Multi-gun top-blowing continuous blowing intelligent system and method

Abstract

The invention relates to the technical field of metal smelting, in particular to a multi-gun top-blowing continuous blowing intelligent system and method. The system comprises a data acquisition module, an analysis processing module and a prediction feedback module. The method comprises the steps that the temperature, the liquid level and the material state in the furnace are monitored in real time and transmitted to the central data processing platform in real time for intelligent feedback control; dimensionality reduction is conducted on sensing data in the smelting process, the least principal components with the contribution rate exceeding 80% are extracted, and dimensionality reduction processing is achieved; carrying out thermal analysis by utilizing a material and energy balance equation, constructing a theoretical model of the blowing process, capturing a long-term dependency relationship, and predicting dynamic parameter change in the smelting process; and an interaction model of the intelligent agent and the smelting environment is constructed, a control strategy is updated in real time, and production parameters are adaptively adjusted. The problem of low-altitude pollution caused by converter blowing is solved, the defect of low efficiency of intermittent operation of current smelting is overcome, and a continuous and stable production process is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of metal smelting, and in particular to an intelligent system and method for multi-gun top-blowing continuous blowing. Background Art

[0002] The production processes of metals such as copper and nickel are mainly divided into two categories: pyrometallurgy and hydrometallurgy. Among them, pyrometallurgy occupies a dominant position, with a share of up to 86%. Pyrometallurgy produces metal matte by smelting matte. The metal matte after blowing contains a high metal grade, and then pure metal is deposited by electrolysis. In modern smelters, horizontal converters are generally used for blowing of metal matte. These traditional converter blowing processes have some limitations, such as periodic operation, poor thermal stability and large fluctuations in flue gas concentration, which limit production efficiency and environmental protection effects. In contrast, the multi-gun continuous top blowing process successfully solves the above-mentioned low-altitude pollution problem and eliminates the safety hazards of melt lifting. At the same time, the continuous production work efficiency is high, the production is stable, and the degree of automation is high. With the improvement of environmental protection and energy consumption requirements, the multi-gun top blowing continuous blowing process is gradually becoming a new trend in the development of the industry. Although the multi-gun top-blowing furnace technology has been widely used in the field of copper matte blowing, the application of this technology in nickel smelting is still blank; in addition, the existing multi-gun top-blowing blowing technology lacks real-time monitoring of material input, furnace temperature and molten pool liquid level. This defect weakens the stability and efficiency of the smelting process and affects the service life of the furnace. At present, the adjustment of the spray gun height mainly depends on manual observation. The operator judges the position of the spray gun with the naked eye through the observation hole of the furnace body and manually raises and lowers it. At the same time, the input amount of top material, the amount of air blown in and the uncertainty of the temperature in the furnace will affect the reaction conditions in the molten pool, which may lead to insufficient or excessive reaction, thereby affecting the metal recovery rate and product quality. This experience-based control method faces high observation difficulty and multiple interference factors in the complex furnace environment, resulting in low overall control accuracy.

[0003] Prior art 1, Chinese patent, application number 202411154041.6 discloses a supersonic multi-gun top blowing converting intelligent spray gun control system and method, including a monitoring module, a feedback control module, a spray gun lifting module, and a spray gun module. The method includes matte, cold material, solvent and limestone entering a multi-gun top blowing converting furnace; the temperature monitoring system and the liquid level monitoring system transmit the corresponding parameters detected in the converting furnace to the intelligent feedback control module; after the feedback control module processes the received data, it calculates the height instruction according to the algorithm, and controls the height of the multi-porous supersonic spray gun through the spray gun lifting module; oxygen-enriched air flows into the multi-porous supersonic spray gun, flows into the multi-gun top blowing converting furnace through the nozzle, and the crude copper flows out from the crude copper discharge port. Although the intelligent control and regulation of the entire smelting process can be realized, it brings important technical support and significance to improving production efficiency, optimizing product quality, and reducing production costs. However, the problems of periodic operation, poor thermal stability and large fluctuations in flue gas concentration limit production efficiency and environmental protection effects.

[0004] Prior art two, Chinese patent, application number 202311689370.6 discloses a multi-gun top blowing slag reduction device and control method for an oxygen-enriched smelting flash smelting furnace, including a silo and a flash smelting furnace; the silo is filled with a solid reducing agent, and the bottom of the silo is a feeding scraper; the feeding scraper is connected to an inclined chute, and the inclined chute is connected to a straight chute; the straight chute is connected to a buffer bin, and the buffer bin is connected to a pneumatic blowing pump; the pneumatic blowing pump is connected to a hose, and the spray gun connected to the hose extends into a sedimentation tank, the upper layer of the sedimentation tank is a liquid smelting slag layer, and the lower layer is a liquid copper matte layer, and the end of the spray gun extends into the liquid smelting slag layer. The pneumatic injection pump of the present invention cooperates with the spray gun to transport the solid reducing agent in the silo to the sedimentation tank, so that it is evenly mixed with the liquid smelting slag layer, so that the oxide and ferroferric oxide are reduced, the flash smelting slag type is improved, the proportion of copper oxide in the flash smelting slag is reduced, and the main loss form of copper is sulfide, which improves the flotation efficiency of the slag selection system, reduces the copper content of the tailings, and improves the direct recovery rate and recovery rate of the system copper. However, there is no strict requirement for the principle particle size, and the batching work in the raw material preparation process is relatively complicated, which increases the workload in the production process.

[0005] Prior art three, Chinese patent, application number 201310243085.1 discloses a double furnace multi-gun top-blowing continuous converting furnace, which divides the slag-making period and copper-making period converting processes in the PS converter into two converting spaces, thereby realizing a continuous converting process of continuous matte feeding, continuous blasting, continuous slag-making, continuous slag-discharging, and continuous or intermittent release of crude copper. Although the side blowing of the converter is changed to a multi-gun suspended top blowing, it not only reduces the scouring of the furnace bricks by the melt splashing and prolongs the service life of the furnace bricks, but also realizes the injection of flux into the melt from the spray gun sleeve, so that the flux and the melt are in contact more evenly and quickly, effectively suppressing the large-scale generation and precipitation of magnetic iron during the converting process. However, its structure is relatively simple, and the utilization rate of the reducing agent is low, resulting in high production costs and waste of resources.

[0006] At present, the existing technologies 1, 2 and 3 have the problems of periodic operation, poor thermal stability and large fluctuation of flue gas concentration. Therefore, in view of the defects of the existing technologies, the present invention proposes an intelligent production process for oxygen-enriched blowing in a multi-gun top-blowing continuous blowing furnace. On the basis of the traditional PS converter blowing technology, it has achieved significant technological innovation and can intelligently adjust the key operating parameters such as the amount of oxygen-enriched air introduced by the spray gun, the amount of material put in and the lifting height of the spray gun according to the real-time state of smelting in the furnace. It not only effectively shortens the blowing cycle and improves production efficiency, but also greatly enhances the stability of the blowing process. Summary of the invention

[0007] The main purpose of the present invention is to provide an intelligent system and method for multi-gun top-blowing continuous refining to solve the problems of periodic operation, poor thermal stability and large fluctuations in flue gas concentration in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A multi-lance top blowing continuous blowing intelligent system, comprising:

[0010] The data acquisition module is used to set up multiple sensors in the furnace to monitor the temperature, liquid level and material status in the furnace in real time, and transmit the collected data to the central data processing platform in real time for intelligent feedback control;

[0011] The analysis and processing module is used to clean various types of data transmitted in real time, use principal component analysis to reduce the dimension of sensor data in the smelting process, extract the minimum principal component with a contribution rate of more than 80%, and achieve dimensionality reduction processing;

[0012] The prediction feedback module is used to perform thermal analysis using material and energy balance equations, construct a theoretical model of the metal matte blowing process, capture long-term dependencies, and predict dynamic parameter changes during the smelting process; it also constructs an interaction model between the intelligent agent and the smelting environment, updates the control strategy in real time, and adaptively adjusts production parameters.

[0013] As a further improvement of the present invention, the data acquisition module includes:

[0014] The temperature acquisition submodule is used to arrange temperature sensors at the furnace wall, furnace roof and furnace top to monitor the overall heat distribution in the furnace, and install temperature sensors in the material to monitor the material temperature; after acquiring the data, it is transmitted to the intelligent feedback control system for processing through the data acquisition module;

[0015] The liquid level acquisition submodule is used to monitor the liquid level using a laser liquid level sensor. A part of the laser is transmitted to the time transmitter as a reference signal, and the other part is converted into parallel light through the optical system to illuminate the liquid surface of the molten pool, and the signal is amplified by the sampling circuit; the collected electrical signal is converted into a digital signal to verify the validity of the signal, and the liquid level data is visualized;

[0016] The material quantity collection submodule is used to install a camera on the top of the conveyor belt to collect material data on the dynamic conveyor belt in real time, and use a filtering algorithm to process the collected images. The processed image data is integrated into the analysis and processing module to calculate the size and trend of the material flow and predict the material flow trend.

[0017] As a further improvement of the present invention, the intelligent feedback control system in the temperature acquisition submodule performs the following processing:

[0018] The spray gun height monitoring unit is used to determine the initial height of the spray gun in the blowing furnace, obtain the image of the spray gun in the furnace through image acquisition, and analyze the image to obtain the height of the spray gun from the molten pool liquid surface;

[0019] A height correction unit is used to obtain real-time height data of the molten pool liquid level and the spray gun height data, and to set a safety threshold and an effective threshold of the distance difference between the molten pool liquid level and the spray gun height. If the current distance difference between the molten pool liquid level and the spray gun height is less than the preset safety threshold, a safety control signal is sent to the spray gun control unit; if the current distance difference between the molten pool liquid level and the spray gun height is greater than the preset safety threshold, an effective control signal is sent to the spray gun control unit;

[0020] The spray gun control unit is used to receive the safety control signal and the effective control signal sent by the height correction unit, and control the lifting and lowering of the spray gun according to the type of signal received. When the safety control signal is received, the spray gun is controlled to rise by electric or hydraulic means; when the effective control signal is received, the spray gun is controlled to descend by electric or hydraulic means.

[0021] As a further improvement of the present invention, the liquid level acquisition submodule includes:

[0022] A reference signal unit is used for emitting a beam of near-infrared light using a laser transmitter, wherein a portion of the laser light passes through a semi-transmitting reflector and is input into a time transmitter as a reference signal to form a periodic time sequence;

[0023] The liquid level acquisition unit is used to process another part of the laser light optically to form a parallel beam of set width to irradiate the surface of the molten pool. After part of the laser light is reflected, it is received by the laser liquid level sensor and the received reflected laser light is converted into an electrical signal.

[0024] The information feedback unit is used to amplify the converted electrical signal and align it with the time series, verify the validity of the signal after converting the electrical signal into a digital signal, and visualize the liquid level data. If the liquid level is detected to be unstable, an alarm will be issued and the spray gun will rise to the top.

[0025] As a further improvement of the present invention, the analysis and processing module includes:

[0026] The data preprocessing submodule is used to obtain the furnace temperature, liquid level and material status collected in real time by the data acquisition module, and perform preprocessing such as cleaning, noise processing, abnormal value detection and elimination, missing value processing, data smoothing and noise reduction, and consistency verification on the acquired real-time data;

[0027] The principal component analysis submodule is used to establish the data matrix under the normal operating conditions of the multi-lance top-blowing converting furnace and calculate the covariance matrix R. The covariance matrix is ​​decomposed by eigenvalues ​​to obtain the eigenvalues ​​and corresponding eigenvectors to reflect the correlation between the features.

[0028] The importance sorting submodule is used to calculate the importance of each feature and evaluate the impact of the input furnace temperature, liquid level and material state on the smelting quality indicators. By analyzing the cumulative variance contribution rate of the principal components, the minimum principal component with a contribution rate of more than 80% is proposed to achieve dimensionality reduction.

[0029] As a further improvement of the present invention, the prediction feedback module includes:

[0030] Theoretical model submodule is used to construct a theoretical model of the metal matte blowing process. By inputting parameters such as ore ratio and oxygen concentration, it predicts the change trend of logistics and energy flow and smelting efficiency.

[0031] The result prediction submodule is used to automatically generate feedback information based on the prediction results of the theoretical model of the metal matte blowing process to inform the operator of possible quality fluctuations or changes in storage conditions;

[0032] The interactive model submodule is used to build an interactive model between the intelligent agent and the smelting environment, define the key parameter states, adjust the process parameter actions, and optimize the rewards for smelting quality, energy consumption, and emissions. The control strategy is updated through real-time data, and the intelligent agent adaptively adjusts the production parameters.

[0033] As a further improvement of the present invention, the theoretical model submodule includes:

[0034] The model building unit is used to obtain the historical data of the spray guns of the multi-gun top-blowing refining furnace, perform thermal analysis using the material and energy balance equations, use a recurrent neural network to process time series data, establish a theoretical model of the refining process, and use the collected real-time furnace temperature, liquid level and material status information as a training set to train the model;

[0035] The model evaluation unit is used to set the inertia weight, learning factor, number of particles and other parameters of the particle swarm optimization method, train the model for each particle and test its performance index, mark the particle with the highest accuracy as the global optimal particle, find the particle with the minimum fitness according to the particle state, and update the model's hyperparameter combination;

[0036] The trend prediction unit is used to input parameters such as ore proportion and oxygen concentration into the theoretical model of the blowing process, predict the changing trend of logistics and energy flow and smelting efficiency, and output the prediction results to the information feedback center.

[0037] As a further improvement of the present invention, the interaction model submodule includes:

[0038] The state definition unit is used to obtain the characteristic value parameters of the furnace temperature, liquid level and material state, define the state as the characteristic value key parameter, define the action as adjusting the process parameters, and define the reward as optimizing the smelting quality, energy consumption and emissions;

[0039] The interactive model unit is used to initialize the neural network weight parameters of the intelligent agent, detect the current working state, select an action according to the current working state, and provide feedback to the intelligent agent after executing the action. According to the prediction results of the theoretical model of the metal matte blowing process, the intelligent agent updates its strategy and value function to achieve the optimal strategy;

[0040] The adaptive adjustment unit is used to obtain the real-time monitored temperature, liquid level and material status in the furnace. The intelligent agent dynamically and adaptively adjusts the smelting process parameters according to the real-time status, and visualizes the adaptive decision with an explainable tool.

[0041] To achieve the above object, the present invention also provides the following technical solutions:

[0042] An intelligent method for multi-lance top blowing continuous blowing, comprising:

[0043] Multiple sensors are installed in the furnace to monitor the temperature, liquid level and material status in the furnace in real time, and the collected data is transmitted to the central data processing platform in real time for intelligent feedback control;

[0044] Clean all kinds of data transmitted in real time, use principal component analysis to reduce the dimension of sensor data in the smelting process, extract the minimum principal component with a contribution rate of more than 80%, and achieve dimensionality reduction processing;

[0045] Use material and energy balance equations to conduct thermal analysis, build a theoretical model of the blowing process, capture long-term dependencies, and predict dynamic parameter changes during the smelting process. Build an interaction model between the intelligent agent and the smelting environment, update the control strategy in real time, and adaptively adjust production parameters.

[0046] To achieve the above object, the present invention also provides the following technical solutions:

[0047] A multi-gun top-blowing continuous refining intelligent device comprises spray guns, wherein the spray guns are installed on the top of the multi-gun top-blowing furnace, and a total of 8 spray guns are provided; a cold material inlet is opened on the furnace wall on the side close to the low-grade metal matte inlet, and the low-grade metal matte inlet is opened obliquely upward and outward; an outlet for refined metal sulfur is opened obliquely downward and outward on the other side of the inlet; a slag outlet is opened on the furnace wall on the side close to the outlet, and the top of the slag outlet is an ascending flue.

[0048] The present invention adopts a new multi-gun top-blowing furnace to replace the traditional PS converter blowing. This technological innovation not only significantly improves the serious low-altitude pollution problem caused by converter blowing, but also overcomes the current disadvantage of low efficiency of intermittent operation in metal smelting, realizes a continuous and stable production process, and greatly improves the production efficiency of metal smelting. In addition, the application of the new multi-gun top-blowing furnace also optimizes the flue gas treatment system, reduces the complexity and investment cost of the system, and makes the flue gas treatment more efficient. It is conducive to promoting the transformation and upgrading of my country's smelting industry and the construction of ecological civilization; the use of a multi-gun top-blowing intelligent control system can achieve energy conservation and emission reduction, reduce labor costs, enhance safety, extend equipment life, and promote industrial upgrading. At the same time, it brings market competitive advantages to enterprises and supports data-driven decision-making, which is a key measure to promote industrial modernization and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a functional module schematic diagram of an embodiment of the multi-lance top-blowing continuous blowing intelligent system of the present invention;

[0050] Figure 2 It is a schematic diagram of an embodiment of the multi-gun top-blowing continuous blowing intelligent system of the present invention;

[0051] Figure 3 This is a functional module diagram of a data acquisition module of an embodiment of an intelligent system for continuous blowing with multiple lances and top blowing according to the present invention;

[0052] Figure 4 This is a functional module schematic diagram of a temperature acquisition submodule of an embodiment of the multi-gun top-blowing continuous blowing intelligent system of the present invention;

[0053] Figure 5 This is a functional module schematic diagram of a liquid level acquisition submodule of an embodiment of the multi-gun top-blowing continuous blowing intelligent system of the present invention;

[0054] Figure 6 This is a functional module schematic diagram of an analysis and processing module of an embodiment of an intelligent system for multi-lance top-blowing continuous blowing according to the present invention;

[0055] Figure 7 This is a functional module schematic diagram of a prediction feedback module of an embodiment of a multi-lance top-blowing continuous blowing intelligent system of the present invention;

[0056] Figure 8 This is a functional module schematic diagram of a theoretical model submodule of an embodiment of a multi-lance top-blowing continuous blowing intelligent system of the present invention;

[0057] Fig. 9 It is a functional module schematic diagram of an interactive model submodule of an embodiment of a multi-lance top-blowing continuous blowing intelligent system of the present invention;

[0058] Fig.10 A schematic diagram of the steps of an embodiment of the intelligent method for multi-gun top-blowing continuous blowing of the present invention;

[0059] Fig.11 It is a schematic diagram of an embodiment of the intelligent method for continuous blowing with multiple lances top blowing of the present invention;

[0060] Fig.12 It is a structural schematic diagram of an embodiment of the multi-gun top-blowing continuous blowing intelligent device of the present invention;

[0061] Fig.13 It is a structural schematic diagram of an embodiment of an electronic device of the present invention;

[0062] Fig.14 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0065] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0066] like Figure 1 As shown, this embodiment provides an embodiment of a multi-gun top-blowing continuous blowing intelligent system. In this embodiment, the multi-gun top-blowing continuous blowing intelligent system specifically includes:

[0067] Data acquisition module 1 is used to set multiple sensors in the furnace to monitor the temperature, liquid level and material status in the furnace in real time, and transmit the collected data to the central data processing platform in real time for intelligent feedback control;

[0068] Analysis and processing module 2 is used to clean various types of data transmitted in real time, use principal component analysis to reduce the dimension of sensor data in the smelting process, extract the minimum principal component with a contribution rate of more than 80%, and achieve dimensionality reduction processing;

[0069] Prediction and feedback module 3 is used to perform thermal analysis using material and energy balance equations, build a theoretical model of the blowing process, capture long-term dependencies, and predict dynamic parameter changes during the smelting process; build an interaction model between the intelligent agent and the smelting environment, update the control strategy in real time, and adaptively adjust production parameters.

[0070] Preferably, the data acquisition module 1 of this embodiment can obtain key information such as the temperature, liquid level and material status in the furnace in real time by setting multiple sensors in the furnace; the collected data is transmitted to the central data processing platform in real time to ensure the timeliness and accuracy of the data; the analysis and processing module 2 removes noise and outliers to improve data quality; through dimensionality reduction processing, the minimum principal component with a contribution rate of more than 80% to the smelting process is extracted, which simplifies the data model and reduces the calculation complexity; the prediction and feedback module 3 uses the material and energy balance equation to construct a theoretical model to capture the long-term dependence in the smelting process; predicts the dynamic parameter changes in the smelting process to provide a scientific basis for production adjustment; constructs an interactive model between the intelligent agent and the smelting environment to realize the real-time update and self-adjustment of the control strategy (for specific schematic diagrams, refer to the attached Figure 2 ).

[0071] To sum up, the data acquisition module 1 of this embodiment monitors the furnace status in real time, which helps to timely discover and solve potential problems and avoid production interruptions; it provides a rich data basis for intelligent analysis and control, and helps to realize the automation and intelligence of the smelting process; the analysis and processing module 2 improves the efficiency and accuracy of data analysis, and provides more reliable data support for thermal analysis and prediction; the dimensionality reduction processing reduces the amount of calculation, which helps to realize real-time online analysis; the prediction feedback module 3 can more accurately predict the parameter changes in the smelting process through theoretical models and prediction models, and provide strong support for production optimization; the intelligent interaction model enables the smelting process to be adaptively adjusted according to actual conditions, thereby improving the stability and flexibility of the production process; through precise control and prediction, the smelting process can be optimized, energy consumption and production costs can be reduced, and economic benefits can be improved.

[0072] Furthermore, if Figure 3 As shown, the data acquisition module 1 specifically includes:

[0073] The temperature acquisition submodule 11 is used to arrange temperature sensors at the furnace wall, furnace roof and furnace top to monitor the overall heat distribution in the furnace, and install temperature sensors in the material to monitor the material temperature; after acquiring the data, it is transmitted to the intelligent feedback control system for processing through the data acquisition module;

[0074] The liquid level acquisition submodule 12 is used to monitor the liquid level using a laser liquid level sensor. A part of the laser is transmitted to the time transmitter as a reference signal, and the other part is converted into parallel light through the optical system to illuminate the liquid surface of the molten pool, and the signal is amplified by the sampling circuit; the collected electrical signal is converted into a digital signal to verify the validity of the signal, and the liquid level data is visualized;

[0075] The material quantity collection submodule 13 is used to install a camera on the top of the conveyor belt to collect material data on the dynamic conveyor belt in real time, use a filtering algorithm to process the collected images, and integrate the processed image data into the analysis and processing module to calculate the size and trend of the material flow and predict the material flow trend.

[0076] Preferably, the temperature acquisition submodule 11 of this embodiment can comprehensively and accurately obtain the temperature distribution information in the furnace by arranging temperature sensors at the furnace wall, furnace top and other positions; installing the temperature sensor in the boring can reflect the temperature change of the material in real time and accurately, which is very important for controlling the material quality and process; the temperature data is transmitted to the intelligent feedback control system through the data acquisition module to realize real-time processing and analysis of the data, providing a basis for optimizing the process parameters; the liquid level acquisition submodule 12 adopts a laser liquid level sensor, which has the characteristics of high precision and high sensitivity, and can accurately monitor the liquid level change of the molten pool; through the optical system and parallel light irradiation, as well as the signal amplification of the sampling circuit The signal quality is improved; the validity of the signal is verified to ensure the accuracy of the data; after the collected liquid level data is converted into a data signal, it is visualized for the operator to intuitively understand the liquid level status; the material quantity acquisition submodule 13 installs a camera on the top of the conveyor belt, which can collect material data on the dynamic conveyor belt in real time, including the shape, size, quantity, etc. of the material; the collected image is processed by a filtering algorithm to improve the image quality; the processed image data is integrated into the analysis and processing module to provide a basis for calculating the material flow; through the calculation of the analysis and processing module, the size and trend of the material flow can be accurately predicted, providing a basis for production scheduling and inventory management.

[0077] To sum up, the temperature acquisition submodule 11 of this embodiment improves the accuracy and stability of temperature control in the furnace, optimizes the process, and improves product quality; monitors the material temperature in real time to prevent material damage or process failure caused by abnormal temperature; provides accurate data support for the intelligent feedback control system to realize automated and intelligent production control; the liquid level acquisition submodule 12 accurately monitors the molten pool liquid level to prevent production accidents caused by abnormal liquid level; improves the accuracy and stability of liquid level control and optimizes the smelting process; data visualization helps operators respond and make decisions quickly, and improves production efficiency and safety; the material quantity acquisition submodule 13 grasps the material flow information in real time and accurately, and improves the flexibility and accuracy of production scheduling; predicts the material flow trend, which helps to optimize inventory management, reduce inventory backlogs and waste; improves production efficiency, reduces production costs, and enhances the competitiveness of the enterprise.

[0078] Furthermore, if Figure 4 As shown, the processing performed by the intelligent feedback control system in the temperature acquisition submodule 11 specifically includes:

[0079] The spray gun height monitoring unit 111 is used to determine the initial height of the spray gun in the blowing furnace, obtain the image of the spray gun in the furnace through image acquisition, and analyze the image to obtain the height of the spray gun from the molten pool liquid surface;

[0080] The height correction unit 112 is used to obtain the real-time height data of the molten pool liquid level and the height data of the spray gun, and set the safety threshold and effective threshold of the distance difference between the molten pool liquid level and the spray gun height. If the current distance difference between the molten pool liquid level and the spray gun height is less than the preset safety threshold, a safety control signal is sent to the spray gun control unit; if the current distance difference between the molten pool liquid level and the spray gun height is greater than the preset safety threshold, an effective control signal is sent to the spray gun control unit;

[0081] The spray gun control unit 113 is used to receive the safety control signal and the effective control signal sent by the height correction unit, and control the lifting and lowering of the spray gun according to the type of signal received. When a safety control signal is received, the spray gun is controlled to rise by electric or hydraulic means; when an effective control signal is received, the spray gun is controlled to descend by electric or hydraulic means.

[0082] Preferably, the spray gun height detection unit 111 of this embodiment determines the initial height of the spray gun in the blowing furnace, and obtains the image information of the spray gun in the furnace in real time through image acquisition technology; analyzes and processes the image to accurately calculate the height of the spray gun from the molten pool liquid surface; the height correction unit 112 obtains the real-time height data of the molten pool liquid surface and the height data of the spray gun; sets a safety threshold and an effective threshold of the distance difference between the molten pool liquid surface and the spray gun height to determine whether the current state is safe or effective; based on the judgment result, sends a response control signal to the spray gun control unit; the spray gun control unit 113 receives the control signal sent by the height correction unit, and controls the lifting and lowering of the spray gun according to the signal type; realizes precise control of the spray gun by electric or hydraulic means to ensure that the spray gun can move according to a predetermined trajectory and speed.

[0083] In summary, the spray gun height monitoring unit 111 of the present embodiment realizes real-time monitoring of the spray gun height, thereby improving the accuracy and safety of the blowing process; through image analysis technology, it avoids the errors and limitations of traditional measurement methods, and improves the accuracy and reliability of measurement; the height correction unit 112 ensures a safe distance between the spray gun and the liquid surface of the molten pool through real-time monitoring and threshold judgment, and prevents safety problems or reduced production efficiency caused by a distance that is too close or too far; it realizes dynamic adjustment of the spray gun height, thereby improving the stability and flexibility of the blowing process; the spray gun control unit 113 realizes precise control of the spray gun height, thereby ensuring the stability and efficiency of the blowing process; through electric or hydraulic means, it improves the reliability and response speed of the spray gun control, and provides strong support for the automation and intelligence of the blowing process.

[0084] Furthermore, if Figure 5As shown, the liquid level acquisition submodule 12 specifically includes:

[0085] The reference signal unit 121 is used to emit a beam of near-infrared light using a laser transmitter, wherein a portion of the laser light passes through a semi-transmitting reflector and is input into a time transmitter as a reference signal to form a periodic time sequence;

[0086] The liquid level acquisition unit 122 is used to optically process another part of the laser light to form a parallel beam of a set width to irradiate the surface of the molten pool, and a part of the laser light is reflected and received by the laser level sensor, and the received reflected laser light is converted into an electrical signal;

[0087] The information feedback unit 123 is used to amplify the converted electrical signal and align it with the time series, verify the validity of the signal after converting the electrical signal into a digital signal, and visualize the liquid level data. If the liquid level is detected to be unstable, an alarm is issued and the spray gun is raised to the top.

[0088] Preferably, the reference signal unit 121 of this embodiment uses a laser transmitter to emit a beam of near-infrared light, thereby ensuring the stability and accuracy of the signal; through a semi-transmitting reflector, part of the laser is input into the time transmitter as a reference signal to form a periodic time series, providing a time reference for signal processing; the liquid level acquisition unit 122 optically processes another part of the laser to form a parallel light beam of a set width to illuminate the surface of the molten pool, thereby ensuring that the light beam can be evenly illuminated on the liquid surface, thereby obtaining more accurate liquid level information; part of the laser is reflected and received by the laser liquid level sensor, and the received reflected laser is converted into an electrical signal; the information feedback unit 123 amplifies the converted electrical signal and aligns it with the time series, thereby ensuring the stability and accuracy of the signal during the processing; the validity of the signal is verified after converting the electrical signal into a digital signal, thereby further improving the reliability and accuracy of the data; the liquid level data is displayed visually, thereby facilitating real-time monitoring and recording by the operator; if the liquid level is detected to be unstable, an alarm is sounded, and the spray gun is raised to the top, thereby ensuring the safety and stability of the system.

[0089] To sum up, the reference signal unit 121 of this embodiment ensures the time synchronization of the system, making the collection and processing of liquid level data more accurate and reliable; it provides a basis for signal alignment and verification, and improves the overall performance of the system; the liquid level acquisition unit 122 realizes non-contact measurement of the molten pool liquid level, avoiding the errors and pollution caused by traditional measurement methods; it improves the accuracy and real-time performance of liquid level measurement, and provides an important basis for industrial production and control; the information feedback unit 123 realizes real-time processing and feedback of liquid level data, and improves the response speed and automation degree of the system; through visual display and alarm functions, it improves the monitoring efficiency and safety of operators; timely measures are taken when the liquid level is unstable to avoid possible production accidents and equipment damage.

[0090] Furthermore, if Figure 6 As shown, the analysis and processing module 2 specifically includes:

[0091] The data preprocessing submodule 21 is used to obtain the furnace temperature, liquid level and material status collected in real time by the data acquisition module, and perform preprocessing such as cleaning, noise processing, abnormal value detection and elimination, missing value processing, data smoothing and noise reduction, and consistency verification on the acquired real-time data;

[0092] The principal component analysis submodule 22 is used to establish a data matrix under normal operating conditions of the multi-lance top-blowing converting furnace and calculate the covariance matrix R. By decomposing the covariance matrix by eigenvalues, eigenvalues ​​and corresponding eigenvectors are obtained to reflect the correlation between features.

[0093] The importance ranking submodule 23 is used to calculate the importance of each feature and evaluate the influence of the input furnace temperature, liquid level and material state on the smelting quality index. By analyzing the cumulative variance contribution rate of the principal component, the minimum principal component with a contribution rate exceeding 80% is proposed to achieve dimensionality reduction processing.

[0094] Among them, the data preprocessing submodule 21 adopts dynamic adaptive data cleaning and noise reduction; the dynamic adaptive noise filtering expression is:

[0095]

[0096] In the formula, y i represents the smoothed data point, x i+j represents the original data point, w ij represents a dynamic adaptive weight based on the deviation of the data point from the local mean, φ(x i+j ) represents a nonlinear transformation function, for example, φ(x) = tanh(x), μ i represents the local mean, calculated as σ i represents the local standard deviation, calculated as ∈ represents a small constant that prevents the denominator from being zero, for example, ∈=10 -6 , k represents the window radius;

[0097] Dynamic outlier detection and removal:

[0098]

[0099] Where D i represents the dynamic Mahalanobis distance, combined with the nonlinear attenuation factor, X i represents the i-th data point, μ represents the data mean vector, ∑ represents the data covariance matrix, β j represents the nonlinear attenuation coefficient, based on the feature importance, ψ(X j ) represents a nonlinear feature transformation function, such as ψ(X j )=log(1+|X j |);

[0100] Dynamic missing value interpolation:

[0101]

[0102] In the formula, represents the estimated value of the missing value, β0 represents the intercept term, β j represents the regression coefficient, φ(x i,j ) represents the nonlinear transformation function, μ j represents the mean of the jth feature, σ j represents the standard deviation of the jth feature;

[0103] The principal component analysis submodule 22 uses multi-level feature decomposition and nonlinear fusion, and dynamic covariance matrix calculation:

[0104]

[0105] Where R represents the dynamic covariance matrix, φ(X i ) represents the data points after nonlinear transformation, Represents the mean of the data after nonlinear transformation;

[0106] Dynamic Eigenvalue Decomposition:

[0107]

[0108] Where Q represents the eigenvector matrix, Λ represents the eigenvalue diagonal matrix, and λ i represents the nonlinear eigenvalue transformation function;

[0109] Dynamic Eigenvector Adjustment:

[0110]

[0111] In the formula, v i represents the dynamically adjusted feature vector, α ij represents the dynamic weight, v ij represents the nonlinear transformation function, μ v represents the mean of the eigenvector, σ v represents the standard deviation of the eigenvector;

[0112] The importance ranking submodule 23 uses a multi-level feature importance assessment and dynamically accumulates variance contribution rates:

[0113]

[0114] In the formula, λ' i represents the i-th eigenvalue, k' represents the number of principal components selected, and n' represents the total number of features;

[0115] Dynamic feature importance calculation:

[0116]

[0117] In the formula, I j represents the importance of the jth feature, α ij represents the dynamic weight, φ(λ i ) represents the nonlinear eigenvalue transformation function, v ij represents the jth component of the i-th eigenvector, μ v represents the mean of the eigenvector, σ v Represents the standard deviation of the feature vector; the dynamic adaptive mechanism introduces dynamic weights and nonlinear transformation functions, so that the model can dynamically adjust according to the data distribution, thereby improving processing efficiency and accuracy; multi-level feature fusion combines nonlinear optimization and multi-level feature decomposition, and the model can capture data features more comprehensively and is suitable for complex scenarios; self-learning weight adjustment introduces a self-learning weight adjustment mechanism, so that the model can dynamically optimize parameters according to historical data, thereby improving the robustness and adaptability of the model.

[0118] Preferably, the data preprocessing submodule 21 of this embodiment removes irrelevant information or duplicate data in the original data to ensure the accuracy and consistency of the data; reduces random fluctuations or errors in the data to improve the signal-to-noise ratio of the data; identifies and removes data points that do not conform to the expected rules to prevent abnormal data from interfering with subsequent analysis; fills in gaps in the data to maintain data integrity; ensures consistency of data at different time points or from different sources to improve data reliability; the principal component analysis submodule 22 organizes the normal operating condition data of the multi-gun top-blowing refining furnace into a matrix form for easy analysis; reveals the correlation between the dimensions of the data; and decomposes the covariance matrix , the eigenvalues ​​and corresponding eigenvectors are obtained, which represent the main change trends in the data; the results of the eigenvalues ​​and eigenvectors can reflect the correlation between the dimensions of the data, which is helpful to understand the internal structure of the data; the importance sorting submodule 23 evaluates the importance of the features by calculating the degree of influence of each feature on the smelting quality indicators; quantifies the specific influence of input factors such as furnace temperature, liquid level and material state on the smelting quality indicators; determines the principal component with the greatest influence on the smelting quality indicators by analyzing the cumulative variance contribution rate of the principal component; proposes the minimum principal component with a contribution rate of more than 80%, realizes data dimensionality reduction processing, and reduces the complexity and calculation amount of subsequent analysis.

[0119] In summary, the data preprocessing submodule 21 of this embodiment can improve the accuracy and efficiency of data analysis through data preprocessing; the preprocessed data is cleaner and more accurate, which helps to reveal the real laws and trends in the data and provide strong support for decision-making; the principal component analysis submodule 22 helps to simplify the data dimension and propose the main changing trends and characteristics in the data; it can understand the data more intuitively and provide support for feature selection and model building; the importance ranking submodule helps to identify the key factors that have the greatest impact on smelting quality indicators, and provide a scientific basis for optimizing the smelting process and improving smelting quality; dimensionality reduction processing can simplify the data model and improve analysis efficiency and accuracy.

[0120] Furthermore, if Figure 7 As shown, the prediction feedback module 3 specifically includes:

[0121] Theoretical model submodule 31 is used to construct a theoretical model of the blowing process, and predict the change trend of the material flow and the smelting efficiency by inputting parameters such as the ore ratio and oxygen concentration;

[0122] A result prediction submodule 32, for automatically generating feedback information based on the prediction results of the theoretical model of the blowing process to inform the operator of possible quality fluctuations or changes in storage conditions;

[0123] The interactive model submodule 33 is used to construct an interactive model between the intelligent agent and the smelting environment, define the key parameter status, adjust the process parameter actions and optimize the rewards for smelting quality, energy consumption and emissions. By updating the control strategy through real-time data, the intelligent agent adaptively adjusts the production parameters.

[0124] Preferably, the theoretical model submodule 31 of this embodiment can accurately simulate the physical and chemical reactions in the metal matte blowing process through key parameters such as ore ratio and oxygen concentration, predict the changing trends of logistics such as material flow and composition change and energy flow such as energy input, output and loss, and provide a scientific basis for the optimization of the smelting process; the result prediction submodule 32 can automatically generate feedback information based on the prediction results of the theoretical model, and promptly notify the operator of possible quality fluctuations or changes in storage conditions; it helps the operator to respond quickly and adjust the smelting parameters to ensure the stability and safety of the smelting process; the interactive model submodule 33 can define the key parameter status, adjust the process parameter action, and update the control strategy in real time according to the reward mechanism for optimizing smelting quality, energy consumption and emissions by constructing an interactive model between the intelligent agent and the smelting environment; the intelligent agent can adaptively adjust the production parameters to realize intelligent control of the smelting process.

[0125] In summary, the theoretical model submodule 31 of this embodiment improves the controllability and predictability of the smelting process, helps to reduce resource waste, and improves smelting efficiency and product quality; the result prediction submodule 32 enhances the dynamic monitoring and timely adjustment capabilities of the smelting process, reduces quality risks and safety hazards, and improves overall production efficiency and product quality; the interactive model submodule 33 promotes the automation and intelligence level of the smelting process, improves smelting efficiency and quality, and at the same time reduces energy consumption and emissions, which is in line with the development trend of green smelting.

[0126] Furthermore, if Figure 8 As shown, the theoretical model submodule 31 specifically includes:

[0127] The model building unit 311 is used to obtain the historical data of the spray guns of the multi-gun top-blowing refining furnace, perform thermal analysis using material and energy balance equations, use a recurrent neural network to process time series data, establish a theoretical model of the refining process, and use the collected real-time furnace temperature, liquid level and material status information as a training set to train the model;

[0128] The model evaluation unit 312 is used to set the inertia weight, learning factor, number of particles and other parameters of the particle swarm optimization method, perform model training on each particle and test its performance index, mark the particle with the highest accuracy as the global optimal particle, find the particle with the minimum fitness according to the particle state, and update the hyperparameter combination of the model;

[0129] The trend prediction unit 313 is used to input parameters such as ore proportion and oxygen concentration into the theoretical model of the blowing process, predict the change trend of the material flow and energy flow and the smelting efficiency, and output the prediction results to the information feedback center.

[0130] Preferably, the model building unit 311 of this embodiment collects historical data from the spray guns of the multi-gun top-blowing refining furnace, and uses the material and energy balance equations to perform thermal analysis, which is helpful to understand the physical and chemical changes in the refining process; uses a recurrent neural network to process time series data, which can capture the time dependence and trend in the data; uses the real-time furnace temperature, liquid level and material state information as a training set to train the theoretical model to improve the accuracy and adaptability of the model; the model evaluation unit 312 sets various parameters of the particle swarm optimization method, such as inertia weight, learning factor and number of particles, and evaluates each particle. The model is trained and its performance indicators are tested to find the optimal combination of model parameters; the particle with the highest marking accuracy is marked as the global optimal particle, and the particle with the smallest fitness is found according to the particle state, which helps to further adjust and optimize the model; the hyperparameter combination of the model is updated according to the evaluation results to improve the performance of the model; the trend prediction unit 313 inputs key parameters such as ore proportion and oxygen concentration into the theoretical model of the blowing process; the changing trend of logistics and energy flow and smelting efficiency are predicted, which helps to understand the possible changes in the blowing process in advance; the prediction results are output to the information feedback center so that corresponding control measures can be taken in time.

[0131] In summary, the model building unit 311 of this embodiment establishes a theoretical model based on actual data that can reflect the blowing process; the time series data is processed by RNN, which improves the model's ability to capture dynamic changes in the blowing process; the model evaluation unit 312 optimizes the model through the PSO method, improves the accuracy and generalization ability of the model, realizes automatic adjustment and optimization of model parameters, and reduces manual intervention; the trend prediction unit 313 realizes real-time monitoring and prediction of the blowing process, improves production efficiency and safety; and guides production operations through prediction results, which helps to optimize the smelting process and reduce energy consumption and costs.

[0132] Furthermore, if Fig. 9 As shown, the interaction model submodule 33 specifically includes:

[0133] The state definition unit 331 is used to obtain characteristic value parameters of the temperature, liquid level and material state in the furnace, define the state as the characteristic value key parameter, define the action as adjusting the process parameters, and define the reward as optimizing the smelting quality, energy consumption and emissions;

[0134] The interactive model unit 332 is used to initialize the weight parameters of the neural network of the intelligent agent, detect the current working state, select an action according to the current working state, and give feedback to the intelligent agent after executing the action, and update its strategy and value function according to the prediction results of the theoretical model of the blowing process to achieve the optimal strategy;

[0135] Among them, the process of updating its strategy and value function is:

[0136] The complex equation of deep deterministic policy gradient, Actor network update equation:

[0137]

[0138] In the formula, θ μ Represents the parameters of the Actor network, α μ represents the learning rate of the Actor network, Q(s,a|θ Q ) represents the Q value function of the Critic network, with parameter θ Q ,μ(s|θ μ ) represents the strategy function of the Actor network, with parameter θ μ , represents the Q value gradient of action a, Represents the Actor network parameter θ μ Policy gradient of

[0139] Critic network update equation:

[0140]

[0141] In the formula, the target value y t Defined as:

[0142] y t =r t+1 +γQ(s t+1 ,μ(s t+1 |θ μ )|θ Q )

[0143] In the formula, y t Represents the parameters of the Critic network, α Q represents the learning rate of the Critic network, y t represents the target Q value, which combines the immediate reward and the Q value of the next state, r t+1 represents the immediate reward, γ represents the discount factor, (s t+1 |θ μ ) represents the next state s t+1 The strategy output of the Actor network;

[0144] The policy update equation for proximal policy optimization is:

[0145]

[0146] In the formula, r t (θ) represents the strategy ratio, represents the estimated value of the advantage function, ∈ represents the clipping range parameter, which is usually 0.1 or 0.2, clip(r t (θ), 1-∈, 1+∈) represents the clipping function, which limits the update amplitude of the strategy ratio;

[0147] Value function update equation:

[0148]

[0149] Where V θ (s t ) indicates the current state s t The value function estimate under Represents the target value function, which usually consists of the immediate reward and the value function of the next state;

[0150] Complex state value function update (combined with multi-step returns):

[0151]

[0152] In the formula, the multi-step return Defined as:

[0153]

[0154] In the formula, represents multi-step returns, combining the rewards of the next n steps and the state value after n steps; γ represents the discount factor; (s t+n” ) represents the state value after n” steps. The above complex equations show how the agent updates its strategy and value function through deep reinforcement learning algorithms (such as DDPG and PPO) in a complex industrial environment; not only the immediate reward is considered, but also the future multi-step rewards and strategy ratio clipping are combined to ensure the stability and efficiency of the strategy update. Through these complex mathematical models, the agent can adaptively adjust the process parameters in the complex smelting process to optimize the smelting quality, energy consumption and emissions;

[0155] The adaptive adjustment unit 333 is used to obtain the real-time monitored temperature, liquid level and material status in the furnace. The intelligent agent dynamically and adaptively adjusts the smelting process parameters according to the real-time status, and visualizes the adaptive decision with an explainable tool.

[0156] Preferably, the state definition unit 331 of this embodiment extracts key eigenvalue parameters by real-time monitoring of the temperature, liquid level and material state in the furnace; explicitly defines the state of the smelting process as these eigenvalue key parameters, and defines the action as adjusting the process parameters; optimizes smelting quality, energy consumption and emissions as reward goals, providing a clear direction for the learning of the intelligent agent; the interactive model unit 332 initializes the neural network weight parameters of the intelligent agent, so that the intelligent agent can detect the current working state in real time and select an optimal action based on these states; based on the feedback obtained from executing the action and the prediction results of the theoretical model of the blowing process, the intelligent agent can continuously update its strategy and value function until the optimal strategy is reached; the adaptive adjustment unit 333 can detect the temperature, liquid level and material state in the furnace in real time, providing a basis for the decision-making of the intelligent agent; the intelligent agent can dynamically and adaptively adjust the smelting process parameters according to the real-time state to achieve flexible control of the smelting process; the adaptive decision is visualized through interpretable tools, which improves the transparency and credibility of the decision.

[0157] In summary, the state definition unit 331 of this embodiment provides a reliable data basis for intelligent decision-making by accurately defining the state of the smelting process; the setting of the reward mechanism makes the learning process of the intelligent agent more targeted and efficient; the interactive model unit 332 realizes the precise control and optimization of the smelting process through the learning and optimization process of the intelligent agent; the automation level and efficiency of the smelting process are improved, and energy consumption and emissions are reduced; the adaptive adjustment unit 333 realizes adaptive control of the smelting process, and improves the robustness and stability of the system; the visual display of the decision helps the operator to understand and trust the decision-making process of the intelligent agent, and enhances the operability and human-computer interactivity of the system.

[0158] like Fig.10 As shown, this embodiment also provides an embodiment of a multi-gun top-blowing continuous blowing intelligent method. In this embodiment, the multi-gun top-blowing continuous blowing intelligent method is applied to the multi-gun top-blowing continuous blowing intelligent system in the above embodiment. The multi-gun top-blowing continuous blowing intelligent method includes:

[0159] Step S100: multiple sensors are installed in the furnace to monitor the temperature, liquid level and material status in the furnace in real time, and the collected data are transmitted to the central data processing platform in real time for intelligent feedback control;

[0160] Step S200: cleaning various data transmitted in real time, using principal component analysis to reduce the dimension of sensor data in the smelting process, extracting the minimum principal component with a contribution rate of more than 80%, and realizing dimensionality reduction processing;

[0161] Step S300: Use material and energy balance equations to perform thermal analysis, build a theoretical model of the blowing process, capture long-term dependencies, and predict dynamic parameter changes during the smelting process; build an interaction model between the intelligent agent and the smelting environment, update the control strategy in real time, and adaptively adjust production parameters.

[0162] Preferably, in step S100 of this embodiment, by setting multiple sensors in the furnace, key information such as furnace temperature, liquid level and material status can be obtained in real time; the collected data is transmitted to the central data processing platform in real time to ensure the timeliness and accuracy of the data; step S200 removes noise and outliers to improve data quality; through dimensionality reduction processing, the minimum principal component with a contribution rate of more than 80% to the smelting process is extracted, the data model is simplified, and the calculation complexity is reduced; step S300 uses material and energy balance equations to construct a theoretical model to capture the long-term dependence of the smelting process; predicts the dynamic parameter changes in the smelting process to provide a scientific basis for production adjustment; constructs an interactive model between the intelligent agent and the smelting environment to realize real-time update and self-adjustment of the control strategy (for specific schematic diagrams, refer to the attached Fig.11 ).

[0163] In summary, the real-time monitoring of the furnace status in step S100 of this embodiment helps to promptly discover and solve potential problems and avoid production interruptions; it provides a rich data basis for intelligent analysis and control, and helps to realize the automation and intelligence of the smelting process; step S200 improves the efficiency and accuracy of data analysis, and provides more reliable data support for thermal analysis and prediction; dimensionality reduction processing reduces the amount of calculation, and helps to realize real-time online analysis; step S300 can more accurately predict parameter changes in the smelting process through theoretical models and predictive models, and provide strong support for production optimization; the intelligent interactive model enables the smelting process to be adaptively adjusted according to actual conditions, thereby improving the stability and flexibility of the production process; through precise control and prediction, the smelting process can be optimized, energy consumption and production costs can be reduced, and economic benefits can be improved.

[0164] like Fig.12 As shown, this embodiment also provides an embodiment of a multi-gun top-blowing continuous blowing intelligent device. In this embodiment, the multi-gun top-blowing continuous blowing intelligent device is applied to the multi-gun top-blowing continuous blowing intelligent system in the above embodiment. The multi-gun top-blowing continuous blowing intelligent device includes: a spray gun 1, a cold material inlet 2, a low-grade metal matte inlet 3, a high-grade metal matte outlet 4, a slag outlet 5 and an ascending flue 6;

[0165] Among them, the spray gun 1 is installed on the top of the multi-gun top-blowing furnace, with a total of 8 guns; the cold material inlet 2 is opened on the furnace wall on the side close to the low-grade metal matte inlet 3, and the low-grade metal matte inlet 3 is opened obliquely upward and outward; the high-grade metal matte outlet 4 is opened obliquely downward and outward on the other side of the inlet 3; the slag outlet 5 is opened on the furnace wall on the side close to the high-grade metal matte outlet 4, and the top of the slag outlet 5 is an ascending flue 6.

[0166] Preferably, in this embodiment, the metal matte (mainly composed of FeS, Ni3S2, Cu2S, PbS, ZnS, CoS and Fe3O4, etc.) produced by matte smelting through smelting equipment such as flash furnace, blast furnace, electric furnace, etc. continuously flows into the multi-gun top-blown furnace through the flow channel. Subsequently, air with an oxygen-enriched concentration of about 37% is sent into the furnace through multiple lances arranged at the top of the multi-gun top-blown furnace. At the same time, an appropriate amount of quartz stone is added. The FeS and other impurities in the metal matte are oxidized to form slag, and part of them and some other volatile impurities are oxidized and discharged with the flue gas, thereby obtaining a metal matte with a high content of valuable metals (mainly composed of NiS and CuS) and a blowing slag with a low content of valuable metals. They are layered due to their different densities, and the blowing slag with a low density floats on the upper layer and is discharged. Most of the nickel and copper in the high-grade matte still exist in the form of sulfides, and a small part exists in the form of alloys, while precious metals and some cobalt also enter the high-grade matte. The high-grade matte is siphoned out from the end of the converting furnace. The heat required for the converting process of the high-grade matte is supplied by the reaction heat of iron and sulfur oxidation, ferrous oxide slag formation, etc. in the low-grade matte. The converting process can not only be carried out by self-heating, but also have excess heat, which can be used to treat cold materials. Treating cold materials is an important measure to prevent overheating during the converting process and extend the life of the masonry.

[0167] Among them, the difference between the blowing of low-nickel matte and copper matte is that the blowing of copper is divided into two stages: the initial stage is the slag-making process of FeS oxidation to form white copper matte (Cu2S), and the second stage is the oxidation of Cu2S to obtain crude copper, which are called the slag-making period and the copper-making period respectively. The blowing of low-nickel matte only goes through the slag-making period and finally generates high-nickel matte (Ni3S2). At the blowing temperature of 1523K, the oxidation reaction of metal sulfide (MeS) can be expressed as:

[0168]

[0169] MeS+02=Me+SO2

[0170] In summary, the top of the multi-gun top-blowing furnace in this embodiment is provided with 8 spray guns 1, and the oxygen-enriched air required for blowing is sprayed into the furnace through the spray guns 1; the material of the spray gun 1 is a high-temperature resistant alloy or corrosion-resistant material, and the lifting device of the spray gun 1 is a detachable motor or cylinder, which is convenient for regular inspection and maintenance; the lifting action of the motor, pneumatic cylinder or hydraulic system is controlled by PLC; the PLC control system can automatically adjust the position of the spray gun according to timely feedback and process requirements; the multi-gun top-blowing furnace is equipped with a cold material inlet 2. In the blowing process of low-grade metallic sulfur, cold material must be added to control the furnace temperature and ensure the stability of operation; the cold material suitable for this process includes steamed bun shells, splashes generated by blowing, slag after smelting, high-metal content ore and other substances; the cold material should maintain low moisture, and the moisture content before entering the furnace shall not exceed 4%, and the large pieces of material shall be pre-crushed to 5 to 25 mm; the low-grade metallic sulfur inlet 3 is mainly responsible for accurately inputting low-grade metallic sulfur into the multi-gun top The slag produced by the multi-gun top-blowing furnace is discharged into the slag bag through the overflow of the slag port. The design of the discharge port needs to be able to withstand high temperature and high pressure while ensuring the smooth outflow of the slag. Regular discharge can maintain the continuity of the smelting process and improve the oxygen utilization rate. The structure of the ascending flue 6 is vertical and made of refractory bricks. In the continuous air supply operation, the flue gas produced by the low-grade metal sulfur blowing has a high sulfur dioxide content. This flue gas is mainly used for sulfuric acid production, so it has a high temperature and is recovered through the waste heat boiler. Fig.13 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 7 includes a processor 71 and a memory 72 coupled to the processor 71.

[0171] The memory 72 stores program instructions for implementing the intelligent method for multi-lance top-blowing continuous refining according to any of the above embodiments.

[0172] The processor 71 is used to execute the program instructions stored in the memory 72 to perform intelligent multi-gun top blowing continuous refining.

[0173] The processor 71 may also be referred to as a CPU (Central Processing Unit). The processor 71 may be an integrated circuit chip having signal processing capabilities. The processor 71 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0174] Further, Fig.14 The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 8 of the embodiment of the present application stores program instructions 81 capable of implementing all the above methods, wherein the program instructions 81 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0175] In the several embodiments provided by the present invention, 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, and 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.

[0176] In addition, each functional unit in each embodiment of the present invention 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 integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0177] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A multi-gun top-blowing continuous refining intelligent system, characterized in that: The multi-lance top-blowing continuous blowing intelligent system comprises: The data acquisition module is used to set up multiple sensors in the furnace to monitor the temperature, liquid level and material status in the furnace in real time, and transmit the collected data to the central data processing platform in real time for intelligent feedback control; The analysis and processing module is used to clean various types of data transmitted in real time, use principal component analysis to reduce the dimension of sensor data in the smelting process, extract the minimum principal components, and achieve dimensionality reduction processing; The prediction feedback module is used to perform thermal analysis using material and energy balance equations, build a theoretical model of the blowing process, capture long-term dependencies, and predict dynamic parameter changes during the smelting process; build an interaction model between the intelligent agent and the smelting environment, update the control strategy in real time, and adaptively adjust production parameters.

2. The multi-gun top-blowing continuous refining intelligent system according to claim 1 is characterized in that: Data acquisition module, including: The temperature acquisition submodule is used to arrange temperature sensors on the furnace wall, furnace top and furnace top to monitor the overall heat distribution in the furnace, and install temperature sensors in the material to monitor the material temperature; after acquiring the data, it is transmitted to the intelligent feedback control system for processing through the data acquisition module; The liquid level acquisition submodule is used to monitor the liquid level using a laser liquid level sensor. A part of the laser is transmitted to the time transmitter as a reference signal, and the other part is converted into parallel light through the optical system to illuminate the liquid surface of the molten pool, and the signal is amplified by the sampling circuit; the collected electrical signal is converted into a digital signal to verify the validity of the signal, and the liquid level data is visualized; The material quantity collection submodule is used to install a camera on the top of the conveyor belt to collect material data on the dynamic conveyor belt in real time, and use a filtering algorithm to process the collected images. The processed image data is integrated into the analysis and processing module to calculate the size and trend of the material flow and predict the material flow trend.

3. The multi-gun top-blowing continuous refining intelligent system according to claim 2 is characterized in that: Temperature acquisition submodule, including: The spray gun height monitoring unit is used to determine the initial height of the spray gun in the blowing furnace, obtain the image of the spray gun in the furnace through image acquisition, and analyze the image to obtain the height of the spray gun from the molten pool liquid surface; A height correction unit is used to obtain real-time height data of the molten pool liquid level and the spray gun height data, and to set a safety threshold and an effective threshold of the distance difference between the molten pool liquid level and the spray gun height. If the current distance difference between the molten pool liquid level and the spray gun height is less than the preset safety threshold, a safety control signal is sent to the spray gun control unit; if the current distance difference between the molten pool liquid level and the spray gun height is greater than the preset safety threshold, an effective control signal is sent to the spray gun control unit; The spray gun control unit is used to receive the safety control signal and the effective control signal sent by the height correction unit, and control the lifting and lowering of the spray gun according to the type of signal received. When the safety control signal is received, the spray gun is controlled to rise by electric or hydraulic means; when the effective control signal is received, the spray gun is controlled to descend by electric or hydraulic means.

4. The multi-gun top-blowing continuous refining intelligent system according to claim 2 is characterized in that: Liquid level collection submodule, including: A reference signal unit is used for emitting a beam of near-infrared light using a laser transmitter, wherein a portion of the laser light passes through a semi-transmitting reflector and is input into a time transmitter as a reference signal to form a periodic time sequence; The liquid level acquisition unit is used to process another part of the laser light optically to form a parallel beam of set width to irradiate the surface of the molten pool. After part of the laser light is reflected, it is received by the laser liquid level sensor and the received reflected laser light is converted into an electrical signal. The information feedback unit is used to amplify the converted electrical signal and align it with the time series, verify the validity of the signal after converting the electrical signal into a digital signal, and visualize the liquid level data. If the liquid level is detected to be unstable, an alarm will be issued and the spray gun will rise to the top.

5. The multi-gun top-blowing continuous blowing intelligent system according to claim 1 is characterized in that: Analysis and processing module, including: The data preprocessing submodule is used to obtain the furnace temperature, liquid level and material status collected in real time by the data acquisition module, and to clean, process noise, detect and eliminate abnormal values, process missing values, smooth and reduce noise, and perform consistency verification preprocessing on the acquired real-time data; The principal component analysis submodule is used to establish the data matrix under the normal operating conditions of the multi-lance top-blowing converting furnace and calculate the covariance matrix R. The covariance matrix is ​​decomposed by eigenvalues ​​to obtain the eigenvalues ​​and corresponding eigenvectors to reflect the correlation between the features. The importance sorting submodule is used to calculate the importance of each feature and evaluate the impact of the input furnace temperature, liquid level and material state on the smelting quality indicators. By analyzing the cumulative variance contribution rate of the principal components, the minimum principal component with a contribution rate of more than 80% is proposed to achieve dimensionality reduction.

6. The multi-gun top-blowing continuous refining intelligent system according to claim 1 is characterized in that: Prediction feedback module, including: Theoretical model submodule is used to construct theoretical models of the blowing process of metal mattes such as copper and nickel mattes. By inputting ore proportion and oxygen concentration parameters, it predicts the changing trend of material flow and energy flow and smelting efficiency. The result prediction submodule is used to automatically generate feedback information based on the prediction results of the theoretical model of the metal matte blowing process such as copper and nickel matte, and inform the operator of possible quality fluctuations or changes in storage conditions; The interactive model submodule is used to build an interactive model between the intelligent agent and the smelting environment, define the key parameter states, adjust the process parameter actions, and optimize the rewards for smelting quality, energy consumption, and emissions. The control strategy is updated through real-time data, and the intelligent agent adaptively adjusts the production parameters.

7. The multi-gun top-blowing continuous refining intelligent system according to claim 6 is characterized in that: Theoretical model submodule, including: The model building unit is used to obtain the historical data of the spray guns of the multi-gun top-blowing refining furnace, perform thermal analysis using the material and energy balance equations, use recurrent neural networks to process time series data, establish a theoretical model of the refining process of metal mattes such as copper and nickel mattes, and use the collected real-time furnace temperature, liquid level and material status information as a training set to train the model; The model evaluation unit is used to set the inertia weight, learning factor, and particle number parameters of the particle swarm optimization method, perform model training on each particle and test its performance index, mark the particle with the highest accuracy as the global optimal particle, find the particle with the minimum fitness based on the particle state, and update the model's hyperparameter combination; The trend prediction unit is used to input parameters such as ore proportion and oxygen concentration into the theoretical model of the blowing process of metal mattes such as copper and nickel mattes, predict the changing trend of logistics and energy flow and smelting efficiency, and output the prediction results to the information feedback center.

8. The multi-gun top-blowing continuous refining intelligent system according to claim 6 is characterized in that: Interaction model submodule, including: The state definition unit is used to obtain the characteristic value parameters of the furnace temperature, liquid level and material state, define the state as the characteristic value key parameter, define the action as adjusting the process parameters, and define the reward as optimizing the smelting quality, energy consumption and emissions; The interactive model unit is used to initialize the weight parameters of the agent's neural network, detect the current working state, select an action based on the current working state, and provide feedback to the agent from the action. Based on the prediction results of the theoretical model of the blowing process of metal mattes such as copper and nickel mattes, the agent updates its strategy and value function to achieve the optimal strategy. The adaptive adjustment unit is used to obtain the real-time monitored temperature, liquid level and material status in the furnace. The intelligent agent dynamically and adaptively adjusts the smelting process parameters according to the real-time status, and visualizes the adaptive decision with an explainable tool.

9. An intelligent method for multi-gun top-blowing continuous blowing, which is applied to the intelligent system for multi-gun top-blowing continuous blowing as claimed in any one of claims 1 to 8, characterized in that: The multi-lance top-blowing continuous blowing intelligent system comprises: Multiple sensors are installed in the furnace to monitor the temperature, liquid level and material status in the furnace in real time, and the collected data is transmitted to the central data processing platform in real time for intelligent feedback control; Clean all kinds of data transmitted in real time, use principal component analysis to reduce the dimension of sensor data in the smelting process, extract the minimum principal component with a contribution rate of more than 80%, and achieve dimensionality reduction processing; Use material and energy balance equations to conduct thermal analysis, build a theoretical model for the blowing process of metal mattes such as copper and nickel mattes, capture long-term dependencies, and predict dynamic parameter changes during the smelting process. Build an interaction model between the intelligent agent and the smelting environment, update the control strategy in real time, and adaptively adjust production parameters.

10. A multi-gun top-blowing continuous blowing intelligent device, which is applied to the multi-gun top-blowing continuous blowing intelligent system as claimed in any one of claims 1 to 8, characterized in that: The multi-gun top-blowing continuous refining intelligent device includes a spray gun; the spray gun is installed on the top of the multi-gun top-blowing furnace; the cold material inlet is opened on the furnace wall on the side close to the inlet of low-grade metal mattes such as copper mattes and low-nickel mattes, and the inlet is opened obliquely upward and outward; the outlet of high-grade metal mattes such as blister copper and high-nickel mattes is opened obliquely downward and outward on the other side of the inlet of low-grade metal mattes such as copper mattes and low-nickel mattes; the slag outlet is opened on the furnace wall on the side close to the outlet of low-grade metal mattes such as copper mattes and low-nickel mattes, and the top of the slag outlet is an ascending flue.

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