Intelligent magnesium smelting equipment system suitable for heat storage circulation combustion refining furnace

Abstract

The invention provides an intelligent magnesium smelting equipment system suitable for a heat storage circulation combustion refining furnace. The intelligent magnesium smelting equipment system is characterized in that S2, a high-temperature-resistant material is adopted for manufacturing a feeding mechanism of the system, and cooling treatment is conducted during overtemperature; s3, the system is provided with an anti-corrosion discharging device, and discharging parameters are adjusted through a rate control algorithm; s4, the system is provided with a slag removal mechanism, and the slag removal track and strength are controlled through image recognition; s5, establishing a material flow management model by the system, and adjusting the feeding and discharging rhythm based on the material data; s6, the system integrates temperature field monitoring, and combustion parameters are optimized through a thermal image and fluid dynamic model; s7, the system constructs a state monitoring network, judges equipment abnormity through mode recognition and triggers protection; and S8, integrating the subsystems by the system, and adjusting operation parameters through a multivariable optimization algorithm.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an intelligent magnesium smelting equipment system suitable for regenerative circulating combustion refining furnaces. Background Technology

[0002] In the complex, high-temperature, and highly corrosive environment of magnesium smelting and refining furnaces, the core technical challenge facing traditional equipment is the inability to simultaneously ensure material durability, operational efficiency, and production process stability under extreme conditions. This results in short equipment lifespan, high production costs, and insufficient safety. This problem stems from the contradiction between the stringent requirements of the high-temperature, highly corrosive environment on material performance and the need for efficient equipment operation: on the one hand, the furnace temperature is extremely high and the concentration of corrosive gases fluctuates drastically, making it difficult for traditional materials to withstand long-term stress, leading to frequent equipment failures and high maintenance costs; on the other hand, improving operational efficiency requires optimizing the feeding, discharging, slag removal, and combustion processes. However, the dynamic changes in these processes and their interaction with the complex environment increase the difficulty of precise control, making it difficult to guarantee material balance and production stability. For example, the uneven distribution of high-temperature, highly corrosive areas within the furnace makes it difficult for traditional equipment to accurately identify and select high-temperature and corrosion-resistant materials, leading to rapid failure in localized areas. Simultaneously, the lack of real-time data support for adjusting slag removal trajectories and discharging parameters makes it difficult to adapt to dynamic changes within the furnace, easily causing material waste or production interruptions. Furthermore, optimizing combustion parameters requires balancing thermal efficiency and in-furnace fluid dynamics; however, traditional equipment lacks multi-dimensional data analysis capabilities, making precise control of the combustion process difficult. The lag in equipment status monitoring further exacerbates the problem, with untimely anomaly warnings easily leading to safety hazards. These seemingly minor issues all revolve around the core challenge: how to achieve synergistic optimization of material selection, operational efficiency, and production stability under extreme operating conditions. This highlights the unique challenge of insufficient intelligence in magnesium smelting equipment in complex environments. Summary of the Invention

[0003] This invention provides an intelligent magnesium smelting equipment system suitable for regenerative circulating combustion refining furnaces, mainly comprising: S1. The system acquires internal environmental data of the combustion furnace and determines the material and structural parameters of the mechanical device through a material performance evaluation mechanism. S2. The system uses high-temperature resistant materials to manufacture the feeding mechanism and performs cooling treatment when the temperature exceeds the limit. S3. The system is equipped with a corrosion-resistant discharge device and adjusts the discharge parameters through a rate control algorithm. S4. The system deploys a slag removal mechanism and controls the slag removal trajectory and intensity through image recognition. S5. The system establishes a material flow management model and adjusts the feeding and discharge rhythm based on material data. S6. The system integrates temperature field monitoring and optimizes combustion parameters through thermal imaging and fluid dynamics models. S7. The system constructs a status monitoring network and determines equipment anomalies and triggers protection through pattern recognition. S8. The system integrates various subsystems and adjusts operating parameters through a multivariate optimization algorithm.

[0004] Furthermore, the acquisition of internal environmental data of the combustion furnace and the determination of material and structural parameters of the mechanical device through a material performance evaluation mechanism include: S11. Collecting internal temperature and corrosive gas concentration data of the combustion furnace through a sensor array to obtain a raw dataset; S12. Denoising and standardizing the raw dataset using a preprocessing algorithm to obtain a standardized dataset; S13. Using a clustering algorithm to partition and determine high-temperature and corrosive regions based on the standardized dataset to obtain regional distribution characteristics; S14. If the temperature or corrosive gas concentration in the regional distribution characteristics exceeds a preset threshold, querying a material performance database for high-temperature resistant or corrosion-resistant materials to obtain a list of candidate materials; S15. Performing stress distribution and deformation analysis on the candidate materials using finite element analysis to determine material selection; S16. Adjusting structural parameters using a topology optimization algorithm based on the selected materials and regional distribution characteristics to obtain an optimized design scheme.

[0005] Furthermore, the method of using high-temperature resistant materials to manufacture the feeding mechanism and performing cooling treatment when the temperature exceeds the limit includes: S21. Real-time acquisition of feeding port temperature data through a temperature sensor to obtain the current temperature value; S22. If the current temperature value exceeds a preset threshold, an over-temperature signal is generated and the water cooling system is activated; S23. Adjustment of water cooling system operating parameters based on cooling water flow data to obtain the cooling rate; S24. Continuous monitoring of temperature changes based on the cooling rate to obtain the temperature decreasing trend; S25. If the temperature decreasing trend does not reach the preset standard, adjustment of water cooling system power and updating of the cooling rate.

[0006] Furthermore, the configured anti-corrosion discharge device adjusts the discharge parameters through a rate control algorithm, including: S31. Collecting discharge port temperature and corrosive gas concentration data through sensors to obtain a raw dataset; S32. Denoising and normalizing the raw dataset using preprocessing techniques to obtain a standardized dataset; S33. Calculating the temperature and corrosive gas concentration change trends using a linear regression algorithm based on the standardized dataset to obtain trend characteristic values; S34. If the trend characteristic value exceeds a preset threshold, adjusting the discharge rate and time through a rate control algorithm to obtain optimized control parameters; S35. Driving the discharge device to perform dynamic adjustments based on the optimized control parameters and updating the discharge parameters in real time.

[0007] Furthermore, the deployment of the slag cleaning mechanism, which controls the slag cleaning trajectory and intensity through image recognition, includes: S41. Collecting real-time image data inside the furnace through an image recognition system to generate slag distribution data; S42. If a high-density area is detected in the slag distribution data, the distribution characteristics are obtained through convolutional neural network analysis; S43. Calculating the movement trajectory of the slag cleaning mechanism based on the distribution characteristics to determine the trajectory parameters; S44. If the trajectory overlaps with an obstacle, adjusting the trajectory through a path optimization algorithm to obtain an optimized path; S45. Adjusting the slag cleaning intensity based on the optimized path and distribution characteristics to complete the slag cleaning.

[0008] Furthermore, the establishment of the material flow management model, which adjusts the feeding and discharging rhythm based on material data, includes: S51. Obtaining data on feeding amount, discharging amount, and slag removal amount, calculating the total material flow, and obtaining the equilibrium state; S52. Analyzing the relationship between feeding amount and discharging amount using a linear regression algorithm to determine the material balance deviation; S53. If the deviation exceeds a preset threshold, adjusting the feeding rhythm to obtain an update rhythm; S54. Using time series analysis based on the update rhythm to predict changes in the discharging rhythm, obtaining an adjustment plan; S55. Calculating the dynamic equilibrium state using the adjustment plan and slag removal amount data, and judging the stability of the equilibrium.

[0009] Furthermore, the integrated temperature field monitoring, through thermal imaging and fluid dynamics modeling to optimize combustion parameters, includes: S61. Acquiring furnace temperature distribution data using a thermal imager to generate a real-time temperature field image; S62. Processing the low-resolution image using image enhancement technology to obtain a high-resolution temperature field image; S63. Extracting spatial temperature distribution features from the high-resolution image to generate a temperature distribution dataset; S64. Combining the temperature distribution dataset with environmental parameters using data fusion technology to obtain a comprehensive dataset; S65. Simulating the combustion process using a fluid dynamics model on the comprehensive dataset to generate optimized combustion parameters.

[0010] Furthermore, the construction of the status monitoring network, which determines equipment anomalies and triggers protection through pattern recognition, includes: S71. Collecting vibration, temperature, and pressure data of the mechanical device through sensors to obtain a raw dataset; S72. Denoising and standardizing the raw dataset using preprocessing methods to obtain a processed dataset; S73. Extracting features using principal component analysis to obtain a feature vector set; S74. If the feature values ​​in the feature vector set exceed a preset threshold, an abnormal state is determined and an early warning signal is triggered; S75. Activating the protection program through the early warning signal, adjusting the equipment operating parameters, and confirming that the equipment status has returned to normal.

[0011] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses an intelligent equipment system for the complex, high-temperature, and highly corrosive environment of magnesium smelting refining furnaces, solving the problems of improper material selection, low operating efficiency, and short equipment lifespan in traditional equipment under extreme conditions. By integrating a sensor array to collect environmental data such as furnace temperature and corrosive gas concentration in real time, this invention employs a clustering algorithm to identify high-temperature and highly corrosive areas, combines a material performance database with finite element analysis to select high-temperature and corrosion-resistant materials, and adjusts the mechanical device structure through topology optimization algorithms, significantly improving equipment durability. Simultaneously, this invention dynamically adjusts the slag removal trajectory through image recognition, optimizes discharge parameters through rate control, and optimizes combustion parameters through thermal imaging and fluid dynamics models, achieving coordinated control of the feeding, discharging, slag removal, and combustion processes to ensure material balance and production stability. A status monitoring network provides real-time early warnings of equipment anomalies through multi-dimensional data analysis, triggering protection mechanisms to ensure operational safety. Through data-driven closed-loop optimization, this invention significantly improves the intelligence level, operating efficiency, and equipment lifespan of magnesium smelting equipment, providing the industry with an efficient and reliable solution. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.

[0013] This embodiment of an intelligent magnesium smelting equipment system suitable for a regenerative circulating combustion refining furnace may specifically include: This invention provides an intelligent magnesium smelting equipment system suitable for regenerative circulating combustion refining furnaces. The method is described in detail below with reference to specific embodiments.

[0014] Step S1: The system acquires internal environmental data of the combustion furnace and determines the material and structural parameters of the mechanical device through a material performance evaluation mechanism. Specifically, the internal environment of the combustion furnace is complex and variable, involving factors such as high temperature and highly corrosive gases. Precise data acquisition and analysis are necessary to ensure the reliability and durability of the mechanical device. Environmental data includes, but is not limited to, key parameters such as temperature, corrosive gas concentration, and pressure, which directly affect material selection and structural design.

[0015] Step S11: Collect temperature and corrosive gas concentration data inside the combustion furnace using a sensor array to obtain a raw dataset. Exemplarily, the sensor array may include multiple high-temperature thermocouples and gas concentration sensors, deployed in key areas of the combustion furnace such as the furnace chamber, feed inlet, and discharge outlet. Thermocouples are used to measure the temperature at different locations inside the furnace; for example, the center of the furnace may reach temperatures above 1500 degrees Celsius, while the temperature near the furnace wall may be lower. Gas concentration sensors are used to detect the concentration of corrosive gases, such as sulfur dioxide or chlorides, which have a significant corrosive effect on the materials of the mechanical device. The collected data is stored in time-series format, forming a raw dataset containing temperature and gas concentration data.

[0016] Step S12 involves denoising and standardizing the original dataset using a preprocessing algorithm to obtain a standardized dataset. It should be noted that the original dataset may be affected by noise, such as sensor drift due to high temperatures or data fluctuations caused by environmental vibrations. Therefore, a sliding window averaging method can be used to smooth the temperature data and reduce the impact of short-term fluctuations. For gas concentration data, outliers can be removed using median filtering. Subsequently, the temperature and gas concentration data are normalized, for example, by mapping temperature values ​​to a range of 0 to 1 to eliminate dimensional differences, resulting in a standardized dataset. This processing facilitates the uniformity and accuracy of subsequent analyses.

[0017] Step S13: Based on the standardized dataset, a clustering algorithm is used to partition and determine the high-temperature and corrosion regions, obtaining the regional distribution characteristics. In one possible implementation, the K-means clustering algorithm can be used to perform partitioning analysis on the standardized dataset. The clustering algorithm divides the furnace space into high-temperature and high-corrosion regions, high-temperature and low-corrosion regions, and low-temperature regions based on the distribution characteristics of temperature and gas concentration. For example, the center of the furnace may be classified as a high-temperature and high-corrosion region, while the area near the discharge port may be a low-temperature and low-corrosion region. The partitioning results are output as regional distribution characteristics, describing the temperature range and corrosive gas concentration range of each region. This partitioning analysis helps identify the differentiated material and structural requirements of different regions within the furnace.

[0018] Step S14: If the temperature or corrosive gas concentration in the regional distribution characteristics exceeds a preset threshold, the system queries a material performance database to obtain a list of candidate materials for high-temperature resistant or corrosion-resistant materials. Preferably, the preset threshold can be set according to the requirements of the magnesium smelting process; for example, the temperature threshold can be set to 1200 degrees Celsius, and the corrosive gas concentration threshold can be set to the safety limit of a specific gas. When the temperature or gas concentration in a certain area exceeds the threshold, the system accesses a pre-built material performance database. The database stores the performance parameters of various materials, such as the high-temperature resistance of nickel-based alloys and the corrosion resistance of ceramic coatings. Through querying, the system filters out candidate materials that meet the requirements for high temperature or corrosion resistance, such as high-temperature resistant chromium-molybdenum alloys or corrosion-resistant zirconia ceramics, forming a list of candidate materials.

[0019] Step S15 involves performing stress distribution and deformation analysis on candidate materials using finite element analysis (FEM) to determine material selection. Specifically, FEM is a numerical simulation method used to evaluate the mechanical properties of materials under high-temperature and highly corrosive environments. The system constructs a finite element model of typical operating conditions within the combustion furnace based on regional distribution characteristics, such as simulating the high-temperature and high-pressure environment at the furnace center. Candidate materials are input into the model, and their deformation and stress distribution under different temperatures and stresses are analyzed. For example, chromium-molybdenum alloys may exhibit smaller deformations at high temperatures, while ceramic coatings have a longer service life in corrosive environments. By comparing the analysis results, the material with the best overall performance, such as a composite material that combines high-temperature resistance and corrosion resistance, is selected as the final material for the mechanical device.

[0020] Step S16: Based on the selected material and regional distribution characteristics, a topology optimization algorithm is used to adjust the structural parameters to obtain an optimized design scheme. In one embodiment, the topology optimization algorithm optimizes the geometry of the mechanical device through mathematical modeling to minimize material usage while meeting strength and durability requirements. For example, for the support structure of the feeding mechanism, the algorithm optimizes the thickness and shape of the structure based on the stress distribution in high-temperature regions to ensure stability at high temperatures while reducing weight. The optimization process considers temperature and corrosion distribution in the regional distribution characteristics to ensure that the structural parameters adapt to the working conditions of different regions. The final output optimized design scheme includes the geometric parameters of the mechanical device, connection method, and installation location.

[0021] In one possible implementation, steps S11 to S16 can be tailored to different types of combustion furnaces. For example, in small magnesium smelting furnaces, the density of the sensor array may be lower, and fewer data points may be collected, but coverage of critical areas must still be ensured. In large industrial furnaces, the number of sensors may be increased to capture more precise changes in temperature and gas concentration. Furthermore, the material performance database can be dynamically updated according to process requirements, such as adding performance data for new composite materials to adapt to emerging magnesium smelting processes. This versatility ensures the applicability of the method across different scales and process environments.

[0022] Step S2: The system uses high-temperature resistant materials to manufacture the feeding mechanism and performs cooling treatment when the temperature exceeds the limit. The feeding mechanism is a key component in the magnesium smelting process that directly contacts high-temperature materials, and its materials and operating conditions directly affect production efficiency and equipment lifespan. The selection of high-temperature resistant materials and the design of the cooling treatment aim to ensure stable operation of the feeding mechanism under extreme environments.

[0023] Step S21: The temperature data at the feeding port is collected in real time using a temperature sensor to obtain the current temperature value. For example, the feeding port temperature sensor can be an infrared thermometer, installed near the feeding mechanism to monitor temperature changes at the feeding port in real time. For instance, in magnesium smelting, the temperature at the feeding port may reach above 1000 degrees Celsius due to the high temperature of the material. The collected temperature data is recorded at a high frequency, such as once per second, forming a continuous temperature time series for subsequent over-temperature detection.

[0024] Step S22: If the current temperature exceeds a preset threshold, an over-temperature signal is generated and the water cooling system is activated. Preferably, the preset threshold can be set according to the temperature resistance limit of the material of the feeding mechanism, for example, 900 degrees Celsius. When the temperature sensor detects that the temperature at the feeding port exceeds this threshold, the system automatically generates an over-temperature signal and activates the water cooling system through the control unit. The water cooling system typically includes cooling water pipes and nozzles, arranged at key parts of the feeding mechanism, such as the outer surface of the feeding port. After activation, cooling water circulates through the pipes, carrying away excess heat.

[0025] Step S23: Adjust the operating parameters of the water cooling system based on the cooling water flow rate data to obtain the cooling rate. In one embodiment, the cooling water flow rate data is collected in real time by a flow sensor, for example, recording the flow rate value once per minute. The system dynamically adjusts the water pump power and valve opening of the water cooling system according to the current temperature value and the target cooling rate. For example, if the temperature at the feed port is too high, the system may increase the water pump power to increase the cooling water flow rate, thereby accelerating the cooling rate. The adjusted operating parameters ensure a smooth cooling process and avoid material stress concentration caused by rapid cooling.

[0026] Step S24: Continuously monitor temperature changes based on the cooling rate to obtain a temperature decreasing trend. Specifically, the system continuously collects temperature data from the feed inlet using a temperature sensor, and analyzes this data in conjunction with the cooling rate to generate a temperature decreasing trend. For example, the trend might show a temperature decrease of 10 degrees Celsius per minute. If the decreasing trend is stable, it indicates that the water cooling system is operating effectively; if the decreasing rate is too slow, further adjustments to the water cooling system parameters may be necessary.

[0027] In one possible implementation, steps S21 to S24 can be optimized by combining different types of feeding mechanisms. For example, for a continuous feeding mechanism, the water cooling system may employ a multi-point spray design to cover a wider feeding port area; for an intermittent feeding mechanism, the system may prioritize initiating cooling after the feeding action is completed to reduce interference with the feeding process. Furthermore, the cooling rate can be customized according to the material characteristics of magnesium smelting; for example, for high-melting-point materials, the cooling rate can be appropriately increased to protect the feeding mechanism. This flexible implementation improves the system's adaptability and reliability.

[0028] Step S3: The system is equipped with a corrosion-resistant discharge device, and the discharge parameters are adjusted through a rate control algorithm. During magnesium smelting, the discharge device is responsible for discharging molten magnesium or byproducts from the combustion furnace, and must withstand the dual challenges of high temperatures and corrosive gases. Corrosion-resistant design and dynamic adjustment of discharge parameters can extend the device's lifespan and improve discharge efficiency.

[0029] Step S31: Collect outlet temperature and corrosive gas concentration data using sensors to obtain a raw dataset. Exemplarily, the outlet sensors include thermocouples and a gas analyzer, used to measure the outlet temperature and corrosive gas concentration, such as chloride or sulfide content, respectively. Sensors are positioned in key areas of the outlet, such as the inlet and outlet of the discharge pipe, to comprehensively capture environmental data. The collected data is compiled into a time series, including temperature and gas concentration values, for subsequent analysis.

[0030] Step S32 involves preprocessing the original dataset to remove noise and normalize it, resulting in a standardized dataset. It should be noted that the outlet environment is complex, and the data may be affected by material flow or gas flow within the furnace, generating noise. Therefore, wavelet transform technology can be used to denoise the temperature and gas concentration data, removing high-frequency noise. Subsequently, linear normalization maps the data to a uniform range, such as 0 to 1, facilitating subsequent algorithm processing. The standardized dataset retains the core variation characteristics of temperature and gas concentration.

[0031] Step S33: Calculate the trends in temperature and corrosive gas concentration using a linear regression algorithm based on the standardized dataset to obtain trend feature values. In one embodiment, the linear regression algorithm analyzes the changes in temperature and gas concentration over time, such as whether the temperature continues to rise or whether the gas concentration exhibits periodic fluctuations. The trend feature values ​​are output as slopes or rates of change, such as a temperature increase of 5 degrees Celsius per minute or a gas concentration increase of 0.1 percentage points per hour. These feature values ​​reflect the dynamic changes in the outlet environment.

[0032] Step S34: If the trend characteristic value exceeds a preset threshold, the discharge rate and time are adjusted using a rate control algorithm to obtain optimized control parameters. Preferably, the preset threshold is set based on the material tolerance and process requirements of the discharge device; for example, the temperature threshold is 800 degrees Celsius, and the gas concentration threshold is the safety limit for a certain corrosive gas. When the trend characteristic value exceeds the threshold, the system calculates the optimal discharge rate and time using the rate control algorithm. For example, if the temperature rises too quickly, the system may reduce the discharge rate to reduce heat accumulation; if the gas concentration is too high, the system may shorten the single discharge time to reduce corrosion risk. Optimized control parameters include discharge rate, single discharge duration, and discharge interval.

[0033] Step S35: The discharge device is dynamically adjusted according to the optimized control parameters, and the discharge parameters are updated in real time. Specifically, the optimized control parameters are transmitted to the actuator of the discharge device, such as a servo motor or hydraulic system, through the control unit. The actuator adjusts the discharge rate according to the parameters, for example, reducing the discharge rate from 100 kg per minute to 80 kg per minute, while shortening the discharge time to reduce the corrosion of the device by corrosive gases. The system continuously monitors the discharge effect. If the environmental data still does not meet the standards after parameter adjustment, steps S31 to S34 are repeated to form a closed-loop control.

[0034] In one possible implementation, steps S31 to S35 can be differentiated based on the type of discharge device. For example, for a rotary discharge device, the rate control algorithm might focus on optimizing the rotation speed and discharge angle; for a sliding plate discharge device, the algorithm might prioritize adjusting the sliding plate opening. The adjustment strategy for discharge parameters can also be customized for different magnesium smelting processes, such as electrolysis or silicothermic processes. For instance, electrolysis might require more frequent short-duration discharges to reduce byproduct accumulation. This diversified implementation ensures efficient operation of the discharge device under various operating conditions.

[0035] In one embodiment, the implementation of steps S1 to S3 can be further optimized to suit the actual production scenarios of magnesium smelting. For example, in high-yield magnesium smelting scenarios, the system may increase the density of the sensor array to improve data accuracy while accelerating the calculation speed for material selection and structural optimization. In low-yield scenarios, the system may simplify the data acquisition range and prioritize monitoring of critical areas. This scenario-based implementation improves the system's flexibility and practicality.

[0036] It should be noted that the above steps, through sensor data, material selection, structural optimization, and dynamic control, jointly ensure the stable operation of the mechanical devices within the combustion furnace. Compared to traditional methods, material and structural optimization based on environmental data can significantly extend the lifespan of the equipment, while dynamic cooling and discharge control further improve production efficiency and safety. The implementation of these steps provides reliable data support and equipment assurance for subsequent slag removal, material management, and temperature field optimization.

[0037] In one possible implementation, the system can be further adjusted to accommodate seasonal variations in the magnesium smelting process. For example, during hot seasons, the system might increase the operating frequency of the water cooling system to cope with rising ambient temperatures; during cold seasons, the system might reduce the cooling rate to conserve energy. This dynamic adjustment strategy improves the system's environmental adaptability.

[0038] For example, the implementation of steps S1 to S3 can be widely applied in small and medium-sized magnesium smelting enterprises. For instance, in a plant with an annual magnesium production of 5,000 tons, the system can operate efficiently using low-cost sensors and a simplified materials database; in a large enterprise with an annual production of 100,000 tons, the system can deploy high-precision sensors and sophisticated optimization algorithms to meet higher production demands. This diverse implementation ensures the applicability of the method to enterprises of different sizes.

[0039] In one embodiment, the system can also incorporate historical data for adaptive optimization. For example, the system can record environmental data and material selection results from the past year, analyze long-term trends in high-temperature and corrosive regions, and dynamically update the material performance database and threshold settings. This adaptive mechanism improves the system's intelligence level and reduces the need for manual intervention.

[0040] Specifically, during steps S1 to S3, the system achieves closed-loop control from environmental perception to equipment optimization through multi-level data processing and optimization design. High-precision data acquisition from the sensor array provides a reliable basis for material selection, preprocessing and cluster analysis improve data utilization efficiency, and topology optimization algorithms ensure the lightweight and durability of the structure. Cooling of the feeding mechanism and dynamic adjustment of the discharge device further guarantee stable operation of the equipment in high-temperature and high-corrosion environments. The synergistic effect of these steps significantly improves the intelligence level and production efficiency of the magnesium smelting equipment.

[0041] In one possible implementation, the system can be integrated with an enterprise's existing production management system, for example, through data sharing via an industrial internet platform. Environmental data collected by sensors can be uploaded to the cloud and analyzed in conjunction with other production parameters to further optimize material selection and equipment operation strategies. This integrated implementation improves the system's compatibility and scalability.

[0042] For example, the implementation of steps S1 to S3 can also be optimized in conjunction with the by-product processing requirements of magnesium smelting. For instance, in processes that handle highly corrosive by-products, the system can prioritize materials with stronger corrosion resistance and increase the cleaning frequency of the discharge device to reduce corrosion accumulation. This targeted implementation improves the system's process adaptability.

[0043] It should be noted that during the implementation of the above steps, the system significantly improved the environmental adaptability and operating efficiency of the equipment through real-time data acquisition and dynamic adjustment. Compared with traditional equipment design with fixed parameters, this method achieves greater flexibility and reliability through data-driven optimization, laying the foundation for subsequent steps such as slag removal, material management, and combustion optimization.

[0044] In one embodiment, the system can further incorporate artificial intelligence technology to optimize the implementation of steps S1 to S3. For example, during material selection, machine learning models can be used to predict the long-term performance of different materials, reducing the computational load of finite element analysis; during cooling, reinforcement learning algorithms can be used to dynamically adjust the parameters of the water cooling system, improving cooling efficiency. This intelligent implementation further enhances the system's performance.

[0045] Specifically, steps S1 to S3, through the collection and processing of multi-dimensional data, achieve precise perception of the complex environment of the combustion furnace and equipment optimization. The deployment of the sensor array ensures high data coverage, preprocessing and analysis algorithms improve data accuracy, and material selection and structural optimization guarantee high equipment reliability. The synergistic effect of these steps provides a solid foundation for the intelligent operation of magnesium smelting equipment.

[0046] In one possible implementation, the system can be optimized to suit the batch production characteristics of the magnesium smelting process. For example, in batch production, the system can adjust material selection and discharge parameters based on the characteristics of each batch of material; in continuous production, the system can prioritize optimizing the stability of the cooling rate and discharge rhythm. This differentiated implementation improves the system's production adaptability.

[0047] For example, the implementation of steps S1 to S3 can also be optimized in conjunction with the energy management needs of magnesium smelting enterprises. For instance, the system can optimize the operating time of the water cooling system by analyzing energy consumption during the cooling process, thereby reducing unnecessary energy waste. This energy-saving implementation method improves the economic efficiency of the system.

[0048] It should be noted that the above steps, through multi-level data processing and dynamic control, achieve end-to-end intelligent management from environmental perception to equipment optimization. Compared to traditional equipment management that relies on manual experience, this method significantly improves operational efficiency and equipment lifespan through data-driven decision-making, providing important support for the intelligent upgrading of the magnesium smelting industry.

[0049] In one embodiment, the system can be further optimized to take into account the regional characteristics of magnesium smelting. For example, in high-humidity areas, the system can increase the frequency of monitoring corrosive gas concentrations to address the exacerbating effects of moisture on equipment corrosion; in arid areas, the system can optimize the water resource utilization efficiency of the water-cooling system. This regionalized implementation improves the system's environmental adaptability.

[0050] Step S4: The system deploys a slag removal mechanism, controlling the slag removal trajectory and intensity through image recognition. This mechanism is responsible for cleaning the slag accumulated inside the combustion furnace. This slag may form due to the high-temperature reaction during magnesium smelting, affecting furnace thermal efficiency and equipment lifespan. The application of image recognition technology enables the slag removal process to accurately identify slag distribution and dynamically adjust the cleaning path and intensity, thereby improving cleaning efficiency and reducing damage to the furnace structure.

[0051] Step S41: Real-time image data of the furnace is acquired using an image recognition system to generate slag distribution data. For example, the image recognition system can employ a high-resolution industrial camera, installed near a high-temperature-resistant observation window inside the combustion furnace, to capture real-time images of the slag distribution. The camera is resistant to high temperatures and corrosive gases, ensuring stable image data acquisition. For instance, in magnesium smelting, slag may concentrate at the bottom of the furnace or near the discharge port. The image data records the shape, density, and location of the slag in a pixel matrix format, forming slag distribution data. This data provides the basis for subsequent slag analysis and slag cleaning operations.

[0052] Step S42: If high-density areas are detected in the slag distribution data, the distribution characteristics are obtained through convolutional neural network analysis. In one possible implementation, the convolutional neural network extracts features from the image data to identify high-density areas of the slag. For example, the network can extract features such as the edges, textures, and accumulation height of the slag through multi-layer convolution operations to generate feature vectors describing the slag distribution. The feature vectors may indicate that there is a thick accumulation of slag at the bottom of the furnace, or that there is loose slag distribution near the discharge port. These distribution characteristics provide a basis for path planning of the slag cleaning mechanism.

[0053] Step S43: Calculate the motion trajectory of the slag cleaning mechanism based on the distribution characteristics and determine the trajectory parameters. It should be noted that the slag cleaning mechanism is typically a robotic arm or scraper device equipped with a multi-degree-of-freedom motion system. The system calculates the robotic arm's motion trajectory based on the distribution characteristics, for example, performing curved cleaning along the bottom of the furnace to cover areas with high-density slag. The trajectory parameters include the robotic arm's moving speed, angle, and cleaning range. For example, if the slag is concentrated on one side of the furnace, the trajectory may be designed to perform multiple reciprocating cleaning along that sidewall. The trajectory parameters ensure that the slag cleaning mechanism can efficiently cover the target area.

[0054] Step S44: If the trajectory overlaps with an obstacle, the trajectory is adjusted using a path optimization algorithm to obtain an optimized path. Preferably, the path optimization algorithm can be based on the A* algorithm or dynamic programming to analyze the position of obstacles (such as pipes or support structures) inside the furnace and adjust the movement trajectory of the slag cleaning mechanism. For example, if the initial trajectory passes through a high-temperature pipe inside the furnace, the algorithm will replan the path to bypass the pipe and maintain slag cleaning efficiency. The optimized path is output in the form of a coordinate sequence to ensure the safe operation of the slag cleaning mechanism in complex environments.

[0055] Step S45: Adjust the slag cleaning intensity based on the optimized path and distribution characteristics to complete slag removal. Specifically, the slag cleaning intensity is determined by the force applied by the robotic arm or the working frequency of the scraper. The system dynamically adjusts the slag cleaning intensity based on the density and hardness of the slag in the distribution characteristics. For example, for high-density, hard slag, the robotic arm may increase the force to completely remove it; for loose slag, the force is reduced to decrease energy consumption. After slag cleaning is completed, the system verifies the cleaning effect through image recognition. If residual slag still exists, steps S41 to S44 are repeated to form a closed-loop control.

[0056] In one embodiment, steps S41 to S45 can be customized according to the size of the combustion furnace and the characteristics of the slag. For example, in a small magnesium smelting furnace, the image recognition system may use a single-point camera to focus on monitoring the bottom of the furnace; in a large furnace, a multi-point camera array can be deployed to cover multiple areas within the furnace. Furthermore, the movement trajectory of the slag cleaning mechanism can be optimized according to the chemical composition of the slag; for example, for highly viscous slag, the cleaning frequency can be increased to prevent accumulation. This diversified implementation improves slag cleaning efficiency and system adaptability.

[0057] Step S5: The system establishes a material flow management model and adjusts the feeding and discharging rhythms based on material data. Material flow management is a core aspect of the magnesium smelting process, directly impacting production efficiency and product quality. By dynamically adjusting the feeding and discharging rhythms, the system can maintain a balanced state of materials within the furnace, reducing waste and improving the stability of the reaction within the furnace.

[0058] Step S51: Obtain data on the feed rate, discharge rate, and slag removal rate, calculate the total material flow, and obtain the equilibrium state. For example, the feed rate is recorded by the flow sensor of the feeding mechanism, e.g., 1000 kg per hour; the discharge rate is measured by the weighing sensor of the discharge device, e.g., 800 kg per hour; the slag removal rate is calculated by the weight sensor of the slag removal mechanism, e.g., 50 kg of slag is removed each time. The system summarizes these data, calculates the total material flow, and determines whether the input and output of the material in the furnace are balanced. For example, if the feed rate is significantly greater than the discharge rate, it may lead to material accumulation, affecting the reaction inside the furnace.

[0059] Step S52 involves analyzing the relationship between the feed rate and the discharge rate using a linear regression algorithm to determine the material balance deviation. In one possible implementation, the linear regression algorithm analyzes the correlation between the feed rate and the discharge rate over time to generate a deviation value. For example, the deviation value might indicate that the feed rate exceeds the discharge rate by 100 kg per hour, leading to material accumulation in the furnace. This deviation value reflects the dynamic imbalance of material flow, providing a basis for subsequent adjustments.

[0060] Step S53: If the deviation exceeds a preset threshold, the feeding rhythm is adjusted to obtain an updated rhythm. Preferably, the preset threshold can be set according to production needs, for example, the deviation should not exceed 50 kg / hour. When the deviation exceeds the threshold, the system reduces the feeding speed of the feeding mechanism through the control unit, for example, from 1000 kg / hour to 900 kg / hour. The updated rhythm is output in the form of a time sequence to ensure that the feeding process and the discharging process are coordinated.

[0061] Step S54: Based on the update rhythm, time series analysis is used to predict changes in the discharge rhythm, resulting in an adjustment plan. Specifically, time series analysis predicts the future trend of the discharge rhythm based on historical feeding and discharging data. For example, the analysis may show that the material quantity gradually increases after the feeding rhythm is adjusted, tending towards a new equilibrium state. The adjustment plan includes optimized values ​​for the discharge rate and time interval, such as increasing the discharge rate to 850 kg per hour and shortening the discharge interval to accelerate material flow.

[0062] Step S55 involves calculating the dynamic equilibrium state by adjusting the scheme and slag removal data, and determining the stability of the equilibrium. In one embodiment, the system inputs the adjusted feeding rhythm, discharging rhythm, and slag removal data into the material flow model to calculate the dynamic equilibrium state of the materials in the furnace. For example, if the adjusted material input-output deviation is controlled within a threshold range, it indicates that the equilibrium state is stable; if the deviation is still large, steps S51 to S54 are repeated to further optimize the rhythm. This closed-loop adjustment improves the accuracy of material management.

[0063] In one possible implementation, steps S51 to S55 can be optimized to accommodate the continuity of the magnesium smelting process. For example, in continuous production, the system may adjust the feeding and discharging rhythms in real time to maintain stable flow; in intermittent production, the system may optimize the slag removal frequency based on batch requirements to reduce material waste. Furthermore, the material flow model can be customized according to the characteristics of the raw materials used in magnesium smelting; for example, for high-purity magnesium raw materials, the feeding rhythm can be appropriately accelerated to improve reaction efficiency.

[0064] Step S6 involves integrating temperature field monitoring into the system and optimizing combustion parameters through thermal imaging and fluid dynamics models. Temperature field monitoring is a crucial step in the magnesium smelting process, directly impacting combustion efficiency and product quality. Through thermal imaging technology and fluid dynamics analysis, the system can accurately perceive the temperature distribution within the furnace and optimize the combustion process.

[0065] Step S61: Temperature distribution data of the furnace body is acquired using a thermal imager to generate a real-time temperature field image. For example, the thermal imager is installed outside the combustion furnace and captures the temperature distribution inside the furnace through a high-temperature resistant window. The thermal imager can capture temperature changes in areas such as the furnace chamber, feed port, and discharge port, forming a two-dimensional temperature field image. For example, the image may show that the temperature at the center of the furnace is higher than 1500 degrees Celsius, while the temperature in the edge areas is lower. This real-time image provides a visual basis for subsequent analysis.

[0066] Step S62 involves processing the low-resolution image using image enhancement techniques to obtain a high-resolution temperature field image. It should be noted that thermal imagers may experience interference from smoke or airflow in high-temperature environments, leading to a decrease in image resolution. Therefore, super-resolution reconstruction techniques can be used to process the low-resolution image, such as increasing the image pixel density through interpolation algorithms. The enhanced high-resolution image can more clearly display details of the temperature distribution, such as high-temperature hotspots in the center of the furnace or the temperature gradient at the discharge port.

[0067] Step S63: Extract spatial temperature distribution features from the high-resolution image to generate a temperature distribution dataset. In one embodiment, the system divides the high-resolution image into multiple regions using image segmentation technology and extracts temperature features for each region, such as average temperature, maximum temperature, and temperature gradient. These features are stored in the form of a dataset, for example, recording the average temperature of the furnace center region as 1450 degrees Celsius and the edge region as 1000 degrees Celsius. The temperature distribution dataset provides basic data for combustion optimization.

[0068] Step S64: The temperature distribution dataset is combined with environmental parameters using data fusion technology to obtain a comprehensive dataset. Preferably, the environmental parameters include furnace pressure, gas flow rate, and oxygen concentration, which are collected in real time by a sensor array. The data fusion technology performs correlation analysis between the temperature distribution data and the environmental parameters, for example, analyzing whether the gas flow rate in the high-temperature region affects the temperature distribution. The comprehensive dataset is output in the form of a multi-dimensional vector, containing the joint features of temperature and environmental parameters.

[0069] Step S65 involves simulating the combustion process using a fluid dynamics model on the comprehensive dataset to generate optimized combustion parameters. Specifically, the fluid dynamics model simulates the transfer of airflow and heat within the furnace, analyzing the impact of temperature distribution and environmental parameters on combustion efficiency. For example, the model might indicate that increasing the oxygen concentration enhances combustion intensity at the furnace center. Optimized combustion parameters include fuel injection rate, air supply, and combustion time interval; for instance, adjusting the fuel injection rate to 50 liters per minute and increasing the air supply by 10%. These parameters are transmitted to the combustion system via the control unit to optimize the combustion process.

[0070] In one possible implementation, steps S61 to S65 can be differentiated based on the structure of the combustion furnace. For example, in a vertical combustion furnace, the thermal imager may focus on monitoring the temperature distribution in the vertical direction; in a horizontal furnace, the thermal imager may cover a wider horizontal area. Furthermore, the fluid dynamics model can be customized according to the airflow characteristics within the furnace, such as optimizing combustion parameters for high-speed airflow environments. This diversified implementation improves the accuracy and applicability of temperature field monitoring.

[0071] Step S7: The system constructs a condition monitoring network, which uses pattern recognition to identify equipment anomalies and trigger protection mechanisms. Through multi-dimensional data analysis, the condition monitoring network monitors the operating status of mechanical devices in real time, promptly detects anomalies, and takes protective measures, thereby improving equipment reliability and production safety.

[0072] Step S71: Vibration, temperature, and pressure data of the mechanical device are collected using sensors to obtain a raw dataset. Exemplarily, the sensors include a vibration sensor, a temperature sensor, and a pressure sensor, which are respectively installed at key locations in the feeding mechanism, the slag-cleaning mechanism, and the discharging device. The vibration sensor records the vibration frequency and amplitude of the robotic arm, the temperature sensor monitors temperature changes on the device surface, and the pressure sensor detects the working pressure of the hydraulic system. For example, the vibration data may show abnormally high-frequency vibrations in the robotic arm during slag cleaning, indicating possible loose components.

[0073] Step S72 involves denoising and standardizing the original dataset using preprocessing methods to obtain a processed dataset. In one embodiment, the preprocessing method includes high-pass filtering to remove low-frequency noise from the vibration data and linear normalization to map the temperature and pressure data to a uniform range. The processed dataset retains the core variation characteristics of vibration, temperature, and pressure, such as periodic changes in vibration frequency or abnormal temperature increases.

[0074] Step S73: Principal component analysis (PCA) is used to extract features, resulting in a feature vector set. Preferably, PCA reduces the dimensionality of multidimensional data and extracts key features reflecting the equipment status. For example, the algorithm may extract principal components from vibration, temperature, and pressure data to generate a feature vector set containing the equipment's operating status. The feature vectors may indicate a strong correlation between vibration frequency and temperature rise, suggesting potential equipment failure.

[0075] Step S74: If the feature values ​​in the feature vector set exceed a preset threshold, an abnormal state is identified and a warning signal is triggered. Specifically, the preset threshold is set according to the characteristic range of normal equipment operation, such as a vibration frequency not exceeding 10 Hz and a temperature not exceeding 500 degrees Celsius. When the feature value exceeds the threshold, the system generates a warning signal, such as notifying the operator through an audible and visual alarm. The warning signal can also record the specific type of abnormality, such as "abnormal vibration of the slag removal mechanism".

[0076] Step S75: The protection program is activated via a warning signal, equipment operating parameters are adjusted, and the equipment status is confirmed to have returned to normal. In one possible implementation, the protection program reduces the equipment's operating speed or suspends operation via a control unit, for example, reducing the movement speed of the slag removal mechanism by 50%. The system continuously monitors the equipment status; if the characteristic values ​​return to the normal range, the equipment status is confirmed to be normal; if the abnormality persists, further shutdown and inspection are conducted. This closed-loop protection mechanism improves equipment safety.

[0077] In one embodiment, steps S71 to S75 can be optimized in conjunction with the operating cycle of magnesium smelting. For example, during high-load production cycles, the system may increase the sensor acquisition frequency to capture more abnormal signals; during low-load cycles, the system may reduce the acquisition frequency to save energy. Furthermore, the condition monitoring network can be integrated with the enterprise maintenance system to automatically generate maintenance plans and improve equipment management efficiency.

[0078] Step S8: The system integrates its various subsystems and adjusts operating parameters using a multivariate optimization algorithm. These subsystems include material selection, feeding and cooling, discharge control, slag removal, material management, and temperature field monitoring. Unified optimization improves overall performance.

[0079] Step S81: Collect data from each subsystem to generate a comprehensive operational dataset. For example, the comprehensive operational dataset includes data such as material selection results, feed inlet temperature, discharge rate, slag removal trajectory, material balance status, and temperature field distribution. This data is collected in real time from each subsystem, updated for example, every minute, forming a multidimensional dataset.

[0080] Step S82 involves analyzing the comprehensive operational dataset using a multivariate optimization algorithm to determine key operational parameters. In one possible implementation, the multivariate optimization algorithm analyzes the interrelationships of data from various subsystems, such as the impact of the feeding rate on the temperature field distribution. The algorithm generates optimization parameters, for example, adjusting the feeding rate to 950 kg per hour and the combustion fuel injection rate to 48 liters per minute. These parameters ensure coordinated operation of the various subsystems.

[0081] Step S83: Adjust the operating status of each subsystem according to the optimized parameters to complete system integration. Specifically, the optimized parameters are transmitted to each subsystem through the control unit, such as adjusting the feeding speed of the feeding mechanism, the cleaning frequency of the slag removal mechanism, and the fuel supply of the combustion system. The system continuously monitors the adjustment effect. If the operating status does not meet expectations, steps S81 to S82 are repeated to form a closed-loop optimization.

[0082] In one embodiment, steps S81 to S83 can be dynamically adjusted according to production needs. For example, under high production demand, the system may prioritize optimizing the feeding and discharging rhythm; under low production demand, the system may focus on optimizing slag removal efficiency to reduce energy consumption. Furthermore, the comprehensive operational dataset can be interfaced with the enterprise production management system to achieve data sharing and remote monitoring.

[0083] In one possible implementation, steps S4 to S8 can be optimized in conjunction with the process characteristics of magnesium smelting. For example, for high-purity magnesium production, the system may increase the slag removal frequency to ensure furnace cleanliness; for low-purity magnesium production, the system may reduce the slag removal intensity to save costs. This differentiated implementation improves the system's process adaptability.

[0084] It should be noted that steps S4 to S8 utilize technologies such as image recognition, material management, and temperature field monitoring to achieve intelligent control of slag cleaning, material balance, and combustion optimization within the combustion furnace. Compared to traditional methods relying on manual operation, this method significantly improves operating efficiency and equipment lifespan through data-driven dynamic adjustments.

[0085] In one embodiment, the system can be optimized to meet the energy management needs of magnesium smelting. For example, by analyzing temperature field data and combustion parameters, the system can optimize fuel efficiency and reduce energy waste. This energy-saving implementation improves the system's economic benefits.

[0086] For example, the implementation of steps S4 to S8 can be extended to magnesium smelting enterprises of different sizes. For instance, in a plant with an annual magnesium production of 10,000 tons, the system can achieve efficient operation by simplifying the slag removal trajectory and optimizing combustion parameters; in a large enterprise with an annual production of 100,000 tons, the system can be deployed with high-precision image recognition and complex optimization algorithms to meet higher demands.

[0087] In one possible implementation, the system can further incorporate artificial intelligence technology to optimize the implementation of steps S4 to S8. For example, in slag removal trajectory planning, reinforcement learning algorithms can be used to dynamically adjust the path; in combustion optimization, neural networks can be used to predict the optimal fuel supply. This intelligent implementation further improves the system's performance.

[0088] Specifically, steps S4 to S8, through multi-level data processing and optimized design, achieve end-to-end intelligent operation from slag cleaning to system integration. High-precision image recognition analysis ensures slag cleaning efficiency, material flow models improve production stability, and temperature field monitoring and condition monitoring networks guarantee combustion efficiency and equipment safety. The synergistic effect of these steps provides crucial support for the intelligent upgrading of the magnesium smelting industry.

[0089] In one embodiment, the system can be optimized to suit the regional characteristics of magnesium smelting. For example, in high-humidity areas, the system can increase monitoring of slag moisture content to optimize slag removal intensity; in arid areas, the system can optimize combustion parameters to reduce water consumption in the water-cooling system. This regionalized implementation improves the system's environmental adaptability.

[0090] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An intelligent magnesium smelting equipment system suitable for regenerative circulating combustion refining furnace, characterized in that, S1. The system acquires internal environmental data of the combustion furnace and determines the material and structural parameters of the mechanical device through a material performance evaluation mechanism; S2. The system uses high-temperature resistant materials to manufacture the feeding mechanism and performs cooling treatment when the temperature exceeds the limit; S3. The system is equipped with a corrosion-resistant discharge device, and the discharge parameters are adjusted through a rate control algorithm; S4. The system deploys a slag removal mechanism, which controls the slag removal trajectory and intensity through image recognition; S5. The system establishes a material flow management model and adjusts the feeding and discharging rhythm based on material data; S6. The system integrates temperature field monitoring and optimizes combustion parameters through thermal imaging and fluid dynamics models; S7. The system constructs a status monitoring network, uses pattern recognition to determine equipment anomalies and trigger protection; S8. The system integrates various subsystems and adjusts operating parameters through a multivariate optimization algorithm.

2. The system as described in claim 1, characterized in that, The acquisition of internal environmental data of the combustion furnace and the determination of material and structural parameters of the mechanical device through a material performance evaluation mechanism include: S11. Collect data on the internal temperature and corrosive gas concentration of the combustion furnace using a sensor array to obtain the raw dataset; S12. The original dataset is denoised and standardized using a preprocessing algorithm to obtain a standardized dataset; S13. Based on the standardized dataset, a clustering algorithm is used to partition and determine the high-temperature and corrosion areas, thus obtaining the regional distribution characteristics; S14. If the temperature or corrosive gas concentration in the regional distribution characteristics exceeds the preset threshold, then query the material performance database for high temperature resistant or corrosion resistant materials to obtain a list of candidate materials. S15. Perform stress distribution and deformation analysis on candidate materials using finite element analysis to determine material selection; S16. Based on the selected materials and regional distribution characteristics, the structural parameters are adjusted using a topology optimization algorithm to obtain an optimized design scheme.

3. The system as described in claim 1, characterized in that, The feeding mechanism, manufactured using high-temperature resistant materials and subjected to cooling treatment when overheating occurs, includes: S21. The temperature data of the feeding port is collected in real time by a temperature sensor to obtain the current temperature value; S22. If the current temperature exceeds the preset threshold, an over-temperature signal is generated and the water cooling system is activated; S23. Adjust the operating parameters of the water cooling system based on the cooling water flow rate data to obtain the cooling rate; S24. Continuously monitor temperature changes based on the cooling rate to obtain the temperature decreasing trend; S25. If the temperature drop trend does not reach the preset standard, adjust the power of the water cooling system and update the cooling rate.

4. The system as described in claim 1, characterized in that, The corrosion-resistant discharge device is configured to adjust discharge parameters through a rate control algorithm, including: S31. Collect data on outlet temperature and corrosive gas concentration using sensors to obtain the raw dataset; S32. Denoise and normalize the original dataset using preprocessing techniques to obtain a standardized dataset; S33. Using a linear regression algorithm based on a standardized dataset, calculate the trends in temperature and corrosive gas concentration to obtain trend characteristic values; S34. If the trend characteristic value exceeds the preset threshold, the discharge rate and time are adjusted through the rate control algorithm to obtain optimized control parameters; S35. Drive the discharge device to perform dynamic adjustments based on optimized control parameters and update the discharge parameters in real time.

5. The system as described in claim 1, characterized in that, The aforementioned slag removal mechanism controls the slag removal trajectory and intensity through image recognition, including: S41. Real-time image data inside the furnace is collected through an image recognition system to generate slag distribution data; S42. If high-density areas are detected in the slag distribution data, the distribution characteristics are obtained through convolutional neural network analysis. S43. Calculate the motion trajectory of the slag removal mechanism based on the distribution characteristics, and determine the trajectory parameters; S44. If the trajectory overlaps with an obstacle, the trajectory is adjusted using a path optimization algorithm to obtain an optimized path; S45. Adjust the slag cleaning intensity according to the optimized path and distribution characteristics to complete the slag cleaning.

6. The system as described in claim 1, characterized in that, The establishment of a material flow management model, which adjusts the feeding and discharging rhythm based on material data, includes: S51. Obtain data on feed rate, discharge rate, and slag removal rate, calculate the total material flow, and obtain the equilibrium state; S52. Analyze the relationship between feed rate and discharge rate using linear regression algorithm to determine material balance deviation; S53. If the deviation exceeds the preset threshold, adjust the feeding rhythm to obtain the updated rhythm; S54. Based on the update rhythm, time series analysis is used to predict changes in the discharge rhythm, and an adjustment plan is obtained; S55. Calculate the dynamic equilibrium state by adjusting the scheme and the amount of slag removed, and determine the stability of the equilibrium.

7. The system as described in claim 1, characterized in that, The integrated temperature field monitoring optimizes combustion parameters through thermal imaging and fluid dynamics models, including: S61. Collect furnace body temperature distribution data using a thermal imager to generate a real-time temperature field image; S62. Low-resolution images are processed using image enhancement techniques to obtain high-resolution temperature field images; S63. Extract spatial temperature distribution features from high-resolution images to generate a temperature distribution dataset; S64. By combining the temperature distribution dataset with environmental parameters through data fusion technology, a comprehensive dataset is obtained; S65. Use a fluid dynamics model to simulate the combustion process on the comprehensive dataset and generate optimized combustion parameters.

8. The system as described in claim 1, characterized in that, The constructed status monitoring network determines device anomalies and triggers protection through pattern recognition, including: S71. Collect vibration, temperature, and pressure data of the mechanical device using sensors to obtain the raw dataset; S72. The original dataset is denoised and standardized using preprocessing methods to obtain the processed dataset; S73. Use principal component analysis to extract features and obtain a feature vector set; S74. If the feature values ​​in the feature vector set exceed the preset threshold, it is judged as an abnormal state and an early warning signal is triggered; S75. Activate the protection program through the warning signal, adjust the equipment operating parameters, and confirm that the equipment status has returned to normal.