Deep learning-based batching model collaboration method and equipment for three-step stainless steel smelting and medium

Abstract

The invention discloses a deep learning-based batching model collaboration method and equipment for three-step stainless steel smelting, and a medium. Comprising the following steps: S1, acquiring historical raw and auxiliary material usage amount data, historical energy medium usage amount, tapping molten steel components, tapping molten steel temperature, raw and auxiliary material cost and energy medium cost data, constructing a data set, and dividing the data set into a training set, a test set and a verification set; s2, obtaining calculation data of raw and auxiliary materials and an energy medium of the batching model; s3, constructing batching model collaboration based on cost optimization; s4, inputting the calibrated batching model calculation data into a batching model based on cost optimization for cooperation, and outputting predicted tapping molten steel components, tapping molten steel temperature and batching prediction cost; s5, collecting current raw and auxiliary materials, energy medium usage amount data and cost in real time; and S6, based on deep learning, realizing cost-optimal batching model cooperation, and performing operation according to the corrected calculation data of the raw and auxiliary materials and the energy medium.

Description

Technical Field

[0001] The present invention relates to a deep learning-based batching model collaboration method, system, equipment and medium for three-step stainless steel smelting. Background Art

[0002] The three-step stainless steel smelting process includes the following steps: the first step is to melt nickel iron in an electric furnace and chromium iron in an intermediate frequency furnace; the second step is to blend the molten nickel iron and chromium iron mother liquors in proportion and pour them into the AOD furnace to smelt stainless steel mother liquor; the third step is to pour the stainless steel mother liquor into the LF furnace or VOD furnace to smelt it into qualified stainless steel liquid. For the stainless steel smelting industry, the steelmaking process is a link in steel production with a high proportion of raw material costs, high energy consumption, and high carbon emissions. In the process of the steel industry's transformation to green and low-carbon, solving the problem of cost-optimal batching for steelmaking and smelting has become the key to promoting the green economic operation of enterprises. By rationally planning the batching of electric furnaces, intermediate frequency furnaces, AOD furnaces, LF furnaces, and VOD furnaces, stainless steel smelting enterprises can achieve the economical use of raw materials, auxiliary materials, and energy media to obtain the maximum cost-effectiveness. Through scientifically optimized batching plans and adjustments, stainless steel smelting enterprises can achieve environmentally friendly and low-carbon production methods. Moreover, this helps to establish a sustainable economic model and promote the steel industry to develop in a more environmentally friendly, efficient, and sustainable direction.

[0003] In the existing technology, the electric furnace is equipped with an electric furnace batching model, the medium frequency furnace is equipped with a medium frequency furnace batching model, the AOD furnace is equipped with an AOD furnace batching model, the LF furnace is equipped with a LF furnace batching model, and the VOD furnace is equipped with a VOD furnace batching model. These five models operate independently and serve their respective processes. Moreover, these batching models are mainly used to smelt molten steel with qualified composition, and basically do not consider the coordination cost between processes. Summary of the invention

[0004] In order to overcome the above-mentioned defects, the purpose of the present invention is to provide a deep learning-based batching model collaborative method, system, equipment and medium for three-step stainless steel smelting.

[0005] To achieve the above object, the present invention provides a deep learning-based batching model collaboration method for three-step stainless steel smelting, wherein the method comprises the following steps:

[0006] S1: Obtain the historical raw material usage data of electric furnace, medium frequency furnace, AOD furnace, LF furnace, VOD furnace, the historical energy medium usage corresponding to the historical raw material usage data, the composition of molten steel, the temperature of molten steel, the cost of raw materials, the cost of energy medium, etc., construct a data set, and divide the data set into a training set, a test set, and a validation set;

[0007] S2: Obtain the calculation data of raw materials and energy media of the batching models of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, and calibrate the calculation data of the batching models of raw materials and energy media through the historical raw materials and energy media usage data;

[0008] S3: Construct a batching model collaboration based on cost optimization for electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, divide the data set into a training set, a test set, and a verification set in turn, input the data set into the model collaboration, train, test, and verify the model, and determine whether the verification result meets the requirements. If so, execute step S4, if not, re-execute step S1;

[0009] S4: input the calibrated batching model calculation data of raw materials, auxiliary materials and energy media into the batching model based on cost optimization of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace, and output the predicted molten steel composition, molten steel temperature and batching prediction cost of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace;

[0010] S5: Real-time collection of the current raw and auxiliary materials, energy medium usage data and the current raw and auxiliary materials, energy medium costs of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, and correction of the predicted costs of raw and auxiliary materials and energy medium based on the current raw and auxiliary materials, energy medium usage data and the current raw and auxiliary materials, energy medium costs;

[0011] S6: The cost-optimal batching model for electric furnaces, medium frequency furnaces, AOD furnaces, LF furnaces, and VOD furnaces based on deep learning is coordinated with the calculated data of corrected raw materials, auxiliary materials, and energy media.

[0012] Furthermore, the raw and auxiliary materials, energy medium usage, and model calculation data include the usage, model calculation data of scrap steel, ferronickel, ferrochrome, iron-containing raw materials, carbon, flux, slag-forming agents, and the energy medium usage, model calculation data of water, electricity, oxygen, nitrogen, argon, and compressed air.

[0013] Furthermore, the cost data of raw materials and energy media include cost data of scrap steel, ferronickel, ferrochrome, iron-containing raw materials, carbon, flux, slag-forming agents, and cost data of energy media such as water, electricity, oxygen, nitrogen, argon, and compressed air.

[0014] Furthermore, the step S2 includes the following specific processes:

[0015] S21: Obtain the calculation data of raw materials, auxiliary materials and energy media of the batching models of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace within the specified period;

[0016] S22: Extract the raw materials and energy medium usage data of the same period from the historical raw materials and energy medium usage data;

[0017] S23: Based on the extracted usage data of raw materials, auxiliary materials and energy media in the same period, compensation calibration is performed on the calculated data of raw materials, auxiliary materials and energy media in the corresponding period according to the specified ratio.

[0018] Furthermore, the step S3 includes the following specific processes:

[0019] S31: Construct the batching model coordination based on cost optimization for electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace;

[0020] S32: inputting the training set into the coordination of the batching model based on cost optimization of the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace, and the VOD furnace to train the model;

[0021] S33: Input the test set into the coordination of the batching model based on cost optimization of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, test the trained model, and judge whether the corresponding indicators of the test results meet the requirements. If so, execute step S34; if not, re-input a new training set to train the model;

[0022] S34: Input the verification set into the cost-optimized batching model collaboration of the tested electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace to verify the test results and determine whether the verification results meet the requirements. If so, execute step S4; if not, re-execute step S1.

[0023] Furthermore, in step S5, the process of correcting the predicted cost of raw materials and energy media by using the current raw materials and energy media usage data and the current raw materials and energy media costs is as follows:

[0024] S51: Calculate the cost of raw materials and energy media based on the calibrated calculation data of raw materials and energy media;

[0025] S52: Create a composite function of the predicted composition of molten steel, the predicted temperature of molten steel and the predicted cost of batching for electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace;

[0026] S53: Calculate the optimal solution for the composite function of the composition of the molten steel, the temperature of the molten steel and the predicted cost of the batching for the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace and the VOD furnace;

[0027] S54: Correcting the electric furnace batching model, the medium frequency furnace batching model, and the AOD furnace batching model of the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace, and the VOD furnace according to the specified ratio through the optimal solution.

[0028] Furthermore, in step S52, the formula for creating a composite function that ensures that the composition and temperature of the molten steel are qualified and the operating cost is optimal is:

[0029] f(x)=f 1EF (x)+f 1MF (x)+f 1AOD (x)+f 1LF (x)+f 1VOD (x)+f 2EF (x)+f 2MF (x)+f 2AOD (x)+f 2LF (x)+f 2VOD (x)+f 3EF (x)+f 3MF (x)+f 3AOD (x)+f 3LF (x)+f 3VOD (x)

[0030] Among them, f 1EF (x) is the composition function of the steel produced by the electric furnace, f 1MF (x) is the steel composition function of the medium frequency furnace, f 1AOD (x) is the composition function of the steel produced by the AOD furnace, f 1LF (x) is the steel composition function of LF furnace, f 1VOD (x) is the COD furnace steel composition function, f 2EF (x) is the temperature function of the electric furnace, f 2MF (x) is the tapping temperature function of the medium frequency furnace, f 2AOD (x) is the tapping temperature function of the AOD furnace, f 2LF (x) is the tapping temperature function of the LF furnace, f 2VOD (x) is the tapping temperature function of the VOD furnace, f 3EF (x) is the cost function of the electric furnace, f 3MF (x) is the cost function of the medium frequency furnace, f 3AOD (x) is the AOD furnace cost function, f 3LF (x) is the LF furnace cost function, f 3VOD (x) is the VOD furnace cost function.

[0031] To achieve the above objectives, the terminal device of the present invention includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor loads and executes the computer program, the above-mentioned deep learning-based batching model collaborative method for three-step stainless steel smelting is adopted.

[0032] To achieve the above-mentioned purpose, the present invention provides a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the above-mentioned deep learning-based batching model collaborative method for three-step stainless steel smelting is adopted.

[0033] The present invention provides a cost-optimized batching model collaboration based on deep learning for three-step stainless steel smelting, which obtains data such as the historical raw and auxiliary material usage, energy medium usage, molten steel composition, molten steel temperature, raw and auxiliary material cost, and energy medium cost of electric furnaces, medium frequency furnaces, AOD furnaces, LF furnaces, and VOD furnaces to build a data set; obtains the calculation data of raw and auxiliary materials and energy medium of each batching model of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, and calibrates the calculation data of raw and auxiliary materials and energy medium through the historical raw and auxiliary materials and energy medium usage; constructs a batching model collaboration based on cost optimization for the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, inputs the data set into the model in turn, and calibrates the model Training, testing and verification, and judging whether the verification results meet the requirements. If so, input the calibrated raw and auxiliary materials, energy medium batching model calculation data into the cost-optimized batching model of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace to output the predicted molten steel composition, molten steel temperature, and batching prediction cost of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace; collect the current raw and auxiliary materials, energy medium usage data and the current raw and auxiliary materials, energy medium costs of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace in real time, and run the batching model based on deep learning to achieve the optimal cost of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace with the corrected calculation data of raw and auxiliary materials and energy medium.

[0034] On the one hand, the coordination of the cost-optimal batching model based on deep learning can make the batching models of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace complement each other and achieve stable molten steel composition and molten steel temperature output. On the other hand, the cost-effectiveness of the three-step stainless steel smelting is improved, the smelting cost is effectively reduced, and the economic benefits are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of a collaborative method for implementing a cost-optimal batching model based on deep learning for three-step stainless steel smelting according to the present invention. DETAILED DESCRIPTION

[0036] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0037] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0038] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0039] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] The three-step stainless steel smelting of the present invention refers to the following steps: the first step is to melt ferronickel in an electric furnace and ferrochromium in a medium frequency furnace; the second step is to blend the molten ferronickel and ferrochromium mother liquors in proportion and pour them into an AOD furnace to smelt them into stainless steel mother liquor; the third step is to pour the stainless steel mother liquor into an LF furnace or a VOD furnace to smelt them into qualified stainless steel molten steel. Depending on the raw materials, the electric furnace can independently provide molten steel mother liquor to the AOD furnace, and the medium frequency furnace can also independently provide molten steel mother liquor to the AOD furnace, that is, the first step can only have an electric furnace, or only have a medium frequency furnace, or both an electric furnace and a medium frequency furnace. According to the composition and temperature requirements of the final molten steel, the stainless steel mother liquor smelted in the AOD furnace can be provided to one of the LF furnace or the VOD furnace, that is, the third step can be either the LF furnace or the VOD furnace.

[0041] The purpose of this invention is to realize the coordination of the batching model with the best cost. The batching model of the electric furnace, the batching model of the medium frequency furnace, the batching model of the AOD furnace, the batching model of the LF furnace, and the batching model of the VOD furnace are used to achieve the coordination of the batching model with the best cost. The batching model of the electric furnace, the batching model of the medium frequency furnace, the batching model of the AOD furnace, the batching model of the LF furnace, and the batching model of the VOD furnace are used to achieve the coordination of the batching model with the best cost. The batching model of the electric furnace and the batching model of the medium frequency furnace are optional.

[0042] See attached Figure 1 As shown, the collaborative method of achieving cost-optimal batching model based on deep learning for three-step stainless steel smelting of the present invention comprises the following steps:

[0043] S1: Obtain the historical raw material usage data of electric furnace, medium frequency furnace, AOD furnace, LF furnace, VOD furnace, the historical energy medium usage corresponding to the historical raw material usage data, the composition of molten steel, the temperature of molten steel, the cost of raw materials, the cost of energy medium, etc., construct a data set, and divide the data set into a training set, a test set, and a validation set;

[0044] S2: Obtain the calculation data of raw materials and energy media of the batching models of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, and calibrate the calculation data of the batching models of raw materials and energy media through the historical raw materials and energy media usage data;

[0045] S3: Construct a batching model collaboration based on cost optimization for electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, divide the data set into a training set, a test set, and a verification set in turn, input the data set into the model, train, test, and verify the model, and determine whether the verification result meets the requirements. If so, execute step S4, if not, re-execute step S1;

[0046] S4: input the calibrated batching model calculation data of raw materials, auxiliary materials and energy media into the batching model based on cost optimization of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace, and output the predicted molten steel composition, molten steel temperature and batching prediction cost of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace;

[0047] S5: Real-time collection of the current raw and auxiliary materials, energy medium usage data and the current raw and auxiliary materials, energy medium costs of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, and correction of the predicted costs of raw and auxiliary materials and energy medium based on the current raw and auxiliary materials, energy medium usage data and the current raw and auxiliary materials, energy medium costs;

[0048] S6: The cost-optimal batching model for electric furnaces, medium frequency furnaces, AOD furnaces, LF furnaces, and VOD furnaces based on deep learning is coordinated with the calculated data of corrected raw materials, auxiliary materials, and energy media.

[0049] Step S1 is mainly to provide a data basis for subsequent model training, testing and verification, so that the model has the learning ability to meet the corresponding requirements. In the process of data preparation, it is necessary to obtain comprehensive historical raw and auxiliary material usage data of electric furnaces, medium frequency furnaces, AOD furnaces, LF furnaces, and VOD furnaces in various periods, historical energy medium usage corresponding to historical raw and auxiliary material usage data, steel-making molten steel composition, steel-making molten steel temperature, raw and auxiliary material costs, energy medium costs and other data. After obtaining the relevant data, it is necessary to pre-process the acquired data, and the pre-processing process includes data cleaning, data conversion and data normalization. The process of data cleaning is mainly to remove invalid data, abnormal data, duplicate data or noise data in the data. The purpose of data conversion is to convert the data that is not easy to analyze into data that is more convenient to analyze. The purpose of data normalization is to limit the processed data to a certain range and summarize the statistical distribution of unified data samples.

[0050] In step S2, the calculation data of raw materials and energy media of each batching model of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace are obtained, because the calculation data is crucial to the smelting system. The smelting system performs smelting according to the calculation data of the aforementioned batching model, so that the smelting system can operate stably and produce molten steel with qualified composition and qualified temperature. However, the calculation data is not completely accurate, and the batching data of the same workshop in the same cycle over a long period of time has a certain regularity. Therefore, it is of great significance to correct the calculation data of the batching model by obtaining the historical raw materials and energy medium usage.

[0051] In steps S3 and S4, a machine learning model that is coordinated with a cost-optimized batching model is constructed, and the constructed model is trained, tested, and verified by obtaining relevant historical data. This allows the model to have a learning ability that meets certain requirements, to be able to achieve the production of qualified products at a lower operating cost based on cost optimization, and to have lower operating costs and higher economic benefits.

[0052] The cost-optimized batching model constructed above works together as a convolutional neural network model. The process of building a convolutional neural network model includes building a convolutional layer, a pooling layer, and a fully connected layer. At the same time, corresponding parameters are set for each constructed layer, including the size and step size of the convolution kernel. At the same time, the activation function and loss function must be selected, and the optimizer must be set.

[0053] In one embodiment, the first convolution layer has 4 input channels, 8 output channels, a filter / kernel sliding step size of 1 in the convolution layer, and 0 is added to the edge of the input data during the convolution operation, and the maximum pooling size is 5. The second convolution layer has 8 input channels, 18 output channels, a filter / kernel sliding step size of 1 in the convolution layer, and 0 is added to the edge of the input data during the convolution operation, and the maximum pooling size is 5. The third fully connected layer has 324 input channels and 135 output channels. The fourth fully connected layer has 135 input channels and 96 output channels, and the output layer has 96 fully connected channels and 12 output channels. The activation function uses the Sigmoid activation function, and the loss function uses the Manhattan distance loss function.

[0054] During the training of the convolutional neural network model, the output of the model is calculated through forward propagation, and the error between the predicted value and the true value is calculated based on the loss function. Then, the gradient is calculated through the backpropagation algorithm, and the network parameters are updated using the optimizer to minimize the loss function. This process will be iterated many times until the preset number of iterations is reached or the loss function reaches a lower value. At the same time, hyperparameters such as the learning rate need to be adjusted during the training process to balance the training speed and generalization ability of the model. The optimizer selects the Adam optimization algorithm to update the model parameters.

[0055] Step S2 includes the following specific processes:

[0056] S21: Obtain the calculation data of raw materials, auxiliary materials and energy media of the batching models of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace within the specified period;

[0057] S22: Extract the usage data of raw materials, auxiliary materials and energy media in the same period from the historical usage data of raw materials, auxiliary materials and energy media;

[0058] S23: Based on the extracted usage data of raw materials, auxiliary materials and energy media in the same period, compensation calibration is performed on the calculated data of raw materials, auxiliary materials and energy media in the corresponding period according to the specified ratio.

[0059] Step S3 includes the following specific processes:

[0060] S31: Construct the batching model coordination based on cost optimization for electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace;

[0061] S32: inputting the training set into the coordination of the batching model based on cost optimization of the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace, and the VOD furnace to train the model;

[0062] S33: Input the test set into the coordination of the batching model based on cost optimization of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, test the trained model, and judge whether the corresponding indicators of the test results meet the requirements. If so, execute step S34; if not, re-input a new training set to train the model;

[0063] S34: Input the verification set into the cost-optimized batching model collaboration of the tested electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace to verify the test results and determine whether the verification results meet the requirements. If so, execute step S4; if not, re-execute step S1.

[0064] In step S5, the process of correcting the predicted cost of raw materials and energy media by using the current raw materials and energy media usage data and the current raw materials and energy media costs is as follows:

[0065] S51: Calculate the cost of raw materials and energy media based on the calibrated calculation data of raw materials and energy media;

[0066] S52: Create a composite function of the predicted composition of molten steel, the predicted temperature of molten steel and the predicted cost of batching for electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace;

[0067] S53: Calculate the optimal solution for the composite function of the composition of the molten steel, the temperature of the molten steel and the predicted cost of the batching for creating the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace and the VOD furnace;

[0068] S54: Correcting the electric furnace batching model, the medium frequency furnace batching model, and the AOD furnace batching model of the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace, and the VOD furnace according to the specified ratio through the optimal solution.

[0069] Preferably, the formula for creating a composite function in step S52 to ensure that the composition and temperature of the molten steel are qualified and the operating cost is optimal is:

[0070] f(x)=f 1EF (x)+f 1MF (x)+f 1AOD (x)+f 1LF (x)+f 1VOD (x)+f 2EF (x)+f 2MF (x)+f 2AOD (x)+f 2LF (x)+f 2VOD (x)+f 3EF (x)+f 3MF (x)+f 3AOD (x)+f 3LF (x)+f3VOD (x)

[0071] Among them, f 1EF (x) is the composition function of the steel produced by the electric furnace, f 1MF (x) is the steel composition function of the medium frequency furnace, f 1AOD (x) is the composition function of the steel produced by the AOD furnace, f 1LF (x) is the steel composition function of LF furnace, f 1VOD (x) is the COD furnace steel composition function, f 2EF (x) is the function of the steel-out temperature of the electric furnace, f 2MF (x) is the tapping temperature function of the medium frequency furnace, f 2AOD (x) is the tapping temperature function of the AOD furnace, f 2LF (x) is the tapping temperature function of the LF furnace, f 2VOD (x) is the tapping temperature function of the VOD furnace, f 3EF (x) is the cost function of the electric furnace, f 3MF (x) is the cost function of the medium frequency furnace, f 3AOD (x) is the AOD furnace cost function, f 3LF (x) is the LF furnace cost function, f 3VOD (x) is the VOD furnace cost function.

[0072] In summary, the present invention provides a cost-optimal batching model collaboration method based on deep learning for three-step stainless steel smelting, which obtains the historical raw and auxiliary material usage, energy medium usage, molten steel composition, molten steel temperature, raw and auxiliary material cost, energy medium cost and other data of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace to construct a data set; obtains the calculation data of raw and auxiliary materials and energy medium of each batching model of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace, and calibrates the calculation data of raw and auxiliary materials and energy medium through the historical raw and auxiliary materials and energy medium usage; constructs the batching model collaboration based on cost optimization for the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace, inputs the data set into the model in turn, and calibrates the model The model is trained, tested and verified, and it is determined whether the verification result meets the requirements. If so, the calibrated raw and auxiliary materials, energy medium batching model calculation data are input into the cost-optimized batching model collaboration of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, and the predicted molten steel composition, molten steel temperature, and batching prediction cost of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace are output; the current raw and auxiliary materials, energy medium usage data and the current raw and auxiliary materials, energy medium costs of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace are collected in real time, and the cost-optimized batching model of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace based on deep learning is run with the corrected calculation data of raw and auxiliary materials and energy medium.

[0073] An embodiment of the present application also discloses a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor adopts the deep learning-based batching model collaborative method for three-step stainless steel smelting of the above-mentioned embodiment when executing the computer program.

[0074] The terminal device may be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device may also include input and output devices, a network access device and a bus.

[0075] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0076] Among them, the memory can be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device, or it can be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the terminal device, etc., and the memory can also be a combination of an internal storage unit and an external storage device of the terminal device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0077] Among them, through this terminal device, the deep learning-based batching model collaborative method for three-step stainless steel smelting of the above-mentioned embodiment is stored in the memory of the terminal device, and is loaded and executed on the processor of the terminal device for user convenience.

[0078] An embodiment of the present application further discloses a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the deep learning-based batching model collaborative method for three-step stainless steel smelting of the above embodiment is adopted.

[0079] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0080] Among them, through this computer-readable storage medium, the deep learning-based batching model collaborative method for three-step stainless steel smelting of the above-mentioned embodiment is stored in a computer-readable storage medium, and is loaded and executed on a processor to facilitate the storage and application of the deep learning-based batching model collaborative method for three-step stainless steel smelting.

[0081] The present invention is described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of ordinary technicians in the field without departing from the purpose of the present invention. Many other changes and modifications that do not depart from the concept and scope of the present invention should be regarded as the protection scope of the present invention.

[0082] In the description of this specification, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0083] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A deep learning-based batching model collaboration method for three-step stainless steel smelting, characterized by: The method comprises the following steps: S1: Obtain the historical raw material usage data of electric furnace, medium frequency furnace, AOD furnace, LF furnace, VOD furnace, the historical energy medium usage corresponding to the historical raw material usage data, the composition of molten steel, the temperature of molten steel, the cost of raw materials, the cost of energy medium, etc., construct a data set, and divide the data set into a training set, a test set, and a validation set; S2: Obtain the calculation data of raw materials and energy media of the batching models of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, and calibrate the calculation data of the batching models of raw materials and energy media through the historical raw materials and energy media usage data; S3: Construct a batching model collaboration based on cost optimization for electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, divide the data set into a training set, a test set, and a verification set in turn, input the data set into the model collaboration, train, test, and verify the model, and determine whether the verification result meets the requirements. If so, execute step S4, if not, re-execute step S1; S4: input the calibrated batching model calculation data of raw materials, auxiliary materials and energy media into the batching model based on cost optimization of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace, and output the predicted molten steel composition, molten steel temperature and batching prediction cost of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace; S5: Real-time collection of the current raw and auxiliary materials, energy medium usage data and the current raw and auxiliary materials, energy medium costs of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, and correction of the predicted costs of raw and auxiliary materials and energy medium based on the current raw and auxiliary materials, energy medium usage data and the current raw and auxiliary materials, energy medium costs; S6: The cost-optimal batching model for electric furnaces, medium frequency furnaces, AOD furnaces, LF furnaces, and VOD furnaces based on deep learning is coordinated with the calculated data of corrected raw materials, auxiliary materials, and energy media.

2. The deep learning-based batching model collaborative method for three-step stainless steel smelting according to claim 1, characterized in that: The raw and auxiliary materials, energy medium usage, and model calculation data include the usage, model calculation data of scrap steel, ferronickel, ferrochrome, iron-containing raw materials, carbon, flux, slag-forming agent, and the energy medium usage, model calculation data of water, electricity, oxygen, nitrogen, argon, and compressed air.

3. The deep learning-based batching model collaborative method for three-step stainless steel smelting according to claim 1, characterized in that: The cost data of raw materials, auxiliary materials and energy media include the cost data of scrap steel, ferronickel, ferrochrome, iron-containing raw materials, carbon, flux, slag-forming agents and the cost data of energy media such as water, electricity, oxygen, nitrogen, argon and compressed air.

4. The deep learning-based batching model collaborative method for three-step stainless steel smelting according to claim 1, characterized in that: The step S2 includes the following specific process: S21: Obtain the calculation data of raw materials, auxiliary materials and energy media of the batching models of the electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace within the specified period; S22: Extract the usage data of raw materials, auxiliary materials and energy media in the same period from the historical usage data of raw materials, auxiliary materials and energy media; S23: Based on the extracted usage data of raw materials, auxiliary materials and energy media in the same period, compensation calibration is performed on the calculated data of raw materials, auxiliary materials and energy media in the corresponding period according to the specified ratio.

5. The deep learning-based batching model collaborative method for three-step stainless steel smelting according to claim 1, characterized in that: The step S3 includes the following specific process: S31: Construct the batching model coordination based on cost optimization for electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace; S32: inputting the training set into the coordination of the batching model based on cost optimization of the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace, and the VOD furnace to train the model; S33: Input the test set into the coordination of the batching model based on cost optimization of the electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace, test the trained model, and judge whether the corresponding indicators of the test results meet the requirements. If so, execute step S34; if not, re-input a new training set to train the model; S34: Input the verification set into the cost-optimized batching model collaboration of the tested electric furnace, medium frequency furnace, AOD furnace, LF furnace, and VOD furnace to verify the test results and determine whether the verification results meet the requirements. If so, execute step S4; if not, re-execute step S1.

6. The deep learning-based batching model collaborative method for three-step stainless steel smelting according to claim 1, characterized in that: In step S5, the process of correcting the predicted cost of raw materials and energy medium by using the current raw materials and energy medium usage data and the current raw materials and energy medium costs is as follows: S51: Calculate the cost of raw materials and energy media based on the calibrated calculation data of raw materials and energy media; S52: Create a composite function of the predicted composition of molten steel, the predicted temperature of molten steel and the predicted cost of batching for electric furnace, medium frequency furnace, AOD furnace, LF furnace and VOD furnace; S53: Calculate the optimal solution for the composite function of the composition of the molten steel, the temperature of the molten steel and the predicted cost of the batching for creating the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace and the VOD furnace; S54: Correcting the electric furnace batching model, the medium frequency furnace batching model, and the AOD furnace batching model of the electric furnace, the medium frequency furnace, the AOD furnace, the LF furnace, and the VOD furnace according to the specified ratio through the optimal solution.

7. The deep learning-based batching model collaborative method for three-step stainless steel smelting according to claim 6, characterized in that: The formula for creating a composite function in step S52 to ensure that the composition and temperature of the molten steel are qualified and the operating cost is optimal is: f(x)=f 1EF (x)+f 1MF (x)+f 1AOD (x)+f 1LF (x)+f 1VOD (x)+f 2EF (x)+f 2MF (x)+f 2AOD (x)+f 2LF (x)+f 2VOD (x)+f 3EF (x)+f 3MF (x)+f 3AOD (x)+f 3LF (x)+f 3VOD (x) Among them, f 1EF (x) is the composition function of the steel produced by the electric furnace, f 1MF (x) is the steel composition function of the medium frequency furnace, f 1AOD (x) is the composition function of the steel produced by the AOD furnace, f 1LF (x) is the steel composition function of LF furnace, f 1VOD (x) is the COD furnace steel composition function, f 2EF (x) is the temperature function of the electric furnace, f 2MF (x) is the tapping temperature function of the medium frequency furnace, f 2AOD (x) is the tapping temperature function of the AOD furnace, f 2LF (x) is the tapping temperature function of the LF furnace, f 2VOD (x) is the tapping temperature function of the VOD furnace, f 3EF (x) is the cost function of the electric furnace, f 3MF (x) is the cost function of the medium frequency furnace, f 3AOD (x) is the AOD furnace cost function, f 3LF (x) is the LF furnace cost function, f 3VOD (x) is the VOD furnace cost function.

8. The deep learning-based batching model collaborative method for three-step stainless steel smelting according to claim 1, characterized in that: The three-step stainless steel smelting method comprises: the first step of melting ferronickel in an electric furnace and melting ferrochrome in a medium frequency furnace; the second step of blending the molten ferronickel and ferrochrome mother liquors in proportion and pouring them into an AOD furnace to smelt stainless steel mother liquor; the third step of pouring the stainless steel mother liquor into an LF furnace or a VOD furnace to smelt qualified stainless steel liquid.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor loads and executes the computer program, the deep learning-based batching model collaborative method for three-step stainless steel smelting described in claim 1 is adopted.

10. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by the processor, the deep learning-based batching model collaborative method for three-step stainless steel smelting described in claim 1 is adopted.

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