Argon closed-loop control method

Abstract

The invention provides an argon closed-loop control method, and relates to the technical field of argon control, and the argon closed-loop control method comprises the following steps: collecting image data of the surface of molten steel in real time; pretreatment: identifying the boundary of the molten steel surface and determining the exposed area of the molten steel; establishing a relation model between the molten steel exposed area and the bottom blowing argon flow; calculating an argon flow adjustment value required for reaching the target exposed area based on the relation model; the molten steel surface change is monitored in real time, and the fourth step is repeated. According to the method, flexible response can be achieved under different production conditions, optimal control over the exposed area of the molten steel is achieved, the situation of excessive or insufficient argon blowing is avoided, the refining effect of the molten steel is improved, and then the quality of the molten steel and the production efficiency are improved.

Description

Technical Field

[0001] The present application relates to the technical field of refined argon gas control, and particularly to an argon gas closed-loop control method. Background Art

[0002] The bottom blowing argon technology is one of the common methods used in modern steel metallurgy to improve the quality of molten steel, remove harmful elements, and adjust the temperature of molten steel. The bottom blowing argon gas contacts the molten steel through the argon blowing device at the bottom of the ladle, forming turbulence and bubbles, promoting the refining reactions such as mixing, deoxidation, and desulfurization of the molten steel, and enhancing the contact area between the molten steel and argon gas, thereby improving the purity of the molten steel. In the traditional bottom blowing argon process, the control of the exposed area of the molten steel is crucial for the quality of the molten steel and the refining effect.

[0003] In the prior art, there are mainly two ways to control the exposed area of the molten steel. One is to rely on manual judgment and empirical adjustment, which results in low control accuracy and is easily affected by human factors. There are problems such as difficulty in quantifying the process standards and lack of standardization in the operation process, leading to relatively arbitrary control of the exposed area of the molten steel and making it difficult to achieve precise adjustment and control. The other is a method of setting the stirring kinetic energy of the molten steel and then converting it into the appropriate flow rate result of the bottom blowing argon gas in the ladle for control. To a certain extent, the experience of the operator is made explicit. Compared with the first method, the entire process of adjusting the argon gas flow rate is easier to control, but it has a high dependence on the stability of the on-site process parameters and is easily affected by various factors such as the quality of the molten steel and the steel grade. Once the relevant process parameters cannot be accurately obtained, the flow rate of the argon gas cannot be accurately obtained either. Summary of the Invention

[0004] The present application is made in view of the above problems, and its purpose is to provide an argon gas closed-loop control method. By visually detecting and real-time monitoring the exposed area of the molten steel surface, it can accurately capture and calculate the change of the exposed area of the molten steel, thereby realizing the precise adjustment of the bottom blowing argon gas flow rate, avoiding errors in manual operation. By real-time adjusting the argon gas flow rate, the present invention can flexibly respond under different production conditions, achieve the optimal control of the exposed area of the molten steel, avoid the situation of excessive or insufficient argon blowing, improve the refining effect of the molten steel, and further improve the quality of the molten steel and production efficiency.

[0005] Specifically, the first aspect of the present application provides an argon gas closed-loop control method, including the following steps:

[0006] Step 1: Real-time collect the image data of the molten steel surface;

[0007] Install a high-precision industrial camera above or on the side of the ladle to real-time collect the image data of the molten steel surface. The camera needs to be selected to be able to adapt to the high-temperature environment and can clearly capture the image of the molten steel surface.

[0008] Step 2: Preprocess the collected image data, identify the boundary on the molten steel surface through image recognition technology, and determine the exposed area of the molten steel;

[0009] Preprocessing the image data can remove noise, distortion, and other irrelevant information in the image, thereby improving the clarity and quality of the image.

[0010] Step 3: Based on historical process data, establish a relationship model between the exposed area of the molten steel and the flow rate of bottom-blown argon gas, and obtain the dynamic relationship between the argon gas flow rate and the exposed area;

[0011] The relationship model is established based on historical process data, and the model verification method includes:

[0012] Step 3.1: Calibrate the permeability of a ladle with normal permeability, set the argon gas flow rate F0 of the ladle and record the argon gas pressure P0 at this time;

[0013] Step 3.2: After the new ladle enters the station and breaks the slag, set the flow rate F0, record the pressure P. If P0 - P < 0.5, it is considered that the permeability is good; if 0.5 ≤ P0 - P2 ≤ 2, it is considered that the permeability is average; if P0 - P > 2, it is considered that the permeability is poor;

[0014] Step 3.3: Establish 3 sets of basic parameters, divide the basic setting flow rate of argon blowing into 3 groups, and the three groups of setting flow rates are F1, F2, and F3. When the permeability of the new ladle is good, use the basic parameter F1; when the permeability is average, use the parameter F2; when the permeability is poor, use the parameter F3;

[0015] According to the permeability calibration data of the ladle, select the corresponding basic argon blowing flow rate, record the actual measurement data of argon blowing for ladles under different process conditions, and use it to verify the corresponding relationship between the argon gas flow rate and the exposed area under different process conditions.

[0016] Step 4: Based on the relationship model, calculate the adjustment value of the argon gas flow rate required to reach the target exposed area according to the current exposed area of the molten steel, and send a control signal through the PID algorithm to automatically adjust the argon gas flow rate;

[0017] Step 5: Monitor the change of the molten steel surface in real time, and repeat Step 4 until the argon blowing ends or the exposed area of the molten steel deviates from the target value beyond the preset range.

[0018] Furthermore, the preprocessing includes denoising and contrast enhancement.

[0019] Furthermore, the denoising is specifically to use the sliding filtering method for denoising, and the specific implementation steps are:

[0020] Step 2.1: Define a filtering window to slide and cover each pixel of the image;

[0021] Step 2.2: The sliding filter window starts from the upper left corner of the image and gradually slides to the lower right corner;

[0022] Step 2.3: Modify the pixel value at the center of the window using mean filtering or median filtering.

[0023] Sliding filter denoising is a technique for processing images by sliding a window, mainly used to reduce noise in images and can stably reduce the random fluctuations of data.

[0024] Further, the contrast enhancement is specifically achieved by using histogram equalization, and the specific implementation steps are as follows:

[0025] Step 4.1: Calculate the histogram of the image, that is, the frequency of occurrence of each pixel brightness value;

[0026] Step 4.2: Calculate the cumulative distribution function according to the histogram;

[0027] Step 4.3: Remap each pixel value in the image through the cumulative distribution function to balance the brightness distribution.

[0028] Histogram equalization is a technique for improving image contrast. By adjusting the gray distribution of the image, it enhances the details and clarity of the image. By transforming the histogram of the original image into a uniform histogram, it expands the dynamic range of pixel values, makes the gray levels of the image more moderate, makes the gray distribution of the image more uniform, and thus improves the overall quality of the image.

[0029] Further, the identification of the boundary of the molten steel surface specifically includes: identifying the edge pixels in the molten steel surface image through the Canny edge detection algorithm and forming a complete boundary by connecting the edge pixels.

[0030] The Canny edge detection algorithm aims to retain key information by reducing the scale of image data, improve the accuracy of edge detection, and reduce the influence of noise at the same time. First, calculate the gradient of each pixel of the image after sliding filtering to find the strong edges of the image, then suppress the non-edge pixels and only retain the strongest edge pixels. Finally, by setting high and low thresholds, determine which pixels are edges and which are not, and form a complete boundary by connecting the edge pixels.

[0031] Further, the determination of the exposed area of the molten steel specifically includes: traversing the exposed area of the molten steel extracted according to the molten steel boundary, counting the number of pixels in this area, and calculating the area of the exposed area based on the conversion of pixel count and the physical size of the image. The formula is as follows:

[0032] S = N × S1;

[0033] Where: S is the area of the exposed area of the molten steel;

[0034] N is the number of pixels in the exposed area;

[0035] S1 is the actual physical area of each pixel.

[0036] Further, the relationship model calculates the adjustment value of the argon flow rate required to reach the target exposed area based on the current exposed area of the molten steel, specifically: based on the corresponding relationship between the exposed area of the molten steel and the argon flow rate in the relationship model, the required adjustment value of the argon flow rate is obtained according to the difference between the argon flow rate corresponding to the target exposed area and the argon flow rate corresponding to the current exposed area of the molten steel.

[0037] Further, the calculation formula of the PID algorithm is as follows:

[0038]

[0039] Where: K p is the proportionality coefficient;

[0040] K i is the integral coefficient;

[0041] K d is the differential coefficient;

[0042] M n is the control quantity at the nth moment;

[0043] e n is the argon flow rate error at the nth moment;

[0044] e n-1 is the argon flow rate error at the (n - 1)th moment;

[0045] K p e n is the proportional operation;

[0046] is the integral operation;

[0047] K d (e n - e n-1 ) is the differential operation.

[0048] Further, the deviation of the exposed area of the molten steel from the target value exceeds the preset range, specifically: the deviation between the exposed area of the molten steel and the target value exceeds ±200 mm 2 .

[0049] Further, when the deviation of the exposed area of the molten steel from the target value exceeds the preset range occurs, the system automatically alarms and starts the emergency adjustment mode, automatically sets it to the starting flow rate of soft blowing and exits the soft blowing automatic control mode, reminding that manual intervention is required for adjustment.

[0050] In a second aspect, the present application further provides a computing device, which has the function of implementing the method described in the first aspect above. The beneficial effects can be referred to the description of the first aspect and will not be elaborated here. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions. In a possible design, the structure of the device includes an acquisition module and a training module. Optionally, it may further include a construction module. These modules can implement the functions of training nodes in the method example of the first aspect above. For specific details, please refer to the detailed description in the method example and will not be elaborated here.

[0051] In a third aspect, the present application further provides a computing device, which is used to implement the function of the method described in the first aspect above. The beneficial effects can be referred to the description of the first aspect and will not be elaborated here. The structure of the computing device includes a processor and a memory. The memory is used to store instructions and / or data. The memory is coupled to the processor. When the processor executes the program instructions stored in the memory, it can implement the functions of training nodes in the example of the first aspect above. The structure of the computing device further includes a communication interface for communicating with other devices.

[0052] In a fourth aspect, the present application further provides a computer-readable storage medium, in which instructions are stored. When it runs on a computer, it causes the computer to execute the methods in the first aspect and all possible designs of the first aspect.

[0053] In a fifth aspect, the present application further provides a computer program product containing instructions. When it runs on a computer, it causes the computer to execute the methods in the first aspect and all possible designs of the first aspect.

[0054] In a sixth aspect, the present application further provides a computing chip. The chip is connected to a memory. The chip is used to read and execute the software program stored in the memory and execute the methods in the first aspect and all possible implementation manners of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present drawings or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present drawings. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0056] Figure 1 It is a flowchart of the steps of the present invention;

[0057] Figure 2It is a graph showing the relationship between the argon gas flow rate and the exposed area of the present invention.

[0058] The realization of the purpose of this attached drawing, its functional features and advantages will be further described with reference to the embodiments and the attached drawings. Detailed implementation manners

[0059] In order to make the purpose, technical solutions and advantages of this application more clear and understandable, the following describes and explains this application in combination with the attached drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0060] Obviously, the attached drawings in the following description are only some examples or embodiments of this application. For those of ordinary skill in the art, without creative efforts, this application can also be applied to other similar scenarios based on these attached drawings. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in this application, some design, manufacturing or production changes based on the technical content disclosed in this application are only conventional technical means and should not be understood as the content disclosed in this application being insufficient.

[0061] If there is no special indication, all implementation manners and optional implementation manners of this application can be combined with each other to form new technical solutions.

[0062] If there is no special indication, all technical features and optional technical features of this application can be combined with each other to form new technical solutions.

[0063] If there is no special indication, all steps of this application can be carried out in sequence or randomly, and preferably in sequence. For example, the method includes steps (a) and (b), which means that the method can include steps (a) and (b) carried out in sequence, or can also include steps (b) and (a) carried out in sequence. For example, it is mentioned that the method may further include step (c), which means that step (c) can be added to the method in any order. For example, the method can include steps (a), (b) and (c), or can also include steps (a), (c) and (b), or can also include steps (c), (a) and (b), etc.

[0064] If there is no special indication, the "including" and "comprising" mentioned in this application mean open-ended, and can also be closed-ended. For example, the "including" and "comprising" can mean that other components not listed can also be included or comprised, or can only include or comprise the listed components.

[0065] Unless otherwise specified, the term "or" is inclusive in this application. For example, the phrase "A or B" means "A, B, or both A and B". More specifically, any of the following conditions satisfies the condition "A or B": A is true (or exists) and B is false (or does not exist); A is false (or does not exist) and B is true (or exists); or both A and B are true (or exist).

[0066] To better understand the solutions of the embodiments of this application, some related terms and concepts that may be involved in the embodiments of this application will be introduced below.

[0067] (1) The sliding filter algorithm, also known as the Moving Average Filter (MAFilter), is a simple and effective filter widely used in digital signal processing. Its main function is to reduce random noise by calculating the average value of a set of consecutive samples of data points, thereby achieving signal smoothing. The sliding filter has a wide range of applications in the fields of signal processing, data processing, and control systems, especially in occasions where short-term fluctuations need to be suppressed and long-term trends need to be highlighted.

[0068] (2) Histogram Equalization is a commonly used image processing technique for improving the contrast of images. It adjusts the histogram distribution of the image so that the gray-scale distribution of the image is more uniform, thereby enhancing the overall visual effect of the image. The basic idea is to redistribute the gray-scale values of the image so that the frequency of each gray-scale value is roughly the same. In this way, the pixel values originally concentrated in a certain gray-scale interval will be spread to the entire gray-scale range, increasing the dynamic range of the image and thus improving the contrast of the image.

[0069] (3) The Canny edge detection algorithm is a classic edge detection method proposed by John F. Canny in 1986. The algorithm aims to find the positions where the gray-scale intensity changes most strongly in the image and evaluates the effect of edge detection through three criteria: low error rate, high localization, and minimum response.

[0070] In this embodiment, as Figure 1 shown, an argon closed-loop control method includes the following steps:

[0071] Step 1: Real-time collect image data on the surface of the molten steel;

[0072] Install a high-precision industrial camera above or on the side of the ladle to real-time collect image data on the surface of the molten steel. The camera should be selected to be able to adapt to high-temperature environments and can clearly capture the image of the molten steel surface.

[0073] Step 2: Preprocess the collected image data, identify the boundary of the molten steel surface through image recognition technology, and determine the exposed area of the molten steel;

[0074] Preprocessing the image data can remove noise, distortion, and other irrelevant information in the image, thereby improving the clarity and quality of the image.

[0075] Step 3: According to the historical process data, establish a relationship model between the exposed area of the molten steel and the flow rate of bottom-blown argon gas, and obtain the dynamic relationship between the argon gas flow rate and the exposed area;

[0076] In this embodiment, according to the historical data, the corresponding relationship diagram between the exposed area of the molten steel and the flow rate of bottom-blown argon gas is as Figure 2 shown.

[0077] The relationship model is established based on historical process data, and the model verification method includes:

[0078] Step 3.1: Calibrate the permeability of the ladle with normal permeability, set the argon gas flow rate F0 of the ladle and record the argon gas pressure P0 at this time;

[0079] Step 3.2: After the new ladle enters the station and breaks the slag, set the flow rate F0, record the pressure P. If P0 - P < 0.5, it is considered that the permeability is good. If 0.5 ≤ P0 - P2 ≤ 2, it is considered that the permeability is average. If P0 - P > 2, it is considered that the permeability is poor;

[0080] Step 3.3: Establish 3 sets of basic parameters, divide the basic blowing argon flow rate into 3 groups, and the three groups of set flow rates are F1, F2, and F3. Use the basic parameter F1 when the permeability of the new ladle is good, use the parameter F2 when the permeability is average, and use the parameter F3 when the permeability is poor;

[0081] According to the permeability calibration data of the ladle, select the corresponding basic blowing argon flow rate, record the actual measurement data of blowing argon for ladles under different process conditions, and use it to verify the corresponding relationship between the argon gas flow rate and the exposed area under different process conditions.

[0082] Step 4: Based on the relationship model, calculate the adjustment value of the argon gas flow rate required to reach the target exposed area according to the current exposed area of the molten steel, and send a control signal through the PID algorithm to automatically adjust the argon gas flow rate;

[0083] Step 5: Monitor the change of the molten steel surface in real time, and repeat Step 4 until the blowing of argon ends or the exposed area of the molten steel deviates from the target value beyond the preset range.

[0084] Furthermore, the preprocessing includes denoising and contrast enhancement.

[0085] Furthermore, the denoising is specifically carried out by using the sliding filtering method, and the specific implementation steps are:

[0086] Step 2.1: Define a filtering window to slide and cover each pixel of the image;

[0087] In this embodiment, the window value of the sliding filter is set to 40. The window size determines the degree of filtering. A larger window can better smooth the data and reduce noise interference, but it will cause an increase in signal delay. The window size needs to be set according to the sampling data volume and filtering experience.

[0088] Step 2.2: The sliding filter window starts from the upper left corner of the image and gradually slides to the lower right corner;

[0089] Step 2.3: Modify the pixel value at the center of the window using mean filtering or median filtering.

[0090] Sliding filter denoising is a technique for processing images by sliding a window, mainly used to reduce noise in images and can stably reduce the random fluctuations of data.

[0091] Furthermore, the contrast enhancement is specifically achieved by using histogram equalization. The specific implementation steps are as follows:

[0092] Step 4.1: Calculate the histogram of the image, that is, the frequency of occurrence of each pixel brightness value;

[0093] Step 4.2: Calculate the cumulative distribution function according to the histogram;

[0094] Step 4.3: Remap each pixel value in the image through the cumulative distribution function to balance the brightness distribution.

[0095] Histogram equalization is a technique for improving image contrast. By adjusting the gray distribution of the image, it enhances the details and clarity of the image. By transforming the histogram of the original image into a uniform histogram, it expands the dynamic range of pixel values, makes the gray levels of the image more moderate, makes the gray distribution of the image more uniform, and thus improves the overall quality of the image.

[0096] Furthermore, identify the boundary of the molten steel surface, specifically including: identifying the edge pixels in the molten steel surface image through the Canny edge detection algorithm and forming a complete boundary by connecting the edge pixels.

[0097] The Canny edge detection algorithm aims to retain key information by reducing the scale of image data, improve the accuracy of edge detection, and reduce the influence of noise at the same time. First, calculate the gradient of each pixel of the image after sliding filtering to find the strong edges of the image, then suppress the non-edge pixels and only retain the strongest edge pixels. Finally, by setting high and low thresholds, determine which pixels are edges and which are not, and form a complete boundary by connecting the edge pixels.

[0098] Further, determine the exposed area of the molten steel, specifically including: traversing and extracting the exposed area of the molten steel based on the molten steel boundary, counting the number of pixels in this area, and calculating the area of the exposed area based on the conversion between pixel counting and the physical size of the image. The formula is as follows:

[0099] S = N × S1;

[0100] Further, based on the relational model, calculate the adjustment value of the argon flow rate required to reach the target exposed area according to the current exposed area of the molten steel. Specifically: based on the corresponding relationship between the exposed area of the molten steel and the argon flow rate in the relational model, obtain the required adjustment value of the argon flow rate according to the difference between the argon flow rate corresponding to the target exposed area and the argon flow rate corresponding to the current exposed area of the molten steel.

[0101] Further, the calculation formula of the PID algorithm is as follows:

[0102]

[0103] Further, the deviation of the exposed area of the molten steel from the target value exceeds the preset range, specifically: the deviation between the exposed area of the molten steel and the target value exceeds ±200 mm 2 .

[0104] Further, when the situation that the deviation of the exposed area of the molten steel from the target value exceeds the preset range occurs, the system automatically alarms and activates the emergency adjustment mode, automatically sets it to the starting flow rate of soft blowing and exits the soft blowing automatic control mode, and reminds that manual intervention is required for adjustment.

[0105] It should be noted that this application is not limited to the above embodiments. The above embodiments are only examples, and embodiments with the same structure and the same function and effect as the technical idea within the scope of the technical solution of this application are all included in the technical scope of this application. In addition, within the scope of not departing from the gist of this application, various deformations that can be thought of by those skilled in the art are imposed on the embodiments, and other ways constructed by combining some constituent elements in the embodiments are also included in the scope of this application.

Claims

1. An argon closed-loop control method, characterized in that, It includes the following steps: Step 1: Real-time collect the image data of the molten steel surface; Step 2: Preprocess the collected image data, identify the boundary of the molten steel surface through image recognition technology and determine the exposed area of the molten steel; Step 3: Based on the historical process data, establish a relationship model between the exposed area of the molten steel and the bottom argon blowing flow rate, and obtain the dynamic relationship between the argon flow rate and the exposed area; Step 4: Based on the relationship model, calculate the adjustment value of the argon flow rate required to reach the target exposed area according to the current exposed area of the molten steel, and send a control signal through the PID algorithm to automatically adjust the argon flow rate; Step 5: Real-time monitor the change of the molten steel surface, and repeat Step 4 until the argon blowing ends or the exposed area of the molten steel deviates from the target value beyond the preset range.

2. The argon closed-loop control method according to claim 1, characterized in that, The preprocessing includes denoising and contrast enhancement.

3. The argon gas closed-loop control method according to claim 2, wherein The denoising is specifically to perform denoising by using the sliding filtering method, and the specific implementation steps are: Step 2.1: Define the filtering window to slide and cover each pixel of the image; Step 2.2: The sliding filter window starts from the upper left corner of the image and gradually slides to the lower right corner; Step 2.3: Modify the pixel value at the center of the window by using mean filtering or median filtering.

4. The argon closed-loop control method according to claim 2, characterized in that The contrast enhancement is specifically to perform contrast enhancement by using histogram equalization, and the specific implementation steps are: Step 3.1: Calculate the histogram of the image, that is, the frequency of occurrence of each pixel brightness value; Step 3.2: Calculate the cumulative distribution function according to the histogram; Step 3.3: Remap each pixel value in the image through the cumulative distribution function to balance the brightness distribution.

5. A method for closed-loop control of argon gas according to claim 1, characterized in that, The identification of the boundary of the molten steel surface specifically includes: identifying the edge pixels in the molten steel surface image through the Canny edge detection algorithm, and forming a complete boundary by connecting the edge pixels.

6. A method for closed-loop control of argon gas according to claim 1, characterized in that, The determination of the exposed area of the molten steel specifically includes: traversing and extracting the molten steel exposed area according to the molten steel boundary, counting the number of pixels in this area, and calculating the area of the exposed area based on the conversion of pixel counting and the physical size of the image. The formula is as follows: s＝ NXS l: Where: S is the area of the molten steel exposed area; N is the number of pixels in the exposed area; S1 is the actual physical area of each pixel.

7. A method for closed-loop control of argon gas according to claim 1, characterized in that The calculation of the adjustment value of the argon flow rate required to reach the target exposed area based on the relationship model according to the current exposed area of the molten steel is specifically: based on the corresponding relationship between the exposed area of the molten steel and the argon flow rate in the relationship model, according to the difference between the argon flow rate corresponding to the target exposed area and the argon flow rate corresponding to the current exposed area of the molten steel, obtain the required adjustment value of the argon flow rate.

8. A method for closed-loop control of argon gas according to claim 1, characterized in that, The calculation formula of the PID algorithm is as follows: Where: K p is the proportionality coefficient; K i is the integral coefficient; K d is the differential coefficient; M n is the control variable at time n; e n is the argon gas flow error at time n; e n-1 is the argon gas flow error quantity at the (n - 1)th moment; K p e n is for proportional operation; is an integral operation; K d (e n -e n-1 ) represents a differential operation.

9. A method for closed-loop control of argon gas according to claim 1, characterized in that The exposed area of the molten steel deviates from the target value by more than the preset range, specifically: the deviation between the exposed area of the molten steel and the target value exceeds ±200 mm 2 .

10. A method for closed-loop control of argon gas according to claim 9, characterized in that, When the situation that the exposed area of the molten steel deviates from the target value beyond the preset range occurs, the system automatically alarms and starts the emergency adjustment mode, automatically sets it to the starting flow rate of soft blowing and exits the soft blowing automatic control mode, reminding that manual intervention is required for adjustment.

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