Methods and systems for quantitative feedback adjustment of acid wash quality

Abstract

This invention relates to a method and system for quantitative feedback control of acid washing quality, including: a quantitative acid washing quality assessment module; a quantitative acid washing quality assessment module that obtains the quantitative acid washing quality assessment deviation by constructing a quantitative acid washing quality assessment model; activation of feedback control at the corresponding feedback level based on the quantitative acid washing quality assessment deviation; and implementation of quantitative acid washing quality feedback control. This invention achieves the benefit of effectively assessing acid washing quality, stabilizing the quality of steel strips, and reducing energy consumption during the acid washing process by establishing a quantitative acid washing quality assessment module and using the deviation value to promptly activate the feedback control method at different reaction levels.The feedback adjustment method of this invention is easy to control, can react faster to shot blasting, plate heating, and catalyst processes, and can also reduce parameters such as acid solution concentration and acid solution temperature, etc. This invention helps achieve high washing quality with low energy consumption by systematically distributing scale removal capabilities such as shot blasting, acid washing, etc.

Description

A method and system for quantitative feedback regulation of pickling quality Technical Field

[0001] The invention belongs to the technical field of surface cleaning, and in particular relates to a quantitative feedback adjustment method and system for pickling quality. Background Art

[0002] Currently, cold-rolling pickling mills require different process parameters, such as acid temperature and concentration, for specific steel coil types. Different pickling endpoint times can lead to varying pickling quality and costs. Current pickling parameter settings are primarily based on operating procedures. To avoid under-pickling, these procedures often set acid temperature and concentration values ​​too high, resulting in high energy consumption and over-pickling, which exacerbates iron losses during pickling.

[0003] Currently, only a rough distinction is made between under-pickling and over-pickling in practical applications, without a quantitative indicator. This makes it difficult to effectively guide the quantitative adjustment of pickling process parameters. Furthermore, current methods for adjusting pickling effects mostly involve adjusting the acid solution temperature and concentration. These two parameters have a significant lag in response, making them ineffective in improving cleaning quality. Therefore, a quantitative determination method is urgently needed to address this significant lag in control methods.

[0004] Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the prior art. The present invention provides a method and system for quantitative feedback regulation of pickling quality, which can stabilize the production process and reduce system energy consumption.

[0006] In order to achieve the expected effect, the present invention adopts the following technical solutions:

[0007] The present invention discloses a method for quantitative feedback regulation of pickling quality, comprising: setting a pickling quality quantitative evaluation module in a pickling system, wherein the pickling quality quantitative evaluation module obtains a pickling quality quantitative evaluation deviation by constructing a pickling quality quantitative evaluation model, and starts a feedback regulation means of a corresponding response level according to the pickling quality quantitative evaluation deviation to perform quantitative feedback regulation of the pickling quality.

[0008] Furthermore, the pickling quality quantitative evaluation module constructs a pickling quality quantitative evaluation model based on the AHP method.

[0009] Furthermore, the pickling quality quantitative evaluation model uses the deviation value between the target value and the current quantitative result as an indicator for measuring the cold rolling pickling quality.

[0010] Furthermore, corresponding indicator weights are set according to the pickling defect level.

[0011] Further, setting corresponding index weights according to the pickling defect level specifically includes: based on at least one defect data among defect area, defect length, defect perimeter, aspect ratio of the defect area, defect shape factor, and defect gray-scale feature value, constructing a defect judgment matrix to solve the index weights corresponding to the pickling defect level.

[0012] Further, the response levels are divided into 5 levels, which are, from high to low, shot blasting motor power, inlet plate surface heating power, accelerator dosage, acid concentration, and acid temperature, and each response level corresponds to its respective adjustment limit.

[0013] Further, the deviation value activates the feedback adjustment means of the corresponding response level within different threshold ranges, and the larger the deviation value, the higher the response level of the activated feedback adjustment means.

[0014] Further, the deviation value is divided into 5 intervals according to its magnitude, corresponding to 5 feedback adjustment means respectively, specifically as follows:

[0015] When 0 < δ < X1, start the acid temperature adjustment to adjust the acid temperature;

[0016] When X1 < δ < X2, start the acid concentration adjustment to adjust the acid concentration;

[0017] When X2 < δ < X3, start the accelerator dosage adjustment to adjust the accelerator dosage;

[0018] When X3 < δ < X4, start the inlet plate surface heating power adjustment to adjust the inlet plate surface heating power;

[0019] When X4 < δ < X5, start the shot blasting motor power adjustment to adjust the shot blasting motor power;

[0020] Wherein, X1 < X2 < X3 < X4 < X5, and X1, X2, X3, X4, X5 are all positive numbers.

[0021] The present invention discloses a pickling quality quantitative feedback adjustment system, including:

[0022] A pickling quality quantitative evaluation module, which obtains the pickling quality quantitative evaluation deviation by constructing a pickling quality quantitative evaluation model, and starts the feedback adjustment means of the corresponding response level according to the pickling quality quantitative evaluation deviation for pickling quality quantitative feedback adjustment.

[0023] The present invention also discloses a pickling adjustment system, including:

[0024] A pickling quality quantitative evaluation module, which is used to obtain the pickling quality by using any one of the above pickling quality quantitative feedback adjustment methods and feedback it to the PLC;

[0025] PLC, used to receive the pickling quality feedback from the pickling quality quantitative evaluation module and calculate the torque of the motor based on it;

[0026] an electric motor for adjusting the speed according to the torque;

[0027] The PC is used to adjust the relevant parameters of the PLC through the neural network model based on the pickling quality feedback from the pickling quality quantitative evaluation module and the preset pickling quality, and send them to the PLC to achieve pickling adjustment.

[0028] Compared to the prior art, the present invention provides a method and system for quantitative feedback control of pickling quality. The method can be applied to the pickling process of steel strip, as well as to the pickling processes of other products. In the steel strip pickling process, the steel strip undergoes mechanical descaling and surface heating before entering the pickling tank for multi-stage rinsing, followed by hot air drying, oven drying, and coiling. After mechanical descaling and pickling, the steel strip is equipped with an image data acquisition module, a visualization module, and a pickling quality quantitative evaluation module. The pickling quality quantitative evaluation module constructs a pickling quality quantitative evaluation model to determine a pickling quality quantitative evaluation deviation. Based on the pickling quality quantitative evaluation deviation, feedback control mechanisms at corresponding response levels are activated to perform quantitative feedback control of the pickling quality. The quantitative feedback control method of the present invention can accelerate product cleaning, reduce equipment energy and acid consumption, and ultimately produce products with high surface quality. By providing a pickling quality quantitative evaluation module and utilizing the deviation value to promptly activate feedback control mechanisms at different response levels, the present invention achieves the beneficial effects of efficiently determining pickling quality, stabilizing strip quality, and reducing pickling energy consumption. The feedback control method of the present invention is easy to control and can respond faster to shot blasting, board heating and accelerators, and can also weaken parameters such as acid concentration and acid temperature. By systematically distributing the descaling capacity of shot blasting, pickling, etc., the present invention helps to achieve low energy consumption and high cleaning quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] FIG1 is a schematic diagram of a pickling process of a pickling quality quantitative feedback adjustment method provided by an embodiment of the present invention.

[0031] FIG2 is a schematic diagram of a pickling adjustment framework provided by an embodiment of the present invention.

[0032] FIG3 is a schematic diagram of another pickling adjustment framework provided by an embodiment of the present invention.

[0033] FIG4 is a schematic diagram of a neural network model provided by an embodiment of the present invention.

[0034] FIG5 is a schematic diagram of adjusting and updating weighted coefficients of a neural network model provided by an embodiment of the present invention.

[0035] FIG6 is a schematic structural diagram of another strip steel pickling system provided in an embodiment of the present invention.

[0036] FIG7 is a schematic structural diagram of an iron sludge processing subsystem provided in an embodiment of the present invention.

[0037] FIG8 is a top view of FIG7 .

[0038] FIG9 is a schematic structural diagram of an iron sludge collection box provided in a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] Referring to Figure 1, the present invention discloses a method for quantitative feedback regulation of pickling quality, including: setting a pickling quality quantitative evaluation module in the pickling system, the pickling quality quantitative evaluation module obtaining a pickling quality quantitative evaluation deviation by constructing a pickling quality quantitative evaluation model, and starting a feedback regulation means of a corresponding response level according to the pickling quality quantitative evaluation deviation to perform quantitative feedback regulation of the pickling quality.

[0041] The present invention can be applied to the pickling process of steel strips, as well as to the pickling processes of other products. In the steel strip pickling process, the steel strip undergoes mechanical descaling and surface heating before entering the pickling tank for multi-stage rinsing, followed by hot air, drying, oven drying, and coiling. After mechanical descaling and pickling, the steel strip is provided with an image data acquisition module, a visualization module, and a pickling quality quantitative evaluation module. The pickling quality quantitative evaluation module obtains a pickling quality quantitative evaluation deviation by constructing a pickling quality quantitative evaluation model, and activates a corresponding response level feedback adjustment means according to the pickling quality quantitative evaluation deviation to perform pickling quality quantitative feedback adjustment.

[0042] As shown in Figure 1, it is a schematic diagram of the pickling process of a quantitative feedback adjustment method for pickling quality provided by an embodiment of the present invention, wherein 1 is a uncoiler, 2 is a welder, 3 is a looper, 4 is a shot blasting machine, 5 is an image data acquisition device, 6 is a fan, 7 is a heater, 8 is an electric heating power regulator, 9 is a pre-cleaning tank, 10 is a pre-cleaning tank, 11 is a 1# acid tank, 12 is a 1# acid tank, 13 is a heat exchanger, 14 is a pump, 15 is a 2# acid tank, 16 is a 2# acid tank, 17 is a rinsing tank, 18 is a rinsing water tank, 19 is a hot air dryer, 20 is a strip steel, 21 is an oxidant generator, and 22 is an ORP detector.

[0043] Preferably, the quantitative feedback adjustment method for pickling quality of the present invention is applied to a pickling system having equipment such as shot blasting, mechanical descaling, plate surface heating, pickling tank, rinsing tank, supporting circulation system, multi-stage rinsing tank, hot air drying, and quality detection system.

[0044] The quantitative feedback regulation method of the present invention can speed up the cleaning speed of the product, reduce the energy consumption and acid consumption of the equipment, and thus obtain products with high surface quality. The present invention sets a quantitative evaluation module for pickling quality and uses the deviation value to timely start the feedback regulation means of different response levels, which can achieve the beneficial effects of efficiently determining the pickling quality, stabilizing the quality of the strip, and reducing the energy consumption of pickling. The feedback regulation means of the present invention is easy to control and can respond to faster shot blasting, plate surface heating and accelerators, and can also weaken parameters such as acid concentration and acid temperature. The present invention helps to achieve low energy consumption and high cleaning quality by systematically allocating descaling capabilities such as shot blasting and pickling.

[0045] In a preferred embodiment, the pickling quality quantitative evaluation module constructs a pickling quality quantitative evaluation model based on the AHP method.

[0046] It is worth noting that the AHP method, or Analytic Hierarchy Process (AHP), is a simple, flexible, and practical multi-criteria decision-making approach for quantitative analysis of qualitative problems. AHP's hallmark is that it organizes the various factors in a complex problem by dividing them into interconnected, ordered hierarchies. It directly and effectively combines expert opinion with the analyst's objective judgment based on a subjective judgment structure of a given objective reality, and quantitatively describes the importance of each element in a hierarchy compared to the others. AHP uses mathematical methods to calculate weights that reflect the relative importance of elements at each hierarchy. It then calculates and ranks the relative weights of all elements using a total ranking across all hierarchies. This is a systematic, hierarchical analytical method that combines qualitative and quantitative analysis. This method, by mathematically analyzing the nature of complex decision-making problems using limited quantitative information, mathematically transforms the decision-making process. This provides a simple approach for multi-objective, multi-criteria, or unstructured decision-making problems. It is a model for making decisions in complex systems that are difficult to quantify. The essence of the hierarchical analysis method is the scoring method: first determine the indicators, then score the indicators of different plans, and finally determine the weights for the indicators. This method is very effective in handling evaluations with unknown data.

[0047] Furthermore, the pickling quality quantitative evaluation model uses the deviation value δ between the target value and the current quantitative result as an indicator for measuring the cold rolling pickling quality.

[0048] Specifically, the target value is typically a preset value for optimal quality. The current quantitative result can be acquired in real time via the image data acquisition module. The deviation value = |target value - current quantitative result|. For example, if the current quantitative result is greater than the target value, it may indicate over-pickling; if the current quantitative result is less than the target value, it may indicate under-pickling. When the deviation value exceeds the threshold, the corresponding response level feedback adjustment method is activated to perform quantitative feedback adjustment of the pickling quality.

[0049] On the one hand, the corresponding indicator weights are set according to the pickling defect level.

[0050] On the other hand, the setting of the corresponding indicator weight according to the pickling defect level specifically includes: solving the indicator weight corresponding to the pickling defect level by constructing a defect judgment matrix based on at least one defect data among the defect area, defect length, defect perimeter, defect area slenderness ratio, defect shape factor and defect grayscale eigenvalue.

[0051] Specifically, pickling defects are divided into over-pickling and under-pickling. According to at least one defect data among defect area, defect length, defect perimeter, defect area aspect ratio, defect shape factor, and defect gray-scale characteristic value, several different levels of pickling defects can be classified, and each pickling defect level has a corresponding index weight. The method of constructing a judgment matrix in the analytic hierarchy process is the consistent matrix method, that is, instead of comparing all factors together, they are compared pairwise. At this time, a relative scale is used to minimize the difficulty of comparing factors with different natures and improve accuracy. The methods for solving the index weights corresponding to pickling defect levels can include the arithmetic mean method for finding weights, the geometric mean method for finding weights, the eigenvalue method for finding weights, etc.

[0052] Preferably, the current quantitative result = defect weight * level weight. The defect weight refers to the weights of the under-pickling and over-pickling defect categories, and the level weight refers to the weight corresponding to the severity of the corresponding defect type. The level weight is determined by factors such as defect area, defect length, defect perimeter, defect area aspect ratio, defect shape factor, and defect gray-scale characteristics. For example, when the target value is 0, it means that neither under-pickling nor over-pickling occurs. The larger the deviation value δ, the more serious the pickling quality problem.

[0053] Further, the response levels are divided into 5 levels, from high to low in order: shot blasting motor power, inlet plate surface heating power, accelerator dosage, acid concentration, acid temperature, and each response level corresponds to its respective adjustment limit. Preferably, the 5 response levels correspond to different weights, ∑pi = 1, i = 1, 2, 3, 4, 5.

[0054] For example, when the activated feedback adjustment means is the acid concentration, within a certain range of the acid concentration target value, the acid concentration is adjusted according to the preset weight of the acid concentration to control the acid concentration to approach the minimum deviation value, that is, to achieve the optimal pickling quality.

[0055] Further, the deviation value activates the feedback adjustment means of the corresponding response level within different threshold ranges. The larger the deviation value, the higher the response level of the activated feedback adjustment means.

[0056] Exemplarily, the selection of the feedback adjustment means corresponding to the response level pi is related to the size of the deviation value δ. When the deviation value δ is large, the feedback adjustment means corresponding to the high response level pi is preferentially activated.

[0057] Further, the deviation value is divided into 5 intervals according to size, respectively corresponding to 5 feedback adjustment means, specifically as follows:

[0058] When 0 < δ < X1, activate the acid temperature adjustment to adjust the acid temperature, that is, adjust the current acid temperature to the acid temperature target value.

[0059] When X1 < δ < X2, start the acid concentration adjustment to adjust the acid concentration, that is, adjust the current acid concentration to the target acid concentration value.

[0060] When X2 < δ < X3, start the accelerator dosage adjustment to adjust the accelerator dosage, that is, adjust the current accelerator dosage to the target accelerator dosage value.

[0061] When X3 < δ < X4, start the inlet plate heating power adjustment to adjust the inlet plate heating power, that is, adjust the current inlet plate heating power to the target inlet plate heating power value.

[0062] When X4 < δ < X5, start the shot blasting motor power adjustment to adjust the shot blasting motor power, that is, adjust the current shot blasting motor power to the target shot blasting motor power value.

[0063] Where X1 < X2 < X3 < X4 < X5, and X1, X2, X3, X4, X5 are all positive numbers.

[0064] The quantitative feedback adjustment method of the present invention can accelerate the cleaning speed of products, reduce the energy consumption and acid consumption of equipment, and thus obtain products with high surface quality. By setting up an acid pickling quality quantitative evaluation module and using the deviation value to promptly start feedback adjustment means at different response levels, the present invention can achieve the beneficial effects of efficiently judging acid pickling quality, stabilizing strip steel quality, and reducing acid pickling energy consumption. The feedback adjustment means of the present invention is easy to control, can respond faster to shot blasting, plate surface heating and accelerators, and can also weaken parameters such as acid concentration and acid temperature. By systematically allocating descaling capabilities such as shot blasting and acid pickling, the present invention helps to achieve low energy consumption and high cleaning quality.

[0065] Based on the same inventive concept, the present invention discloses an acid pickling quality quantitative feedback adjustment system, including:

[0066] An acid pickling quality quantitative evaluation module, which obtains the acid pickling quality quantitative evaluation deviation by constructing an acid pickling quality quantitative evaluation model, and starts the corresponding response-level feedback adjustment means according to the acid pickling quality quantitative evaluation deviation for acid pickling quality quantitative feedback adjustment.

[0067] The embodiments of the system can be correspondingly implemented one by one with the foregoing method embodiments, and will not be elaborated herein.

[0068] As shown in Figures 2 and 3, the present invention also discloses an acid pickling adjustment system, including:

[0069] An acid pickling quality quantitative evaluation module, which is used to obtain the acid pickling quality by using any one of the above acid pickling quality quantitative feedback adjustment methods and feedback it to the PLC;

[0070] PLC, used to receive the pickling quality feedback from the pickling quality quantitative evaluation module and calculate the torque of the motor based on it;

[0071] an electric motor for adjusting the speed according to the torque;

[0072] The PC is used to adjust the relevant parameters of the PLC through the neural network model based on the pickling quality feedback from the pickling quality quantitative evaluation module and the preset pickling quality, and send them to the PLC to achieve pickling adjustment.

[0073] For example, in actual application, after the steel strip undergoes mechanical descaling and plate surface heating before entering the pickling tank, it undergoes multi-stage rinsing and then hot air drying and coiling. An image data acquisition module, a visualization module, and a pickling quality quantitative evaluation module are provided after the steel strip undergoes mechanical descaling and pickling. During the pickling process of the steel strip, the image data of the steel strip is collected by the image data acquisition module (e.g., a camera device) and visualized by the visualization module. The cleaning quality of the steel strip is then evaluated and scored by the pickling quality quantitative evaluation module. In addition, the present invention also has a feedback adjustment function for neural network self-learning. After multiple training and learning, the pickling adjustment system can quantitatively adjust the pickling quality of the steel strip.

[0074] The pickling control system, based on a quantitative pickling quality evaluation module, implements feedback control, boasts a high degree of digitization and enables quantitative control. This system offers shorter strip speed control response times than traditional PID control, better real-time performance, and less overshoot. Furthermore, PID controller parameters can be adjusted based on operating conditions, achieving optimal control results.

[0075] In one embodiment, the training samples of the neural network model on the PC are collected from the PLC, and the samples include r(k), y(k), K P , K I and K D , where r(k) is the preset pickling quality at the kth iteration, y(k) is the pickling quality fed back by the pickling quality quantitative evaluation module at the kth iteration, K P , K I , K D They are the adjustable parameters P, I, and D of the PLC’s PID controller, as shown in Figure 4.

[0076] In another embodiment, the PC collects training samples through OPC Client communicating with OPC Server, and OPC Server communicating with PLC. After obtaining the samples, the samples are stored in a database of the PC for training the neural network model.

[0077] Furthermore, the neural network model is trained and learned using the BP algorithm, wherein the performance indicator function is: Among them, r(k) is the preset pickling quality at the kth iteration, and y(k) is the pickling quality feedback from the pickling quality quantitative evaluation module at the kth iteration.

[0078] Preferably, the number of neurons in the input layer, hidden layer and output layer of the neural network model are 2, 6 and 3 respectively.

[0079] Preferably, the number of neurons in the hidden layer of the neural network model can be adjusted according to actual needs, and the connection relationship between each neuron corresponds to a different weighting coefficient that can be continuously adjusted and updated.

[0080] On the one hand, the activation function of the hidden layer is:

[0081] The hidden layer may also use other activation functions.

[0082] On the other hand, the activation function of the output layer is:

[0083] The output layer may also use other activation functions.

[0084] In one embodiment, the weighted coefficients of the neural network model are adjusted and updated using a gradient descent method.

[0085] Furthermore, the step of adjusting and updating the weighted coefficients of the neural network model using the gradient descent method specifically includes:

[0086] S1. Initialize the weighted coefficients of each layer of the neural network model, and set the number of iterations of the neural network model k = 1;

[0087] S2. Calculate the error between the input value and the output value of the input layer. If the error is less than the first threshold, execute S3; otherwise, optimize the input value and the output value of the input layer so that the error is less than the first threshold.

[0088] S3. Calculate the input and output of each layer of neurons in the neural network model, wherein the output value of the output layer of the neural network model is an adjustable parameter of the PID controller of the PLC;

[0089] S4. The output value of the PID controller is calculated based on the adjustable parameters of the PLC PID controller;

[0090] S5. Perform neural network model learning and adjust weighting coefficients to achieve adaptive adjustment of adjustable parameters of the PID controller;

[0091] S6. If the adjustable parameter of the PID controller is greater than the second threshold, the neural network model training is completed; otherwise, the neural network model iteration number k=k+1 is set, and the process returns to S2.

[0092] As shown in Figure 5, the sampling period is set to 1 second, meaning the pickling control system calls the neural network model every 1 second. Once the neural network model is trained, the pickling control system can effectively control the acid solution quality. When encountering disturbances or parameter changes, the pickling control system quickly readjusts the parameter values ​​to achieve optimal control. This invention accelerates cleaning speed, reduces energy and acid consumption, and ultimately produces strip steel with high surface cleaning quality.

[0093] Based on the same inventive concept, the present invention also discloses a pickling adjustment method, comprising:

[0094] The pickling quality quantitative evaluation module adopts any of the above pickling quality quantitative feedback adjustment methods to obtain the pickling quality and feed it back to the PLC;

[0095] The PLC receives the pickling quality feedback from the pickling quality quantitative evaluation module and calculates the torque of the motor accordingly;

[0096] The motor adjusts the speed of the strip according to the torque;

[0097] Based on the pickling quality feedback from the pickling quality quantitative evaluation module and the preset pickling quality, the PC adjusts the relevant parameters of the PLC through the neural network model and sends them to the PLC to achieve pickling adjustment.

[0098] The embodiments of the pickling adjustment method and the embodiments of the aforementioned pickling adjustment system can be implemented one by one, and will not be described in detail here.

[0099] As shown in Figure 6, another strip pickling system provided by an embodiment of the present invention includes a uncoiler 1, a pre-cleaning unit, a multi-stage pickling unit 8 and a final rinsing unit connected in sequence, and the pickling unit 8 includes a pickling tank 81, an intermediate rinsing tank 82 and a strip heating device 83 connected in sequence.

[0100] In one embodiment, as shown in Figure 6 , an annealing furnace 3 is positioned between the uncoiling device 1 and the pre-cleaning unit to selectively anneal the steel strip. A looper 2 can be positioned between the uncoiling device 1 and the annealing furnace 3. Furthermore, a heat preservation chamber 4 is positioned at the exit of the annealing furnace 3 to insulate the steel strip exiting the annealing furnace 3, ensuring that it enters the pre-cleaning unit at a relatively high temperature.

[0101] In one embodiment, as shown in FIG6 , a preheater 5 is disposed between the unwinding device 1 and the pre-cleaning unit to heat the steel strip so that the steel strip enters the pre-cleaning unit at a relatively high temperature. The preheater 5 includes, but is not limited to, a flame heating device.

[0102] Preferably, a temperature measuring instrument 6 is arranged at the strip outlet of the pre-heater 5 to measure the temperature of the outlet strip, which can guide the operation of the pre-heater 5 and save energy when heating the strip to a preset temperature.

[0103] It is preferred that an annealing furnace 3 and a preheater 5 are arranged simultaneously, and the annealing furnace 3 and the preheater 5 are arranged in sequence along the running direction of the strip.

[0104] It is preferred that the temperature of the strip entering the pre-cleaning unit is higher than 100°C, so that a strong physical and chemical reaction can occur on the contact surface of the high-temperature strip and the low-temperature acid solution to quickly pickle and peel off the coarse iron oxide scale on the surface.

[0105] In one embodiment, as shown in FIG6 , the pre-cleaning unit includes a pre-cleaning tank 7 and may further be equipped with a pre-cleaning tank to realize the circulation of the pre-cleaning liquid. Preferably, the pickling medium of the pre-pickling tank 81 is HCl, the concentration of HCl is 20 to 60 g / L, and the pickling medium contains Fe 2+ and Fe 3+ .

[0106] In one embodiment, the pickling medium in the pickling tank 81 is HCl, the concentration of HCl is 20-150 g / L, and the pickling medium contains Fe 2+ and Fe 3+ .

[0107] Preferably, as shown in FIG6 , each pickling tank 81 is respectively provided with a circulating acid tank 84 to achieve recycling of the pickling solution and ensure the concentration of the acid solution in the pickling tank 81 .

[0108] In one embodiment, the acid solution in each of the circulating acid tanks 84 is overflowed in a step-by-step manner through a connecting pipe, and the acid solution concentration increases successively (from the first-stage pickling unit 8 to the final-stage pickling unit 8), the total iron content decreases successively, and the oxidizing property of the acid solution in each circulating acid tank 84 is gradually improved. Preferably, the acid solution in the second-stage pickling unit 8 is a reducing system, and the pickling medium in the final-stage pickling unit 8 is a strongly oxidizing system, which can ensure the pickling quality of the strip.

[0109] In one embodiment, the strip heating device 83 adopts a flame heating device or a hot air drying device, and the heat can be directly applied to the strip, eliminating acid heating devices such as graphite heat exchangers that are prone to clogging and have low heat exchange efficiency; the strip temperature rises quickly, and the unit can be started directly and put into production in a short time, avoiding the situation where the main acid liquid of conventional indirect heating is reheated to the strip.

[0110] Preferably, a temperature measuring instrument 6 is arranged at the strip outlet of the strip heating device 83 to measure the temperature of the outlet strip, which can guide the operation of the strip heating device 83 and save energy when heating the strip to a preset temperature.

[0111] In one embodiment, the strip heating device 83 is used to heat the strip to a temperature of 60-170° C., ensuring that a high pickling rate is always maintained at the strip interface.

[0112] Preferably, in each stage of the pickling unit 8, the number of intermediate rinsing tanks 82 is 1 to 3.

[0113] In this embodiment, a modular pickling tank 81, an intermediate rinsing tank 82, and a strip heating device 83 are used. The multi-stage pickling unit 8 peels off the iron oxide scale layer by layer. The iron oxide scale removed by pickling in the pickling tank can be promptly rinsed off in the intermediate rinsing tank 82, exposing the underlying iron oxide scale for continued pickling, thereby significantly improving the pickling efficiency. The strip heating device 83 can increase or maintain the temperature of the strip, preventing the acid solution or strip temperature from decreasing, allowing the strip to continuously maintain a high temperature, and maintaining a high pickling rate at the strip interface, thereby ensuring pickling efficiency and pickling quality. In addition, heat can be directly applied to the strip, eliminating acid heating devices such as graphite heat exchangers that are prone to clogging and have low heat exchange efficiency. The strip temperature rises rapidly, and the unit can be started directly and put into production in a short time, avoiding the situation where the acid solution in conventional indirect heating is reheated to the strip.

[0114] The modular pickling unit 8 offers high production flexibility, allowing for selective opening and closing of multiple pickling units 8 based on the difficulty of pickling the strip. For example, the presence of acid in the pickling tank 81 can be controlled by a tank vent valve and an acid pump. When the pickling tank 81 is empty, the strip can pass through empty, effectively placing the pickling unit 8 in a closed / standby state.

[0115] FIG6 shows only two-stage pickling units 8 , but the present invention is not limited thereto. The number of pickling units 8 may be increased or decreased according to actual working conditions. For example, space for arranging the pickling units 8 may be reserved.

[0116] In one embodiment, the final rinse unit includes multiple stages of final rinse tanks 9 connected in series, each stage equipped with a rinse tank 91. Each final rinse tank 9 / rinsing tank 91 is equipped with a pH meter and conductivity meter. Preferably, the multiple stages of final rinse tanks 9 are connected by pumps to achieve cascaded rinsing water utilization. Optionally, the rinse water pressure in the first few stages is 2-6 kg, and the rinse water pressure in the final stage is 5-10 kg.

[0117] In addition, there are multiple pickling units 8, and the rinsing water of the intermediate rinsing tank 82 of each pickling unit 8 comes from the rinsing water of the intermediate rinsing tank 82 of the subsequent pickling unit 8, and the rinsing water of the intermediate rinsing tank 82 of the final pickling unit 8 preferably comes from the rinsing tank 91 of the final rinsing unit, thereby realizing multi-stage recycling of rinsing water.

[0118] In one embodiment, as shown in FIG6 , a drying device 10 is arranged at the outlet of the final rinsing unit to facilitate drying and coiling the strip or sending it to the downstream process. The drying device includes but is not limited to a hot air drying device.

[0119] In one embodiment, as shown in FIG6 , the strip pickling system further comprises an oxidant generator 11 , which is connected to each of the circulating acid tanks 84 , and is used to add oxidant to each of the circulating acid tanks 84 to regulate the Fe 2+ 、Fe 3+ Content and ratio.

[0120] Furthermore, each circulating acid tank 84 is respectively equipped with an oxidation potential detection, which determines the redox characteristics of the pickling medium by detecting the redox potential of the acid solution in each pickling tank 81, and thereby controls whether to add an oxidant to the pickling tank 81 and the amount of the oxidant added, so as to realize the redox characteristics of the pickling medium in each pickling tank 81, thereby ensuring the effect and consistency of strip cleaning.

[0121] In one embodiment, an image acquisition module is provided at the strip outlet of each pickling unit 8 for acquiring strip surface images to facilitate pickling quality evaluation, thereby better controlling and optimizing the pickling process.

[0122] Preferably, an image acquisition module is provided at the strip outlet of each pickling unit 8 for collecting the surface image of the strip; thereby, the pickling quality of each stage of pickling unit 8 can be evaluated separately, which facilitates the control of the pickling process of each stage of pickling unit 8 separately; in addition, according to the pickling quality at the outlet side of the final pickling unit 8, in addition to being used to evaluate the final pickling unit 8, the overall pickling quality of the system can also be evaluated.

[0123] During the strip pickling process, the image data acquisition module (e.g., a camera) captures the strip image data and visualizes it using the visualization module. The pickling quality quantitative evaluation module then evaluates and scores the strip cleaning quality. Furthermore, the system features a neural network self-learning feedback control function that, after repeated training, can quantitatively adjust the strip pickling quality.

[0124] In this embodiment, feedback control is implemented based on a quantitative pickling quality evaluation module, achieving a high degree of digitization and enabling quantitative adjustment. This embodiment achieves shorter strip speed control response times than traditional PID control, better real-time performance, and less overshoot. Furthermore, PID controller parameters can be adjusted based on operating conditions, achieving optimal control results.

[0125] In addition, in addition to adjusting the motor torque to accelerate or decelerate the strip, the adjustment of the pickling process may also include at least one of the following parameters: strip temperature at the inlet side of the pickling unit, acid concentration, and redox characteristics of the acid.

[0126] Furthermore, the acid tank 84 is equipped with an iron sludge processing subsystem for online cleaning of iron sludge impurities in the acid tank 84, which can improve the operational stability and reliability of the pickling system and reduce the downtime and frequency of desilting.

[0127] As shown in Figures 7 and 8, the iron sludge processing subsystem includes an intermediate medium circulation mechanism and an iron sludge recovery mechanism. The intermediate medium circulation mechanism includes a plurality of intermediate media 330 capable of extracting iron sludge from the bottom of the acid tank 84, and a medium conveying unit 331, a medium transfer unit 332 and a medium reflux unit 333 connected in sequence. The medium conveying unit 331 is connected to the intermediate medium outlet of the acid tank 84, and the medium reflux unit 333 is connected to the intermediate medium inlet of the acid tank 84; the iron sludge recovery mechanism includes a flushing unit arranged above the medium transfer unit 332 and an iron sludge collection box 321 arranged below the medium transfer unit 332.

[0128] In one embodiment, the intermediate medium 330 includes steel balls for entraining iron sludge, which facilitates the removal of iron sludge from the bottom of the acid tank 84. The iron sludge at the bottom of the acid tank 84 is entrained by the cascading flow of steel balls and carried out of the acid tank 84 by the medium conveying unit 331. The surface roughness of the steel balls is designed to enhance the iron sludge entrainment. In one embodiment, the surface roughness of the steel balls is Ra ≥ 0.8 μm, and more preferably, Ra ≤ 12 μm.

[0129] In one embodiment, as shown in FIG7 , a slope is provided at the bottom of the acid tank 84 , and the slope slopes from the intermediate medium inlet to the intermediate medium outlet, so as to facilitate the circulation of the intermediate medium 330 in the acid tank 84 . For example, the medium steel balls can rely on gravity to move from the intermediate medium inlet to the intermediate medium outlet, and the medium steel balls at a higher position exert an extrusion and driving effect on the medium steel balls at a lower position and the iron mud on the slope. Based on the circulation of the medium steel balls, the bottom of the acid tank 84 is ensured to be in motion at all times, which can reduce the accumulation of iron mud and thus save the intervention of power equipment. At the same time, the slope design is also conducive to the deposition of iron mud at the intermediate medium outlet, thereby facilitating the intermediate medium 330 to bring the iron mud out.

[0130] In one embodiment, the medium conveying unit 331 adopts a screw pump or a screw conveyor. According to the relative position relationship between the intermediate medium outlet and the medium transfer unit 332, the screw pump or the screw conveyor can be arranged tilted or horizontally.

[0131] In one embodiment, as shown in FIG7 and FIG8 , the medium transfer unit 332 is a chain conveying unit, such as a chain plate conveyor or a drag chain conveyor. Accordingly, the medium transfer unit 332 includes an upper chain layer 3321 and a lower chain layer 3322 .

[0132] The gap between the chain plates of the chain conveying unit is smaller than the size of the intermediate medium 330 , for example, smaller than the diameter of the medium steel ball.

[0133] Among them, the medium conveying unit 331 is connected with the upper chain layer 3321. For example, the medium output port of the medium conveying unit 331 is located directly above the upper chain layer 3321, and the intermediate medium 330 can be conveyed to the upper chain layer 3321; optionally, a hopper is arranged above the upper chain layer 3321, and the intermediate medium 330 output by the medium conveying unit 331 is received by the hopper and transferred to the upper chain layer 3321, which can avoid the intermediate medium 330 from popping out of the upper chain layer 3321 due to excessive falling distance.

[0134] The medium return unit 333 is arranged at the outlet side of the chain conveyor unit. Optionally, the medium return unit 333 uses a conveyor roller to transport the cleaned intermediate medium 330 back to the acid tank 84.

[0135] The flushing unit is used to flush the intermediate medium 330 on the medium transfer unit 332, thereby separating the iron sludge from the intermediate medium 330. In one embodiment, as shown in Figure 8, the flushing unit includes a flushing pipe 351. At least one set of spray structures can be arranged at the bottom of the flushing pipe 351. When there are multiple sets of spray structures, the spray structures are arranged sequentially along the conveying direction of the intermediate medium 330. Each spray structure includes at least one nozzle. When there are multiple nozzles in the spray structure, the nozzles in the spray structures are preferably arranged sequentially along the width of the medium transfer unit 332.

[0136] 8 , the flushing unit further includes a flushing liquid supply pipe 352 connected to the flushing pipe 351 for supplying flushing liquid. Preferably, surface water from the acid tank 84 is used as the flushing liquid, and accordingly, the flushing liquid supply pipe 352 is connected to the upper portion of the acid tank 84.

[0137] The flushing liquid can exit through both sides of the medium transfer unit 332, and / or, if the medium transfer unit 332 is a hollow conveying device, for example, it can exit through the gaps between the chain plates of the chain conveyor unit. In one embodiment, as shown in Figures 7 and 9, the iron sludge recovery mechanism also includes a drainage unit 322, which is arranged between the upper chain layer 3321 and the lower chain layer 3322 of the medium transfer unit 332. The top inlet of the drainage unit 322 is located directly below the flushing unit, and the bottom outlet of the drainage unit 322 is located directly above the iron sludge collection box 321. Based on this design, the flushing liquid can be reliably drained into the iron sludge collection box 321, making the on-site environment cleaner. At the same time, it prevents flushing water carrying iron sludge from contaminating the lower chain layer 3322, thereby improving the operating reliability of the medium transfer unit 332 and reducing its maintenance frequency.

[0138] Preferably, as shown in Figures 7 and 9, the drainage unit 322 is in an inverted Y-shaped structure, forming one drainage inlet pipe and two drainage outlet pipes; the two drainage outlet pipes can ensure the drainage efficiency and effect of the flushing fluid on the one hand, and on the other hand, it is also convenient for the arrangement of the lower chain layer 3322, for example, the lower chain layer 3322 is located between the two drainage outlet pipes.

[0139] The upper chain layer 3321 may be arranged in the drainage inlet pipe, so as to better capture the intermediate medium 330 and iron mud splashed by the high-pressure jet.

[0140] Preferably, as shown in Figure 9, the above-mentioned drainage unit 322 is connected to the iron mud collection box 321 to form an integrated structure. For example, for the above-mentioned inverted Y-shaped drainage unit 322, its outer frame 3221 is integrally formed with the iron mud collection box 321 to form a top-closed box body, and an inverted V-shaped mudguard 3222 is arranged in the box body, which correspondingly constitutes the inner frame of the drainage unit 322.

[0141] In one embodiment, a protective net 323 is further arranged around the upper chain layer 3321 of the medium transfer unit 332. The protective area of ​​the protective net 323 at least covers the flushing area of ​​the upper chain layer 3321. The provision of the protective net 323 can prevent the high-pressure jet from spraying the intermediate medium 330 out of the medium transfer unit 332.

[0142] Among them, the protective net 323 can provide side protection. Optionally, the protective net 323 includes two side mesh panels 3231, and the two side mesh panels 3231 are arranged on both sides of the conveying channel of the medium transfer unit 332; the side mesh panels 3231 preferably do not move together with the medium transfer unit 332, for example, they are installed through mesh panel brackets. For the above-mentioned solution with a drainage unit 322, the side mesh panels 3231 can also be installed on the outer frame 3221 of the drainage unit 322.

[0143] And / or, the protective net 323 can provide upper protection. Optionally, the protective net 323 includes a top mesh panel 3232, which is installed above the medium transfer unit 332; the top mesh panel 3232 preferably does not move with the medium transfer unit 332, and its installation method can refer to the installation method of the side mesh panel 3231.

[0144] The iron sludge processing subsystem is further optimized. As shown in FIG7 and FIG8 , the iron sludge recovery mechanism further includes a filtering unit, and the iron sludge collection box 321 is provided with a flushing liquid recovery pipe connected to the filtering unit.

[0145] Alternatively, the filtrate produced by the filter unit can be reused as a flushing liquid. For example, the filtrate outlet pipe of the filter unit is connected to a flushing liquid storage tank, and the flushing liquid supply pipe 352 is also connected to the flushing liquid storage tank. When the flushing liquid is the surface water of the acid tank 84, the filtrate produced by the filter unit can be refluxed into the acid tank 84. Accordingly, the filtrate outlet pipe of the filter unit is connected to the acid tank 84.

[0146] The iron sludge collection box 321 can control the direction of the flushing liquid by overflowing, and the flushing liquid recovery pipe is connected to the overflow level of the iron sludge collection box 321. Heavier impurities are deposited at the bottom of the iron sludge collection box 321 and can be cleaned regularly or irregularly.

[0147] In one embodiment, the filtering unit includes an electromagnetic filter 100 for removing ferromagnetic impurities in the flushing liquid, which can reliably adsorb and remove the ferromagnetic impurities suspended in the flushing liquid.

[0148] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A quantitative feedback adjustment method for pickling quality, characterized in that: A pickling quality quantitative evaluation module is set in the pickling system. The pickling quality quantitative evaluation module obtains the pickling quality quantitative evaluation deviation by constructing a pickling quality quantitative evaluation model, and starts the feedback adjustment means of the corresponding response level according to the pickling quality quantitative evaluation deviation for pickling quality quantitative feedback adjustment.

2. A pickling quality quantitative feedback adjustment method according to claim 1, characterized in that: The pickling quality quantitative evaluation module constructs a pickling quality quantitative evaluation model based on the AHP method.

3. A pickling quality quantitative feedback adjustment method as claimed in claim 2, characterized in that: The pickling quality quantitative evaluation model uses the deviation value between the target value and the current quantitative result as an index to measure the cold rolling pickling quality.

4. A pickling quality quantitative feedback adjustment method as claimed in claim 3, characterized in that: Set corresponding index weights according to the pickling defect level.

5. A pickling quality quantitative feedback adjustment method as claimed in claim 4, characterized in that: The setting of the corresponding index weights according to the pickling defect level specifically includes: according to at least one defect data among the defect area, defect length, defect perimeter, defect area aspect ratio, defect shape factor, and defect gray feature value, constructing a defect judgment matrix and solving the index weights corresponding to the pickling defect level.

6. A pickling quality quantitative feedback adjustment method as claimed in claim 3, characterized in that: The response levels are divided into 5 levels, from high to low in order: shot blasting motor power, inlet plate surface heating power, accelerator dosage, acid concentration, acid temperature, and each response level corresponds to its own adjustment limit.

7. A pickling quality quantitative feedback adjustment method according to claim 6, characterized in that: The deviation value starts the feedback adjustment means of the corresponding response level within different threshold ranges. The larger the deviation value, the higher the response level of the started feedback adjustment means.

8. A pickling quality quantitative feedback adjustment method as claimed in claim 7, characterized in that: The deviation value is divided into 5 intervals according to its size, corresponding to 5 feedback adjustment means respectively, specifically as follows: When 0 < δ < X1, start the acid temperature adjustment to adjust the acid temperature; When X1 < δ < X2, start the acid concentration adjustment to adjust the acid concentration; When X2 < δ < X3, start the accelerator dosage adjustment to adjust the accelerator dosage; When X3 < δ < X4, start the inlet plate surface heating power adjustment to adjust the inlet plate surface heating power; When X4 < δ < X5, start the shot blasting motor power adjustment to adjust the shot blasting motor power; Among them, X1 < X2 < X3 < X4 < X5, and X1, X2, X3, X4, X5 are all positive numbers.

9. A pickling quality quantitative feedback control system, characterized in that: Include: A pickling quality quantitative evaluation module. The pickling quality quantitative evaluation module obtains the pickling quality quantitative evaluation deviation by constructing a pickling quality quantitative evaluation model, and starts the feedback adjustment means of the corresponding response level according to the pickling quality quantitative evaluation deviation for pickling quality quantitative feedback adjustment.

10. A pickling adjustment system, characterized in that: Include: A pickling quality quantitative evaluation module, which is used to obtain the pickling quality by using any one of the pickling quality quantitative feedback adjustment methods described in claims 1-8 and feedback it to the PLC; The PLC is used to receive the pickling quality fed back by the pickling quality quantitative evaluation module and calculate the torque of the motor accordingly; The motor is used to adjust the speed according to the torque; The PC is used to adjust the relevant parameters of the PLC through a neural network model according to the pickling quality fed back by the pickling quality quantitative evaluation module and the preset pickling quality, and send them to the PLC to achieve pickling adjustment.

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