CN118111250A - Top-blown lance control method, top-blown lance, smelting apparatus, and computer-readable medium - Google Patents
Top-blown lance control method, top-blown lance, smelting apparatus, and computer-readable medium Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
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Abstract
Embodiments of the present disclosure disclose a top-blowing lance control method, a top-blowing lance, a computer readable medium, and a smelting apparatus. One embodiment of the method comprises the following steps: collecting working parameters of a top-blowing spray gun in real time, wherein the working parameters at least comprise flow information, pressure information, temperature information and gun position information; inputting the collected working parameters into a pre-trained state analysis model to obtain working state information, wherein the working state information comprises injection effect, abrasion condition and potential fault information; generating a working state image according to the working parameters and the working state information, and judging whether abnormal working parameters exist in the working parameters according to the working state image; and in response to determining that the working parameter has abnormal working parameters, adjusting the abnormal working parameters to preset working conditions of the top-blowing spray gun. According to the embodiment, the real-time monitoring and prediction of the top-blowing spray gun can be realized, and the accuracy of parameter adjustment is improved.
Description
Technical Field
Embodiments of the present disclosure relate to the technical field of metal smelting, and in particular, to a top-blowing lance control method, a top-blowing lance, a smelting apparatus, and a computer readable medium.
Background
In the metallurgical industry, a top-blowing lance is a vital technical equipment used for precisely blowing gas, oxygen or fuel into a smelting furnace, thereby ensuring efficient refining of nonferrous metals. The top-blowing spray gun is easy to wear and corrode under the action of high-temperature and high-speed air flow, needs to be maintained or replaced regularly, and when technological parameters are inappropriate, the air flow and the spraying mode of the top-blowing spray gun can cause uneven temperature distribution in the furnace, the efficiency and the product quality of the smelting process are seriously affected, and the parameters are required to be found and adjusted in time. Therefore, it is important to monitor and control the top-blowing lance in real time.
However, the inventor finds that the multiphase flow and the high-temperature reaction and other physical and chemical processes inside the top-blowing spray gun are extremely difficult to monitor and control in real time. Currently, smelters still rely heavily on the experience and intuition of the operators to make adjustments, which is not only inefficient, but also presents great uncertainty and risk.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose a top-blowing lance control method, a top-blowing lance, and a computer readable medium to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a top-blowing lance control method applied to a top-blowing lance, the method comprising: collecting working parameters of the top-blowing spray gun in real time, wherein the working parameters at least comprise flow information, pressure information, temperature information and heat distribution information; inputting the collected working parameters into a pre-trained state analysis model to obtain working state information, wherein the working state information comprises injection effect, abrasion condition and potential fault information; generating a working state image according to the working parameters and the working state information, and judging whether abnormal working parameters exist in the working parameters according to the working state image; and in response to determining that the working parameters have abnormal working parameters, adjusting the abnormal working parameters to preset working conditions of the top-blowing spray gun.
In a second aspect, some embodiments of the present disclosure provide a top-blowing lance comprising: one or more processors; a spray gun assembly; a gun position controller; an image display unit; and a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a third aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a smelting plant that includes a top-blowing lance as described in any one of the implementations of the second aspect above.
The above embodiments of the present disclosure have the following advantageous effects: the working state of the top-blowing spray gun is accurately tracked and predicted by utilizing a pre-trained state analysis model through real-time data acquisition and processing, an intuitive visual monitoring means is provided for workers, a clear guiding basis is provided for parameter adjustment, and the real-time monitoring and control of the top-blowing spray gun are realized without depending on manual experience and judgment, so that the working efficiency and the accuracy of parameter adjustment are improved.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of a top-blowing lance control method according to the present disclosure;
Fig. 2 is a schematic structural view of a top-blowing lance suitable for use in practicing some embodiments of the present disclosure.
FIG. 3 is a schematic diagram of a smelting apparatus that implements some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates a flow 100 of some embodiments of a top-blowing lance control method according to the present disclosure. The control method of the top-blowing spray gun is applied to smelting equipment and comprises the following steps:
Step 101, collecting working parameters of a top-blowing spray gun in real time, wherein the working parameters at least comprise flow information, pressure information, temperature information and gun position information;
In some embodiments, the body of execution of the top-blowing lance control method (e.g., the top-blowing lance) may collect the operating parameters of the top-blowing lance in real time or in response to determining that the collection instruction was received. The inlet of the top-blowing spray gun is provided with a gas flow sensor, a temperature sensor, a pressure sensor, a position sensor and the like; gas flow sensors, temperature sensors, pressure sensors, position sensors, etc. may be connected to the distributed control system and displayed.
In some embodiments, the flow information refers to the flow of gas, fuel, or oxygen through the lance. The magnitude of the flow directly influences the injection force and effect generated by the spray gun, and further influences the speed and efficiency of smelting reaction. If the flow rate is too low, the movement of the material in the furnace may not be sufficiently stimulated; if the flow rate is too high, energy waste may be caused, and even uneven temperature distribution in the furnace may be caused.
In some embodiments, the pressure information is another key parameter. The pressure changes may reflect the clogging of the lance, the stability of the gas supply and the degree of wear of the nozzle. Too high or too low a pressure may negatively impact the performance and life of the spray gun. Temperature information is also one of the core parameters in the smelting process. For top-blowing lances, the temperatures that need to be monitored include the temperature of the lance itself and the temperature of the different regions within the furnace. Excessive temperature of the lance may cause deformation or failure of the material, and non-uniformity of temperature in the furnace may affect the quality and efficiency of the smelting reaction.
In some embodiments, the real-time collection of the operating parameters of the top-blowing lance further includes heat distribution information, which refers to the heat distribution of different areas within the furnace. By analyzing the heat distribution, whether the spray mode of the spray gun is reasonable, the motion state of the materials in the furnace and the utilization efficiency of heat energy can be known. Uneven heat distribution may lead to uneven composition of the smelting products or unnecessary energy consumption.
Step 102, inputting the collected working parameters into a pre-trained state analysis model to obtain working state information, wherein the working state information comprises injection effect, abrasion condition and potential fault information.
In some embodiments, the injection effect may include gas flow conditions within the lance, pulverized coal particle flow conditions, heat transfer and distribution conditions, thermal stresses experienced by the lance body, gas-solid distribution conditions of the end mixing zone, and the like. The abrasion condition may include a flushing condition of particles against the inner wall surface, a wearing condition of pulverized coal particles against the cyclone sheet, and the like. Potential fault information may include lance life, operating performance, fault predictions, etc. that are derived by predicting operating conditions within the lance.
In some embodiments, the execution body of the pre-trained state analysis model may be a model calculation host, and the state analysis model may calculate and obtain the working state information of the top-blowing spray gun through integrating and training the following several mathematical models, such as a gas motion equation, a pulverized coal particle motion equation, a momentum conservation equation, a particle drag force model, a fluid turbulence model, an energy conservation model (a gas and particle heat transfer equation and a gas and wall heat transfer equation), and the like.
In some embodiments, the model calculation host is main equipment for performing iterative calculation, and the iterative calculation mode can be to accurately describe the cyclone gas and the material particles in the top-blowing spray gun in the smelting equipment through a self-defined multi-model coupling platform. The coupling multi-model refers to the combination of a plurality of groups of mathematical models related to gas-solid two-phase flow and heat transfer process, wherein the mathematical models comprise a momentum conservation equation of gas and particles, a mass conservation equation, an energy conservation equation and the like, and the three equations are mainly used for calculating the momentum, mass and heat transfer process between the gas and the particles in the spray gun. Wherein the momentum conservation equation is used to calculate the relationship between the change in the velocity field and the pressure gradient, viscous forces and other external forces; the mass conservation equation is used to calculate the distribution and variation of mass in the fluid; while the energy conservation equation is used to calculate the energy transfer and change inside the fluid. Further, a drag model that calculates interactions between particles and fluid, and a turbulence model that calculates turbulence energy of fluid are included.
As an example, the detailed process of iterative computation is divided into the following six steps:
First, a spray gun geometric model is established. The method comprises the steps of defining the structure and the size of a spray gun in actual smelting equipment in detail through modeling software, and outputting a geometric model file in a specific format as a physical model of iterative computation;
and secondly, meshing. The geometric model of the spray gun is accurately meshed, a solving area is divided into a limited number of small areas, and physical characteristics are converted into discrete mathematical forms from continuous forms to be solved.
Third, setting initial boundary conditions: parameters such as gas flow, material feeding flow, gas supply temperature, gas pressure and the like at an inlet of the spray gun in an initial state are defined, and the step is to give initial iteration values before formally calculating a momentum conservation equation, a mass conservation equation and an energy conservation equation based on a Navier-Stokes equation, so that smooth calculation is ensured.
Fourth, a suitable mathematical model is selected. The LES (LARGE EDDY formulation) turbulence model is selected prior to iterative calculations for solving the turbulence intensity of the air flow in the lance past the multi-stage paddle blades and the Wen-Yu model is selected for calculating the drag force magnitude experienced by the material particles, both of which are described in detail below.
Fifth, a suitable discretization method is selected. The finite volume method is selected for discretization calculation of the spray gun, and is one of the most commonly used discretization methods in computational fluid mechanics. It divides the space into discrete finite volumes and then solves the mass, momentum and energy conservation equations for the gas and particles over each volume.
And sixthly, performing formal iterative computation. And calculating the motion process of gas and particles in any space area in the spray gun at each moment and the stress distribution of the inner wall of the spray gun and the paddle blade through an iterative mathematical model.
In some embodiments, the physical model used in the calculation of the model calculation host is completely constructed according to the geometrical structure of the real industrial smelting equipment and the top-blowing spray gun, and the physical model needs to be grid-divided before the calculation of the model calculation host, and the existing grid types are as follows: the invention adopts a tetrahedral grid, a hexahedral grid, a polyhedral grid, a self-adaptive grid and the like, and divides smelting equipment and a spray gun in a mode of combining the tetrahedral grid and the hexahedral grid with higher calculation stability.
In some embodiments, the model calculation host has more selectable model combination schemes, such as a scheme of mutually coupling a gas control equation, a particle control equation, a heat transfer equation, a particle drag model and a fluid turbulence model, and the scheme can truly predict the running state in the spray gun;
As an example, the gas phase control equation to be calculated by the model calculation host involves solving a hydrodynamic equation, which is based on the Navier-Stokes equation to describe the motion of the gas in space and time, and the mass and momentum conservation equation of the gas phase is as follows:
Where θ g,ρg and u g represent the volume fraction, density and velocity, respectively, of the gas phase, P represents the gas pressure, F represents the momentum transfer between the gas and solid phases, τ g represents the gas phase stress tensor. The control equation above may calculate the flow conditions of the gas or oxygen into the lance.
As an example, a particle phase control equation to be calculated by a model calculation host is implemented by a Lagrange method, a particle motion equation describes the motion of each particle by using a lagrangian frame, and the particle motion and momentum equations are as follows:
Where u p denotes the velocity of movement of the particles, x p denotes the distance of movement of the particles, D p denotes the drag coefficient, ρ p denotes the density of the particles, θ p denotes the volume fraction of the particles, τ p denotes the normal stress gradient between the particles, and g denotes the gravitational acceleration of the particles. The above equation describes the flow of pulverized coal particles in the top-blowing lance.
As an example, the fluid phase turbulence model to be calculated by the model calculation host is a large vortex simulation (LES), which has the advantage that a larger scale turbulence structure can be calculated simultaneously with a locally smaller location turbulence structure. The model is suitable for solving the turbulence intensity in the top-blowing spray gun with a complex internal structure, and the basic equation of the LES is obtained by carrying out spatial filtering on the Navier-Stokes equation. The following is the basic equation for LES:
Wherein the method comprises the steps of Is the filtered density,/>Is the filtered velocity component,/>Is the filtered pressure,/>Is the filtered stress tensor, ρu' iu'j is the small scale turbulence stress represented by the subgrid model. Here, the superscript "bar" denotes the filtering operation, while ρu' iu'j is a sub-grid model, representing modeling on small scale turbulence structures. Specific small vortex models are typically based on local characteristics of the flow field, such as simulation of sub-network dimensions, energy transfer, etc.
As an example, the particle phase drag model to be calculated by the model calculation host is a Wen-Yu model, which is suitable for calculating the pneumatic conveying condition in the pipeline, and the expression is as follows:
Where C d is the drag fraction associated with the Reynolds number (Re) and d p is the diameter of the particle. The model is mainly used for calculating the drag force condition of pulverized coal particles in the spray gun in the operation process.
As an example, the solution of the gas-solid two-phase heat transfer process to be calculated by the model calculation host is implemented by an energy conservation model, wherein the energy conservation equations of the gas phase and the particle phase are respectively:
Wherein H g and Δh rg represent the enthalpy and the reaction heat of the gas phase, respectively, S g,p and S g,w represent the heat transfer amounts between the gas phase and the particulate phase, and between the gas phase and the wall surface, respectively, The enthalpy diffusion is represented by lambda g, the gas thermal conductivity, T g, the temperature of the gas, m p, the mass of the particles, C V, the gas specific heat capacity, T p, the temperature of the particles, and Q p,g,Qradi and Q react, respectively, the heat flux between the gas and the solid, the radiant heat flux and the heat flux. The above control equation mainly describes the heat transfer process between the gas or oxygen and the pulverized coal particles in the top-blowing lance (5) and between the gas or oxygen and the wall surface, so as to obtain the situation that the local temperature of the lance is too high.
As an example, the Solver employed by the present disclosure at the model calculation host may be a Pressure-Based Solver (SIMPLE algorithm) that solves the calculation of the internal gas-solid motion state of the top-blowing lance within the smelting plant.
In some embodiments, the pre-trained state analysis model may be trained by:
in step 1021, a sample set is obtained. The samples in the sample set include sample operating parameters and sample operating state information corresponding to characteristics of the sample operating parameters. The purpose of this step is to collect enough data to train the model. The sample set is composed of a plurality of samples, and each sample contains a set of working parameters (such as flow information, pressure information, temperature information and the like) and working state information (such as injection effect, abrasion condition, potential faults and the like) corresponding to the parameters. These samples may be collected from actual operating smelting equipment or may be generated by simulation or emulation experiments. Importantly, the sample set should be sufficiently rich and diverse to cover a wide variety of possible operating conditions and anomalies.
Step 1022, the sample working parameters of at least one sample in the sample set are respectively input into the initial state analysis model, so as to obtain sample working state information corresponding to each sample in the at least one sample.
In some embodiments, the process of training the model is an iterative process that optimizes its performance by continually adjusting the parameters of the model. In each iteration, one or more samples are selected from the sample set and their operating parameters are input into the initial state analysis model. This initial model may be a simple model or a model built based on some prior knowledge. A batch (or all) of samples is randomly selected from the sample set. The operating parameters of these samples are input into an initial state analysis model. The model processes these inputs according to current parameters and internal structure and outputs predicted operating state information.
Step 1023, comparing the working state information corresponding to each sample in the at least one sample with the corresponding sample working state information.
In some embodiments, the model may be processed and analyzed based on the input operating parameters and then output a set of predicted operating state information. This information is the interpretation and judgment of the current input by the model. And comparing the predicted working state information output by the model with the real working state information in the sample. Calculating the difference or error between the two can be done by a loss function. The choice of the loss function depends on the particular problem and model type.
Step 1024, determining whether the initial state analysis model reaches a preset optimization target according to the comparison result.
In some embodiments, an optimization algorithm (e.g., gradient descent) is used to adjust parameters of the model based on the calculated loss. The optimization algorithm updates the parameters of the model according to the gradient information of the loss to reduce the loss of the next iteration. This process is repeated until the performance of the model reaches a satisfactory level or a certain stopping condition is met (e.g., a preset number of iterations is reached, the loss is below a certain threshold, etc.).
And step 1025, in response to determining that the initial state analysis model reaches the optimization target, determining the initial state analysis model as a trained state analysis model.
In some embodiments, during the training process, it is also necessary to evaluate the performance of the model periodically to ensure that it does not suffer from problems such as over-fitting or under-fitting. The evaluation may be accomplished by testing the performance of the model on a validation set (a separate data set from the training set). If the model is found to perform poorly, it may be necessary to adjust the structure of the model, increase the amount of data, use more complex optimization algorithms, etc. When the model achieves satisfactory performance, the model can be saved and deployed into an actual application scene. After deployment, the performance of the model also needs to be monitored periodically and updated and maintained as needed.
Through the training steps, a state analysis model capable of accurately analyzing the working state of the top-blowing spray gun can be obtained, and powerful support is provided for subsequent real-time monitoring and adjustment.
And step 103, generating a working state image according to the working parameters and the working state information, and judging whether the working parameters have abnormal working parameters according to the working state image.
In some embodiments, the working state image is an important element in the top-blowing gun control method, and intuitively shows the working state of the gun, so that an operator or an automatic control system can quickly identify potential abnormality. This image is typically generated based on real-time acquisition of operating parameters (e.g., flow, pressure, temperature, gun position, etc.) and operating state information (e.g., spray effect, wear, potential failure, etc.) obtained by a state analysis model. The image may contain various forms of visualization elements, such as: graph diagram: and showing the change trend of the working parameters with time, and helping to find sudden abnormal fluctuation. Bar graph: the current or relative magnitudes of the different operating parameters are displayed to facilitate comparison and identification of values that deviate from the normal range. Thermodynamic diagrams: the temperature distribution or working intensity of different areas of the spray gun is represented by color change, and possible hot spots or cold spots are revealed. Status indicator lamp: color or symbols are used to indicate the overall operating condition (e.g., normal, warning, malfunction, etc.) of the gun.
In some embodiments, the anomaly determination is based on an analysis of the operating state image and the operating parameters. The system may detect abnormal patterns in the images and parameters or values deviating from normal ranges according to preset rules or algorithms. These rules or algorithms may include: threshold judgment: setting a normal range of the working parameter, and judging as abnormal when the value of a certain parameter exceeds the range. Trend analysis: monitoring the trend of the operating parameter may mean that an abnormal situation occurs if a certain parameter suddenly increases or decreases. Pattern recognition: the model is trained using a machine learning algorithm to identify abnormal patterns, such as abnormal flow fluctuations, pressure changes, etc.
In some embodiments, the generating of the working state image may include the following steps:
step 1031, generating a first sub-image according to the working parameter.
In some embodiments, operating parameters (e.g., flow, pressure, temperature, gun position, etc.) acquired in real-time may be converted into a visual graphical representation. Each parameter may be represented by a different color, line or icon to facilitate quick identification by the operator.
A chart library (e.g., matplotlib, seaborn, etc.) may be employed to plot the parameter over time or histogram. For example, the flow may be represented by a blue curve, the pressure by a red curve, and the temperature and gun position by corresponding graphical elements.
Step 1032, generating a second sub-image based on the operating parameter information.
In some embodiments, the present disclosure may convert operational state information (e.g., injection effects, wear conditions, potential faults, etc.) output by the state analysis model into a visual graphical representation. Such information may be inferred from the model based on operating parameters, which may be relatively abstract to the operator and thus may need to be presented graphically. Color coding, icons or symbols, etc. may be used to represent the different operational status information. For example, the spray effect may be represented by a gradual change from green to red, with green representing good effect and red representing poor effect; wear can be expressed in terms of percentage of wear; the potential failure may be represented by a warning icon or flashing light.
And 1033, performing image fusion processing on the first sub-image and the second sub-image to obtain a fusion parameter image.
In some embodiments, the present disclosure may combine the first sub-image and the second sub-image into a fused parameter image, where the image includes information of the working parameters and the working state at the same time, so as to facilitate the operator to observe and analyze in a unified view. The fusion process of the images may be performed using an image processing library (e.g., openCV). Specific methods include image superimposition, transparency adjustment, color mixing, and the like. The fused image should be able to clearly show the position and trend of each parameter and status information.
In step 1034, image quality detection processing is performed on the fused parameter image to obtain image quality score information.
In some embodiments, the present disclosure may evaluate the quality of the fused parametric image to ensure that the sharpness and accuracy of the image meets the requirements. If the image quality is poor, the judgment of an operator can be affected or misjudgment of an automatic control system can be caused. Quality assessment of the fused parametric image may employ image quality assessment algorithms (e.g., PSNR, SSIM, etc.) to calculate the quality score of the image. These algorithms evaluate the quality of an image based on its brightness, contrast, structural similarity, etc. If the quality score is below a preset threshold, the image may be regenerated or image enhancement processing may be performed.
And step 1035, performing feature extraction processing on the fusion parameter image to obtain fusion image feature information in response to determining that the image quality score information meets a preset score condition.
In some embodiments, the present disclosure may extract useful feature information from the fused parametric image for subsequent operating state image generation. The characteristic information may be texture, shape, color, etc. of the image, which can reflect the operating state and abnormal situation of the spray gun. Specifically, a computer vision library (e.g., openCV, scikit-image, etc.) may be used for the feature extraction process. The specific method comprises edge detection, corner detection, texture analysis and the like. The extracted characteristic information can be expressed in a vector or matrix form, so that the subsequent classification and identification processing is facilitated.
Step 1036, generating a working state image according to the fused image characteristic information and the fused parameter image.
In some embodiments, a final working state image is generated according to the extracted fusion image characteristic information and the original fusion parameter image, and the image intuitively displays the working state of the top-blowing spray gun and possible abnormal conditions, and provides decision basis for operators or provides input signals for an automatic control system.
In some embodiments, determining whether the operating parameter has an abnormal operating parameter based on the operating state image may include the sub-steps of:
a first sub-step of carrying out feature extraction processing on a preset working state image to obtain reference image feature information;
a second sub-step of generating feature similarity information according to the fused image feature information and the reference image feature information;
A third sub-step of determining that the working parameters have no abnormal working parameters in response to determining that the generated feature similarity information meets a preset similarity condition; and or, in response to determining that the generated feature similarity information does not meet the preset similarity condition, determining that the working parameter has an abnormal working parameter.
In some embodiments, the present disclosure may provide for one or more sets of working state images representing normal working states prepared in advance. These images were taken with the top-blowing lance in known good operating conditions and used as a benchmark for comparison. By extracting the characteristics of the images in the preset working states, a group of reference image characteristic information can be obtained. These features may include color, texture, shape, edges, etc., which together constitute a unique identification of the image.
As an example, in a real-time operation, a current working state image is generated according to the collected working parameters and working state information, and feature information, i.e. fused image feature information, is extracted from these real-time generated working state images. And then, comparing the fused image characteristic information with the reference image characteristic information extracted from the preset working state image. This comparison may be achieved by calculating the similarity between the two sets of feature information, and various image processing and machine learning algorithms may be used, such as cosine similarity, structural Similarity Index (SSIM), and the like. The generated similarity information quantifies the degree of similarity between the current working state image and the preset working state image.
Optionally, according to the calculated feature similarity information, the system determines whether the current working state deviates from the normal working state, which can be achieved by comparing the feature similarity information with a preset threshold. If all or most of the feature similarity information is higher than the preset threshold value, the current working state is considered to be similar to the preset normal working state, and therefore no abnormality exists in the working parameters. Conversely, if one or more of the feature similarity information is below a predetermined threshold, this indicates that the current operating state is significantly different from the predetermined normal operating state. In this case, the system may determine that there is an abnormality in the operating parameters and may trigger an alarm or other response.
And step 104, adjusting the abnormal working parameters to the preset working conditions of the top-blowing spray gun in response to the fact that the abnormal working parameters exist in the working parameters.
In some embodiments, when the system detects an abnormality in an operating parameter, appropriate action may be taken to address such an abnormality. May include automatically adjusting operating parameters to attempt to revert to normal operating conditions, sending an alert to an operator for manual intervention, or initiating more detailed fault diagnosis and repair procedures. These response measures aim to ensure the continuity and safety of the smelting process. The preset working conditions are a set of ideal working parameter values preset according to smelting process requirements, equipment performance limitations, operation experience and other factors. When the system detects abnormal operating parameters, it automatically or through manual intervention adjusts these parameters so that they re-conform to the preset operating conditions. Abnormal working parameters can be timely adjusted, the interruption of the smelting process can be avoided, and the production loss is reduced. By keeping the lance operating in a stable operating state, continuity of the smelting process and consistency of product quality can be ensured.
The foregoing is an invention point of the embodiments of the present disclosure, solving the technical problems mentioned in the background art, where the physicochemical processes such as multiphase flow and high temperature reaction inside the top-blown lance are very difficult to monitor and control in real time, and relying on experience and intuition of an operator to perform adjustment is not only inefficient, but also has great uncertainty and risk. Firstly, working parameters of the top-blowing spray gun are collected in real time, and are analyzed by utilizing a pre-trained state analysis model, and the method can realize real-time monitoring and intelligent evaluation of the working state of the spray gun. The transparency and the controllability of the smelting process are improved, and potential problems can be found and solved in time, so that the continuity and the stability of the smelting process are ensured. Secondly, the method can accurately identify the information such as the injection effect, the abrasion condition and the potential faults of the spray gun. The method provides precious decision support for operators, so that the operators can adjust working parameters of the spray gun according to actual conditions, optimize smelting process and improve product quality and smelting efficiency. In addition, by automatically generating the working state image and judging whether the working parameters are abnormal, the method realizes visual display and intelligent diagnosis of the working state of the spray gun. The working strength of operators is greatly reduced, the possibility of human errors is reduced, and the safety and reliability of the smelting process are improved.
Referring now to FIG. 2, a schematic structural diagram of a top-blowing lance 200 suitable for use in practicing some embodiments of the present disclosure is shown. The top-blowing lance illustrated in fig. 2 is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure in any way.
As shown in fig. 2, the top-blowing lance 200 may include a processing device (e.g., a central processor, a graphics processor, etc.) 201 that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 202 or a program loaded from a storage device 208 into a Random Access Memory (RAM) 203. In the RAM 203, various programs and data necessary for the operation of the top-blowing lance 200 are also stored. The processing device 201, ROM 202, and RAM 203 are connected to each other through a bus 204. An input/output (I/O) interface 205 is also connected to bus 204.
In general, the following devices may be connected to the I/O interface 205: input devices 206 including, for example, a touch screen, touchpad, keyboard, image capture device, microphone, accelerometer, gyroscope, etc.; an output device 207 including, for example, a Liquid Crystal Display (LCD), a sound playing apparatus, a vibrator, and the like; storage 208 including, for example, magnetic tape, hard disk, etc.; a communication device 209. The communication means 209 may allow the top-blowing lance 200 to communicate wirelessly or by wire with other devices to exchange data; a spray gun assembly 210. While fig. 2 illustrates a top-blowing lance 200 having various devices, it is to be understood that not all of the illustrated devices are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 2 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication device 209, or from the storage device 208, or from the ROM 202. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 201.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the top-blowing lance; or may be present alone without being fitted into the top-blowing lance. The computer readable medium carries one or more programs which, when executed by the top-blowing lance, cause the top-blowing lance to: collecting working parameters of a top-blowing spray gun in real time, wherein the working parameters at least comprise flow information, pressure information, temperature information and gun position information; inputting the collected working parameters into a pre-trained state analysis model to obtain working state information, wherein the working state information comprises injection effect, abrasion condition and potential fault information; generating a working state image according to the working parameters and the working state information, and judging whether abnormal working parameters exist in the working parameters according to the working state image; and in response to determining that the working parameters have abnormal working parameters, adjusting the abnormal working parameters to preset working conditions of the top-blowing spray gun. Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
Referring now to FIG. 3, a schematic diagram of a smelting apparatus suitable for use in practicing some embodiments of the present disclosure is shown. The smelting apparatus shown in fig. 3 is only one example and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure in any way.
The smelting plant includes a top-blowing lance as described in any one of the implementations of the second aspect above.
In some embodiments, as shown in fig. 3, the smelting apparatus 300 may include a smelting furnace 1, a distributed control system 2, a model calculation host 3, an image display unit 4, a top-blowing lance 5, a lance position controller 6, an oxygen supply controller 7, a feed controller 8, an air compressor 9, and a silo 10.
As an example, a temperature sensor, a pressure sensor and a gas flow sensor are installed on the top-blowing lance 5 of the smelting furnace 1, an electric signal on the sensor is converted into a digital signal by the distributed control system 2 to be displayed, the temperature, the flow and the pressure of the inlet of the lance are taken into the model calculation host 3 as initial conditions to be calculated, the calculation result is displayed by the image display unit 4, the physical abrasion and bending deformation of the blades in the lance are reduced by adjusting the temperature and the flow of the gas at the inlet of the lance and the feeding quantity, the position of the lance is controlled by the lance position controller 6, and the output flow of the air compressor 9 and the feed bin 10 is controlled by the oxygen supply controller 7 and the feed controller 8.
In some embodiments, to reduce physical wear and bending deformation of the blades within the lance, the operator may flexibly adjust the gas temperature, flow rate, and feed rate at the lance inlet based on feedback from the image display unit 4. The gun position controller 6 ensures accurate control of the position of the spray gun, and the oxygen supply controller 7 and the feed controller 8 respectively and accurately regulate the output flow of the air compressor 9 and the feed bin 10, so as to jointly maintain the optimal working state of the spray gun.
In some embodiments, the distributed control system 2 is configured to closely connect a plurality of subsystems through a communication network, so as to realize comprehensive monitoring, real-time adjustment and continuous optimization of the whole smelting process. The top-blowing spray gun 5 enhances the stirring, mixing and gas phase reaction of a smelting molten pool by spraying gas at a high speed, thereby remarkably improving the smelting efficiency and the control precision. Meanwhile, the gun position controller 6, the oxygen supply controller 7 and the feeding controller 8 work cooperatively, so that accurate control of the immersion depth of the top-blowing spray gun, the oxygen supply flow of the pipeline and the powder feeding flow is ensured.
In some embodiments, the image display unit 4 is used as a core component of the model computing system, not only to receive and display the computation results from the model computing host 3, but also to provide a complete set of image processing functions for in-depth analysis, visual presentation and interpretation of the simulation results. The set of visual tools with comprehensive and powerful functions, including charts, images and animations, can clearly show the changes of physical quantities such as flow fields, temperature distribution, stress conditions and the like in space-time dimensions; the track tracking function can accurately analyze the motion track of the tracking objects such as fluid, particles and the like; the drawing function of the field lines and the contour lines can intuitively reveal the distribution rules of physical fields such as a flow field, a temperature field, a pressure field and the like; the statistical analysis tool can carry out deep statistical analysis on the simulation result and extract key statistical information such as mean, variance, probability density function and the like; the dynamics analysis function can be used for researching dynamics characteristics of the spray gun system, such as vibration, strain, stress and the like; thermodynamic analysis can be used to analyze thermodynamic properties such as heat flow and heat conduction of gas and particles.
In some embodiments, in the application scenario of the metallurgical top-blowing lance smelting technology, the image display unit 4 can comprehensively and finely display the multidimensional physical parameter distribution inside the lance, such as the temperature, speed and pressure distribution of gas and particles, the gas turbulence characteristics, the particle residence time, the volume fraction, the particle size distribution, the abrasion loss of the wall surface, the thermal stress distribution of the lance body and the swirl plate, the gas-solid flow field and the temperature distribution of the mixing area at the tail end of the lance body, and the like. This rich and detailed data provides researchers with a window of deep insight into the operating conditions and performance of the lance.
In some embodiments, researchers can visually observe the real-time state of key physical quantities such as temperature, speed, pressure, turbulence energy, thermal stress distribution and the like at any position in the spray gun through the image display unit 4. Once the local or overall severe wear and deformation conditions are found, they can quickly determine the irrational nature of the current operating parameters and make optimal adjustments accordingly. The adjusting mode comprises that the air supply flow entering the top-blowing spray gun is increased or decreased, the air supply flow is increased or decreased, the air supply temperature is adjusted, and the like, so that the fine control on the gas-solid two-phase flow process of the spray gun is realized, and the abrasion and deformation risks are reduced. These adjustments may be precisely made by the distributed control system 2, wherein key components of the gun position controller 6, oxygen supply controller 7, and feed controller 8 work cooperatively to ensure that the gun is operating in an optimal condition.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (10)
1. A top-blowing spray gun control method is applied to smelting equipment and comprises the following steps:
Collecting working parameters of the top-blowing spray gun in real time, wherein the working parameters at least comprise flow information, pressure information, temperature information and gun position information;
Inputting the collected working parameters into a pre-trained state analysis model to obtain working state information, wherein the working state information comprises injection effect, abrasion condition and potential fault information;
Generating a working state image according to the working parameters and the working state information, and judging whether abnormal working parameters exist in the working parameters according to the working state image;
And in response to determining that the working parameters have abnormal working parameters, adjusting the abnormal working parameters to preset working conditions of the top-blowing spray gun.
2. The method of claim 1, wherein the state analysis model is trained by:
obtaining a sample set, wherein a sample in the sample set comprises a sample working parameter and sample working state information corresponding to the characteristics of the sample working parameter;
the following training steps are performed based on the sample set:
Respectively inputting sample working parameters of at least one sample in a sample set into an initial state analysis model to obtain sample working state information corresponding to each sample in the at least one sample;
Comparing the working state information corresponding to each sample in the at least one sample with the corresponding sample working state information;
determining whether the initial state analysis model reaches a preset optimization target according to the comparison result;
and in response to determining that the initial state analysis model reaches the optimization target, determining the initial state analysis model as a trained state analysis model.
3. The method of claim 2, wherein training the state analysis model further comprises:
in response to determining that the initial state analysis model does not meet the optimization objective, adjusting training parameters of the initial state analysis model, and using unused samples to form a sample set, using the adjusted initial state analysis model as the initial state analysis model, performing the training step again.
4. The method of claim 1, wherein the method further comprises:
and in response to determining that the abnormal working parameters are adjusted to the preset working conditions, controlling the top-blowing spray gun to execute blowing work according to the adjusted working parameters.
5. The method of claim 1, wherein the method further comprises:
And transmitting potential fault information to a preset terminal in response to determining that the working parameter does not have abnormal working parameters.
6. The method of claim 1, wherein the generating an operating state image comprises the steps of:
generating a first sub-image according to the working parameters;
Generating a second sub-image according to the working parameter information;
performing image fusion processing on the first sub-image and the second sub-image to obtain a fusion parameter image;
performing image quality detection processing on the fusion parameter image to obtain image quality score information;
responding to the fact that the image quality score information meets a preset score condition, and carrying out feature extraction processing on the fusion parameter image to obtain fusion image feature information;
And generating the working state image according to the fusion image characteristic information and the fusion parameter image.
7. The method of claim 6, wherein the method further comprises:
Performing feature extraction processing on the image in the preset working state to obtain feature information of the reference image;
Generating feature similarity information according to the fused image feature information and the reference image feature information;
determining that the working parameters do not have abnormal working parameters in response to determining that the generated feature similarity information meets a preset similarity condition;
And/or, the number of the groups,
And determining that the working parameters have abnormal working parameters in response to the fact that the generated feature similarity information does not meet the preset similarity condition.
8. A top-blowing lance comprising:
One or more processors;
a spray gun assembly;
A storage device having one or more programs stored thereon,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.
9. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-7.
10. A smelting plant comprising a top-blowing lance defined in claim 8.
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