Ammonia hydrogen zero carbon fuel heating furnace simulation system, method and related equipment

Abstract

The present invention discloses an ammonia-hydrogen zero-carbon fuel heating furnace simulation system, method and related equipment, which relate to the field of heating furnaces in the steel industry, and are mainly used to solve the problem that existing methods are difficult to better simulate and predict the combustion process in an ammonia-hydrogen zero-carbon fuel furnace. The system includes: an equipment layer, which is used to receive and send basic data of an ammonia-hydrogen zero-carbon fuel heating furnace; a data layer, which is used to receive, store and analyze the basic data of an ammonia-hydrogen zero-carbon fuel heating furnace sent by the equipment layer; a simulation layer, which is used to build and run an ammonia-hydrogen zero-carbon fuel heating furnace simulation model; and a display layer, which is used to display the operation results of the real-time simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace to the user. The present invention is used for the simulation and prediction process of an ammonia-hydrogen zero-carbon fuel heating furnace.

Description

Technical Field

[0001] The present invention relates to the field of heating furnaces in the steel industry, and in particular to an ammonia-hydrogen zero-carbon fuel heating furnace simulation system, method and related equipment. Background Art

[0002] The steel industry is an important basic industry of my country's national economy and an important field for achieving green and low-carbon development. At present, carbon emissions from steel enterprises account for about 17% of my country's total carbon emissions, so controlling carbon emissions from steel enterprises is an important task. Among them, the use of ammonia hydrogen zero-carbon fuel to replace carbon-based coal gas fuel can achieve carbon reduction from the fuel side. This method can achieve the goal of reducing carbon emissions without changing the structure of the heating furnace and the heating process. And as the cost of photovoltaic hydrogen production and ammonia production decreases year by year, pipeline transportation is convenient, and the use of ammonia hydrogen zero-carbon fuel has also become feasible. However, there is a gap in the research on the combustion of ammonia and hydrogen in heating furnaces, and the proportion of blending and the conditions in the furnace still need to be explored. Summary of the invention

[0003] In view of the above problems, the present invention provides an ammonia-hydrogen zero-carbon fuel heating furnace simulation system, method and related equipment, the main purpose of which is to solve the problem that existing methods are difficult to better simulate and predict the combustion process in an ammonia-hydrogen zero-carbon fuel furnace.

[0004] In order to solve at least one of the above technical problems, in a first aspect, the present invention provides an ammonia hydrogen zero-carbon fuel heating furnace simulation system, the system comprising:

[0005] The equipment layer is used to receive and send basic data of the ammonia hydrogen zero-carbon fuel heating furnace;

[0006] A data layer, the data layer is used to receive, store and analyze the basic data of the ammonia hydrogen zero-carbon fuel heating furnace sent by the equipment layer;

[0007] A simulation layer, which is used to build and run an ammonia-hydrogen zero-carbon fuel heating furnace simulation model;

[0008] A display layer is used to display the operating results of the real-time simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace to the user.

[0009] Optionally, the above system further includes:

[0010] An interface layer, the interface layer being used to establish a connection with the device layer;

[0011] A data gateway layer, which is connected to the interface layer and includes a signal conversion unit, a data acquisition controller unit and a communication access terminal unit, and is used to transmit the basic data of the ammonia hydrogen zero-carbon fuel heating furnace transmitted by the interface layer to the network transmission layer;

[0012] A network transmission layer, which is connected to the data layer and is used to transmit the basic data of the ammonia hydrogen zero-carbon fuel heating furnace to the data layer.

[0013] In a second aspect, an embodiment of the present invention further provides a method for simulating an ammonia-hydrogen zero-carbon fuel heating furnace, comprising:

[0014] Obtaining basic data of the ammonia hydrogen zero-carbon fuel heating furnace in real time;

[0015] Running the basic data based on the ammonia hydrogen zero-carbon fuel heating furnace simulation model to obtain simulation results;

[0016] The simulation result is displayed on the display layer.

[0017] Optionally, the above method further includes:

[0018] The simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is constructed based on the design information of the furnace of the ammonia-hydrogen zero-carbon fuel heating furnace and the gas characteristics of the ammonia-hydrogen zero-carbon fuel and coal gas, wherein the simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is a CFD model constructed based on a fully structured hexahedral high-quality grid and a DO radiation model;

[0019] The simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is optimized based on the ammonia-hydrogen fuel oxidation mechanism, the nitrogen conversion skeleton reaction mechanism and the EDC model.

[0020] Optionally, the above method further includes:

[0021] Performing a three-dimensional CFD simulation based on the basic data of the ammonia hydrogen zero-carbon fuel heating furnace to obtain CFD simulation data;

[0022] Obtain the numerical simulation results of the mixed gas low-carbon fuel heating furnace under different ammonia-hydrogen blending ratios;

[0023] A CFD simulation database is constructed based on the CFD simulation data and the numerical simulation results of the mixed coal gas low-carbon fuel heating furnace.

[0024] Optionally, the above method further includes:

[0025] Building a training set based on a CFD simulation database, and using a deep learning AI model to train the ammonia hydrogen zero-carbon fuel heating furnace simulation model;

[0026] The optimal model of the ammonia-hydrogen zero-carbon fuel heating furnace simulation model is determined based on cross-validation and hyperparameter tuning.

[0027] Optionally, the above method further includes:

[0028] In case that there is an abnormality in the simulation result, the flow rate of the ammonia hydrogen zero-carbon fuel and the coal gas is changed through feedforward regulation to adjust the combustion environment in the furnace.

[0029] In a third aspect, an embodiment of the present invention further provides an ammonia hydrogen zero-carbon fuel heating furnace simulation device, comprising:

[0030] An acquisition unit, used for acquiring basic data of the ammonia hydrogen zero-carbon fuel heating furnace in real time;

[0031] An operating unit, configured to operate the basic data based on the ammonia-hydrogen zero-carbon fuel heating furnace simulation model to obtain a simulation result;

[0032] The display unit is used to display the simulation result on the display layer.

[0033] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the above-mentioned program is executed by a processor, the steps of the above-mentioned ammonia-hydrogen zero-carbon fuel heating furnace simulation method are implemented.

[0034] In order to achieve the above-mentioned purpose, according to the fifth aspect of the present invention, there is provided an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the steps of the above-mentioned ammonia-hydrogen zero-carbon fuel heating furnace simulation method.

[0035] By means of the above technical scheme, the simulation system, method and related equipment of the ammonia hydrogen zero carbon fuel heating furnace provided by the present invention are difficult to better simulate and predict the combustion process in the ammonia hydrogen zero carbon fuel furnace by the existing methods. The present invention constructs an equipment layer, the equipment layer is used to receive and send the basic data of the ammonia hydrogen zero carbon fuel heating furnace; the data layer, the data layer is used to receive, store and analyze the basic data of the ammonia hydrogen zero carbon fuel heating furnace sent by the equipment layer; the simulation layer, the simulation layer is used to build and run the simulation model of the ammonia hydrogen zero carbon fuel heating furnace; the display layer, the display layer is used to display the operation results of the real-time simulation model of the ammonia hydrogen zero carbon fuel heating furnace to the user. In the above scheme, through the integration and information interaction of different control layers at the production level, artificial intelligence, edge computing, and data mining technologies are comprehensively applied to realize the simulation process of all-round synchronous mapping from the physical world to the virtual world. By using digital twin technology, a digital entity of a heating furnace that is highly similar to the original physical entity of the heating furnace can be "cloned" without changing the original physical entity of the heating furnace. Various characteristics of the physical entity of the heating furnace can be seen in the digital world, and real-time monitoring and feedforward control of the heating furnace system can be realized through digital twin technology. Compared with traditional numerical simulation, it can synchronously track and evolve in real time with the changes in the state and operating conditions of the physical entity of the heating furnace, thereby compensating for the uncertainty of the combustion process in the furnace using ammonia hydrogen zero-carbon fuel, and can perform real-time regulation of the heating furnace according to the twin results, thereby improving the combustion efficiency of the steel enterprises using ammonia hydrogen zero-carbon fuel heating furnace. In addition, through real-time regulation, the emission control of pollutants such as NOx can be achieved.

[0036] Correspondingly, the ammonia-hydrogen zero-carbon fuel heating furnace simulation device, equipment and computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects.

[0037] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0039] Figure 1 A schematic diagram of a digital twin system architecture provided by an embodiment of the present invention is shown;

[0040] Figure 2A schematic diagram of a digital twin process of an ammonia-hydrogen zero-carbon fuel heating furnace provided by an embodiment of the present invention is shown;

[0041] Figure 3 A schematic flow chart of a method for simulating an ammonia-hydrogen zero-carbon fuel heating furnace provided in an embodiment of the present invention is shown;

[0042] Figure 4 A client twin data temperature field interface diagram provided by an embodiment of the present invention is shown;

[0043] Figure 5 A client twin data gas composition O2 cloud map interface provided by an embodiment of the present invention is shown;

[0044] Figure 6 A schematic block diagram of the composition of an ammonia-hydrogen zero-carbon fuel heating furnace simulation device provided by an embodiment of the present invention is shown;

[0045] Figure 7 A schematic block diagram of the composition of an ammonia-hydrogen zero-carbon fuel heating furnace simulation electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0046] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully conveyed to those skilled in the art.

[0047] In order to solve the problem that the existing methods are difficult to better simulate and predict the combustion process in the ammonia hydrogen zero-carbon fuel furnace, the embodiment of the present invention provides an ammonia hydrogen zero-carbon fuel heating furnace simulation system, such as Figure 1-2 As shown, including:

[0048] The equipment layer is used to receive and send basic data of the ammonia hydrogen zero-carbon fuel heating furnace;

[0049] A data layer, the data layer is used to receive, store and analyze the basic data of the ammonia hydrogen zero-carbon fuel heating furnace sent by the equipment layer;

[0050] A simulation layer, which is used to build and run an ammonia-hydrogen zero-carbon fuel heating furnace simulation model;

[0051] A display layer is used to display the operating results of the real-time simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace to the user.

[0052] For example, digital twin technology refers to the use of digital means to twin the real physical world into a nearly identical virtual simulation world. Through the integration and information interaction of different control layers at the production level, and the comprehensive application of artificial intelligence, edge computing, and data mining technology, a simulation process of all-round synchronous mapping of the physical world to the virtual world is realized.

[0053] Specifically, Figure 1 As shown in the figure, the digital twin system includes the equipment layer, data layer, simulation platform, display layer and other parts. The equipment layer is mainly responsible for receiving and sending basic information. The data layer is mainly a local server platform, responsible for receiving, storing and analyzing data. The simulation platform is mainly responsible for modeling and simulating the heating furnace. The display layer is responsible for the three-dimensional display of the furnace, the display of prediction results, the display of data analysis, and the display of abnormal reminders in the furnace.

[0054] It can be understood that the equipment layer is equipped with a field equipment signal source receiving device to receive the flow and temperature of the gas and air from the ammonia hydrogen zero-carbon fuel heating furnace system, the heating furnace wall temperature, the temperature, composition and flow of the flue gas, and other data related to the combustion process, such as temperature, flow, flue gas composition, etc.

[0055] Considering that there is a gap in the current research on ammonia and hydrogen combustion in heating furnaces, and the proportion of combustion and the real-time status in the furnace still need to be explored, this application combines digital twin technology to study the combustion of zero-carbon fuel in heating furnaces of steel enterprises. Using digital twin technology, the constructed equipment layer, data layer, simulation layer and display layer can map the physical entity of the heating furnace in real time.

[0056] In one embodiment, the system further comprises:

[0057] An interface layer, the interface layer is used to establish a connection with the device layer; a data gateway layer, the data gateway layer is connected to the interface layer, and includes a signal conversion unit, a data acquisition controller unit and a communication access terminal unit, and is used to transmit the basic data of the ammonia hydrogen zero-carbon fuel heating furnace transmitted by the interface layer to the network transmission layer; a network transmission layer, the network transmission layer is connected to the data layer, and is used to transmit the basic data of the ammonia hydrogen zero-carbon fuel heating furnace to the data layer.

[0058] Furthermore, the data gateway layer is connected to the control system of the ammonia hydrogen zero-carbon fuel heating furnace system, and the data is sent to the data acquisition controller unit through the signal conversion unit. The data acquisition control unit automatically parses and processes the data according to the data interface type and uploads it to the server platform. In addition, it can also receive signals from the local server platform, convert them and act on the control system of the heating furnace, thereby realizing the control of the heating furnace by the server platform.

[0059] The main function of the server of the data layer is to provide hardware support for data storage, calculation and call, and mainly adopts MySQL / MongoDB / Red is mainstream database technology. After receiving information from the data gateway, the server establishes corresponding databases, including real-time databases, historical databases, and relational databases for storage.

[0060] It is understandable that the equipment side of the digital twin system is equipped with a field signal source receiving device, which is connected to the data gateway to realize information conversion in the data gateway, and then upload the field information to the local server. The server receives information such as the temperature, flow rate, and furnace wall temperature of ammonia hydrogen zero-carbon fuel, coal gas, and flue gas from the equipment layer, and stores it to establish a real-time equipment database, a historical database, and a relational database for control closed-loop data.

[0061] like Figure 2 As shown, the main process of the ammonia hydrogen zero-carbon fuel heating furnace system is that air is blown into the gas heater E1 by the fan F1 to heat the air. The heated air is transported to the furnace nozzle by the air pipes 1 and 2 through the valve V3, and then the heated air is sprayed into the furnace through different nozzles. On the fuel side, ammonia (or hydrogen) and coal gas are mixed in the fuel pipeline 3, transported to the furnace nozzle, and then sprayed into the furnace by the fuel nozzle. After fully burning and reacting with the air in the furnace, the generated exhaust gas is discharged from the furnace through the flue gas pipeline 5, and then further processed. In addition, the flue gas pipeline 5 is provided with a flue gas analyzer FG, which analyzes the flue gas components in the exhaust gas, and sends the analysis results to the digital twin system through the signal transmission line 6. The ammonia hydrogen zero-carbon fuel heating furnace system also includes pipeline flow meters P1, P2, P3, P4, temperature sensors T1 to T7 and a flue gas analyzer FG. Among them, P1 is responsible for the gas flow of air duct 1, P2 is responsible for the air flow of air duct 2, P3 is responsible for the gas flow, and P4 is responsible for the flue gas flow. Thermometer T1 is mainly responsible for monitoring the air temperature, T2 is responsible for monitoring the gas temperature, T3 is responsible for monitoring the flue gas temperature, and T4~T7 are mainly responsible for monitoring the furnace wall temperature.

[0062] Based on the above structure, air is blown into the gas heater E1 by the fan F1 to heat the air, and then sprayed into the furnace by the DC nozzle through the air pipe 1, wherein the air pipe 1 is provided with a pipe flow meter P1 to record the air flow, and also has a temperature sensor T1 to monitor the air temperature. Pipeline 2 is also responsible for transporting air, and the pipe flow meter P2 is responsible for recording the air flow injected by the swirl nozzle. Pipeline 3 is mainly responsible for transporting gas, including ammonia hydrogen zero carbon fuel and coal gas, wherein P3 is responsible for monitoring the fuel flow, and T2 is responsible for monitoring the gas temperature. The injected gas and air will burn in the furnace, and T4 to T7 monitor the furnace wall temperature. The flue gas after combustion is discharged through the flue gas pipe 5, which is also equipped with a pipe flow meter P4 and a thermometer T3 to monitor the flue gas, and is also equipped with a flue gas analyzer FG to analyze the flue gas. The abnormality of the heating furnace overcomes the disadvantage that it can only be monitored manually in the past, such as the detection of the furnace wall temperature by the temperature sensor, and the monitoring of the flue gas, the abnormality is monitored and alarmed, and then regulated.

[0063] It can be understood that the flow signal, temperature signal and flue gas signal of the ammonia hydrogen zero-carbon fuel heating furnace system are all transmitted to the digital twin system via the signal transmission line 6.

[0064] The embodiment of the present invention also provides a method for simulating an ammonia hydrogen zero carbon fuel heating furnace, such as Figure 3 As shown, the method includes:

[0065] S101, basic data of the ammonia hydrogen zero-carbon fuel heating furnace;

[0066] S102, running the basic data based on the ammonia hydrogen zero-carbon fuel heating furnace simulation model to obtain simulation results;

[0067] S103: Display the simulation result on the display layer.

[0068] This application establishes a full operating condition database through quasi-real-time numerical simulation, etc., and enters multiple test conditions such as mixing ratio on the client page to perform a three-dimensional visual display of the CFD prediction results of the heating furnace under these conditions, including the combustion state in the furnace, pollutant generation and emission, etc., and the physical entity of the heating furnace can be controlled through the data transmission line 6, which can greatly reduce the cost of testing and trial and error.

[0069] Based on the above scheme, the digital twin system can realize three-dimensional modeling of the heating furnace based on the physical parameters of the heating furnace, and visualize the real operating status of the heating furnace according to the real-time monitoring data such as the operating conditions and furnace wall temperature. In addition, the digital twin heating furnace can perform feedforward control on the heating furnace according to the simulation operation results, such as controlling the gas flow rate and adjusting the valve opening, etc. By realizing the visualization and accurate prediction of the effects of various transformation and debugging work, the cost of trial and error can be greatly reduced. In this way, the integration of digital technology and the real economy can be realized, the digital transformation process of steel enterprises can be promoted, and the production efficiency and energy efficiency of ammonia hydrogen zero-carbon fuel heating furnaces in the steel industry can be greatly improved.

[0070] In one embodiment, the method further includes:

[0071] The simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is constructed based on the design information of the furnace of the ammonia-hydrogen zero-carbon fuel heating furnace and the gas characteristics of the ammonia-hydrogen zero-carbon fuel and coal gas, wherein the simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is a CFD model constructed based on a fully structured hexahedral high-quality grid and a DO radiation model;

[0072] The simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is optimized based on the ammonia-hydrogen fuel oxidation mechanism, the nitrogen conversion skeleton reaction mechanism and the EDC model.

[0073] Specifically, the simulation platform models the heating furnace based on the design information of the heating furnace hearth in the data layer, as well as the gas characteristics of ammonia hydrogen zero-carbon fuel and coal gas. In order to improve the simulation accuracy, a fully structured hexahedral high-quality grid is used for simulation.

[0074] In addition, this application fully considers the high H2 and CO ratio and selects the DO radiation model. In order to further improve the prediction accuracy, the simulation is carried out by coupling the experimentally verified ammonia hydrogen fuel oxidation and nitrogen conversion skeleton reaction mechanism and the EDC model that can consider the detailed mechanism.

[0075] In summary, a high-precision numerical simulation of the mixed combustion of ammonia-hydrogen zero-carbon fuel and coal gas was established, which can obtain more accurate data on the temperature field and component field of the heating furnace and better reflect the state of the physical entity heating furnace.

[0076] In one embodiment, the method further includes:

[0077] Performing a three-dimensional CFD simulation based on the basic data of the ammonia hydrogen zero-carbon fuel heating furnace to obtain CFD simulation data;

[0078] Obtain the numerical simulation results of the mixed gas low-carbon fuel heating furnace under different ammonia-hydrogen blending ratios;

[0079] A CFD simulation database is constructed based on the CFD simulation data and the numerical simulation results of the mixed coal gas low-carbon fuel heating furnace.

[0080] Furthermore, the simulation platform receives real-time data of the heating furnace from the data layer to perform three-dimensional CFD simulation, and carries out numerical simulation of the mixed gas low-carbon fuel heating furnace under different ammonia-hydrogen blending ratios, and transmits the simulation structure and simulation results to the data layer for storage.

[0081] It is understandable that CFD simulation data has more redundant information and unnecessary details, so the server platform takes pre-processing measures such as dimensionality reduction, feature selection, and data standardization and normalization on the numerical simulation data to improve the efficiency and speed of model training.

[0082] Specifically, the dimensionality reduction mainly adopts the POD intrinsic orthogonal decomposition method. The key goal of the POD method is to reduce a large number of interdependent variables to a smaller number of unrelated variables. The specific method is to obtain a series of basis functions from the known experimental solutions or numerical solutions of the system. These basis functions are usually called intrinsic orthogonal modes, and it is necessary to ensure that these basis functions are the optimal orthogonal basis functions in the least squares sense. The linear combination of these orthogonal bases is used to approximate the initial problem, and the combination coefficients are called intrinsic orthogonal components. Usually, the size of the intrinsic orthogonal eigenvalue (POV) determines the dominance of the mode. The larger the eigenvalue, the greater the contribution of the corresponding mode. Therefore, the dimensionality reduction of the original complex problem can be achieved by discarding the higher-order modes corresponding to the smaller POV.

[0083] The preprocessing process cleans the data and removes abnormal data and noise to ensure the quality and reliability of the data. Then, the data is normalized to scale the numerical range of each feature to a similar scale to facilitate model training and optimization.

[0084] In summary, this application uses a deep learning AI model to create an efficient and accurate prediction model. The model can learn spatial features from flow field data and predict future flow field distribution. Then, a full-condition CFD simulation database based on real-time monitoring data is established, including a model structure database, a simulation result database, etc.

[0085] The deep learning AI model mainly adopts a convolutional neural network (CNN), which has a convolution layer, a pooling layer, and a fully connected layer. The convolution layer is mainly responsible for extracting image features, which is achieved by convolving the image using a convolution kernel (a filter). The convolution kernel is a small matrix that slides on the image and performs a convolution operation with each part of the image. For each position of the image, the convolution kernel will perform an inner product with the image pixels it covers, and then add all the results of the inner product to get a new value. This new value is the output of the convolution kernel at that position. The process of sliding the convolution kernel on the image is a convolution operation. The role of the pooling layer is to reduce the dimension of the image while retaining useful features. Commonly used pooling operations are maximum pooling and average pooling. Maximum pooling retains the maximum value within the pooling window, while average pooling retains the average value within the pooling window. The effect of pooling is to reduce the dimension of the image while effectively suppressing noise and reducing the size of the model. The fully connected layer is an ordinary neural network layer that further processes the previous output and obtains the final output result. Each unit in the fully connected layer is connected to all units in the previous layer.

[0086] The above solution establishes a multi-condition database by integrating the CFD offline database, on-site real-time data and historical data. Based on a large amount of actual data and CFD numerical simulation data, it uses deep learning and AI training to achieve full-condition numerical simulation and quasi-real-time prediction of the combustion process.

[0087] In one embodiment, the method further includes:

[0088] Building a training set based on a CFD simulation database, and using a deep learning AI model to train the ammonia hydrogen zero-carbon fuel heating furnace simulation model;

[0089] The optimal model of the ammonia hydrogen zero-carbon fuel heating furnace simulation model is determined based on cross-validation and hyperparameter tuning. Specifically, the server platform uses the existing CFD simulation data as a training set to train CNN, and uses cross-validation and hyperparameter tuning to find the best model, thereby verifying the prediction model and ensuring the verification accuracy of the new working conditions. The deep learning AI model mainly uses convolutional neural network CNN.

[0090] This application realizes real-time deductive speculation of the combustion process based on the CNN model in the deep learning model, and establishes a multi-condition CFD simulation database, including a model structure database and a simulation result database.

[0091] Furthermore, the cross-validation is to select a part of the samples in the training set for testing the model. A part of the training set data is retained as the validation set, and the parameters generated by the training set are tested to relatively objectively judge the degree of conformity of these parameters to the data outside the training set, thereby obtaining a reliable and stable model. The hyperparameter tuning mainly optimizes the model performance by adjusting the hyperparameters, where the hyperparameters include the learning rate, batch size, number of layers, number of neurons, etc.

[0092] The local application combines computational fluid dynamics (CFD) and convolutional neural network (CNN) methods to simulate the heating furnace with high precision. First, the simulation platform uses CFD methods to perform three-dimensional numerical simulations based on the heating furnace design information, air and fuel temperature flow and other related information to obtain data on velocity field, temperature field, component field, etc. The server platform processes the data using the intrinsic orthogonal decomposition method. This includes data cleaning to ensure the reliability of the data, and then normalizing the data to make it have a similar scale. In addition, the CNN model in the deep learning model is used to realize real-time deductive speculation of the combustion process, and a multi-condition CFD simulation database is established, including a model structure database and a simulation result database.

[0093] For example, considering that the current simulation of digital twin systems is mostly limited to real-time mapping of a small amount of existing monitoring data, it is not possible to achieve full-condition numerical simulation. However, this application uses AI deep learning to integrate CFD offline database, on-site real-time data and historical data to achieve full-condition numerical simulation, thereby predicting the state inside the furnace, and feedforward adjusting the heating furnace system according to the abnormal conditions inside the furnace to restore it to normal.

[0094] Based on the above scheme, the heating furnace is modeled and simulated with high precision through the CFD method, and then a multi-condition database is established by integrating the CFD offline database, on-site real-time data and historical data. Based on a large amount of actual data and CFD numerical simulation data, AI models such as deep learning are used to realize the full-condition numerical simulation and quasi-real-time prediction of the combustion process of the ammonia-hydrogen zero-carbon fuel heating furnace.

[0095] In one embodiment, the method further includes:

[0096] In case that there is an abnormality in the simulation result, the flow rate of the ammonia hydrogen zero-carbon fuel and the coal gas is changed through feedforward regulation to adjust the combustion environment in the furnace.

[0097] Specifically, this application integrates multi-source heterogeneous data such as physical entities, real-time sensors, simulations, and equipment operation data, and displays the operation process of the heating furnace through the software client, including two-dimensional cloud maps and working conditions of different stages and furnace heights. It can manage the equipment operation status, equipment alarm reminders, equipment maintenance instructions, etc.

[0098] In summary, the client can accurately and dynamically display the physical heating furnace status in real time and give an alarm for abnormalities in the furnace. In addition, the flow of air and gas can be adjusted through the data transmission line 6 to achieve feedforward control and optimization of the heating furnace, change the combustion atmosphere in the furnace in advance, and prevent the occurrence of abnormal conditions in the furnace.

[0099] The furnace working conditions display effect is as follows Figure 4-5 As shown, the present application can simulate the quasi-real-time state of the heating furnace according to the flow rate, temperature and other related signal inputs of gas, air, etc., and perform real-time three-dimensional visualization of the combustion state in the furnace, retrieve relevant data from the full-operating CFD simulation database, and then simulate and predict the operating condition, predict abnormal conditions in the furnace in advance and give an alarm, and the digital twin system can automatically feedforward control the flow rate of air and gas input to the heating furnace through the signal transmission line 6 according to the abnormal condition, and then adjust the combustion atmosphere in the furnace in advance to reduce the occurrence of abnormal conditions in the heating furnace. The basic group can only rely on the abnormal alarm of the instrument or manual monitoring to perform lagging control after the abnormality is discovered. In comparison, the improved group can feedforward adjust the heating furnace system at least 10-15s in advance. In addition, the present application realizes full-operating condition simulation by combining CFD numerical simulation methods and AI models such as deep learning, and can then realize continuous analysis of flue gas components, temperature, etc. The digital twin system can predict the flue gas composition and temperature, and issue control instructions to the heating furnace control system through data transmission line 6. By adjusting the opening of the air and gas valves and changing the atmosphere in the furnace, the flue gas composition can be controlled.

[0100] By means of the above technical scheme, the simulation system and method of the ammonia hydrogen zero carbon fuel heating furnace provided by the present invention can solve the problem that the existing methods are difficult to better simulate and predict the combustion process in the ammonia hydrogen zero carbon fuel furnace. The present invention constructs an equipment layer, which is used to receive and send the basic data of the ammonia hydrogen zero carbon fuel heating furnace; a data layer, which is used to receive, store and analyze the basic data of the ammonia hydrogen zero carbon fuel heating furnace sent by the equipment layer; a simulation layer, which is used to build and run the simulation model of the ammonia hydrogen zero carbon fuel heating furnace; and a display layer, which is used to display the operation results of the real-time simulation model of the ammonia hydrogen zero carbon fuel heating furnace to the user. In the above scheme, through the integration and information interaction of different control layers at the production level, artificial intelligence, edge computing, and data mining technologies are comprehensively applied to realize the simulation process of all-round synchronous mapping from the physical world to the virtual world. By using digital twin technology, a digital entity of a heating furnace that is highly similar to the original physical entity of the heating furnace can be "cloned" without changing the physical entity of the original heating furnace. Various characteristics of the physical entity of the heating furnace can be seen in the digital world, and real-time monitoring and feedforward control of the heating furnace system can be realized through digital twin technology. Compared with traditional numerical simulation, it can synchronously track and evolve in real time with the changes in the state and operating conditions of the physical entity of the heating furnace, thereby compensating for the uncertainty of the combustion process in the furnace using ammonia hydrogen zero-carbon fuel, and can perform real-time regulation of the heating furnace according to the twin results, thereby improving the combustion efficiency of the steel enterprises using ammonia hydrogen zero-carbon fuel heating furnace. In addition, through real-time regulation, the emission control of pollutants such as NOx can be achieved.

[0101] Furthermore, as a response to the above Figure 3 In order to realize the method shown in the figure, the embodiment of the present invention also provides an ammonia hydrogen zero carbon fuel heating furnace simulation device for Figure 3 The device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Figure 6 As shown, the device includes: an acquisition unit 21, an operation unit 22 and a display unit 23, wherein:

[0102] An acquisition unit 21 is used to acquire basic data of the ammonia hydrogen zero-carbon fuel heating furnace in real time;

[0103] An operation unit 22 is used to operate the basic data based on the ammonia hydrogen zero-carbon fuel heating furnace simulation model to obtain a simulation result;

[0104] The display unit 23 is used to display the simulation result on the display layer.

[0105] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and a simulation method of an ammonia hydrogen zero-carbon fuel heating furnace is implemented by adjusting kernel parameters, which can solve the problem that the existing method is difficult to better simulate and predict the combustion process in the ammonia hydrogen zero-carbon fuel furnace.

[0106] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, and when the program is executed by a processor, the ammonia-hydrogen zero-carbon fuel heating furnace simulation method is implemented.

[0107] An embodiment of the present invention provides a processor, which is used to run a program, wherein the ammonia-hydrogen zero-carbon fuel heating furnace simulation method is executed when the program is running.

[0108] An embodiment of the present invention provides an electronic device, the electronic device comprising at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the above-mentioned ammonia hydrogen zero-carbon fuel heating furnace simulation method

[0109] An embodiment of the present invention provides an electronic device 30, such as Figure 7 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned ammonia hydrogen zero-carbon fuel heating furnace simulation method.

[0110] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0111] The present application also provides a computer program product, which, when executed on a process management electronic device, is suitable for executing a program that initializes the above-mentioned ammonia-hydrogen zero-carbon fuel heating furnace simulation method steps.

[0112] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0114] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0117] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 3 This corresponds to the flow of control of the memory in the embodiment.

[0118] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server, or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server, a data center, etc. that includes one or more available media integration. Available media can be magnetic media, (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state drive (SSD)), etc.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0120] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0123] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0124] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An ammonia hydrogen zero carbon fuel heating furnace simulation system, characterized in that: include: The equipment layer is used to receive and send basic data of the ammonia hydrogen zero-carbon fuel heating furnace; A data layer, the data layer is used to receive, store and analyze the basic data of the ammonia hydrogen zero-carbon fuel heating furnace sent by the equipment layer; A simulation layer, which is used to build and run an ammonia-hydrogen zero-carbon fuel heating furnace simulation model; A display layer is used to display the operating results of the real-time simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace to the user.

2. The system according to claim 1, characterized in that Also includes: An interface layer, the interface layer being used to establish a connection with the device layer; A data gateway layer, which is connected to the interface layer and includes a signal conversion unit, a data acquisition controller unit and a communication access terminal unit, and is used to transmit the basic data of the ammonia hydrogen zero-carbon fuel heating furnace transmitted by the interface layer to the network transmission layer; A network transmission layer, which is connected to the data layer and is used to transmit the basic data of the ammonia hydrogen zero-carbon fuel heating furnace to the data layer.

3. A method for simulating an ammonia-hydrogen zero-carbon fuel heating furnace, used in the ammonia-hydrogen zero-carbon fuel heating furnace simulation system according to any one of claims 1-2, characterized in that: include: Obtaining basic data of the ammonia hydrogen zero-carbon fuel heating furnace in real time; Running the basic data based on the ammonia hydrogen zero-carbon fuel heating furnace simulation model to obtain simulation results; The simulation result is displayed on the display layer.

4. The method according to claim 3, characterized in that Also includes: The simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is constructed based on the design information of the furnace of the ammonia-hydrogen zero-carbon fuel heating furnace and the gas characteristics of the ammonia-hydrogen zero-carbon fuel and coal gas, wherein the simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is a CFD model constructed based on a fully structured hexahedral high-quality grid and a DO radiation model; The simulation model of the ammonia-hydrogen zero-carbon fuel heating furnace is optimized based on the ammonia-hydrogen fuel oxidation mechanism, the nitrogen conversion skeleton reaction mechanism and the EDC model.

5. The method according to claim 3, characterized in that: Also includes: Performing a three-dimensional CFD simulation based on the basic data of the ammonia hydrogen zero-carbon fuel heating furnace to obtain CFD simulation data; Obtain the numerical simulation results of the mixed gas low-carbon fuel heating furnace under different ammonia-hydrogen blending ratios; A CFD simulation database is constructed based on the CFD simulation data and the numerical simulation results of the mixed coal gas low-carbon fuel heating furnace.

6. The method according to claim 5, characterized in that Also includes: Building a training set based on a CFD simulation database, and using a deep learning AI model to train the ammonia hydrogen zero-carbon fuel heating furnace simulation model; The optimal model of the ammonia-hydrogen zero-carbon fuel heating furnace simulation model is determined based on cross-validation and hyperparameter tuning.

7. The method according to claim 3, characterized in that Also includes: In case that there is an abnormality in the simulation result, the flow rate of the ammonia hydrogen zero-carbon fuel and the coal gas is changed through feedforward regulation to adjust the combustion environment in the furnace.

8. An ammonia hydrogen zero carbon fuel heating furnace simulation device, characterized in that: Also includes: An acquisition unit, used for acquiring basic data of the ammonia hydrogen zero-carbon fuel heating furnace in real time; An operating unit, configured to operate the basic data based on the ammonia-hydrogen zero-carbon fuel heating furnace simulation model to obtain a simulation result; The display unit is used to display the real-time simulation result on the display layer.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the ammonia-hydrogen zero-carbon fuel heating furnace simulation method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the ammonia-hydrogen zero-carbon fuel heating furnace simulation and prediction method as described in any one of claims 1 to 7.

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