A cold continuous rolling high-pressure large-flow hydraulic energy-saving control method and system

Abstract

The present application relates to the field of steel rolling control, and particularly relates to a cold continuous rolling high-pressure large-flow hydraulic energy-saving control method and system, which comprises the following steps: collecting and preprocessing rolling system parameters and real-time data to obtain basic data for pressure regulation; performing model coupling analysis on steel coil process parameters to obtain adaptive rolling pressure data; performing nonlinear fusion evaluation on acceleration and thickness disturbance to obtain adaptive dynamic safety margin; performing real-time synthesis on the adaptive rolling pressure data and the adaptive dynamic safety margin to obtain dynamic target pressure setting data; and performing closed-loop energy-saving control based on the dynamic target pressure setting data, so as to improve the energy-saving effect of the hydraulic system.

Description

Technical Field

[0001] This invention relates to the field of steel rolling control, and in particular to a high-pressure, high-flow hydraulic energy-saving control method and system for cold continuous rolling. Background Technology

[0002] Cold rolling is a crucial production stage in the modern steel industry, tasked with rolling steel plates to a precise target thickness at high speeds. To achieve micron-level thickness accuracy control, cold rolling mills widely employ electro-hydraulic servo automatic thickness control systems (AGC) with extremely fast response times. This system precisely adjusts the gap between rolls using high-pressure hydraulic cylinders to compensate for disturbances such as fluctuations in incoming material thickness and changes in rolling force. This requires the hydraulic system to be able to provide enormous flow and pressure in response to these disturbances, representing a typical high-pressure, high-flow hydraulic application scenario. To balance the demands of the automatic thickness control system for instantaneous high flow rates with the economic goal of energy conservation and emission reduction, the industry's advanced solutions generally employ a combination of variable displacement pumps and accumulators for oil supply. In this solution, the variable displacement pump, driven by a variable frequency motor, provides the system's average flow rate to meet basic energy requirements; while the large-capacity accumulator acts as a buffer unit for high-pressure energy, responsible for responding to millisecond-level or peak-level flow impacts during rolling, such as when the strip is threaded at the beginning or end, accelerating or decelerating, or encountering sudden changes in local thickness. The accumulator can rapidly release or absorb energy to compensate for the insufficient response speed of the variable displacement pump.

[0003] In such combined oil supply systems, the core of the control strategy is to maintain the pressure within the accumulator within a preset safe operating range. Existing control methods involve real-time monitoring of the accumulator pressure using pressure sensors and comparing it to a fixed target pressure setpoint. For example, a PID controller adjusts the variable pump speed based on this pressure deviation. When the actual pressure is lower than the setpoint, the PID controller instructs the variable pump to operate at high speed, replenishing oil to the system and accumulator; when the pressure reaches or exceeds the setpoint, the pump speed is reduced or it enters a low-power standby state. While this closed-loop control method based on a fixed pressure threshold ensures system reliability, its inherent control logic has inherent energy consumption drawbacks. Specifically, the target pressure setpoint used in existing methods is a static value. This setpoint is typically calculated once and fixed in the control program during the hydraulic system design and commissioning phases to ensure equipment safety, based on the most demanding operating conditions the production line can handle (e.g., rolling the highest strength grade of steel with the largest reduction). The inherent flaw of this control strategy lies in the fact that the control system itself is only responsible for maintaining pressure, and its logic is not linked to the higher-level production scheduling system. Therefore, it cannot identify the specific process parameters of the currently rolled steel coil, resulting in the fixed target pressure setpoint failing to adaptively adjust according to the differences in process parameters of different steel coils in the production plan. When rolling mild steel with a lower rolling load or thinner products, the system still maintains excessively high energy reserves for an extreme operating condition that will never occur. Furthermore, this control strategy also has certain limitations in dealing with changes in operating conditions within a single steel coil, as its control logic is essentially a lag feedback. Summary of the Invention

[0004] In view of this, the present invention aims to propose a high-pressure, high-flow hydraulic energy-saving control method for cold continuous rolling, so as to solve the problems of energy waste and insufficient dynamic response caused by static pressure setting in traditional systems.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows: In a first aspect, this application provides a method for energy-saving control of high-pressure, high-flow hydraulic systems in cold continuous rolling mills, the method comprising: By collecting and preprocessing rolling system parameters and real-time data, basic data for pressure regulation can be obtained; By performing model coupling analysis on the process parameters of steel coils, adaptive rolling pressure data can be obtained. An adaptive dynamic safety margin is obtained by nonlinearly fusing acceleration and thickness disturbances. Dynamic target pressure setting data is obtained by real-time synthesis of adaptive rolling pressure data and adaptive dynamic safety margin. Closed-loop energy control is performed based on dynamic target pressure setting data. The basic data used for pressure control includes steel coil process parameters, acceleration input during steel coil rolling, and thickness disturbance during the steel coil rolling process.

[0006] Preferably, the step of acquiring and preprocessing rolling system parameters and real-time data to obtain basic data for pressure control further includes: Initialize the control system by setting the average deformation resistance, target total thickness reduction, coil width, rated rolling speed, minimum physical pressure of the hydraulic system, and reserve pressure under the reference working conditions. Before the start of each rolling task for a steel coil, the preset macroscopic process parameters for that steel coil are obtained from the upper-level manufacturing execution system, including the target total thickness reduction, width, average deformation resistance, and rated rolling speed of the current steel coil; after entering the rolling process, the real-time running speed data of the rolling mill and the real-time entry thickness deviation data of the steel coil are collected. The real-time operating speed data of the rolling mill and the real-time entry thickness deviation data of the steel coil are filtered by a digital filtering algorithm, and the sensor data are smoothed by a moving average filter.

[0007] Preferably, the step of obtaining adaptive rolling pressure data by performing model coupling analysis on the steel coil process parameters includes: By performing multidimensional normalization and coupled modeling on the process parameters of steel coil rolling, dimensionless macroscopic working condition adaptability factors are obtained; by integrating the working condition adaptability index with the nonlinear mapping relationship of the accumulator, adaptive rolling pressure data are obtained.

[0008] Preferably, the step of obtaining a dimensionless macroscopic working condition adaptability factor by performing multidimensional normalization and coupled modeling on the process parameters of steel coil rolling includes: Divide the average deformation resistance, target total thickness reduction, coil width, and rated rolling speed of the current steel coil by the average deformation resistance, target total thickness reduction, coil width, and rated rolling speed under the reference conditions, respectively, to obtain the first deformation resistance assessment, first target total thickness reduction assessment, first coil width assessment, and first rated rolling speed assessment of the steel coil. The square root of the first target total thickness reduction assessment is multiplied by the first deformation resistance assessment, the first steel coil width assessment, and the first rated rolling speed assessment, and the result is used as the first macroscopic working condition adaptation assessment; the square root of the first macroscopic working condition adaptation assessment is used as the dimensionless macroscopic working condition adaptability factor.

[0009] Preferably, the step of integrating the operating condition adaptability index with the nonlinear mapping relationship of the accumulator to obtain adaptive rolling pressure data includes: The minimum physical pressure required to maintain the basic function of the hydraulic system is obtained. The result of multiplying the dimensionless macroscopic working condition adaptability factor by the reserve pressure under the benchmark working condition and adding it to the minimum physical pressure is used as the adaptive rolling pressure data.

[0010] Preferably, the step of obtaining the adaptive dynamic safety margin by performing a nonlinear fusion evaluation of acceleration and thickness disturbances includes: The dynamic acceleration disturbance intensity of the system is obtained by performing nonlinear mapping processing on real-time acceleration data; the nonlinear impact intensity of material disturbance is obtained by joint feature analysis of thickness deviation and change rate; and the adaptive dynamic safety margin is obtained by further fusion analysis of the dynamic acceleration disturbance intensity of the system and the nonlinear impact intensity of material disturbance.

[0011] Preferably, the step of obtaining the system's dynamic acceleration disturbance intensity by performing nonlinear mapping processing on real-time acceleration data includes: Set an acceleration sensitivity coefficient; obtain real-time running acceleration data of the rolling mill through real-time running speed data; perform hyperbolic tangent function mapping on the calculation result of dividing the real-time running acceleration data of the rolling mill by the rated rolling speed and multiplying it by the acceleration sensitivity coefficient, and take the larger value between the corresponding mapping result and the constant 0 as the dynamic acceleration disturbance intensity of the system.

[0012] Preferably, the step of obtaining the nonlinear impact strength of material disturbance through joint characteristic analysis of thickness deviation and rate of change includes: The real-time inlet thickness deviation data of the steel coil is differentiated to obtain the real-time inlet thickness deviation change rate. The square of the product of the real-time inlet thickness deviation data and the real-time inlet thickness deviation change rate is used as the first impact strength assessment. The first impact strength assessment is divided by the fourth power of the nominal inlet thickness of the steel coil to obtain the second impact strength assessment. The negative of the second impact strength assessment is mapped by an exponential function with the natural constant as the base. The result of subtracting the constant 1 from the corresponding mapping result is used as the nonlinear impact strength of the material disturbance.

[0013] Preferably, the step of further fusing analysis of the system's dynamic acceleration disturbance intensity and the nonlinear impact intensity of material disturbance to obtain the adaptive dynamic safety margin includes: The calculation result of subtracting the adaptive rolling pressure data from the minimum physical pressure of the hydraulic system is used as the first safety margin assessment; the square root of the sum of the square of the system's dynamic acceleration disturbance intensity and the square of the nonlinear impact intensity of the material disturbance is used as the safety margin assessment factor; the calculation result of multiplying the safety margin assessment factor by the first safety margin assessment is used as the adaptive dynamic safety margin.

[0014] Secondly, this application provides a high-pressure, high-flow hydraulic energy-saving control system for cold continuous rolling mills, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a high-pressure, high-flow hydraulic energy-saving control method for cold continuous rolling mills is implemented.

[0015] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: This invention introduces a load equivalent modeling method based on rolling theory to transform steel coil process parameters into adaptive rolling pressures that match the energy storage mechanism of the hydraulic system, significantly reducing the system's ineffective energy output under routine production tasks. Furthermore, by combining a nonlinear fusion evaluation mechanism of real-time acceleration and inlet thickness disturbances, adaptive compensation for dynamic safety margins is achieved, maintaining system stability while avoiding energy redundancy caused by excessively reserved safety pressures.

[0016] In practical cold rolling production line applications, this method can effectively reduce the operating frequency and output power of the variable pump during low-load steel coil rolling, while simultaneously completing pressure regulation before high-speed rolling or abnormal incoming material disturbances occur, ensuring product thickness accuracy and system response speed. Under complex operating conditions such as mixed rolling of multiple steel grades, frequent start-stop operations, or rolling of thin-gauge materials, this invention can continuously maintain the hydraulic system in a precise, energy-saving, and highly responsive state, providing strong support for the high efficiency and high reliability of cold rolling production lines. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for energy-saving control of high-pressure, high-flow hydraulic systems in cold continuous rolling, provided by an embodiment of the present invention. Detailed Implementation

[0019] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0020] See Figure 1 This is a flowchart of a method for energy-saving control of high-pressure, high-flow hydraulic systems in cold continuous rolling mills, provided by an embodiment of the present invention. Figure 1 As shown, the method may include: Step S1: By collecting and preprocessing the rolling system parameters and real-time data, basic data for pressure regulation is obtained.

[0021] In the implementation of a high-pressure, high-flow hydraulic energy-saving control method for cold continuous rolling, it is first necessary to collect and preprocess rolling system parameters and real-time data to obtain basic data for pressure regulation. Specifically, during the initialization or debugging phase of the control system, constants reflecting the inherent characteristics of the system need to be configured and stored at one time, including: the average deformation resistance of the set reference working condition, the target total thickness reduction, the steel coil width, the rated rolling speed, the minimum physical pressure of the hydraulic system, and the reserve pressure. Before the start of each rolling task for a steel coil, the preset macroscopic process parameters of the steel coil are obtained from the upper manufacturing execution system, including the target total thickness reduction, width, average deformation resistance and rated rolling speed of the current steel coil; after entering the rolling process, this method continuously collects real-time dynamic data through sensors in the rolling equipment, including: real-time running speed data of the rolling mill and real-time entry thickness deviation data of the steel coil. After acquiring the real-time dynamic data from the sensors in the rolling mill, the real-time running speed data of the mill and the real-time entry thickness deviation data of the steel coil are filtered by a digital filtering algorithm, and the sensor data are smoothed by a moving average filter to complete the data preprocessing process and reduce data noise. It should be noted that in this embodiment of the invention, a moving average filter is selected for filtering.

[0022] Thus, by collecting and preprocessing the rolling system parameters and real-time data, basic data for pressure control is obtained.

[0023] Step S2: Obtain adaptive rolling pressure data by performing model coupling analysis on the steel coil process parameters.

[0024] In existing cold rolling hydraulic control practices, a common inherent problem stems from the structural limitations of its core control algorithm, the PID (Proportional-Integral-Derivative) controller. Specifically, this type of controller, used to maintain the target setpoint of system pressure, is configured as a static value within the control system. On the one hand, cold rolling production tasks are highly diverse. Production plans include various steel coils with vastly different process parameters such as yield strength, target thickness, plate width, and rolling speed. For example, rolling a low-strength thin steel plate for food packaging results in a relatively low actual load on the hydraulic system; while rolling a high-strength steel plate for automotive structural components results in a much higher load. From an energy-saving perspective, the ideal control method should be to set a minimum economic operating pressure that precisely meets the needs of each different production task. On the other hand, for the sake of production safety and product quality, engineers prefer to use a peak-selection principle when configuring the aforementioned static pressure target value. That is, the pressure value is set based on the highest load condition among all production tasks the production line can handle, with an additional safety margin. It should be noted that the safety margin in this invention refers to an additional pressure compensation dynamically added on top of the adaptive rolling pressure to cope with instantaneous disturbances such as mill acceleration or fluctuations in incoming material thickness. This ensures the stability and control accuracy of the hydraulic system under sudden load changes. If the set value is too low, the system will inevitably trigger an alarm and shutdown due to insufficient pressure when rolling high-load steel coils, causing significant production interruptions and economic losses.

[0025] This peak-selection strategy, stemming from flaws in the algorithm's structure, directly results in the hydraulic system being continuously maintained at a high pressure level far exceeding actual requirements when handling conventional steel coils, where the load is far below the most demanding conditions for the majority of the production schedule. To maintain this unnecessary high pressure, the variable pump continuously outputs inefficient energy. This persistent energy waste is a deep-seated and difficult-to-solve technical bottleneck for existing technologies when facing diverse production tasks.

[0026] Therefore, the core objective of this invention is to solve the problem of wasted planned energy consumption caused by the inability of steel coils to adapt to different macroscopic operating conditions. Instead of treating the pressure target as a static parameter, it is optimized as a dynamic variable based on the upcoming production task. Specifically, this step proactively retrieves all macroscopic process parameters of each new steel strip from the upper-level production scheduling system before rolling begins. These parameters from different physical dimensions are then used to obtain an index that measures the overall rolling difficulty of the current task. Based on this index, adaptive rolling pressure data that meets the basic operating requirements of the coil is calculated. This ensures that the basic energy supply level of the hydraulic system can accurately match the actual needs of each production task, eliminating energy waste caused by differences in operating conditions.

[0027] First, a dimensionless macroscopic working condition adaptability factor is obtained by performing multidimensional normalization and coupled modeling on the process parameters of steel coil rolling. Then, adaptive rolling pressure data is obtained by integrating the working condition adaptability index with the nonlinear mapping relationship of the accumulator. Specifically, the average deformation resistance, target total thickness reduction, coil width, and rated rolling speed of the current steel coil are divided by the average deformation resistance, target total thickness reduction, coil width, and rated rolling speed under the baseline working condition, respectively, to obtain the first deformation resistance assessment, the first target total thickness reduction assessment, the first coil width assessment, and the first rated rolling speed assessment of the steel coil. The square root of the first target total thickness reduction assessment is multiplied by the first deformation resistance assessment, the first coil width assessment, and the first rated rolling speed assessment, and the result is used as the first macroscopic working condition adaptability assessment. The square root of the first macroscopic working condition adaptability assessment is used as the dimensionless macroscopic working condition adaptability factor.

[0028] In one embodiment, it is assumed that the average deformation resistance of the current steel coil is The current target total thickness reduction for the steel coil is: The current width of the steel coil is The current rated rolling speed of the steel coil is The average deformation resistance under the reference working condition is The target total thickness reduction under the benchmark condition is: The width under the reference working condition is Rated rolling speed under reference operating conditions Then the expression for calculating the dimensionless macroscopic working condition adaptability factor is: in, Represents the dimensionless macroscopic operating condition adaptability factor; This indicates the average deformation resistance of the current steel coil; This represents the average deformation resistance under the reference working condition; This indicates the target total thickness reduction of the current steel coil; This indicates the target total thickness reduction under the baseline operating conditions; Indicates the current width of the steel coil; Indicates the width of the steel coil under the reference operating conditions; This indicates the rated rolling speed of the current steel coil; This indicates the rated rolling speed under the reference operating conditions.

[0029] It should be noted that the dimensionless macroscopic working condition adaptability factor aims to integrate process parameters of different physical dimensions into a unified index that can represent their comprehensive load level. Therefore, the design structure of this invention directly stems from the logical derivation of the basic physical model of the rolling process. First, the fundamental physical quantity of the system load level is the rolling power, which is proportional to the product of the total rolling force and the rolling speed. According to the generally accepted classical theory in the field of metal plastic processing, the Sims rolling force model gives that the total rolling force is proportional to the average deformation resistance, the workpiece width, and the square root of the reduction. Integrating these physical relationships and substituting the proportional relationship of the total rolling force into the power calculation, it is derived that the rolling power ultimately has a physical multiplicative coupling relationship with the four core process parameters: average deformation resistance, the square root of the reduction, the workpiece width, and the rolling speed. Based on this theoretical foundation, this invention constructs a product term... This structure directly maps the coupling relationships of the parameters in the aforementioned physical model through a series of multiplications. By dividing each term by its own baseline value, this design transforms absolute physical quantities into dimensionless relative values, eliminating dimensional issues between physical quantities and enabling the factor to characterize the load multiple of the current operating condition relative to a standard operating condition. Finally, the square root of the product terms is calculated. This design, based on the energy storage physical characteristics of hydraulic accumulators, aims to accurately convert a load index in a "power domain" into a regulation factor in a "pressure domain." The energy reserve required by the system to cope with disturbances should be proportional to the rolling power. According to the physical equations of the accumulator, the stored energy has an approximate square relationship with its internal pressure. Therefore, the required reserve pressure should be proportional to the square root of the rolling power. Thus, by calculating the square root of the relative power index, this mapping from the "power domain" to the "pressure domain" is simulated.

[0030] After obtaining the dimensionless macroscopic working condition adaptability factor, the adaptive rolling pressure data can be obtained by integrating the working condition adaptability index with the nonlinear mapping relationship of the accumulator. Specifically, the minimum physical pressure for the hydraulic system to maintain basic functions is obtained. The result of multiplying the dimensionless macroscopic working condition adaptability factor by the reserve pressure under the benchmark working condition and adding it to the minimum physical pressure is used as the adaptive rolling pressure data.

[0031] In one embodiment, it is assumed that the minimum physical pressure required for the hydraulic system to maintain basic function under baseline operating conditions is: The reserve pressure under the baseline operating condition is The formula for calculating the adaptive rolling pressure data is: in, This indicates adaptive rolling pressure data; This indicates the minimum physical pressure required for the hydraulic system to maintain basic functions under baseline operating conditions. Represents the dimensionless macroscopic operating condition adaptability factor; This indicates the reserve pressure under the baseline operating conditions.

[0032] It should be noted that by multiplying the obtained dimensionless macroscopic working condition adaptability factor by the reserve pressure under the benchmark working condition, the required reserve pressure increment under the current working condition is calculated. This increment is then superimposed on the minimum physical pressure required for system operation to obtain the adaptive rolling pressure data.

[0033] This completes the process of obtaining adaptive rolling pressure data by performing model coupling analysis on the steel coil process parameters.

[0034] Step S3: Obtain the adaptive dynamic safety margin by performing a nonlinear fusion evaluation of acceleration and thickness disturbances.

[0035] In step S2, adaptive rolling pressure data for the entire steel coil to be rolled was obtained. This adaptive rolling pressure data addresses the energy consumption differences between different steel coils. However, this adaptive rolling pressure data is calculated based on the dimensionless macroscopic average operating conditions of the steel coil, and it can only meet the ideal rolling state of stability and no disturbance. In actual production, the rolling process of a single steel coil is not static, but rather subject to sudden disturbances. These disturbances mainly originate from two aspects: firstly, changes in the internal operating conditions of the system, especially the acceleration stage from low-speed threading to high-speed main rolling, at which point the demand for power and hydraulic flow from the mill will increase sharply; secondly, fluctuations in the quality of external incoming materials, such as incompletely eliminated local thickness defects upstream of the steel strip. When these defects enter the rolls at high speed, they will cause a sudden surge in rolling force. If the system relies solely on the adaptive rolling pressure data set in step S2, then when any of the above instantaneous disturbances occur, this adaptive rolling pressure data will be insufficient to cope with the sudden energy consumption, inevitably leading to a sharp drop in accumulator pressure. This pressure instability will not only directly affect the response accuracy of the hydraulic servo system, causing quality problems such as thickness deviation in the product, but in severe cases, it may even trigger low-pressure protection, leading to production interruption.

[0036] To address the aforementioned problems, this invention obtains the dynamic acceleration disturbance intensity of the system by performing nonlinear mapping processing on real-time acceleration data; obtains the nonlinear impact intensity of material disturbance through joint feature analysis of thickness deviation and change rate; and obtains an adaptive dynamic safety margin by further fusing analysis of the system's dynamic acceleration disturbance intensity and the nonlinear impact intensity of material disturbance. This adaptive dynamic safety margin is then used to dynamically compensate for adaptive rolling pressure data, thereby preventing pressure instability during the rolling process.

[0037] First, an acceleration sensitivity coefficient is set. In this embodiment, the acceleration sensitivity coefficient is set to 2. This coefficient can be adjusted according to the actual scenario and is not required. The real-time running acceleration data of the rolling mill is obtained by using the real-time running speed data of the rolling mill. The calculation result of dividing the real-time running acceleration data of the rolling mill by the rated rolling speed and multiplying it by the acceleration sensitivity coefficient is mapped by a hyperbolic tangent function. The larger value between the corresponding mapping result and the constant 0 is taken as the dynamic acceleration disturbance intensity of the system.

[0038] In one embodiment, it is assumed that the rolling mill is in The running speed at time is The current rated rolling speed of the steel coil is Then in The expression for calculating the intensity of the dynamic acceleration disturbance of the system at any given time is: in, Indicates in The intensity of dynamic acceleration disturbance in the system at any given moment; This represents the function for calculating the maximum value. Represents the hyperbolic tangent function; Indicates the acceleration sensitivity coefficient; Indicates the rolling mill is in The speed of operation at any given moment; This indicates the rated rolling speed of the current steel coil; Let be the differential value of with respect to t, representing the rolling mill at t. The acceleration of movement at any given moment.

[0039] It should be noted that, in handling system acceleration disturbances, this invention uses the current rated speed of the steel coil as the scale benchmark for real-time acceleration to perform adaptive pressure compensation. This allows the compensation to adapt to different speed levels, avoiding the problems of over-compensation or under-compensation under certain conditions caused by using a fixed threshold. Simultaneously, mapping using a hyperbolic tangent function achieves nonlinear processing of the input signal. The S-curve characteristic of this function ensures that the calculation results remain responsive when the input acceleration data is small; while when the input acceleration data reaches a maximum value due to noise or abnormal conditions, the output smoothly tends towards the upper limit of saturation, thus avoiding excessive amplification of instantaneous spike noise in the input acceleration data and ensuring output stability. The function ensures that the intensity of dynamic acceleration disturbances in the system only has a compensating effect when the real-time operating acceleration of the mill is positive, accurately corresponding to the physical process of increased system load.

[0040] After obtaining the system's dynamic acceleration disturbance intensity, the real-time inlet thickness deviation data of the steel coil is further differentiated to obtain the real-time inlet thickness deviation change rate of the steel coil. The square of the product of the real-time inlet thickness deviation data and the real-time inlet thickness deviation change rate of the steel coil is used as the first impact strength assessment. The first impact strength assessment is divided by the fourth power of the nominal inlet thickness of the steel coil as the second impact strength assessment. It should be noted that in the technical solution of this invention, the nominal inlet thickness refers to the theoretical reference value determined according to the preset process parameters of the steel coil or the system design standard, which is used for normalization calculation or as a benchmark scale for assessing the actual deviation. The negative number of the second impact strength assessment is mapped by an exponential function with the natural constant as the base, and the result of subtracting the constant 1 from the corresponding mapping result is used as the nonlinear impact strength of the material disturbance.

[0041] In one embodiment, it is assumed that the nominal entry thickness of the current steel coil is ; The current steel coil is Inlet thickness deviation data at time Then in The expression for calculating the nonlinear impact strength of the material disturbance at time t is: in, Let be the differential value with respect to t; Indicates in The nonlinear impact strength of material disturbance at any given time; Denotes the natural constant e; Indicates the current state of the steel coil. Inlet thickness deviation data at any given time; This indicates the nominal entry thickness of the current steel coil.

[0042] It should be noted that, in order to identify truly threatening defects when dealing with material thickness defect disturbances, this invention employs... As an impact characteristic, multiplying the magnitude of the thickness deviation by the degree of its change allows for the differentiation between gradual large deviations and drastic small deviations, accurately reflecting the physical nature of the impact on the system. Furthermore, by using the nominal entry thickness of the current steel coil as a benchmark, the disturbance assessment can adapt to products of different thickness specifications, resolving the issue of varying impacts of the same absolute defect on different sheet metal sizes. Finally, through exponential mapping, the second impact strength assessment is nonlinearly mapped to a bounded and smoothly varying range. The value.

[0043] After obtaining the system's dynamic acceleration disturbance intensity and the nonlinear impact intensity of the material disturbance, an adaptive dynamic safety margin is obtained by further fusing analysis of these two indices. Specifically, the calculation result of subtracting the adaptive rolling pressure data from the minimum physical pressure of the hydraulic system is used as the first assessment of the safety margin; the square root of the sum of the square of the system's dynamic acceleration disturbance intensity and the square of the nonlinear impact intensity of the material disturbance is used as the safety margin assessment factor; and the calculation result of multiplying the safety margin assessment factor by the first assessment of the safety margin is used as the adaptive dynamic safety margin.

[0044] In one embodiment, in The expression for calculating the adaptive dynamic safety margin at time t is: in, Indicates in Adaptive dynamic safety margin at any time; This indicates adaptive rolling pressure data; This indicates the minimum physical pressure required for the hydraulic system to maintain basic functions under baseline operating conditions. Indicates in The intensity of dynamic acceleration disturbance in the system at any given moment; Indicates in The nonlinear impact strength of material disturbance at any given time.

[0045] It should be noted that after quantifying the two independent disturbance sources (i.e., the intensity of the system's dynamic acceleration disturbance and the nonlinear impact intensity of the material disturbance), the two are then... The disturbance intensity index is fused in a certain way to obtain the safety margin assessment factor, and the safety margin assessment factor is then combined with the basic load level of the system. Multiplying these values ​​yields an adaptive dynamic safety margin, which not only reflects the severity of real-time disturbances but also adapts to the overall load level of the current steel coil.

[0046] Thus, the adaptive dynamic safety margin was obtained by nonlinearly fusing the evaluation of acceleration and thickness disturbances.

[0047] Step S4: Dynamic target pressure setting data is obtained by real-time synthesis of adaptive rolling pressure data and adaptive dynamic safety margin.

[0048] After obtaining the adaptive dynamic safety margin in step S3, the calculation result of adding the adaptive dynamic safety margin to the minimum physical pressure for the hydraulic system to maintain basic functions under the reference operating condition is used as the dynamic target pressure setting data.

[0049] Step S5: Perform closed-loop energy control based on dynamic target pressure setting data.

[0050] This step is the final execution stage of the control method, and its purpose is to convert the dynamic target pressure setpoint data calculated in the preceding steps into the final execution stage. This translates into precise physical control of the hydraulic pump unit. It should be noted that the implementation of this invention presupposes that the PID controller itself has been properly tuned and possesses the basic ability to track the dynamic setpoint, which is a standard requirement of modern industrial control systems. The core contribution of this invention lies not in the PID controller itself, but in providing it with an adaptively changing target setpoint. Solve the problem of energy waste at the source.

[0051] The specific control process is as follows: The dynamic target pressure setting data obtained in real time in step S4... The target setpoint is transmitted in real time to the PID controller in the hydraulic system, which controls the pressure of the variable pump. This PID controller also receives the actual pressure measured by a pressure sensor mounted on the accumulator. As feedback input, the algorithm inside the controller uses this dynamic target pressure setting data. Compared with actual pressure value Continuous comparisons are performed to generate a real-time pressure deviation signal. ,Right now .

[0052] The PID controller responds to the pressure deviation signal The processing is not a single operation, but rather it analyzes deviations from different dimensions and generates a comprehensive control action through three parallel control loops within it: Proportional adjustment mechanism: This mechanism provides an immediate response. It generates a value relative to the current pressure deviation. The control input is proportional to the magnitude of the deviation. When the deviation is large, it outputs a strong control action to quickly correct the deviation; when the deviation is small, its control action weakens accordingly.

[0053] Integral terminator: This terminator eliminates steady-state error. It continuously accumulates all deviation values ​​over a period of time. If the system has a small but persistent deviation (e.g., the actual pressure is always slightly lower than the target pressure), the proportional terminator may not be able to completely eliminate it, but the cumulative effect of the integral terminator will become stronger and stronger until the persistent deviation is completely eliminated, ensuring that the system can accurately reach the target pressure.

[0054] Differential element: This element provides predictability and suppresses system oscillations. It calculates the deviation... It operates based on the rate of change of pressure. If it detects that the pressure is dropping rapidly, it will also output a large control action in advance to stop the downward trend, thereby improving the system's response speed and stability.

[0055] The PID controller ultimately performs a weighted sum of the control quantities calculated from the three stages mentioned above, forming a single, precise control command signal that comprehensively considers the current deviation magnitude, historical cumulative deviation, and future trends. This command signal is sent to the frequency converter driving the variable pump motor. Based on the received command signal, the frequency converter adjusts the AC frequency output to the motor in real time, thereby precisely controlling the motor speed. Since the motor speed directly determines the displacement of the connected variable pump, the entire control chain achieves precise real-time adjustment from a dynamic pressure target to the actual energy output of the pump group, forming a complete closed-loop control system.

[0056] Due to the dynamic target pressure setting data provided by this invention Instead of static, conservative values, these are dynamic values ​​that accurately reflect the system's actual pressure requirements at any given moment. Therefore, the ultimate effect of this closed-loop control is that during most of the rolling process, in stable operation or low-load phases, It will maintain a low economic level; the PID controller will instruct the frequency converter to drive the pump unit at a lower speed, thereby significantly reducing energy consumption. It will only operate when dynamic disturbances are anticipated or detected. This allows the pump unit to instantly increase its output to cope with the impact. This control method completely solves the problem of continuous energy waste caused by maintaining static high pressure in existing technologies. Under the premise of fully ensuring the dynamic response capability of the system and production safety, it achieves a precise match between energy supply and actual demand, thereby achieving a significant energy-saving effect in the hydraulic system.

[0057] The present invention also provides a high-pressure, high-flow hydraulic energy-saving control system for cold continuous rolling mill, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a high-pressure, high-flow hydraulic energy-saving control method for cold continuous rolling mill according to the present invention is implemented.

[0058] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for energy-saving control of high-pressure, high-flow hydraulic systems in cold continuous rolling mills, characterized in that: include: By collecting and preprocessing rolling system parameters and real-time data, basic data for pressure regulation can be obtained; By performing model coupling analysis on the process parameters of steel coils, adaptive rolling pressure data can be obtained. An adaptive dynamic safety margin is obtained by nonlinearly fusing acceleration and thickness disturbances. Dynamic target pressure setting data is obtained by real-time synthesis of adaptive rolling pressure data and adaptive dynamic safety margin. Closed-loop energy control is performed based on dynamic target pressure setting data. The basic data used for pressure control includes steel coil process parameters, acceleration input during steel coil rolling, and thickness disturbance during the steel coil rolling process. The method of obtaining adaptive rolling pressure data by performing model coupling analysis on steel coil process parameters includes: obtaining dimensionless macroscopic working condition adaptability factors by performing multidimensional normalization and coupled modeling on the steel coil rolling process parameters; and obtaining adaptive rolling pressure data by integrating the working condition adaptability index with the nonlinear mapping relationship of the accumulator. The process involves multidimensional normalization and coupled modeling of the steel coil rolling process parameters to obtain a dimensionless macroscopic working condition adaptability factor. This includes: dividing the current average deformation resistance, target total thickness reduction, coil width, and rated rolling speed of the steel coil by the average deformation resistance, target total thickness reduction, coil width, and rated rolling speed under the reference working condition, respectively, to obtain the first deformation resistance assessment, the first target total thickness reduction assessment, the first coil width assessment, and the first rated rolling speed assessment of the steel coil; multiplying the square root of the first target total thickness reduction assessment by the first deformation resistance assessment, the first coil width assessment, and the first rated rolling speed assessment, and using the result as the first macroscopic working condition adaptability assessment; and using the square root of the first macroscopic working condition adaptability assessment as the dimensionless macroscopic working condition adaptability factor. The method of integrating the working condition adaptability index with the nonlinear mapping relationship of the accumulator to obtain adaptive rolling pressure data includes: obtaining the minimum physical pressure for the hydraulic system to maintain basic functions, and using the result of multiplying the dimensionless macroscopic working condition adaptability factor with the reserve pressure under the benchmark working condition and adding it to the minimum physical pressure as adaptive rolling pressure data. The method of obtaining an adaptive dynamic safety margin by performing a nonlinear fusion evaluation of acceleration and thickness disturbance includes: obtaining the system's dynamic acceleration disturbance intensity by performing nonlinear mapping processing on real-time acceleration data; obtaining the nonlinear impact intensity of material disturbance by performing joint feature analysis of thickness deviation and change rate; and obtaining the adaptive dynamic safety margin by further performing a fusion analysis on the system's dynamic acceleration disturbance intensity and the nonlinear impact intensity of material disturbance. The step of obtaining the system dynamic acceleration disturbance intensity by performing nonlinear mapping processing on real-time acceleration data includes: setting an acceleration sensitivity coefficient; obtaining real-time running acceleration data of the rolling mill through real-time running speed data of the rolling mill; performing hyperbolic tangent function mapping on the calculation result of dividing the real-time running acceleration data of the rolling mill by the rated rolling speed and multiplying it by the acceleration sensitivity coefficient; and taking the larger value between the obtained mapping result and the constant 0 as the system dynamic acceleration disturbance intensity. The method of obtaining the nonlinear impact strength of material disturbance through joint characteristic analysis of thickness deviation and change rate includes: obtaining the real-time inlet thickness deviation change rate of the steel coil by differentiating the real-time inlet thickness deviation data of the steel coil; taking the square of the product of the real-time inlet thickness deviation data of the steel coil and the real-time inlet thickness deviation change rate of the steel coil as the first impact strength assessment; taking the calculation result of dividing the first impact strength assessment by the fourth power of the nominal inlet thickness of the steel coil as the second impact strength assessment; mapping the negative of the second impact strength assessment to an exponential function with the natural constant as the base, and taking the calculation result of subtracting the constant 1 from the corresponding mapping result as the nonlinear impact strength of material disturbance.

2. The energy-saving control method for high-pressure, high-flow hydraulic systems in cold continuous rolling mills according to claim 1, characterized in that, The basic data also includes: Initialize the control system by setting the average deformation resistance, target total thickness reduction, coil width, rated rolling speed, minimum physical pressure of the hydraulic system, and reserve pressure under the reference working conditions. Before the start of each rolling task for a steel coil, the preset macroscopic process parameters for that steel coil are obtained from the upper-level manufacturing execution system, including the target total thickness reduction, width, average deformation resistance, and rated rolling speed of the current steel coil; after entering the rolling process, the real-time running speed data of the rolling mill and the real-time entry thickness deviation data of the steel coil are collected. The real-time operating speed data of the rolling mill and the real-time entry thickness deviation data of the steel coil are filtered by a digital filtering algorithm, and the sensor data are smoothed by a moving average filter.

3. The energy-saving control method for high-pressure, high-flow hydraulic systems in cold continuous rolling mills according to claim 1, characterized in that, The process of further fusing analysis of the system's dynamic acceleration disturbance intensity and the nonlinear impact intensity of material disturbance to obtain an adaptive dynamic safety margin includes: The calculation result of subtracting the minimum physical pressure of the hydraulic system from the adaptive rolling pressure data is used as the first safety margin assessment; the square root of the sum of the square of the system's dynamic acceleration disturbance intensity and the square of the nonlinear impact intensity of the material disturbance is used as the safety margin assessment factor; the calculation result of multiplying the safety margin assessment factor and the first safety margin assessment is used as the adaptive dynamic safety margin.

4. A high-pressure, high-flow hydraulic energy-saving control system for cold continuous rolling mills, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a cold continuous rolling high-pressure, high-flow hydraulic energy-saving control method according to any one of claims 1-3.

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