A CFD-based digital twin monitoring method and system for hot-dip galvanizing furnace nose

Abstract

This invention relates to the field of digital twin technology, specifically to a CFD-based digital twin monitoring method and system for hot-dip galvanizing furnace noses. The CFD-based digital twin monitoring method for hot-dip galvanizing furnace noses includes: acquiring real-time production data and CAD drawing data; constructing a model using digital twin technology to obtain a digital twin model of the hot-dip galvanizing furnace nose; performing simulation based on computational fluid dynamics and real-time production data; generating monitoring information for the hot-dip galvanizing furnace nose using a Prophet-GAN model based on the simulation results; inputting the monitoring information into the digital twin model to obtain monitoring results; determining an anomaly based on a preset alarm threshold; issuing an anomaly alarm when the monitoring verification result is abnormal; displaying the anomaly alarm on a monitor; and continuing real-time monitoring when the monitoring verification result is normal. This invention provides a real-time and efficient method for monitoring hot-dip galvanizing furnace noses based on digital twin technology.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a CFD-based digital twin monitoring method and system for the nose of a hot-dip galvanizing furnace. Background Technology

[0002] In the hot-dip galvanizing process, the furnace nose is the part of the equipment used to guide and transport the items to be galvanized into and out of the molten zinc. This part is typically a pipe or channel with a specific shape, and its design and manufacture must take into account both the efficient guidance of the items into and out of the molten zinc and the ability to withstand high temperatures and corrosion. During hot-dip galvanizing, the items are first fed into the furnace nose, guided by the furnace nose into the molten zinc for galvanizing, and then the molten zinc is discharged through the furnace nose. Precise control of this process ensures the quality and efficiency of the galvanizing process.

[0003] The design of the furnace nose typically considers the following aspects: the shape and size of the furnace nose need to effectively guide the items to be galvanized into and out of the molten zinc; since the furnace nose needs to be exposed to high temperature and corrosive environment for a long time, high temperature and corrosion resistant materials, such as stainless steel or special alloys, are generally selected; during the hot-dip galvanizing process, the temperature of the furnace nose needs to be maintained within a suitable range to ensure the quality of galvanizing; the design and manufacture of the furnace nose also need to take safety factors into account, such as preventing overheating and preventing explosions.

[0004] Digital twin technology fully utilizes models, data, and intelligent algorithms, integrating multidisciplinary technologies to serve as a bridge connecting the physical and information worlds throughout the product lifecycle. This technology can systematically plan production processes, equipment, and resources, monitor production conditions in real time, and promptly detect and respond to various anomalies and instabilities in the production process, achieving the goals of cost reduction, efficiency improvement, quality assurance, and meeting environmental protection requirements. However, existing production monitoring technologies cannot effectively digitize the furnace nose and zinc pot areas, and the monitoring results are not intuitive, failing to provide real-time and effective risk monitoring of production outcomes.

[0005] In the existing technology, there is a lack of a real-time and efficient method for monitoring the nose of a hot-dip galvanizing furnace based on digital twin technology. Summary of the Invention

[0006] To address the technical problems of low visualization, transparency, and intelligence levels in existing hot-dip galvanizing furnace nose production lines, this invention provides a CFD-based digital twin monitoring method and system for hot-dip galvanizing furnace noses. The technical solution is as follows:

[0007] On the one hand, a CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace is provided. This method is implemented by a digital twin monitoring device and includes:

[0008] Acquire real-time production data and CAD drawing data; based on the real-time production data and CAD drawing data, use digital twin technology to construct a model and obtain a digital twin model of the hot-dip galvanizing furnace nose;

[0009] Based on computational fluid dynamics, simulations are performed using the real-time production data to obtain simulation results of the production situation.

[0010] Based on the simulation results of the production situation, data was generated using the Prophet-GAN model to obtain monitoring information of the hot-dip galvanizing furnace nose.

[0011] The monitoring information of the hot-dip galvanizing furnace nose is input into the digital twin model of the hot-dip galvanizing furnace nose to obtain the monitoring results; the monitoring results are then input into the display for display.

[0012] The monitoring results are verified according to the preset alarm threshold to obtain the monitoring verification result; when the monitoring verification result is abnormal, an abnormal alarm is issued; the abnormal alarm is input to the display for display; when the monitoring verification result is normal, real-time monitoring continues.

[0013] The real-time production data includes DCS data, PLC data, and sensor data.

[0014] The data types of the real-time production data include business data, historical data, and statistical data;

[0015] The business data includes production data, equipment data, process quality data, and material tracking data.

[0016] The digital twin model of the hot-dip galvanizing furnace nose includes a furnace nose area model and a zinc pot area model.

[0017] The furnace nose area model includes a furnace nose equipment model and a zinc ash pump equipment model;

[0018] The zinc pot area model includes a zinc pot equipment model and a zinc pot three-roller equipment model.

[0019] Optionally, the step of performing simulation based on computational fluid dynamics and the real-time production data to obtain simulation results of the production situation includes:

[0020] Based on computational fluid dynamics, the strip temperature inside the furnace nose is simulated according to the real-time production data to obtain the strip temperature simulation results.

[0021] Based on computational fluid dynamics, the water vapor concentration distribution and temperature distribution in the vertical strip movement direction inside the furnace nose are simulated according to the real-time production data to obtain the simulation results of the atmosphere inside the furnace nose.

[0022] Based on computational fluid dynamics, the overflow of zinc liquid in the furnace nose is simulated according to the real-time production data, and the simulation results of the overflow at the lower end of the furnace nose are obtained.

[0023] Based on computational fluid dynamics, the zinc liquid in the zinc pot is simulated based on the temperature field and velocity field according to the real-time production data, and the simulation results of the zinc liquid condition are obtained.

[0024] Based on computational fluid dynamics, the distribution field of zinc slag inside the zinc pot is simulated according to the real-time production data, and the simulation results of zinc slag condition are obtained.

[0025] The Prophet-GAN model consists of a Prophet network model and a generative adversarial network model.

[0026] The Prophet-GAN model is used to assist the digital twin model of the hot-dip galvanizing furnace nose in real-time monitoring of production.

[0027] The Prophet network model is used to enhance the temporal prediction capability of the generative adversarial network model.

[0028] The generative adversarial network model includes a generator and a discriminator; the generator and discriminator continuously adjust their parameters while monitoring the real-time production data.

[0029] Optionally, the step of generating data using the Prophet-GAN model based on the simulation results of the production situation to obtain monitoring information of the hot-dip galvanizing furnace nose includes:

[0030] Based on the simulation results of the production situation, feature extraction is performed using the Prophet network model to obtain time-series features;

[0031] Based on the simulation results of the production situation and the time series characteristics, data is generated by the generator to obtain realistic monitoring information.

[0032] Based on the discriminator, the realistic monitoring information is discriminated to obtain the discrimination result;

[0033] When the discrimination result is failure, the realistic monitoring information is identified as the hot-dip galvanizing furnace nose monitoring information; when the discrimination result is success, the realistic monitoring information is discarded.

[0034] On the other hand, a CFD-based digital twin monitoring system for hot-dip galvanizing furnace noses is provided. This system is applied to a CFD-based digital twin monitoring method for hot-dip galvanizing furnace noses. The system includes electronic equipment and a display, wherein:

[0035] The electronic device is used to acquire real-time production data and CAD drawing data; based on the real-time production data and CAD drawing data, a digital twin technology is used to construct a model to obtain a digital twin model of the hot-dip galvanizing furnace nose; based on computational fluid dynamics, simulation is performed on the real-time production data to obtain simulation results of the production situation; based on the simulation results of the production situation, data is generated using a Prophet-GAN model to obtain monitoring information of the hot-dip galvanizing furnace nose; the monitoring information of the hot-dip galvanizing furnace nose is input into the digital twin model of the hot-dip galvanizing furnace nose to obtain monitoring results; the monitoring results are verified according to a preset alarm threshold to obtain monitoring verification results; when the monitoring verification result is abnormal, an abnormal alarm is issued; when the monitoring result is normal, real-time monitoring continues.

[0036] The display is used to input and display the monitoring results; and to input and display the abnormal alarms.

[0037] The real-time production data includes DCS data, PLC data, and sensor data.

[0038] The data types of the real-time production data include business data, historical data, and statistical data;

[0039] The business data includes production data, equipment data, process quality data, and material tracking data.

[0040] The digital twin model of the hot-dip galvanizing furnace nose includes a furnace nose area model and a zinc pot area model.

[0041] The furnace nose area model includes a furnace nose equipment model and a zinc ash pump equipment model;

[0042] The zinc pot area model includes a zinc pot equipment model and a zinc pot three-roller equipment model.

[0043] Optionally, the electronic device is further configured to:

[0044] Based on computational fluid dynamics, the strip temperature inside the furnace nose is simulated according to the real-time production data to obtain the strip temperature simulation results.

[0045] Based on computational fluid dynamics, the water vapor concentration distribution and temperature distribution in the vertical strip movement direction inside the furnace nose are simulated according to the real-time production data to obtain the simulation results of the atmosphere inside the furnace nose.

[0046] Based on computational fluid dynamics, the overflow of zinc liquid in the furnace nose is simulated according to the real-time production data, and the simulation results of the overflow at the lower end of the furnace nose are obtained.

[0047] Based on computational fluid dynamics, the zinc liquid in the zinc pot is simulated based on the temperature field and velocity field according to the real-time production data, and the simulation results of the zinc liquid condition are obtained.

[0048] Based on computational fluid dynamics, the distribution field of zinc slag inside the zinc pot is simulated according to the real-time production data, and the simulation results of zinc slag condition are obtained.

[0049] The Prophet-GAN model consists of a Prophet network model and a generative adversarial network model.

[0050] The Prophet-GAN model is used to assist the digital twin model of the hot-dip galvanizing furnace nose in real-time monitoring of production.

[0051] The Prophet network model is used to enhance the temporal prediction capability of the generative adversarial network model.

[0052] The generative adversarial network model includes a generator and a discriminator; the generator and discriminator continuously adjust their parameters while monitoring the real-time production data.

[0053] Optionally, the electronic device is further configured to:

[0054] Based on the simulation results of the production situation, feature extraction is performed using the Prophet network model to obtain time-series features;

[0055] Based on the simulation results of the production situation and the time series characteristics, data is generated by the generator to obtain realistic monitoring information.

[0056] Based on the discriminator, the realistic monitoring information is discriminated to obtain the discrimination result;

[0057] When the discrimination result is failure, the realistic monitoring information is identified as the hot-dip galvanizing furnace nose monitoring information; when the discrimination result is success, the realistic monitoring information is discarded.

[0058] On the other hand, a digital twin monitoring device is provided, the digital twin monitoring device comprising: a processor; a memory storing computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, any one of the methods described above for monitoring the digital twin of a hot-dip galvanizing furnace nose based on CFD is implemented.

[0059] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement any of the above-described CFD-based digital twin monitoring methods for hot-dip galvanizing furnace noses.

[0060] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0061] This invention proposes a CFD-based digital twin monitoring method for hot-dip galvanizing furnace noses. Utilizing digital twin technology, it digitally models the furnace nose and zinc pot area using 3D modeling, and employs real-time production data to achieve real-time mapping of production line equipment and material status. Simultaneously, it visualizes the 3D equipment model and CFD simulation results using digital twin technology, making the production process readily apparent. Alarm thresholds are set according to galvanizing quality control rules to provide real-time alerts for abnormal production. This method systematically plans production processes, equipment, and resources, monitors the production conditions of the furnace nose and zinc pot area in real time, and promptly detects and responds to various anomalies and instabilities in the production process. It increasingly achieves the goals of cost reduction, efficiency improvement, quality assurance, and environmental protection requirements through intelligent means. This invention is a real-time and efficient hot-dip galvanizing furnace nose monitoring method based on digital twin technology. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0063] Figure 1 This is a flowchart of a CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace provided in an embodiment of the present invention;

[0064] Figure 2 This is a vector diagram showing the velocity distribution of molten zinc along the length of the strip inside the furnace nose, provided in an embodiment of the present invention.

[0065] Figure 3 This is a vector diagram of the velocity distribution of molten zinc in a cross-section of the moving direction of the strip steel inside a zinc pot, provided by an embodiment of the present invention.

[0066] Figure 4 This is a block diagram of a CFD-based digital twin monitoring system for the nose of a hot-dip galvanizing furnace provided in an embodiment of the present invention.

[0067] Figure 5 This is a schematic diagram of the structure of a digital twin monitoring device provided in an embodiment of the present invention. Detailed Implementation

[0068] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0069] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0070] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0071] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0072] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0073] This invention provides a CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace. This method can be implemented using a digital twin monitoring device, which can be a terminal or a server. Figure 1 The flowchart shown is a CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace. The processing flow of this method may include the following steps:

[0074] S1. Obtain real-time production data and CAD drawing data; based on the real-time production data and CAD drawing data, use digital twin technology to build a model and obtain a digital twin model of the hot-dip galvanizing furnace nose.

[0075] In one feasible implementation, the present invention is specifically described in conjunction with the actual production information of a cold rolling mill. The production process of the cold rolling mill mainly includes pickling-rolling mill combined unit, continuous annealing unit and continuous hot-dip galvanizing unit.

[0076] The three-dimensional spatial information of the furnace nose and zinc pot area equipment was digitally processed through 3D modeling to establish a digital twin model of the hot-dip galvanizing furnace nose. The motion states of each key piece of equipment are shown in Table 1 (Key Equipment Actions Table).

[0077] Table 1

[0078] Serial Number equipment Real-time synchronization of device movement content 1 steering rollers Rotational motion 2 Furnace nose Stretching motion, translational motion, swinging motion, tilting motion 3 Submerged rollers Rotational motion

[0079] Real-time production data includes DCS data, PLC data, and sensor data.

[0080] The types of data used in real-time production include business data, historical data, and statistical data;

[0081] Business data includes production data, equipment data, process quality data, and material tracking data.

[0082] In one feasible implementation, the digital twin model of the hot-dip galvanizing furnace nose in this invention communicates with automation and information systems to read relevant business data such as production, equipment, process quality, and material tracking. The data type is Internet of Things Data (IoT) data, which mainly includes Distributed Control System (DCS) data type, Programmable Logic Controller (PLC) data type, and sensor data type.

[0083] Among them, the digital twin model of the hot-dip galvanizing furnace nose includes a furnace nose area model and a zinc pot area model;

[0084] The furnace nose area model includes the furnace nose equipment model and the zinc ash pump equipment model;

[0085] The zinc pot area model includes a zinc pot equipment model and a zinc pot three-roller equipment model.

[0086] In one feasible implementation, this invention uses an automated and digital application platform for the production site as its data foundation, digitizes the three-dimensional spatial information of the hot-dip galvanizing furnace nose, establishes a 3D virtual model, and fuses real-time data within the 3D virtual model of the equipment at a fusion layer, thereby establishing a visual monitoring platform to realize a 3D digital twin of the hot-dip galvanizing furnace nose. The equipment dimensions of the digital twin model of the hot-dip galvanizing furnace nose are modeled according to a 1:1 scale based on CAD drawing data.

[0087] S2. Based on computational fluid dynamics, simulations are performed using real-time production data to obtain simulation results of the production situation.

[0088] Optionally, based on computational fluid dynamics, simulations are performed using real-time production data to obtain simulation results of the production situation, including:

[0089] Based on computational fluid dynamics, the temperature of the strip inside the furnace nose is simulated according to real-time production data, and the simulation results of the strip temperature are obtained.

[0090] Based on computational fluid dynamics, the water vapor concentration distribution and temperature distribution in the vertical direction of strip movement inside the furnace nose are simulated according to real-time production data, and the simulation results of the atmosphere inside the furnace nose are obtained.

[0091] Based on computational fluid dynamics, the overflow of zinc liquid in the furnace nose is simulated according to real-time production data, and the simulation results of the overflow at the lower end of the furnace nose are obtained.

[0092] Based on computational fluid dynamics, and using real-time production data, the zinc liquid inside the zinc pot is simulated based on the temperature field and velocity field to obtain simulation results of the zinc liquid condition.

[0093] Based on computational fluid dynamics, the distribution field of zinc slag inside the zinc pot is simulated according to real-time production data, and the simulation results of zinc slag condition are obtained.

[0094] In one feasible implementation, computational fluid dynamics (CFD) is used in this invention to simulate the strip temperature, atmosphere, overflow in the furnace nose, and the temperature field, fluid field, and zinc slag distribution in the zinc pot based on real-time data from the production process.

[0095] The simulation of the strip temperature inside the furnace nose involves slicing along the length / width of the strip and using cloud maps to simulate the temperature distribution along the width / length of the strip. The simulation of the atmosphere inside the furnace nose is divided into two cases: humidified and unhumidified. Slices are taken along the length perpendicular to the strip, and cloud maps are used to simulate the water vapor concentration and temperature distribution perpendicular to the strip's movement direction. The simulation of the overflow at the lower end of the furnace nose simulates the overflow of the molten zinc surface inside the furnace nose, specifically the overflow at the center and edges of the strip, using vector field maps. The simulation of the molten zinc in the zinc pot is based on temperature and velocity fields. The simulation of the zinc dross distribution field in hot-dip galvanized iron (GA) and hot-dip pure zinc (GI) zinc pots is also performed.

[0096] S3. Based on the simulation results of production conditions, data is generated using the Prophet-GAN model to obtain monitoring information of the hot-dip galvanizing furnace nose.

[0097] In one feasible implementation, monitoring information for the hot-dip galvanizing furnace nose under new operating conditions is established based on the calculation results of the Prophet-GAN model. This includes vector fields or cloud maps of the atmosphere, fluid, and temperature of the furnace nose. The inputs and outputs of the Prophet-GAN model are shown in Table 2 (Input and Output Parameter Table). Figure 2 This refers to the zinc melt velocity distribution along the length of the strip inside the furnace nose. Figure 3 This shows the flow of molten zinc in the cross-section of the zinc pot along the direction of the steel strip's movement.

[0098] Table 2

[0099]

[0100]

[0101] The Prophet-GAN model consists of a Prophet network model and a generative adversarial network model.

[0102] The Prophet-GAN model is used to assist the digital twin model of the hot-dip galvanizing furnace nose in real-time monitoring of production.

[0103] In one feasible implementation, to enhance the time-series prediction capability of Generative Adversarial Networks (GANs), this invention uses Prophet to extract new features from time series data as input to the GAN. The actual predictions of the Prophet model, the upper and lower bounds of the confidence interval, daily and weekly seasonality and trends, etc., can all be used as its new features.

[0104] Prophet is a method for predicting time series data based on an additive model. It is robust to missing data and trend changes, and can usually handle outliers well. The expression of Prophet can be described as follows (1):

[0105] y(m)=g(m)+s(m)+h(m)+ε(m) (1)

[0106] Where m represents time; y(m) is the original time series in the real-time production data; ε(m) is the error term; g(m) is the trend function, used to explain the non-periodic trend in y(m), and g(m) is represented by the following equation (2) using a non-linearly growing logistic regression model:

[0107]

[0108] Where C determines the upper limit of growth; τ is the growth rate; and b is the offset.

[0109] s(m) is a periodic variation function used to describe trend quantities with periodicity, and its mathematical expression is as follows (3):

[0110]

[0111] Where Q represents the number of periods in the model; p represents the period of the time series.

[0112] h(m) is the holiday influence function, and its mathematical expression is as follows (4):

[0113]

[0114] Where L represents the number of segments in the time series; κ l D is the coefficient; l Let l represent the set of time periods during which the l-th holiday lasts.

[0115] Among them, the Prophet network model is used to enhance the temporal prediction capability of the generative adversarial network model;

[0116] Generative Adversarial Network (GAN) models consist of a generator and a discriminator; the generator and discriminator continuously adjust their parameters while monitoring real-time production data.

[0117] In one feasible implementation, a generative adversarial network (GAN) includes two models: a generator and a discriminator. These two models are typically implemented using neural networks.

[0118] The general mathematical expression for the generator's loss function is as follows (5):

[0119] L gen =-E z～p(z) [logD(G(z))] (5)

[0120] Where z is a random vector sampled from the noise distribution, G(z) is the data output by the generator, and D(·) is the discriminator.

[0121] The general mathematical expression for the discriminator loss function is as follows (6):

[0122]

[0123] Where x is the actual data collected during the hot-dip galvanizing production process, and p data It is the actual distribution of real data.

[0124] Optionally, based on the simulation results of production conditions, data is generated using the Prophet-GAN model to obtain monitoring information of the hot-dip galvanizing furnace nose, including:

[0125] Based on the simulation results of production conditions, feature extraction is performed using the Prophet network model to obtain time-series features;

[0126] Based on the simulation results of production conditions and time-series characteristics, data is generated through a generator to obtain realistic monitoring information.

[0127] Based on the discriminator, realistic monitoring information is discriminated to obtain the discrimination result;

[0128] When the judgment result is failure, the realistic monitoring information is identified as the monitoring information of the hot-dip galvanizing furnace nose; when the judgment result is success, the realistic monitoring information is abandoned.

[0129] In one feasible implementation, the generative adversarial network (GAN) consists of two competing networks: a generator and a discriminator. The generator aims to generate realistic monitoring information of a hot-dip galvanizing production line, while the discriminator aims to accurately distinguish between real production line information and realistic monitoring information. During training, these two networks continuously engage in a game-like process, making the monitoring information generated by the generator increasingly closer to the real monitoring information of a hot-dip galvanizing production line, and enabling the discriminator to more accurately determine the authenticity of the data. This process continues until neither network can improve further.

[0130] When the discriminator cannot accurately distinguish between realistic monitoring information and real-time production monitoring information, it indicates that the generator has successfully mimicked the distribution of real data. By observing the generator's realistic monitoring information, the operating status of the hot-dip galvanizing production line can be understood, thereby achieving real-time monitoring and prediction of the entire production line.

[0131] S4. Input the monitoring information of the hot-dip galvanizing furnace nose into the digital twin model of the hot-dip galvanizing furnace nose to obtain the monitoring results; input the monitoring results into the display screen.

[0132] In one feasible implementation, the monitoring results of the furnace nose and the space inside the zinc pot under stable conditions specifically refer to the temperature distribution along the width / length of the strip, the water vapor concentration and temperature distribution perpendicular to the strip movement direction, the overflow of zinc liquid inside the furnace nose, the cross-section of the thermocouple installation location inside the zinc pot, and the distribution of bottom slag in the GA pot and floating slag in the GI pot. The operating data of each stage and equipment in the hot-dip galvanizing production process are shown in Table 3 (Operating Data Variable Name Table):

[0133] Table 3

[0134]

[0135]

[0136] S5. Verify the monitoring results according to the preset alarm threshold and obtain the monitoring verification results; when the monitoring verification results are abnormal, issue an abnormal alarm; input the abnormal alarm into the display; when the monitoring verification results are normal, continue real-time monitoring.

[0137] In one feasible implementation, the present invention monitors production data in real time based on the galvanizing quality in the monitoring results, using anomaly judgment rules combined with preset alarm thresholds. When an anomaly occurs, a visual alarm is triggered. For example, during outer plate production, humidification at the furnace nose can lead to zinc ash defects, triggering a visual alarm; similarly, when the strip temperature exceeds a threshold, it can easily cause roller mark defects, also triggering a visual alarm.

[0138] Based on the method described in this invention, information and data can be better provided to users. By combining the information needs of the personnel in the position, the overall, partial, sectional or perspective views of the furnace nose and zinc pot can be used to visualize the production status, provide production auxiliary decision support for operators, and improve product quality.

[0139] This invention proposes a CFD-based digital twin monitoring method for hot-dip galvanizing furnace noses. Utilizing digital twin technology, it digitally models the furnace nose and zinc pot area using 3D modeling, and employs real-time production data to achieve real-time mapping of production line equipment and material status. Simultaneously, it visualizes the 3D equipment model and CFD simulation results using digital twin technology, making the production process readily apparent. Alarm thresholds are set according to galvanizing quality control rules to provide real-time alerts for abnormal production. This method systematically plans production processes, equipment, and resources, monitors the production conditions of the furnace nose and zinc pot area in real time, and promptly detects and responds to various anomalies and instabilities in the production process. It increasingly achieves the goals of cost reduction, efficiency improvement, quality assurance, and environmental protection requirements through intelligent means. This invention is a real-time and efficient hot-dip galvanizing furnace nose monitoring method based on digital twin technology.

[0140] Figure 4 This is a block diagram illustrating a CFD-based digital twin monitoring system for hot-dip galvanizing furnace noses, according to an exemplary embodiment. The system is used in a CFD-based digital twin monitoring method for hot-dip galvanizing furnace noses. (Refer to...) Figure 4 The system includes electronic device 410 and display 420, wherein:

[0141] The electronic device is used to acquire real-time production data and CAD drawing data; based on the real-time production data and CAD drawing data, it uses digital twin technology to construct a model and obtain a digital twin model of the hot-dip galvanizing furnace nose; based on computational fluid dynamics, it performs simulation based on the real-time production data to obtain simulation results of the production situation; based on the simulation results of the production situation, it generates data through the Prophet-GAN model to obtain monitoring information of the hot-dip galvanizing furnace nose; it inputs the monitoring information of the hot-dip galvanizing furnace nose into the digital twin model of the hot-dip galvanizing furnace nose to obtain monitoring results; it verifies the monitoring results according to a preset alarm threshold to obtain monitoring verification results; when the monitoring verification result is abnormal, it issues an abnormal alarm; when the monitoring result is normal, it continues real-time monitoring;

[0142] A monitor is used to input and display monitoring results; it is also used to input and display abnormal alarms.

[0143] Real-time production data includes DCS data, PLC data, and sensor data.

[0144] The types of data used in real-time production include business data, historical data, and statistical data;

[0145] Business data includes production data, equipment data, process quality data, and material tracking data.

[0146] Among them, the digital twin model of the hot-dip galvanizing furnace nose includes a furnace nose area model and a zinc pot area model;

[0147] The furnace nose area model includes the furnace nose equipment model and the zinc ash pump equipment model;

[0148] The zinc pot area model includes a zinc pot equipment model and a zinc pot three-roller equipment model.

[0149] Optionally, the electronic device 410 is further used for:

[0150] Based on computational fluid dynamics, the temperature of the strip inside the furnace nose is simulated according to real-time production data, and the simulation results of the strip temperature are obtained.

[0151] Based on computational fluid dynamics, the water vapor concentration distribution and temperature distribution in the vertical direction of strip movement inside the furnace nose are simulated according to real-time production data, and the simulation results of the atmosphere inside the furnace nose are obtained.

[0152] Based on computational fluid dynamics, the overflow of zinc liquid in the furnace nose is simulated according to real-time production data, and the simulation results of the overflow at the lower end of the furnace nose are obtained.

[0153] Based on computational fluid dynamics, and using real-time production data, the zinc liquid inside the zinc pot is simulated based on the temperature field and velocity field to obtain simulation results of the zinc liquid condition.

[0154] Based on computational fluid dynamics, the distribution field of zinc slag inside the zinc pot is simulated according to real-time production data, and the simulation results of zinc slag condition are obtained.

[0155] The Prophet-GAN model consists of a Prophet network model and a generative adversarial network model.

[0156] The Prophet-GAN model is used to assist the digital twin model of the hot-dip galvanizing furnace nose in real-time monitoring of production.

[0157] Among them, the Prophet network model is used to enhance the temporal prediction capability of the generative adversarial network model;

[0158] Generative Adversarial Network (GAN) models consist of a generator and a discriminator; the generator and discriminator continuously adjust their parameters while monitoring real-time production data.

[0159] Optionally, the electronic device 410 is further used for:

[0160] Based on the simulation results of production conditions, feature extraction is performed using the Prophet network model to obtain time-series features;

[0161] Based on the simulation results of production conditions and time-series characteristics, data is generated through a generator to obtain realistic monitoring information.

[0162] Based on the discriminator, realistic monitoring information is discriminated to obtain the discrimination result;

[0163] When the judgment result is failure, the realistic monitoring information is identified as the monitoring information of the hot-dip galvanizing furnace nose; when the judgment result is success, the realistic monitoring information is abandoned.

[0164] This invention proposes a CFD-based digital twin monitoring method for hot-dip galvanizing furnace noses. Utilizing digital twin technology, it digitally models the furnace nose and zinc pot area using 3D modeling, and employs real-time production data to achieve real-time mapping of production line equipment and material status. Simultaneously, it visualizes the 3D equipment model and CFD simulation results using digital twin technology, making the production process readily apparent. Alarm thresholds are set according to galvanizing quality control rules to provide real-time alerts for abnormal production. This method systematically plans production processes, equipment, and resources, monitors the production conditions of the furnace nose and zinc pot area in real time, and promptly detects and responds to various anomalies and instabilities in the production process. It increasingly achieves the goals of cost reduction, efficiency improvement, quality assurance, and environmental protection requirements through intelligent means. This invention is a real-time and efficient hot-dip galvanizing furnace nose monitoring method based on digital twin technology.

[0165] Figure 5 This is a schematic diagram of the structure of a digital twin monitoring device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the digital twin monitoring device may include the above-mentioned Figure 4 The illustrated digital twin monitoring system for the nose of a hot-dip galvanizing furnace, based on CFD (Computational Fluid Dynamics), may optionally include a first processor 2001.

[0166] Optionally, the digital twin monitoring device 510 may also include a memory 2002 and a transceiver 2003.

[0167] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0168] The following is combined with Figure 5 A detailed introduction to each component of the digital twin monitoring device 510:

[0169] The first processor 2001 is the control center of the digital twin monitoring device 510. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0170] Optionally, the first processor 2001 can perform various functions of the digital twin monitoring device 510 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0171] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 5 CPU0 and CPU1 are shown in the diagram.

[0172] In a specific implementation, as one example, the digital twin monitoring device 510 may also include multiple processors, for example... Figure 5 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0173] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0174] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be accessed through the interface circuit of the digital twin monitoring device 510. Figure 5 (Not shown in the figure) is coupled to the first processor 2001, and the embodiments of the present invention do not specifically limit this.

[0175] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0176] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 5 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0177] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently and be connected to the interface circuit of the digital twin monitoring device 510. Figure 5 (Not shown in the figure) is coupled to the first processor 2001, and the embodiments of the present invention do not specifically limit this.

[0178] It should be noted that, Figure 5 The structure of the digital twin monitoring device 510 shown does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0179] Furthermore, the technical effect of the digital twin monitoring device 510 can be referred to the technical effect of the CFD-based digital twin monitoring method for hot-dip galvanizing furnace nose described in the above method embodiments, and will not be repeated here.

[0180] It should be understood that the first processor 2001 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0181] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0182] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0183] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0184] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0185] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0186] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0187] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, systems, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0188] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0189] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0190] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0191] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0192] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace, characterized in that, The method includes: Acquire real-time production data and CAD drawing data; based on the real-time production data and CAD drawing data, use digital twin technology to construct a model and obtain a digital twin model of the hot-dip galvanizing furnace nose; Based on computational fluid dynamics, simulations are performed using the real-time production data to obtain simulation results of the production situation. Based on the simulation results of the production situation, data was generated using the Prophet-GAN model to obtain monitoring information of the hot-dip galvanizing furnace nose. The monitoring information of the hot-dip galvanizing furnace nose is input into the digital twin model of the hot-dip galvanizing furnace nose to obtain the monitoring results; the monitoring results are then input into the display for display. The monitoring results are verified according to a preset alarm threshold to obtain a monitoring verification result; when the monitoring verification result is abnormal, an abnormal alarm is issued; the abnormal alarm is input to the display for display; when the monitoring verification result is normal, real-time monitoring continues.

2. The CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace according to claim 1, characterized in that, The real-time production data includes DCS data, PLC data, and sensor data; The data types of the real-time production data include business data, historical data, and statistical data; The business data includes production data, equipment data, process quality data, and material tracking data.

3. The CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace according to claim 1, characterized in that, The digital twin model of the hot-dip galvanizing furnace nose includes a furnace nose area model and a zinc pot area model; The furnace nose area model includes a furnace nose equipment model and a zinc ash pump equipment model; The zinc pot area model includes a zinc pot equipment model and a zinc pot three-roller equipment model.

4. The CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace according to claim 1, characterized in that, The simulation based on computational fluid dynamics, using the real-time production data, yields simulation results of the production situation, including: Based on computational fluid dynamics, the strip temperature inside the furnace nose is simulated according to the real-time production data to obtain the strip temperature simulation results. Based on computational fluid dynamics, the water vapor concentration distribution and temperature distribution in the vertical strip movement direction inside the furnace nose are simulated according to the real-time production data to obtain the simulation results of the atmosphere inside the furnace nose. Based on computational fluid dynamics, the overflow of zinc liquid in the furnace nose was simulated according to the real-time production data, and the simulation results of the overflow at the lower end of the furnace nose were obtained. Based on computational fluid dynamics, the zinc liquid in the zinc pot is simulated based on the temperature field and velocity field according to the real-time production data, and the simulation results of the zinc liquid condition are obtained. Based on computational fluid dynamics, the distribution field of zinc slag inside the zinc pot is simulated according to the real-time production data, and the simulation results of zinc slag condition are obtained.

5. The CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace according to claim 1, characterized in that, The Prophet-GAN model consists of a Prophet network model and a generative adversarial network model. The Prophet-GAN model is used to assist the digital twin model of the hot-dip galvanizing furnace nose in real-time monitoring of production.

6. The CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace according to claim 5, characterized in that, The Prophet network model is used to enhance the temporal prediction capability of the generative adversarial network model; The generative adversarial network model includes a generator and a discriminator; the generator and discriminator continuously adjust their parameters while monitoring the real-time production data.

7. The CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace according to claim 6, characterized in that, Based on the simulation results of the production situation, data is generated using the Prophet-GAN model to obtain monitoring information of the hot-dip galvanizing furnace nose, including: Based on the simulation results of the production situation, feature extraction is performed using the Prophet network model to obtain time-series features; Based on the simulation results of the production situation and the time series characteristics, data is generated by the generator to obtain realistic monitoring information. Based on the discriminator, the realistic monitoring information is discriminated to obtain the discrimination result; When the discrimination result is failure, the realistic monitoring information is identified as the hot-dip galvanizing furnace nose monitoring information; when the discrimination result is success, the realistic monitoring information is discarded.

8. A CFD-based digital twin monitoring system for the nose of a hot-dip galvanizing furnace, wherein the CFD-based digital twin monitoring system for the nose of a hot-dip galvanizing furnace is used to implement the CFD-based digital twin monitoring method for the nose of a hot-dip galvanizing furnace as described in any one of claims 1-7, characterized in that, The system includes electronic devices and a display, wherein: The electronic device is used to acquire real-time production data and CAD drawing data; based on the real-time production data and CAD drawing data, a digital twin technology is used to construct a model to obtain a digital twin model of the hot-dip galvanizing furnace nose; based on computational fluid dynamics, simulation is performed on the real-time production data to obtain simulation results of the production situation; based on the simulation results of the production situation, data is generated using a Prophet-GAN model to obtain monitoring information of the hot-dip galvanizing furnace nose; the monitoring information of the hot-dip galvanizing furnace nose is input into the digital twin model of the hot-dip galvanizing furnace nose to obtain monitoring results; the monitoring results are verified according to a preset alarm threshold to obtain monitoring verification results; when the monitoring verification result is abnormal, an abnormal alarm is issued; when the monitoring verification result is normal, real-time monitoring continues. The display is used to input and display the monitoring results; and to input and display the abnormal alarms.

9. A digital twin monitoring device, characterized in that, The digital twin monitoring device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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