Method for realizing carbon neutralization by hydrogen-rich carbon circulating oxygen blast furnace

Abstract

The invention discloses a method for realizing carbon neutralization of a hydrogen-rich carbon circulating oxygen blast furnace, and relates to the technical field of metallurgical engineering, and the method comprises the following steps: deploying a gas analysis device and a sensor, and collecting multi-dimensional data of environmental gas in the blast furnace; establishing a carbon emission baseline model through a machine learning algorithm in combination with historical carbon emission data, and generating a real-time carbon emission trend prediction report; constructing an in-furnace multiphase collaborative reaction model, and calculating carbon utilization efficiency distribution and carbon emission source positioning according to a carbon emission trend prediction report; on the basis of carbon utilization efficiency distribution and carbon emission source positioning, the injection strategy of the hydrogen-rich gas is adjusted, and a dynamic injection plan is obtained; according to the dynamic injection plan, reaction process parameters of a solid phase and a liquid phase in the furnace are adjusted, and regionalized distribution of a thermal phase is regulated and controlled; through optimization of the machine learning algorithm, the multiphase collaborative reaction model and the dynamic injection strategy, the carbon utilization efficiency is improved, carbon emission is reduced, and the overall production efficiency and energy efficiency of the blast furnace are improved.

Description

Technical Field

[0001] The invention relates to the technical field of metallurgical engineering, and in particular to a method for achieving carbon neutralization in a hydrogen-rich carbon circulating oxygen blast furnace. Background Art

[0002] As the problem of global climate change becomes increasingly serious, carbon emission control has become a key issue that needs to be urgently addressed in all walks of life. Especially in the steel smelting industry, the blast furnace, as one of the traditional production equipment, accounts for a large proportion of industrial emissions in terms of carbon emissions. The traditional blast furnace ironmaking process uses coke as a reducing agent, and the amount of carbon dioxide emitted is huge, which cannot meet the increasingly stringent environmental protection regulations. Therefore, how to achieve the low-carbonization, energy-saving and ultimate carbon neutrality goals of the blast furnace process has become a major challenge facing the industry. In recent years, researchers have proposed a variety of methods, such as introducing hydrogen-rich gas to replace traditional reducing agents and optimizing gas injection strategies, but these methods still face a series of problems in practical applications, such as how to accurately control carbon emissions and improve carbon utilization efficiency.

[0003] The shortcomings of existing technologies in carbon emission control are mainly reflected in two aspects: first, the lack of accurate modeling and real-time monitoring of complex multiphase reactions inside blast furnaces, resulting in poor prediction and regulation of carbon emissions; second, traditional carbon emission monitoring methods often rely on static data, making it difficult to obtain real-time changes in the furnace and dynamically adjust operating strategies. Although some studies have attempted to optimize carbon emission predictions through machine learning and sensor data analysis, most of these methods only focus on a single gas component or a single process variable, and fail to effectively integrate multidimensional data in the furnace to dynamically adapt to complex changes in the production process. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for achieving carbon neutrality in a hydrogen-rich carbon circulating oxygen blast furnace to solve the problem of poor prediction and regulation of carbon emissions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides a method for achieving carbon neutrality in a hydrogen-rich carbon-cycle oxygen blast furnace, which includes deploying gas analysis devices and sensors to collect multi-dimensional data of ambient gases in the blast furnace; establishing a carbon emission baseline model through a machine learning algorithm based on the multi-dimensional data of ambient gases in combination with historical carbon emission data, and generating a real-time carbon emission trend forecast report; constructing a multi-phase synergistic reaction model in the furnace, and calculating the carbon utilization efficiency distribution and the carbon emission source location according to the carbon emission trend forecast report; based on the carbon utilization efficiency distribution and the carbon emission source location, adjusting the hydrogen-rich gas injection strategy to obtain a dynamic injection plan; according to the dynamic injection plan, adjusting the reaction process parameters of the solid and liquid phases in the furnace, and regulating the regional distribution of the hot phase.

[0008] As a preferred solution of the method for achieving carbon neutrality in the hydrogen-rich carbon cycle oxygen blast furnace of the present invention, the gas analysis device and the sensor are deployed to collect multi-dimensional data of the ambient gas in the blast furnace, and the specific steps are as follows:

[0009] Select and layout gas analysis equipment and sensors;

[0010] Install fixed guards for sensors that complete the layout;

[0011] Connect the fixed sensors to the data acquisition and transmission central controller to build a data acquisition network;

[0012] Calibrate and debug sensors based on the established data acquisition network;

[0013] During normal operation of the blast furnace, multi-dimensional data of environmental gases are collected to verify the sensor functions.

[0014] As a preferred solution of the method for achieving carbon neutrality in the hydrogen-rich carbon cycle oxygen blast furnace of the present invention, wherein: according to the multi-dimensional data of environmental gases, combined with historical carbon emission data, a carbon emission baseline model is established through a machine learning algorithm to generate a real-time carbon emission trend forecast report, the specific steps are as follows:

[0015] Preprocess the multidimensional data of environmental gases and historical carbon emission data, extract feature vectors, and perform feature dimension reduction through principal component analysis;

[0016] Based on the feature vector after dimensionality reduction, a carbon emission baseline model is trained using a deep learning framework combined with a variational autoencoder.

[0017] Based on the trained carbon emission baseline model and combined with multi-dimensional data of environmental gases, the short-term carbon emission trend is predicted through the long short-term memory network to generate a real-time carbon emission trend forecast report.

[0018] As a preferred solution of the method for achieving carbon neutrality in the hydrogen-rich carbon cycle oxygen blast furnace of the present invention, wherein: the multiphase coordinated reaction model in the furnace is constructed, and the specific steps are as follows:

[0019] Extract the furnace gas concentration and reaction condition parameters from the multi-dimensional data of ambient gases, and use the historical carbon emission distribution provided by the carbon emission trend forecast report as input data;

[0020] Through the principles of mass conservation and energy conservation, the synergistic effect of gas phase reaction and solid phase reaction is modeled, and the kinetic model expression is:

[0021]

[0022] Among them, r i is the reaction rate of the ith reaction, k i is the reaction rate constant, E i is the activation energy, R is the ideal gas constant, T is the absolute temperature, C A is the concentration of reactant A, C B is the concentration of reactant B, a i is the reaction order of reactant A for the i-th reaction rate, b i is the reaction order of reactant B for the i-th reaction rate, i is the reaction number index coefficient;

[0023] Particle swarm optimization is used to optimize the dynamic model parameter k i 、E i 、a i and b i Perform the fitting.

[0024] As a preferred solution of the method for achieving carbon neutrality in the hydrogen-rich carbon cycle oxygen blast furnace of the present invention, wherein: the carbon utilization efficiency distribution and carbon emission source location are calculated according to the carbon emission trend forecast report, and the specific steps are as follows:

[0025] Calculate the carbon utilization efficiency distribution, the expression is:

[0026]

[0027] Among them, η C is the carbon utilization efficiency distribution, is the mass concentration of carbon dioxide emitted per unit time, m CO,out is the mass concentration of carbon monoxide emitted per unit time, v g is the gas flow rate, m C,in is the mass concentration of carbon raw material input per unit time, t 1 is the reaction start time, t 2 is the reaction end time, dt is the integral identifier;

[0028] The Bayesian network is used to construct a carbon emission source probability model, which is expressed as:

[0029]

[0030] Among them, P(S|D) is the posterior probability of observing carbon emission data D under the condition of source S, P(D|S) is the likelihood function of carbon emission data under the condition of carbon emission source S, P(S) is the prior probability under the condition of carbon emission source S, and P(D) is the marginal probability of the data;

[0031] Based on the carbon emission trend forecast report, the Bayesian network model is trained to obtain the parameters of the prior probability P(S) and the likelihood function P(D|S);

[0032] Based on the multi-dimensional data of environmental gases and combined with the variational inference algorithm, the probability distribution of the main sources of carbon emissions is calculated and a source location map is generated.

[0033] As a preferred solution of the method for achieving carbon neutrality in the hydrogen-rich carbon circulating oxygen blast furnace of the present invention, wherein: based on the carbon utilization efficiency distribution and the location of the carbon emission source, the hydrogen-rich gas injection strategy is adjusted to obtain a dynamic injection plan, and the specific steps are as follows:

[0034] Based on the distribution of carbon utilization efficiency and the location of carbon emission sources, identify hydrogen-rich gas injection areas and quantify injection requirements;

[0035] Dynamically adjust the hydrogen injection flow rate in the injection area, the expression is:

[0036]

[0037] Among them, H j (t) is the hydrogen injection flow rate of j injection areas, η C (t) is the carbon utilization efficiency distribution at time t, S(t) is the location of carbon emission sources, CO 2 (t) is the time function of carbon dioxide emissions, CO(t) is the time function of carbon monoxide emissions, α 1 is the carbon utilization efficiency weight coefficient, α 2 is the weight coefficient for carbon emission source positioning, α 3 is the weight coefficient of carbon monoxide emissions, β 1 is the weight coefficient of carbon dioxide emissions, R is the ideal gas constant, and T is the absolute temperature;

[0038] Based on the injection strategy of hydrogen-rich gas, a dynamic injection plan is generated through a timing optimal control algorithm.

[0039] As a preferred solution of the method for achieving carbon neutralization in the hydrogen-rich carbon circulating oxygen blast furnace of the present invention, wherein: the reaction process parameters of the solid phase and the liquid phase in the furnace are adjusted according to the dynamic injection plan, and the specific steps are:

[0040] According to the multi-dimensional data of the ambient gas in the blast furnace and the dynamic injection plan, the solid phase reaction zone in the furnace is identified;

[0041] Based on the hydrogen injection amount and position in the dynamic injection plan, the flow rate of combustion air in the furnace is adjusted to control the temperature and air flow velocity in the reaction zone;

[0042] According to the reaction efficiency and hydrogen injection amount, the particle size of the charge is adjusted through the feeding device;

[0043] Identify the liquid phase reaction zone in the furnace through online gas analysis instruments and temperature sensors;

[0044] The melting temperature and gas composition of the liquid phase reaction zone are adjusted based on the hydrogen injection amount and position in the dynamic injection plan.

[0045] As a preferred solution of the method for achieving carbon neutrality in the hydrogen-rich carbon circulating oxygen blast furnace of the present invention, wherein: the regional distribution of the heat phase is regulated, and the specific steps are:

[0046] According to the hydrogen injection amount, reaction heat and carbon emission trend of each area, the hot zone distribution in the furnace is dynamically adjusted. The heat distribution expression is:

[0047]

[0048] Where Q(t) is the heat distribution in the furnace at time t, K s (t) is the temperature of the solid phase region in the furnace at time t, K l (t) is the temperature of the liquid phase area in the furnace at time t, A(t) is the boundary A of the area that needs temperature difference adjustment, and B(t) is the boundary B of the area that needs temperature difference adjustment.

[0049] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for achieving carbon neutrality in a hydrogen-rich carbon circulating oxygen blast furnace as described in the first aspect of the present invention.

[0050] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for achieving carbon neutrality in a hydrogen-rich carbon circulating oxygen blast furnace as described in the first aspect of the present invention.

[0051] The beneficial effects of the present invention are as follows: by deploying gas analysis devices and sensors, multi-dimensional data of environmental gases in the blast furnace are collected in real time, and combined with historical carbon emission data, a carbon emission baseline model is established through a machine learning algorithm, and a carbon emission trend forecast report is generated. Based on the carbon emission trend forecast report, a multiphase synergistic reaction model in the furnace is constructed, the carbon utilization efficiency distribution and carbon emission sources are accurately calculated, the injection strategy of hydrogen-rich gas is optimized, and a dynamic injection plan is obtained. By adjusting the reaction process parameters of the solid and liquid phases in the furnace and finely controlling the regional distribution of the hot phase, the present invention effectively improves the carbon utilization efficiency of the blast furnace, reduces carbon emissions, and achieves the low-carbonization goal while improving production efficiency and energy efficiency, with certain environmental and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 This is a flow chart of the method for achieving carbon neutrality in the hydrogen-rich carbon circulating oxygen blast furnace in Example 1.

[0054] Figure 2 This is a flow chart for adjusting the reaction process parameters of the solid phase and liquid phase in Example 1. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0058] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for achieving carbon neutrality in a hydrogen-rich carbon circulating oxygen blast furnace, comprising:

[0059] S1: Deploy gas analysis devices and sensors to collect multi-dimensional data of the ambient gas in the blast furnace.

[0060] Specifically, the following steps are included:

[0061] S1.1: Select and arrange gas analysis equipment and sensors.

[0062] Specifically, the gas analysis device is used to detect H 2 ,CO,CO 2 , CH 4 To measure gas concentrations, use devices based on infrared spectroscopy (NDIR), electrochemical sensors or laser sensors.

[0063] The temperature sensor selects thermocouple or fiber optic temperature sensor to ensure stable measurement in high temperature areas.

[0064] The pressure sensor uses a high-pressure piezoresistive or capacitive sensor to detect pressure changes in different areas of the blast furnace.

[0065] Flow rate and flow sensors monitor gas dynamic changes through thermal flow meters or ultrasonic flow meters.

[0066] Sensors are rationally arranged in different areas of the blast furnace (such as furnace roof, furnace bosh, and tuyere) to cover the main paths of gas flow and reaction and ensure the representativeness of the data.

[0067] S1.2: Install fixed guards for sensors that have completed the layout.

[0068] It should be understood that the fixed protective device is installed to ensure the reliability of the sensor in high temperature, high pressure and dusty environments.

[0069] Installation measures include:

[0070] Protective shell: Install a heat-resistant, corrosion-resistant, and dust-proof shell on the outside of the sensor, using high-temperature resistant materials (such as stainless steel or ceramics).

[0071] Cooling unit: Temperature-sensitive sensors (such as gas analysis equipment) are equipped with circulating cooling water jackets or air cooling devices.

[0072] Fixed bracket: Use anti-vibration bracket or suspension unit to ensure the stability and accuracy of the sensor in the vibration environment of the blast furnace.

[0073] Sealed interface: Use high temperature sealing gasket at the connection between sensor and blast furnace to prevent gas leakage or interference from external impurities.

[0074] S1.3: Connect the fixed sensors to the data acquisition and transmission central controller to build a data acquisition network.

[0075] Specifically, use industrial transmission protocols (such as RS485) to connect sensors to the central controller, install local data collectors near the blast furnace to achieve preliminary data storage and processing. Arrange high-temperature and anti-interference signal transmission lines, or use industrial wireless communication modules (such as LoRa or industrial Wi-Fi) to achieve remote data transmission.

[0076] S1.4: Calibrate and debug the sensors based on the established data acquisition network.

[0077] Specifically, the gas sensor is calibrated with standard gas to ensure that the measurement error is within an acceptable range. A trial run is carried out in a simulated blast furnace environment to detect the sensor's response speed and error range, and the sampling frequency is adjusted (such as 1 time / second). Alarm thresholds and data fault tolerance mechanisms are set to ensure timely response when data is abnormal.

[0078] S1.5: Collect multi-dimensional data of ambient gases during normal operation of the blast furnace and verify the sensor functions.

[0079] Specifically, we continuously collect multi-dimensional data of the ambient gas in the blast furnace to evaluate whether the sensor can adapt to the high temperature and high pressure environment. We compare it with the laboratory gas analysis results to ensure the accuracy and consistency of the sensor data. We also test the anti-interference performance of the collected sensors to observe whether they can operate stably under harsh conditions.

[0080] Preferably, through the precise selection and reasonable layout of gas analysis devices and sensors, multi-dimensional data collection of environmental gases in the blast furnace is achieved, ensuring the accuracy and reliability of the data. The sensor's protective measures, cooling units, and anti-seismic bracket designs improve the adaptability of the equipment in high temperature, high pressure, and dusty environments, and enhance the long-term stability of the acquisition equipment. The data acquisition and transmission module uses high-temperature-resistant, anti-interference signal transmission lines or wireless modules to ensure the real-time nature of the data and remote monitoring capabilities. The calibration and debugging measures of the sensor ensure that the measurement error is controllable, and through continuous data verification, the stability and reliability of the equipment in actual blast furnace operation are ensured.

[0081] S2: Based on the multi-dimensional data of environmental gases and combined with historical carbon emission data, a carbon emission baseline model is established through machine learning algorithms to generate a real-time carbon emission trend forecast report.

[0082] Specifically, the following steps are included:

[0083] S2.1: Preprocess the multidimensional data of ambient gases and historical carbon emission data, extract feature vectors, and perform feature dimensionality reduction through principal component analysis.

[0084] It should be noted that the preprocessing includes data denoising, missing value filling and standardization.

[0085] The extracted feature vectors include gas component concentration, temperature, pressure, flow rate, and blast furnace operating parameters (such as tuyere temperature and material layer distribution in the furnace).

[0086] The principal component analysis operation retains the principal components with 95% cumulative contribution rate for feature dimensionality reduction.

[0087] S2.2: Based on the reduced feature vector, a deep learning framework combined with a variational autoencoder is used to train a carbon emission baseline model.

[0088] Specifically, the processed data is segmented by time windows to generate training sets and validation sets.

[0089] Define the loss function of the variational autoencoder (VAE), expressed as:

[0090]

[0091] in, is the loss function, q φ (z|X') is the posterior distribution of the latent variable z, z is the latent variable, D KL is the KL divergence, p θ (X'|z) is the likelihood function of the generative model, is the expectation, p(z) is the prior distribution of the latent variable z, φ is the parameter of the encoder, θ is the parameter of the decoder, and X' is the training set.

[0092] Train the carbon emission baseline model based on the defined loss function.

[0093] S2.3: Based on the trained carbon emission baseline model and combined with multi-dimensional data of environmental gases, the short-term carbon emission trend is predicted through the long short-term memory network to generate a real-time carbon emission trend forecast report.

[0094] Specifically, the feature data after real-time dimension reduction is input into the long short-term memory network (LSTM) model to predict future carbon emissions. The prediction results are visualized, including carbon emission trend curves, peak moments, and high emission area prompts.

[0095] Preferably, the data quality is improved and the dimension is reduced through preprocessing, feature dimension reduction and principal component analysis of multidimensional data of environmental gases and historical carbon emission data, ensuring the efficiency and accuracy of subsequent model training. The carbon emission baseline model is trained by variational autoencoder (VAE), combined with reconstruction error and KL divergence, to avoid overfitting and learn the potential data structure, laying a solid foundation for short-term carbon emission trend forecasting. The long short-term memory network (LSTM) is used to further predict carbon emission trends, capture dependencies in time series, and improve forecasting accuracy. The real-time carbon emission trend forecast report displays trend curves, peak moments and high emission areas through visual processing, providing intuitive data support to help managers adjust production strategies in a timely manner, optimize carbon emission management, and promote the realization of carbon neutrality goals.

[0096] S3: Construct a multiphase synergistic reaction model in the furnace, and calculate the carbon utilization efficiency distribution and carbon emission source location based on the carbon emission trend forecast report.

[0097] Specifically, the following steps are included:

[0098] S3.1: Construct a multiphase coordinated reaction model in the furnace.

[0099] S3.1.1: Extract furnace gas concentration and reaction condition parameters from the multidimensional data of ambient gases, and use the historical carbon emission distribution provided in the carbon emission trend forecast report as input data.

[0100] Specifically, features related to the gas concentration in the furnace, such as carbon dioxide (CO 2 ), carbon monoxide (CO), hydrogen (H 2 ), methane (CH 4 ), the concentration of gases such as oxygen, and reaction conditions such as temperature and pressure.

[0101] S3.1.2: Based on the principles of conservation of mass and energy, the synergistic effect of gas phase reaction and solid phase reaction is modeled. The kinetic model expression is:

[0102]

[0103] Among them, r i is the reaction rate of the ith reaction, k i is the reaction rate constant, E i is the activation energy, R is the ideal gas constant, T is the absolute temperature, C A is the concentration of reactant A, C B is the concentration of reactant B, a i is the reaction order of reactant A for the i-th reaction rate, b iis the reaction order of reactant B for the i-th reaction rate, i is the reaction number index coefficient;

[0104] S3.1.3: Use particle swarm optimization to optimize the dynamic model parameter k i 、E i 、a i and b i Perform the fitting.

[0105] It should be understood that particle swarm optimization is suitable for optimization problems of high-dimensional, nonlinear, and multi-peak functions. The goal is to minimize the error between the reaction rate predicted by the model and the actual measured data, so as to obtain the optimal reaction rate constant and other parameters.

[0106] S3.2: Based on the carbon emission trend forecast report, calculate the carbon utilization efficiency distribution and carbon emission source location.

[0107] S3.2.1: Calculate the carbon utilization efficiency distribution, expressed as:

[0108]

[0109] Among them, η C is the carbon utilization efficiency distribution, is the mass concentration of carbon dioxide emitted per unit time, m CO,out is the mass concentration of carbon monoxide emitted per unit time, v g is the gas flow rate, m C,in is the mass concentration of carbon raw material input per unit time, t 1 is the reaction start time, t 2 is the reaction end time, and dt is the integral identifier.

[0110] Preferably, calculating the carbon utilization efficiency distribution can help evaluate the carbon utilization effect in different regions or different reaction pathways, identify links with low carbon emission efficiency, and provide a basis for further optimization.

[0111] S3.2.2: Use Bayesian network to construct a carbon emission source probability model, expressed as:

[0112]

[0113] Among them, P(S|D) is the posterior probability of observing carbon emission data D under the condition of source S, P(D|S) is the likelihood function of carbon emission data under the condition of carbon emission source S, P(S) is the prior probability under the condition of carbon emission source S, and P(D) is the marginal probability of the data;

[0114] Preferably, the model helps to identify the contribution of different carbon emission sources to the overall emissions and to quantify emissions in different regions.

[0115] S3.2.3: Based on the carbon emission trend forecast report, train the Bayesian network model to obtain the parameters of the prior probability P(S) and the likelihood function P(D|S).

[0116] S3.2.4: Based on the multi-dimensional data of ambient gases and combined with the variational inference algorithm, calculate the probability distribution of the main sources of carbon emissions and generate a source location map.

[0117] Preferably, by constructing a multiphase synergistic reaction model in the furnace, calculating the distribution of carbon utilization efficiency and locating the source of carbon emissions, an accurate management method is provided for optimizing carbon emissions in the blast furnace process. Through multidimensional data analysis and synergistic reaction modeling, the accuracy of carbon emission prediction is improved, and the carbon utilization efficiency of different reaction paths can be evaluated, and the links with low carbon emission efficiency can be identified. Using Bayesian networks and variational inference algorithms, the main sources of carbon emissions are accurately located to provide data support for emission reduction measures. The particle swarm optimization algorithm further optimizes the reaction model, and combines carbon emission trend reports and real-time data to improve the intelligence level of carbon emission management.

[0118] S4: Based on the carbon utilization efficiency distribution and the location of carbon emission sources, the injection strategy of the hydrogen-rich gas is adjusted to obtain a dynamic injection plan.

[0119] Specifically, the following steps are included:

[0120] S4.1: Based on the distribution of carbon utilization efficiency and the location of carbon emission sources, identify hydrogen-rich gas injection areas and quantify injection requirements.

[0121] Specifically, by analyzing the distribution of carbon utilization efficiency and the location of carbon emission sources, high carbon emission areas and low carbon utilization efficiency links are identified as priority areas for injecting hydrogen-rich gas. Based on the carbon utilization efficiency and carbon emission contribution of different areas, the injection amount of each area is allocated.

[0122] S4.2: Dynamically adjust the hydrogen injection flow rate in the injection area, the expression is:

[0123]

[0124] Among them, H j (t) is the hydrogen injection flow rate of j injection areas, η C (t) is the carbon utilization efficiency distribution at time t, S(t) is the location of carbon emission sources, CO 2 (t) is the time function of carbon dioxide emissions, CO(t) is the time function of carbon monoxide emissions, α 1 is the carbon utilization efficiency weight coefficient, α 2 is the weight coefficient for carbon emission source positioning, α 3 is the weight coefficient of carbon monoxide emissions, β1 is the weight coefficient of carbon dioxide emissions, R is the ideal gas constant, and T is the absolute temperature;

[0125] Preferably, the carbon utilization efficiency η is collected and updated regularly C (t) and carbon emission data CO 2 (t) and CO(t) to ensure the real-time adjustment of the injection flow rate. Adjust the weight coefficients according to the actual production situation and emission targets to make the hydrogen injection flow rate adjustment meet the actual emission requirements. Allocate the adjusted injection flow rate to different injection areas, and continuously optimize the injection strategy according to the changes in carbon emissions and carbon utilization.

[0126] S4.3: Based on the injection strategy of hydrogen-rich gas, a dynamic injection plan is generated through a timing optimal control algorithm.

[0127] Specifically, model predictive control (MPC) is used to generate a dynamic injection plan based on the determined injection flow rate. The goal is to dynamically adjust the hydrogen injection strategy based on real-time carbon emission data and carbon utilization efficiency to ensure efficient and low-emission hydrogen injection. The dynamic injection plan includes the hydrogen injection volume of each injection area at different time points and the adjustment curve of the injection flow rate. The dynamic plan can adapt to fluctuations in blast furnace operation, such as load changes, temperature fluctuations, etc.

[0128] Preferably, adjusting the hydrogen-rich gas injection strategy based on the carbon utilization efficiency distribution and the location of carbon emission sources can effectively improve the carbon utilization efficiency of the blast furnace and optimize carbon emission control. By identifying high-emission and low-efficiency areas, hydrogen-rich gas injection is prioritized, and the injection flow rate is dynamically adjusted to adapt to real-time carbon emissions and carbon utilization data. Through precise mathematical models and dynamic control, the injection flow rate is optimized, ineffective emissions are reduced, and energy utilization is improved. In addition, the dynamic injection plan generated by the timing optimal control algorithm can cope with fluctuations in blast furnace operation, continuously optimize the injection strategy, reduce carbon dioxide and carbon monoxide emissions, and promote green manufacturing and intelligent production.

[0129] S5: According to the dynamic injection plan, the reaction process parameters of the solid and liquid phases in the furnace are adjusted, and the regional distribution of the hot phase is controlled.

[0130] Specifically, the following steps are included:

[0131] S5.1: Adjust the reaction process parameters of the solid and liquid phases in the furnace according to the dynamic injection plan.

[0132] S5.1.1: Based on the multi-dimensional data of the ambient gas in the blast furnace and the dynamic injection plan, identify the solid phase reaction zone in the furnace.

[0133] It should be understood that the solid phase reaction zone is usually formed in a high temperature, high oxygen environment, and the increase in the amount of hydrogen injection will change the gas composition, thereby affecting the location of the reaction area. The solid phase reaction zone can be identified by setting sensors (such as temperature sensors, gas analyzers) to monitor the gas composition and temperature distribution in different areas of the furnace.

[0134] S5.1.2: Based on the hydrogen injection amount and location in the dynamic injection plan, adjust the flow rate of combustion air in the furnace to control the reaction zone temperature and air flow velocity.

[0135] Specifically, when increasing the hydrogen injection flow rate, the air flow rate needs to be adjusted accordingly to ensure that the temperature and air flow rate of the reaction zone are in the optimal range. By controlling the flow rate of the tuyere in the furnace, adjusting the position of the tuyere, adjusting the oxygen supply, etc., the temperature and air flow rate of the reaction zone can be optimized to ensure efficient combustion and reaction.

[0136] S5.1.3: Adjust the particle size of the charge through the feeding device according to the reaction efficiency and the amount of hydrogen injection.

[0137] Specifically, smaller particle size can increase the contact area with the gas and promote the reaction, while larger particle size can slow down the reaction process and maintain the stability of the reaction. It is necessary to regularly monitor the gas composition and temperature in the furnace and adjust the feeding equipment to ensure that the particle size of the charge meets the reaction conditions.

[0138] S5.1.4: Identify the liquid phase reaction zone in the furnace using online gas analysis instruments and temperature sensors.

[0139] It should be understood that liquid phase reactions usually occur in areas with higher temperatures and lower airflows. Through the coordination of gas analysis and temperature sensors, the location and state of the liquid phase reaction can be identified.

[0140] S5.1.5: Adjust the melt temperature and gas composition of the liquid phase reaction zone based on the hydrogen injection rate and location in the dynamic injection plan.

[0141] Specifically, the injection of hydrogen will affect the atmosphere in the furnace, and then affect the temperature and gas composition of the liquid phase reaction zone. By adjusting the amount of hydrogen injection and the reaction atmosphere (such as the ratio of oxygen and hydrogen), the melting temperature and gas composition of the liquid phase reaction can be precisely controlled to ensure the efficient progress of the reaction.

[0142] S5.2: Regulate the regional distribution of thermal phase.

[0143] S5.2.1: Dynamically adjust the heat zone distribution in the furnace according to the hydrogen injection amount, reaction heat and carbon emission trend of each area. The heat distribution expression is:

[0144]

[0145] Where Q(t) is the heat distribution in the furnace at time t, K s (t) is the temperature of the solid phase region in the furnace at time t, K l (t) is the temperature of the liquid phase area in the furnace at time t, A(t) is the boundary A of the area that needs temperature difference adjustment, and B(t) is the boundary B of the area that needs temperature difference adjustment.

[0146] It should be understood that the heat distribution in the furnace affects the efficiency of the solid-phase and liquid-phase reactions, so accurate heat distribution is crucial. According to the generation of reaction heat and heat transfer, the distribution of hot zones in the furnace can be optimized by adjusting parameters such as temperature difference and gas flow rate. Specifically, the optimal distribution of heat between zones can be achieved by controlling the amount of hydrogen sprayed in different zones of the furnace, gas flow distribution, temperature regulation, etc.

[0147] Preferably, the solid-phase and liquid-phase reaction process parameters are adjusted according to the dynamic injection plan to ensure the adaptation of the hydrogen injection amount, temperature, air flow velocity, charge particle size, etc. in the reaction zone, which can effectively improve the reaction rate and reaction efficiency. Dynamically adjust the combustion air flow rate and hydrogen injection amount to maintain the stability of the reaction zone, avoid the effects of overheating or supercooling, and optimize the combustion process. By real-time monitoring and adjusting the melting temperature and gas composition of the liquid-phase reaction zone, the liquid-phase reaction is made more efficient and the energy conversion efficiency is further improved. Regulate the regional distribution of the hot phase, accurately distribute the heat in the furnace, and ensure that the heat is efficiently transferred to different reaction zones by adjusting the temperature difference and airflow distribution, thereby maximizing the efficiency of solid-phase and liquid-phase reactions.

[0148] This embodiment also provides a computer device, which is suitable for the method of achieving carbon neutrality in a hydrogen-rich carbon-cycle oxygen blast furnace, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method of achieving carbon neutrality in a hydrogen-rich carbon-cycle oxygen blast furnace as proposed in the above embodiment.

[0149] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0150] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for achieving carbon neutrality in a hydrogen-rich carbon cycle oxygen blast furnace as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0151] In summary, the present invention achieves this by: deploying gas analysis devices and sensors to collect multi-dimensional data of ambient gases in the blast furnace in real time, and combining historical carbon emission data, establishing a carbon emission baseline model through a machine learning algorithm, and generating a carbon emission trend forecast report. Based on the carbon emission trend forecast report, a multiphase synergistic reaction model in the furnace is constructed to accurately calculate the carbon utilization efficiency distribution and carbon emission sources, optimize the injection strategy of hydrogen-rich gas, and obtain a dynamic injection plan. By adjusting the reaction process parameters of the solid and liquid phases in the furnace and finely controlling the regional distribution of the hot phase, the present invention effectively improves the carbon utilization efficiency of the blast furnace, reduces carbon emissions, and achieves the goal of low carbonization while improving production efficiency and energy efficiency, with certain environmental and economic benefits.

[0152] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for achieving carbon neutrality in a hydrogen-rich carbon cycle oxygen blast furnace, characterized in that: include, Deploy gas analysis devices and sensors to collect multi-dimensional data of the ambient gas in the blast furnace; Based on the multi-dimensional data of environmental gases and historical carbon emission data, a carbon emission baseline model is established through machine learning algorithms to generate a real-time carbon emission trend forecast report; Construct a multiphase synergistic reaction model in the furnace, calculate the carbon utilization efficiency distribution and carbon emission source location based on the carbon emission trend forecast report; Based on the carbon utilization efficiency distribution and carbon emission source location, the injection strategy of hydrogen-rich gas is adjusted to obtain a dynamic injection plan; According to the dynamic injection plan, the reaction process parameters of the solid and liquid phases in the furnace are adjusted, and the regional distribution of the hot phase is controlled.

2. The method for achieving carbon neutralization in a hydrogen-rich carbon circulating oxygen blast furnace according to claim 1, characterized in that: The gas analysis device and the sensor are deployed to collect multi-dimensional data of the ambient gas in the blast furnace, and the specific steps are: Select and layout gas analysis equipment and sensors; Install fixed guards for sensors that complete the layout; Connect the fixed sensors to the data acquisition and transmission central controller to build a data acquisition network; Calibrate and debug sensors based on the established data acquisition network; During normal operation of the blast furnace, multi-dimensional data of environmental gases are collected to verify the sensor functions.

3. The method for achieving carbon neutralization in a hydrogen-rich carbon circulating oxygen blast furnace according to claim 2, characterized in that: The carbon emission baseline model is established based on the multi-dimensional data of environmental gases and combined with historical carbon emission data through machine learning algorithms to generate a real-time carbon emission trend forecast report. The specific steps are: Preprocess the multidimensional data of environmental gases and historical carbon emission data, extract feature vectors, and perform feature dimension reduction through principal component analysis; Based on the feature vector after dimensionality reduction, a carbon emission baseline model is trained using a deep learning framework combined with a variational autoencoder. Based on the trained carbon emission baseline model and combined with multi-dimensional data of environmental gases, the short-term carbon emission trend is predicted through the long short-term memory network to generate a real-time carbon emission trend forecast report.

4. The method for achieving carbon neutralization in a hydrogen-rich carbon circulating oxygen blast furnace according to claim 3, characterized in that: The concrete steps of constructing the multiphase coordinated reaction model in the furnace are as follows: Extract the furnace gas concentration and reaction condition parameters from the multi-dimensional data of ambient gases, and use the historical carbon emission distribution provided by the carbon emission trend forecast report as input data; Through the principles of mass conservation and energy conservation, the synergistic effect of gas phase reaction and solid phase reaction is modeled, and the kinetic model expression is: Among them, r i is the reaction rate of the ith reaction, k i is the reaction rate constant, E i is the activation energy, R is the ideal gas constant, T is the absolute temperature, C A is the concentration of reactant A, C B is the concentration of reactant B, a i is the reaction order of reactant A for the i-th reaction rate, b i is the reaction order of reactant B for the i-th reaction rate, i is the reaction number index coefficient; Particle swarm optimization is used to optimize the dynamic model parameter k i 、E i 、a i and b i Perform the fitting.

5. The method for achieving carbon neutralization in a hydrogen-rich carbon circulating oxygen blast furnace according to claim 4, characterized in that: The specific steps of calculating the carbon utilization efficiency distribution and carbon emission source location according to the carbon emission trend forecast report are as follows: Calculate the carbon utilization efficiency distribution, the expression is: Among them, η C is the carbon utilization efficiency distribution, is the mass concentration of carbon dioxide emitted per unit time, m CO,out is the mass concentration of carbon monoxide emitted per unit time, v g is the gas flow rate, m C,in is the mass concentration of carbon raw material input per unit time, t1 is the reaction start time, t2 is the reaction end time, and dt is the integral identifier; The Bayesian network is used to construct a carbon emission source probability model, which is expressed as: Among them, P(S|D) is the posterior probability of observing carbon emission data D under the condition of source S, P(D|S) is the likelihood function of carbon emission data under the condition of carbon emission source S, P(S) is the prior probability under the condition of carbon emission source S, and P(D) is the marginal probability of the data; Based on the carbon emission trend forecast report, the Bayesian network model is trained to obtain the parameters of the prior probability P(S) and the likelihood function P(D|S); Based on the multi-dimensional data of environmental gases and combined with the variational inference algorithm, the probability distribution of the main sources of carbon emissions is calculated and a source location map is generated.

6. The method for achieving carbon neutralization in a hydrogen-rich carbon circulating oxygen blast furnace according to claim 5, characterized in that: The specific steps of adjusting the injection strategy of hydrogen-rich gas based on the carbon utilization efficiency distribution and the location of carbon emission sources and obtaining a dynamic injection plan are as follows: Based on the distribution of carbon utilization efficiency and the location of carbon emission sources, identify hydrogen-rich gas injection areas and quantify injection requirements; Dynamically adjust the hydrogen injection flow rate in the injection area, the expression is: Among them, H j (t) is the hydrogen injection flow rate of j injection areas, η C (t) is the carbon utilization efficiency distribution at time t, S(t) is the location of carbon emission sources, CO2(t) is the time function of carbon dioxide emissions, CO(t) is the time function of carbon monoxide emissions, α1 is the carbon utilization efficiency weight coefficient, α2 is the carbon emission source location weight coefficient, α3 is the carbon monoxide emission weight coefficient, β1 is the carbon dioxide emission weight coefficient, R is the ideal gas constant, and T is the absolute temperature; Based on the injection strategy of hydrogen-rich gas, a dynamic injection plan is generated through a timing optimal control algorithm.

7. The method for achieving carbon neutrality in a hydrogen-rich carbon circulating oxygen blast furnace according to claim 6, characterized in that: The reaction process parameters of the solid phase and the liquid phase in the furnace are adjusted according to the dynamic injection plan, and the specific steps are: According to the multi-dimensional data of the ambient gas in the blast furnace and the dynamic injection plan, the solid phase reaction zone in the furnace is identified; Based on the hydrogen injection amount and position in the dynamic injection plan, the flow rate of combustion air in the furnace is adjusted to control the temperature and air flow velocity in the reaction zone; According to the reaction efficiency and hydrogen injection amount, the particle size of the charge is adjusted through the feeding device; Identify the liquid phase reaction zone in the furnace through online gas analysis instruments and temperature sensors; The melting temperature and gas composition of the liquid phase reaction zone are adjusted based on the hydrogen injection amount and position in the dynamic injection plan.

8. The method for achieving carbon neutralization in a hydrogen-rich carbon circulating oxygen blast furnace according to claim 7, characterized in that: The specific steps of regulating the regional distribution of the thermal phase are: According to the hydrogen injection amount, reaction heat and carbon emission trend of each area, the hot zone distribution in the furnace is dynamically adjusted. The heat distribution expression is: Where Q(t) is the heat distribution in the furnace at time t, K s (t) is the temperature of the solid phase region in the furnace at time t, K l (t) is the temperature of the liquid phase area in the furnace at time t, A(t) is the boundary A of the area that needs temperature difference adjustment, and B(t) is the boundary B of the area that needs temperature difference adjustment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for achieving carbon neutrality in a hydrogen-rich carbon circulating oxygen blast furnace as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for achieving carbon neutrality in a hydrogen-rich carbon circulating oxygen blast furnace as described in any one of claims 1 to 8 are implemented.

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