specifically, the scale removal early warning system aims to alert operators when scale problems reach a certain level, requiring manual intervention or more thorough cleaning. There are two triggering conditions for issuing a scale removal early warning, or a combination thereof. The first triggering condition is "when the dispersion continuously exceeds the dispersion range of the corresponding material batch in a clean steel strip state." Here, dispersion reflects the uniformity of the steel strip surface temperature response. When scale accumulates on the back of the steel strip, its distribution is often uneven, leading to increased differences in heat transfer efficiency across different areas of the steel strip, thus increasing the dispersion of the instantaneous thermal response characteristics of the polymer droplets. If this dispersion continues to exceed the normal range, it indicates that the scale problem is quite serious and persistent, requiring cleaning. The second triggering condition is "when the steel strip's operating speed and / or cooling water parameters reach the preset maximum compensation limit, and the current average thermal response characteristics still have not recovered to the preset matching range of the average thermal response characteristics of the corresponding material batch in a clean steel strip state." The preset maximum compensation limit refers to the system's ability boundary to compensate for the decrease in heat transfer efficiency by adjusting the steel strip's operating speed and / or cooling water parameters. When the system has tried its best to maintain stable production by adjusting these parameters, but the average thermal response characteristics of the polymer droplets (such as the average rate of temperature change, average recovery time, etc.) still fail to return to the normal matching range under the clean steel belt condition, it indicates that parameter adjustment alone cannot effectively solve the problem. At this time, it is also necessary to issue a scale cleaning warning to prompt manual cleaning.

[0085] This application's solution addresses the problem of merely detecting scale presence without timely intervention by introducing a scale removal early warning mechanism. When the system detects scale accumulation leading to decreased heat transfer efficiency, it does not immediately issue an early warning. Instead, it first attempts to compensate by adjusting the running speed of the steel belt and / or the parameters of the cooling water. A scale removal early warning is only triggered when the scale problem continues to worsen, manifested as a persistent deviation in the dispersion of instantaneous thermal response characteristics from the normal range, or when the system has reached its maximum compensation capacity through parameter adjustments but the thermal response characteristics still fail to return to normal levels. This mechanism ensures the timeliness and necessity of early warnings, avoiding unnecessary downtime or maintenance, while simultaneously alerting operators to take action before the problem becomes severe.

[0086] Through the above technical solution, this application provides a more intelligent and proactive process control for steel strip granulators. It not only identifies scale buildup problems, but more importantly, it issues cleaning warnings at appropriate times based on the severity of the scale problem and the system's own compensation capabilities. This helps avoid a continuous decline in production efficiency, fluctuations in product quality, and even equipment damage caused by long-term scale accumulation. By issuing warnings when the system's compensation capabilities reach their limit or the problem continues to worsen, it effectively guides operators to perform preventative maintenance, thereby extending the service life of the steel strip, reducing maintenance costs, and ensuring the stability of the production process and the consistency of product quality.

[0087] In some preferred embodiments, assuming that during the production process of a rotary steel belt granulator, the system analyzes the instantaneous thermal response characteristics of polymer droplets and determines that scale accumulation on the back of the steel belt is causing a decrease in heat transfer efficiency. In this case, the system will first attempt to reduce the operating speed of the steel belt and / or increase the flow rate of cooling water to compensate for the decrease in heat transfer efficiency. If, after a period of compensation adjustment, the system finds that the dispersion of the instantaneous thermal response characteristics of the polymer droplets is consistently higher than the preset range under clean steel belt conditions (e.g., continuous monitoring for 30 minutes with dispersion indicators all exceeding a threshold), the system will immediately issue a scale cleaning warning, prompting the operator to check and clean the scale on the back of the steel belt. Alternatively, if the system has already adjusted the operating speed of the steel belt to the minimum allowable limit and the cooling water flow rate to the maximum allowable limit, i.e., reached the preset maximum compensation limit, but the current average thermal response characteristics of the polymer droplets (e.g., average temperature change rate) are still lower than the matching range under clean steel belt conditions and have not recovered to a normal level, the system will also issue a scale cleaning warning. Both of these situations indicate that the scale problem has exceeded the scope of the system's automatic adjustment and requires manual intervention for cleaning, thereby avoiding further loss of production efficiency and potential equipment failure.

[0088] In the above method, the preset maximum compensation limit is a pre-set compensation boundary. The compensation boundary is determined based on the lower limit of the running speed of the steel strip and the allowable adjustment boundary of the cooling water parameters. The allowable adjustment boundary of the cooling water parameters includes the upper limit of the cooling water flow rate and / or the lower limit of the cooling water temperature.

[0089] Specifically, the preset maximum compensation limit refers to the maximum adjustment range achievable when adjusting the steel strip running speed and / or cooling water parameters to compensate for a decrease in heat transfer efficiency. This maximum compensation limit is not arbitrarily set but is predetermined based on the physical limitations of the production equipment and process requirements. Determining the compensation boundary requires comprehensive consideration of the lower allowable limit of the steel strip running speed and the allowable adjustment boundaries of the cooling water parameters. The lower allowable limit of the steel strip running speed refers to the minimum speed at which the steel strip can operate safely while ensuring product quality and production stability. The allowable adjustment boundaries of the cooling water parameters specifically include the upper allowable limit of the cooling water flow rate and / or the lower allowable limit of the cooling water temperature. The upper allowable limit of the cooling water flow rate refers to the maximum flow rate that the cooling system can provide; exceeding this limit may lead to equipment overload or energy waste. The lower allowable limit of the cooling water temperature refers to the lowest temperature that the cooling water can reach without affecting the polymer granulation effect or causing other process problems. By clearly defining these boundaries, it can be ensured that adjustments to the steel strip running speed and cooling water parameters are always made within a safe and effective range during automated control.

[0090] The solution proposed in this application defines a preset maximum compensation limit, enabling the system to operate within a reasonable and safe range when making compensatory adjustments to address a decrease in heat transfer efficiency. When the system detects a decrease in heat transfer efficiency between the steel strip and cooling water, it attempts to restore normal thermal response characteristics by adjusting the running speed of the steel strip and / or the parameters of the cooling water. However, this adjustment is not unlimited. By setting compensation boundaries, the system can identify situations where the adjustment capacity reaches its limit. Once the running speed of the steel strip is adjusted to the lower limit of the allowable range, or the cooling water flow rate reaches the upper limit of the allowable range, or the cooling water temperature reaches the lower limit of the allowable range, i.e., the preset maximum compensation limit is reached, the system will determine that the current compensation measures are no longer effective in solving the problem, thereby triggering a scale removal warning. This mechanism avoids unlimited parameter adjustments, preventing production instability, product quality degradation, or equipment damage due to over-adjustment, while ensuring timely manual intervention when necessary, such as scale removal.

[0091] Through the above technical solution, this application provides a clear and safe compensation adjustment range for the automated control of the rotary steel strip granulator production process. This not only helps prevent production problems caused by excessive or ineffective parameter adjustments, such as product quality fluctuations, increased energy consumption, or accelerated equipment wear, but also ensures timely issuance of scale cleaning warnings when the automated compensation capacity reaches its limit. This effectively guides operators to perform necessary maintenance, avoiding long-term low production efficiency and potential equipment damage caused by scale accumulation. Therefore, it improves the system's intelligence level and operational reliability, extends equipment lifespan, and optimizes overall production costs.

[0092] This application further proposes a step of comparing the instantaneous thermal response characteristics with a preset reference thermal response characteristic to obtain the characteristic differences, including:

[0093] Obtain the material grade, steel strip running speed, and cooling water parameters currently being produced;

[0094] Based on the material grade, steel strip running speed, and cooling water parameters currently being produced, reference thermal response features that match the current production conditions are retrieved from a pre-established set of reference thermal response features.

[0095] When there is no reference thermal response feature in the reference thermal response feature set that matches the current production conditions, a reference thermal response feature that adapts to the current production conditions is generated based on the reference thermal response feature corresponding to the production conditions adjacent to the current production conditions in the reference thermal response feature set. The production conditions adjacent to the current production conditions are those where the values ​​of the steel strip running speed and / or cooling water parameters are less than and greater than the corresponding values ​​of the current production conditions, respectively, and form an interpolation interval with the current production conditions.

[0096] The instantaneous thermal response features are compared with the retrieved or generated reference thermal response features to obtain the feature differences.

[0097] Specifically, obtaining the material grade, steel belt speed, and cooling water parameters currently being produced refers to the system monitoring or reading the current production status information from the production control system in real time. These parameters are key factors affecting the cooling behavior of polymer droplets on the steel belt, and therefore need to be accurately obtained to determine the current production conditions.

[0098] Among them, retrieving the reference thermal response characteristics matching the current production conditions from the pre-established reference thermal response characteristics set according to the material grade currently being produced, the running speed of the steel strip, and the parameters of the cooling water can be understood as the system maintaining a data set containing the ideal thermal response characteristics under various production conditions. After obtaining the current production conditions, the system will search for the reference thermal response characteristics that are exactly the same or closest to the current conditions in this set. This reference thermal response characteristics set is usually established through experiments or simulations of different combinations of materials, speeds, and cooling parameters under the condition of a clean steel strip.

[0099] In practical applications, when there is no reference thermal response characteristic in the reference thermal response characteristics set that exactly matches the current production conditions, the system will generate a reference thermal response characteristic suitable for the current production conditions based on the reference thermal response characteristics corresponding to the production conditions adjacent to the current production conditions in the reference thermal response characteristics set. Here, the "production conditions adjacent to the current production conditions" refers to the production conditions whose values are respectively less than and greater than the corresponding values of the current production conditions in the dimension of the running speed of the steel strip and / or the parameters of the cooling water, and jointly form an interpolation interval with the current production conditions. For example, if the current running speed of the steel strip is V, and there are only reference characteristics corresponding to speeds V1 and V2 (V1 < V < V2) in the reference set, then the reference characteristics at speed V can be obtained by interpolating the reference characteristics corresponding to V1 and V2. This interpolation generation mechanism ensures that even when the reference set does not fully cover all possible production conditions, a reasonable and current-condition-adapted reference thermal response characteristic can be obtained.

[0100] Finally, the obtained instantaneous thermal response characteristics are compared with the retrieved or generated reference thermal response characteristics to obtain the characteristic difference. This comparison result will more accurately reflect the deviation between the current actual thermal response and the ideal thermal response, providing a reliable basis for the subsequent judgment of the heat transfer efficiency.

[0101] This application's solution dynamically acquires current production conditions and, based on these conditions, retrieves or generates adaptive reference thermal response characteristics from a preset set. This solves the problem in traditional solutions where preset reference characteristics may not fully match the actual, changing production conditions. Specifically, when production conditions change, such as material grade, steel strip speed, or cooling water parameter adjustments, the system no longer relies on a single fixed reference value but can acquire reference thermal response characteristics that highly match the current actual operating conditions. This dynamic matching mechanism ensures that the comparison between instantaneous thermal response characteristics and reference thermal response characteristics is performed on a "fair" and "accurate" benchmark, avoiding misjudgments caused by inaccurate reference benchmarks. By introducing an interpolation generation mechanism, even when facing production conditions not directly included in the reference set, the system can make reasonable calculations using adjacent data points, further enhancing the method's universality and robustness. Therefore, the obtained characteristic differences can more realistically reflect the actual changes in heat transfer efficiency between the steel strip and cooling water, rather than normal fluctuations caused by changes in production conditions.

[0102] Through the above technical solution, this application can significantly improve the accuracy and adaptability of automated control in the production process of a rotary steel strip granulator. Since the reference thermal response characteristics can be dynamically adjusted or generated according to current production conditions (including material grade, steel strip running speed, and cooling water parameters), when comparing instantaneous thermal response characteristics, interference caused by changes in production conditions can be eliminated, and changes in heat transfer efficiency caused by abnormal conditions such as scale accumulation on the back of the steel strip can be identified more accurately. This precise judgment of characteristic differences makes subsequent adjustments to the steel strip running speed and / or cooling water parameters more accurate and effective, avoiding over- or under-adjustment, thereby ensuring the stability of granulated product quality, extending the steel strip cleaning cycle, and reducing maintenance costs.

[0103] In some preferred embodiments, suppose a rotary steel strip granulator is producing a novel polymer material, designated "P-XYZ," with the current steel strip speed set at 1.5 m / s, cooling water flow rate at 10 m³ / h, and cooling water temperature at 25°C. The system first acquires these current production conditions. Then, the system searches a pre-established set of reference thermal response characteristics. If the set contains a reference thermal response characteristic for the "P-XYZ" material grade at a steel strip speed of 1.5 m / s, a cooling water flow rate of 10 m³ / h, and a cooling water temperature of 25°C, this characteristic is directly used as the reference benchmark for the current production conditions.

[0104] However, if the reference thermal response feature set does not contain a reference thermal response feature that perfectly matches the material grade "P-XYZ" at a strip speed of 1.5 m / s, a cooling water flow rate of 10 m³ / h, and a cooling water temperature of 25°C, but there are reference features for the material grade "P-XYZ" under other adjacent production conditions, for example: Condition A: strip speed 1.0 m / s, cooling water flow rate 8 m³ / h, cooling water temperature 20°C, corresponding to reference feature R_A; Condition B: strip speed 2.0 m / s, cooling water flow rate 12 m³ / h, cooling water temperature 30°C, corresponding to reference feature R_B.

[0105] At this point, the system generates a reference thermal response feature R_current that adapts to the current production conditions (1.5 m / s, 10 m³ / h, 25°C) based on the reference thermal response features R_A and R_B corresponding to conditions A and B, using a multidimensional interpolation algorithm (such as linear interpolation or more complex spline interpolation). Subsequently, the instantaneous thermal response features of the polymer droplets acquired in real time are compared with this R_current to obtain accurate feature differences. This approach ensures that even with minor changes in production conditions or the use of new materials, the system can obtain a highly matched reference benchmark, thereby achieving precise automated control.

[0106] This application further proposes that the steps for obtaining the material grade of the currently produced material can specifically include the following methods:

[0107] Obtain the production control information corresponding to the current production;

[0108] Extract the identification information of the materials currently being produced from the production control information corresponding to the current production process;

[0109] Use the identification information of the material being produced as the material grade number for the current production.

[0110] Specifically, production control information refers to various data and instructions used to guide and monitor production operations during the rotary steel belt granulator process, such as production plans, formula information, and process parameter settings. This information is typically generated and stored by the production management system or automated control system. The identification information of the currently produced material is a key component of the production control information, used to uniquely identify the type or grade of the polymer material currently being produced. This identification information can be a material code, product model, batch number, etc., and its purpose is to ensure that the corresponding reference thermal response characteristics of the material can be accurately matched during subsequent granulation, thereby enabling precise comparison and judgment. By directly using the extracted identification information as the grade of the currently produced material, the data processing flow can be simplified, and the accuracy and real-time nature of material information can be ensured.

[0111] This application's solution acquires production control information and extracts material identification information from it, enabling automated and accurate identification of the material grade currently being produced. This method avoids errors and delays that may arise from manual input or verification, ensuring the accuracy of subsequent reference thermal response feature retrieval or generation. By directly using material identification information as the material grade, seamless integration of production data and control logic is achieved, providing reliable basic data for subsequent feature comparison and efficiency assessment.

[0112] The above technical solution enables automated and accurate identification of the grade of materials currently being produced, effectively avoiding errors that may be introduced by manual operation and improving the accuracy and real-time nature of material information acquisition. This is crucial for subsequently retrieving or generating matching reference thermal response features from the reference thermal response feature set, thereby ensuring the accuracy and reliability of the automated control of the steel strip granulator production process and further enhancing the intelligence level and production efficiency of the entire control system.

[0113] This application further proposes a step for generating reference thermal response features adapted to the current production conditions based on reference thermal response features corresponding to production conditions adjacent to the current production conditions in a reference thermal response feature set. The steps include:

[0114] In the reference thermal response feature set, identify at least two sets of production conditions that are adjacent to the current production conditions and correspond to the material grade currently being produced.

[0115] Obtain at least two sets of reference thermal response characteristics corresponding to each of the production conditions;

[0116] Based on the current production steel strip running speed, cooling water parameters and parameter differences between at least two sets of production conditions, interpolation is performed on the reference thermal response characteristics corresponding to each of the at least two sets of production conditions.

[0117] The interpolation result is used as a reference thermal response characteristic to adapt to the current production conditions.

[0118] Specifically, in the reference thermal response feature set, identifying at least two sets of production conditions adjacent to the current production conditions for the material grade currently being produced means, within the pre-established reference thermal response feature set, identifying at least two sets of recorded production conditions that are close to the actual current production conditions in terms of steel strip running speed and / or cooling water parameters for the specific material grade currently being produced. These adjacent production conditions typically form an interpolation interval around the current production conditions to facilitate accurate interpolation calculations later.

[0119] Obtaining reference thermal response characteristics for at least two sets of production conditions can be understood as extracting pre-measured or calculated reference thermal response characteristic data corresponding to the at least two sets of adjacent production conditions identified above from the reference thermal response characteristic set. These data will serve as input for interpolation calculations.

[0120] In practical applications, based on the current production strip running speed, cooling water parameters, and parameter differences between at least two sets of production conditions, interpolation processing is performed on the reference thermal response characteristics corresponding to each of the at least two sets of production conditions. Specifically, this involves using mathematical interpolation algorithms, such as linear interpolation, polynomial interpolation, or more complex multivariate interpolation methods, to weight or fit the reference thermal response characteristics corresponding to at least two sets of adjacent production conditions based on the specific differences between the current production conditions and adjacent production conditions in terms of strip running speed and cooling water parameters, thereby deriving the reference thermal response characteristics under the current production conditions. The purpose is to derive unknown data points from known data points when direct matching data is unavailable, thereby improving the accuracy of the reference characteristics.

[0121] Therefore, using the interpolation result as a reference thermal response feature adapted to the current production conditions means that the reference thermal response feature obtained after the above interpolation calculation, which is highly matched with the current actual production conditions, will be used in the subsequent feature difference comparison process.

[0122] The proposed solution provides a sufficient data foundation for interpolation calculations by identifying at least two sets of production conditions adjacent to the current production conditions for a specific material grade within a reference thermal response feature set, and obtaining their corresponding reference thermal response features. The use of at least two sets of adjacent conditions, rather than a single adjacent condition, makes interpolation possible in a multi-dimensional parameter space, enabling a more comprehensive capture of the impact of parameter changes on the thermal response features. Subsequently, by interpolating based on the current production strip running speed, cooling water parameters, and the parameter differences between these adjacent production conditions, the reference thermal response features under the current production conditions can be accurately calculated. This multi-point interpolation method effectively compensates for situations where the data points in the reference thermal response feature set are sparse or do not completely cover all production conditions, ensuring highly accurate reference features even without directly matching data.

[0123] Through the above technical solution, this application can significantly improve the accuracy and reliability of generating reference thermal response characteristics adapted to current production conditions when no directly matching reference thermal response characteristics exist. Compared to relying solely on a single adjacent condition or simple extrapolation, using at least two sets of adjacent conditions for interpolation processing can more precisely reflect the nonlinear influence of production parameters (such as steel strip running speed and cooling water parameters) on the thermal response characteristics of polymer droplets, thereby making the generated reference thermal response characteristics more consistent with actual production conditions. Therefore, in subsequent comparisons of feature differences, the changes in heat transfer efficiency between the steel strip and cooling water can be more accurately determined, avoiding misjudgments or control deviations caused by inaccurate reference characteristics, and thus improving the accuracy and stability of the automated control of the rotary steel strip granulator production process.

[0124] In some preferred embodiments, it is assumed that a specific grade of polymer is currently being produced, with a steel belt running speed of V_current, a cooling water flow rate of F_current, and a cooling water temperature of T_current. Within a pre-established set of reference thermal response characteristics, there may not be reference data that perfectly matches (V_current, F_current, T_current).

[0125] At this point, the system will first determine at least two sets of production conditions adjacent to the current production conditions for the material grade from the reference thermal response feature set. For example, four sets of adjacent conditions can be determined: Condition A: (V_low, F_low, T_low) and its corresponding reference thermal response feature R_A; Condition B: (V_high, F_low, T_low) and its corresponding reference thermal response feature R_B; Condition C: (V_low, F_high, T_low) and its corresponding reference thermal response feature R_C; Condition D: (V_low, F_low, T_high) and its corresponding reference thermal response feature R_D; where V_low <V_current<V_high,F_low<F_current<F_high,T_low<T_current<T_high。

[0126] Subsequently, the system acquires the reference thermal response characteristics R_A, R_B, R_C, and R_D corresponding to each of the four sets of production conditions.

[0127] Next, based on the differences between the current production steel strip running speed V_current, cooling water flow rate F_current, cooling water temperature T_current and the parameters of conditions A, B, C, and D, a trilinear interpolation (or a more complex multivariate interpolation) algorithm is used to interpolate R_A, R_B, R_C, and R_D. For example, interpolation can be performed first in the V dimension, then in the F dimension, and finally in the T dimension, or multivariate interpolation calculations can be performed directly.

[0128] Ultimately, the interpolation process yields a reference thermal response characteristic R_current that adapts to the current production conditions (V_current, F_current, T_current). This R_current will be used to compare with the real-time acquired instantaneous thermal response characteristics, thereby more accurately assessing the production status.

[0129] refer to Figure 2 This application proposes an automated control system for the production process of a rotary steel strip granulator, the system comprising:

[0130] The disturbance application module adjusts the flow rate and / or temperature of the cooling water supplied to the steel strip according to preset disturbance parameters to form a controlled disturbance.

[0131] The acquisition module acquires the instantaneous temperature response information of polymer droplets on the steel strip under controlled perturbation.

[0132] The analysis module analyzes the instantaneous temperature response information to obtain the instantaneous thermal response characteristics of the polymer droplets;

[0133] The comparison module compares the instantaneous thermal response characteristics with the preset reference thermal response characteristics to obtain the characteristic differences;

[0134] The judgment module determines the change in heat transfer efficiency between the steel strip and the cooling water based on the differences in characteristics.

[0135] The adjustment module adjusts the running speed of the steel strip and / or the parameters of the cooling water according to the changes in heat transfer efficiency, wherein the parameters of the cooling water include at least one of cooling water flow rate and temperature.

[0136] Specifically, the disturbance application module is configured to adjust the flow rate and / or temperature of the cooling water supplied to the steel strip according to preset disturbance parameters. Its purpose is to proactively introduce controllable, minute disturbances without significantly affecting normal production, so that subsequent modules can capture the instantaneous thermal response of polymer droplets to these disturbances. For example, the disturbance application module can be a controller-driven flow regulating valve and / or temperature regulator for precisely controlling the flow rate and temperature of the cooling water.

[0137] The acquisition module is configured to acquire the instantaneous temperature response information of polymer droplets on the steel strip under controlled perturbation. This module typically includes a non-contact infrared temperature sensor or other high-precision temperature measurement equipment, and its purpose is to capture the temperature change process of the polymer droplets after being disturbed by cooling water in real time and accurately, providing raw data for subsequent feature analysis.

[0138] In practical applications, the analysis module is configured to analyze instantaneous temperature response information to obtain the instantaneous thermal response characteristics of the polymer droplets. This module can be a data processor or an embedded computing unit, and its purpose is to extract key thermal response features from the raw instantaneous temperature response data, such as the rate of temperature change, the magnitude of temperature change, the response time, and the recovery time. These features can quantify the heat exchange efficiency between the polymer droplets and the steel strip.

[0139] Furthermore, the comparison module is configured to compare the instantaneous thermal response characteristics with preset reference thermal response characteristics to obtain the characteristic differences. This module can be a data processing unit, the purpose of which is to quantify the deviation between the thermal response characteristics under the current operating conditions and the reference characteristics under ideal or standard operating conditions by comparing the thermal response characteristics under the current operating conditions and providing a basis for judging changes in heat transfer efficiency.

[0140] Furthermore, the judgment module is configured to determine the changes in heat transfer efficiency between the steel strip and cooling water based on characteristic differences. This module can be a logical judgment unit, whose purpose is to accurately identify specific situations that cause abnormal changes in heat transfer efficiency, such as whether there is scale accumulation on the back of the steel strip or whether the steel strip surface is contaminated, based on the characteristic differences output by the comparison module and in combination with preset judgment rules or models.

[0141] Finally, the adjustment module is configured to adjust the running speed of the steel belt and / or the parameters of the cooling water based on changes in heat transfer efficiency. The cooling water parameters include at least one of cooling water flow rate and temperature. This module typically includes actuators, such as frequency converters, for adjusting the steel belt running speed, and flow control valves and temperature regulators for adjusting the cooling water parameters. Its purpose is to automatically and in real-time compensate for production parameters based on the conclusions of the judgment module, in order to maintain the stability of the granulation process and product quality.

[0142] The solution proposed in this application decomposes the aforementioned automated control method into multiple functional modules, which work collaboratively to achieve automated and intelligent control of the rotary steel strip granulator production process. Specifically, the disturbance application module actively introduces a controlled disturbance, providing the system with an observable input signal. The acquisition module monitors the polymer droplet response to this disturbance in real time, converting the physical process into processable digital information. Subsequently, the analysis module performs in-depth processing on this information, extracting key features that reflect the heat transfer state. The comparison module quantifies the deviation between the current state and the ideal state by comparing it with preset reference features. Based on these deviations, the judgment module can accurately diagnose the specific causes of changes in heat transfer efficiency. Finally, the adjustment module automatically adjusts the steel strip running speed and / or cooling water parameters according to the diagnostic results, thereby forming a closed-loop control system. It is precisely because of this modular design and collaborative working mechanism that the entire control process can be carried out efficiently and accurately, effectively solving the limitations of traditional methods, such as excessive manual intervention, slow response, and difficulty in achieving continuous optimization.

[0143] Through the above technical solution, this application provides a system that can concretely implement the automated control method for the production process of a rotary steel strip granulator. This system, through modular design, achieves automated execution of each step in the method, significantly improving the real-time performance and accuracy of control. Compared to situations where there is only a method but no concrete implementation, this system can achieve continuous monitoring and automatic adjustment of the production process, effectively avoiding errors and lags that may be caused by manual operation, thereby ensuring the stability of granulated product quality and improving production efficiency. Furthermore, through the close cooperation of each module, the system can more quickly and accurately identify and respond to changes in the heat transfer efficiency between the steel strip and cooling water, making timely compensatory adjustments, effectively reducing production fluctuations and product defects caused by abnormal heat transfer efficiency, thereby improving the automation level and operational reliability of the entire production line.

[0144] In some preferred embodiments, the automated control system for the rotary steel strip granulator production process can be implemented as follows:

[0145] The disturbance application module can consist of an electrically controlled regulating valve and an electric heater, both controlled by a programmable logic controller (PLC). The electrically controlled regulating valve is installed on the cooling water supply line to precisely regulate the cooling water flow rate; the electric heater is used to fine-tune the cooling water temperature. Based on preset disturbance parameters, the PLC periodically sends control signals to the regulating valve and the electric heater, causing them to fluctuate within a small range, thus creating a controlled disturbance.

[0146] The acquisition module can employ an array of multiple non-contact infrared temperature sensors, deployed above the steel strip and directly facing the cooling area of ​​the polymer droplets. These sensors collect surface temperature data of multiple adjacent polymer droplets on the steel strip in real time, and the analog signals are converted into digital signals by a data acquisition card and transmitted to the central processing unit.

[0147] The analysis and comparison modules can be integrated into an industrial computer or high-performance PLC. This computer runs specialized algorithm software, receives temperature data from the acquisition module, performs real-time data processing and feature extraction, and calculates the instantaneous thermal response characteristics of the polymer droplets. Subsequently, the software compares the current instantaneous thermal response characteristics with preset reference thermal response characteristics stored in a database, calculating the difference in characteristics.

[0148] The judgment module is also integrated into the industrial computer. Based on the feature differences output by the comparison module, combined with the preset judgment logic (such as based on fuzzy logic or machine learning model), it determines whether there is scale accumulation or surface contamination on the back of the steel strip, and assesses the degree of change in heat transfer efficiency.

[0149] The adjustment module communicates with the granulator's frequency converter and the electric regulating valves and electric heaters of the cooling water system via an industrial Ethernet or fieldbus from the industrial computer. When the judgment module identifies an abnormality in heat transfer efficiency, the adjustment module automatically calculates the required adjustment of the steel belt running speed and / or cooling water parameters (flow rate and temperature) according to a preset control strategy, and sends control commands to the corresponding actuators to achieve real-time compensation and optimization of the production process. For example, when it is determined that scale accumulation is causing a decrease in heat transfer efficiency, the system may instruct the frequency converter to reduce the steel belt running speed and instruct the electric regulating valve to increase the cooling water flow rate to enhance the cooling effect.

[0150] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. An automated control method for the production process of a rotary steel strip granulator, characterized in that, The method includes the following steps: According to preset disturbance parameters, the flow rate and / or temperature of the cooling water supplied to the steel strip are adjusted to form a controlled disturbance; Acquire the instantaneous temperature response information of polymer droplets on a steel strip under controlled perturbation; By analyzing the instantaneous temperature response information, the instantaneous thermal response characteristics of the polymer droplets are obtained; The instantaneous thermal response characteristics are compared with the preset reference thermal response characteristics to obtain the characteristic differences; Based on the differences in characteristics, determine the changes in heat transfer efficiency between the steel strip and the cooling water; Adjust the running speed of the steel strip and / or the parameters of the cooling water according to the changes in heat transfer efficiency, wherein the parameters of the cooling water include at least one of cooling water flow rate and temperature.

2. The automated control method for the production process of a rotary steel strip granulator as described in claim 1, characterized in that, The steps for analyzing instantaneous temperature response information to obtain the instantaneous thermal response characteristics of polymer droplets include: The instantaneous temperature response information of multiple adjacent polymer droplets under controlled perturbation is extracted to obtain temperature response features, which include the temperature change rate, temperature change amplitude, response time and recovery time of each polymer droplet. Based on the temperature change rate, temperature change amplitude, response time, and recovery time corresponding to each polymer droplet, calculate the average value and standard deviation of the temperature change rate, temperature change amplitude, response time, and recovery time corresponding to each polymer droplet. When any standard deviation exceeds the preset range relative to the corresponding average, abnormal droplet data is removed. Abnormal droplet data refers to droplet data whose calculated value is greater than the preset first threshold. The calculated value is the absolute value of the difference between the corresponding temperature response characteristic and the corresponding average value. For the remaining droplet data after removing abnormal droplet data, the average values ​​of temperature change rate, temperature change amplitude, response time, and recovery time for each polymer droplet are recalculated, and the recalculated average values ​​are used as the instantaneous thermal response characteristics of the polymer droplets.

3. The automated control method for the production process of a rotary steel strip granulator as described in claim 2, characterized in that, Multiple adjacent polymer droplets are adjacent polymer droplets along the width direction of the steel strip.

4. The automated control method for the production process of a rotary steel strip granulator as described in claim 3, characterized in that, The steps for determining the change in heat transfer efficiency between the steel strip and cooling water based on characteristic differences include: The instantaneous thermal response characteristics of multiple adjacent polymer droplets are statistically analyzed to obtain the current average thermal response characteristics and dispersion. The current average thermal response characteristics and dispersion are compared with a preset material batch reference set to determine the characteristic differences of the current average thermal response characteristics and dispersion relative to the material batch reference set, wherein the material batch reference set includes the average thermal response characteristics and dispersion of different material batches in the clean steel strip state; When the current average thermal response characteristics fall within the preset matching range of the average thermal response characteristics of a certain material batch in the material batch reference set, and the degree of dispersion is within the degree of dispersion of that material batch in the clean steel strip state, it is determined that the heat transfer efficiency between the steel strip and the cooling water has not changed abnormally, and the characteristic difference is caused by the fluctuation of the material's own properties. When the average temperature change rate in the current average thermal response characteristics is lower than the corresponding rate reference value and / or the average recovery time is higher than the corresponding time reference value, and the dispersion exceeds the dispersion range of the material batch in the clean steel strip state, it is determined that there is scale accumulation on the back of the steel strip, and the scale accumulation causes a decrease in the heat transfer efficiency between the steel strip and the cooling water. The rate reference value and the time reference value are obtained from the material batch reference set.

5. The automated control method for the production process of a rotary steel strip granulator as described in claim 4, characterized in that, When it is determined that scale accumulation exists on the back of the steel strip, and that the scale accumulation reduces the heat transfer efficiency between the steel strip and the cooling water, the method further includes a scale removal early warning step, which includes: When the dispersion continues to exceed the dispersion range of the corresponding material batch in the clean steel belt state, a scale cleaning warning will be issued; And / or, when the running speed of the steel strip and / or the parameters of the cooling water reach the preset maximum compensation limit, and the current average thermal response characteristics have not yet recovered to the preset matching range of the average thermal response characteristics of the corresponding material batch in the state of clean steel strip, a scale cleaning warning will be issued.

6. The automated control method for the production process of a rotary steel strip granulator as described in claim 5, characterized in that, The preset maximum compensation limit is a pre-defined compensation boundary, which is determined based on the lower limit of the running speed of the steel strip and the allowable adjustment boundary of the cooling water parameters. The allowable adjustment boundary of the cooling water parameters includes the upper limit of the cooling water flow rate and / or the lower limit of the cooling water temperature.

7. The automated control method for the production process of a rotary steel strip granulator as described in claim 1, characterized in that, The steps for comparing the instantaneous thermal response characteristics with the preset reference thermal response characteristics to obtain the characteristic differences include: Obtain the material grade, steel strip running speed, and cooling water parameters currently being produced; Based on the material grade, steel strip running speed, and cooling water parameters currently being produced, reference thermal response features that match the current production conditions are retrieved from a pre-established set of reference thermal response features. When there is no reference thermal response feature in the reference thermal response feature set that matches the current production conditions, a reference thermal response feature that adapts to the current production conditions is generated based on the reference thermal response feature corresponding to the production conditions adjacent to the current production conditions in the reference thermal response feature set. The production conditions adjacent to the current production conditions are those where the values ​​of the steel strip running speed and / or cooling water parameters are less than and greater than the corresponding values ​​of the current production conditions, respectively, and form an interpolation interval with the current production conditions. The instantaneous thermal response features are compared with the retrieved or generated reference thermal response features to obtain the feature differences.

8. The automated control method for the production process of a rotary steel strip granulator as described in claim 7, characterized in that, The steps to obtain the material grade currently in production include: Obtain the production control information corresponding to the current production; Extract the identification information of the materials currently being produced from the production control information corresponding to the current production process; Use the identification information of the material being produced as the material grade number for the current production.

9. The automated control method for the production process of a rotary steel strip granulator as described in claim 7, characterized in that, The steps for generating reference thermal response features adapted to the current production conditions based on the reference thermal response feature set corresponding to the production conditions adjacent to the current production conditions include: In the reference thermal response feature set, identify at least two sets of production conditions that are adjacent to the current production conditions and correspond to the material grade currently being produced. Obtain at least two sets of reference thermal response characteristics corresponding to each of the production conditions; Based on the current production steel strip running speed, cooling water parameters and parameter differences between at least two sets of production conditions, interpolation is performed on the reference thermal response characteristics corresponding to each of the at least two sets of production conditions. The interpolation result is used as a reference thermal response characteristic to adapt to the current production conditions.

10. An automated control system for the production process of a rotary steel strip granulator, employing the automated control method for the production process of a rotary steel strip granulator as described in claim 1, characterized in that... The system includes: The disturbance application module adjusts the flow rate and / or temperature of the cooling water supplied to the steel strip according to preset disturbance parameters to form a controlled disturbance. The acquisition module acquires the instantaneous temperature response information of polymer droplets on the steel strip under controlled perturbation. The analysis module analyzes the instantaneous temperature response information to obtain the instantaneous thermal response characteristics of the polymer droplets; The comparison module compares the instantaneous thermal response characteristics with the preset reference thermal response characteristics to obtain the characteristic differences; The judgment module determines the change in heat transfer efficiency between the steel strip and the cooling water based on the differences in characteristics. The adjustment module adjusts the running speed of the steel strip and / or the parameters of the cooling water according to the changes in heat transfer efficiency, wherein the parameters of the cooling water include at least one of cooling water flow rate and temperature.

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