A control method for treating slag from submerged arc furnace based on water quenching method

Abstract

The present invention belongs to the technical field of slag treatment control, and specifically discloses a control method for submerged arc furnace slag treatment based on the water quenching method, including: obtaining the chemical element content and dynamic viscosity in the raw materials, inputting them into a fuzzy inference metallurgical model to obtain the expected composition matrix, viscosity and output range of the slag, collecting the temperatures at N temperature measurement points, calculating the preset control instruction of the slag flushing pump by using an adaptive control algorithm. During the slag flushing process, the water quenching data is detected in real time, and the control instruction of the slag flushing pump is dynamically adjusted accordingly. After the water quenching is completed, through processes such as precipitation and cooling, the cold water is separated and recycled; by calculating the control instruction of the slag flushing pump according to the expected composition matrix, viscosity, output range and temperature of the slag, the present invention accurately adapts to the complex characteristics of the slag and temperature fluctuations, dynamically optimizes the water quenching parameters, thereby improving the water quenching effect and reducing the wear of equipment and the blockage of pipelines.

Description

Technical Field

[0001] The present invention belongs to the technical field of slag treatment control, and relates to a control method for submerged arc furnace slag treatment based on the water quenching method. Background Art

[0002] During the smelting process of submerged arc furnaces, slag (such as ferroalloy slag) poses a large number of environmental hazards. The traditional water quenching method directly contacts high-temperature liquid slag with water, and uses the latent heat of vaporization of water to quickly cool and break the slag into small particles. This method can effectively inhibit dust generation and reduce pollution to the atmospheric environment. Moreover, the water-quenched slag has good activity and can be used as a cement admixture, concrete admixture, etc. in the field of building materials, realizing the secondary utilization of resources and improving resource utilization efficiency. Although the water quenching method can achieve rapid cooling and resource utilization, there are still significant technical bottlenecks in practical applications.

[0003] For example, the water quenching system for submerged arc furnace slag disclosed in the existing invention patent with the publication number CN118516563A improves the treatment efficiency by optimizing the chute structure and the layout of water quenching nozzles, but its technical solution has essential defects: 1. Using fixed water quenching parameters cannot respond to the dynamic changes in slag composition and temperature, resulting in uneven cooling, incomplete crushing, abnormal wear of equipment and pipeline blockage.

[0004] 2. Relying on physical structure improvement and lacking a process parameter control mechanism, it is difficult to establish a dynamic response relationship between water quenching data and process parameters, resulting in fluctuations in treatment effects and reduced resource utilization rate. These problems seriously restrict the stability and economy of industrial production, and a water quenching treatment system with intelligent control functions needs to be developed. Summary of the Invention

[0005] In view of this, to solve the problems raised in the above background art, a control method for submerged arc furnace slag treatment based on the water quenching method is proposed.

[0006] The object of the present invention can be achieved by the following technical solutions: The present invention provides a control method for submerged arc furnace slag treatment based on the water quenching method, including: S1. Obtain the content and dynamic viscosity of each chemical element in the raw materials, and input the raw material data into the Takagi-Sugeno fuzzy inference metallurgical model to obtain the expected composition matrix, expected viscosity value and expected output range of the slag.

[0007] S2. Obtain the temperatures of

[0008] a number of temperature measurement points, and calculate the preset control command of the slag flushing pump through an adaptive control algorithm.

[0009] S4. After the water quenching is completed, the cold water is separated through precipitation, cooling tower cooling and inclined plate sedimentation device and recycled.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By calculating the control instruction of the slag sluicing pump according to the expected composition matrix, viscosity, production range and temperature of the slag, and applying the adaptive algorithm, the present invention accurately adapts to the complex characteristics and temperature fluctuations of the slag, dynamically optimizes the water quenching parameters, thereby improving the water quenching effect and reducing the wear of equipment and the blockage of pipelines.

[0011] (2) By detecting the water quenching data, analyzing the angle regulation demand coefficient, water volume regulation demand coefficient and water pressure regulation demand coefficient of the slag sluicing pump, and determining the current control instruction of the slag sluicing pump, the present invention establishes a dynamic response relationship between the water quenching data and the adjustment instruction, effectively ensuring the consistency of the slag water quenching effect and realizing the refined control of the slag water quenching.

[0012] (3) By counting the water pressure regulation demand coefficient of the slag sluicing pump according to the diameter of each water quenched slag in the water quenched slag pool, the present invention accurately adapts to the slag sluicing requirements of slag with different particle sizes, avoiding the problems of insufficient slag scouring or excessive scouring of equipment caused by improper water pressure, and thus improving the slag sluicing efficiency and quality.

[0013] (4) By inputting the content of each chemical element and the dynamic viscosity in the raw material into the Takagi-Sugeno fuzzy inference metallurgical model, the present invention obtains the expected composition matrix, expected viscosity value and expected production range of the slag, accurately predicts the composition parameters of the slag, and thus improves the accuracy of the subsequent preset control of the slag sluicing pump, thereby realizing the improvement of energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 It is a connection schematic diagram of each step of the method of the present invention.

[0016] Figure 2 It is a connection schematic diagram of the steps for calculating the preset control instruction of the slag sluicing pump by the adaptive control algorithm of the present invention.

[0017] Figure 3 It is a connection schematic diagram of each process of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1

[0020] Please refer to Figure 1 As shown, the present invention provides a control method for the slag treatment of a submerged arc furnace based on the water quenching method. The method includes: S1. Obtain the content of each chemical element and the dynamic viscosity in the raw materials, and input the raw material data into the Takagi-Sugeno fuzzy inference metallurgical model to obtain the expected composition matrix, expected viscosity value and expected output range of the slag.

[0021] It should be added that the content of each chemical element and the dynamic viscosity of the raw materials are obtained through a multi-sensor fusion device. The multi-sensor fusion device refers to a multi-sensor fusion detection device that uses an integrated laser-induced breakdown spectrometer (LIBS) and a rotating viscosity probe. The advantages of choosing the multi-sensor fusion device are as follows: 1. A single sensor can only detect a certain characteristic of the raw materials, while the multi-sensor fusion device can simultaneously obtain multi-dimensional data such as the content of chemical elements and the dynamic viscosity coefficient, providing more comprehensive information for subsequent analysis. 2. Improve the detection accuracy: The measurement principles and error sources of different sensors are different. Through multi-sensor fusion, the data of multiple sensors can be used for cross-verification and data fusion, thereby reducing the measurement error and improving the detection accuracy. 3. Real-time and high efficiency: This device can work simultaneously and obtain various data in a short time without multiple detections or transfers of the raw materials, saving the detection time and improving the detection efficiency, providing data support for real-time decision-making in the production process. In the preparation stage of submerged arc furnace smelting, the physical property parameters of the raw materials can be quickly obtained, enabling the operators to adjust the production process and parameters in a timely manner to ensure the efficient progress of production.

[0022] It should be added that the Takagi-Sugeno fuzzy inference metallurgical model is a hybrid inference system that combines fuzzy logic and mathematical models. The following is a detailed explanation of the model in the slag treatment scenario: The Takagi-Sugeno (T-S) fuzzy model consists of the following two parts: The premise part is a rule base based on fuzzy logic, which divides input variables (such as chemical element ratios and dynamic viscosities) through fuzzy sets. The conclusion part is that each fuzzy rule corresponds to a local linear mathematical model (such as a polynomial equation), and the output result is a deterministic numerical value or interval. The application process in the metallurgical scenario: (1) Fuzzification of input variables: The contents of various chemical elements in the input raw materials and the dynamic viscosity of the raw materials. Fuzzy set division: The input parameters are divided into multiple fuzzy sets according to metallurgical experience, and membership functions are defined.

[0023] (2) Fuzzy rule base: Each rule is generated by training with expert experience or historical data, and the parameters in the conclusion part are determined through regression analysis or optimization algorithms.

[0024] (3) Inference and defuzzification: Calculate the activation weights of each rule according to the membership degrees of the input parameters, and the final output is the weighted average of the outputs of each rule. Output results: Expected composition matrix: The predicted ratios of each component in the slag; Expected viscosity value: A key indicator of the fluidity of the slag; Expected output interval: The output range after comprehensively considering process fluctuations.

[0025] Exemplarily, inputting the raw material data into the Takagi-Sugeno fuzzy inference metallurgical model to obtain the expected composition matrix, expected viscosity value, and expected output interval of the slag includes: S1-1. Normalize the contents of various chemical elements to obtain the ratios of various chemical elements, and define the membership functions of their fuzzy subsets.

[0026] It should be added that the normalization process is: Divide the content value of each chemical element by the sum of the content values of all chemical elements to obtain the ratio of each chemical element.

[0027] S1-2. Based on the membership degrees of the fuzzy subsets to which various chemical elements belong, match the corresponding rules in the pre-set fuzzy inference rule base, and perform inference calculations according to the membership degrees of their antecedents and the functional relationships of their consequents to obtain the fuzzy inference results of each chemical component.

[0028] It should be added that the process of establishing a pre-set fuzzy inference rule base is as follows: (1) Collect and organize data: Under laboratory conditions, conduct submerged arc furnace slag smelting experiments with different raw material ratios and different process parameters (such as temperature, pressure, reaction time, etc.), record the chemical element content of the raw materials, the final composition, viscosity, and output data of the slag in each experiment, and at the same time collect a large amount of data in the actual production process, including raw material information of different batches, production operation records, and corresponding slag product data.

[0029] (2) Determine fuzzy linguistic variables and fuzzy subsets: Select factors related to slag composition, viscosity, and output as fuzzy linguistic variables. For example, the content of each chemical element in the raw materials, smelting temperature, reaction time, etc. are used as input variables, and the chemical composition, viscosity, and output of the slag are used as output variables. At the same time, define appropriate fuzzy subsets for each fuzzy linguistic variable. For example, for the chemical element content, define fuzzy subsets such as "low", "medium", "high", etc.; for the slag viscosity, "extremely low", "low", "medium", "high", "extremely high", etc. can be defined. Each fuzzy subset has a corresponding membership function to describe the degree to which the variable belongs to the subset.

[0030] (3) Summarize expert knowledge and experience: Invite experts in the metallurgy field. According to their professional knowledge and rich experience, evaluate and judge the output characteristics of the slag under different combinations of input variables, and then transform the experts' judgments and experience into specific rule forms. For example, "If the content in the raw materials is high and the content is low, then the silicate component in the slag is high". These rules are the basis of the fuzzy inference rule base.

[0031] (4) Data analysis and rule optimization: Use data mining techniques to analyze the collected experimental data and production data, and explore the potential relationships and laws between the data. For example, through association analysis, find the strong association rules between the raw material element content and the slag composition, and then use the existing data to verify the initially established rules, check the accuracy and rationality of the rules. When it is found that some rules deviate greatly from the actual data, adjust and optimize the rules in time, such as redefining the fuzzy subsets, adjusting the membership function, or modifying the conditions and conclusions of the rules.

[0032] (5) Improvement and update of the rule base: With the continuous accumulation of new experimental data and production data, regularly update and improve the rule base. Through continuous learning, make the rule base adapt to different production conditions and changing raw material characteristics. At the same time, establish a feedback mechanism to compare and analyze the slag detection results in actual production with the prediction results of the fuzzy inference model. When there are large differences, find the reasons in time and correct the rule base to improve the accuracy and reliability of the fuzzy inference model.

[0033] S1-3. Corresponding multiply the fuzzy inference results of each chemical component with the preset weights of each chemical component to obtain the expected content of each chemical component, and then generate the component expected matrix of the slag.

[0034] In a specific embodiment, assume that we have a submerged arc furnace, and the raw materials processed mainly involve four chemical elements: iron (Fe), silicon (Si), calcium (Ca), and aluminum (Al). Through detection and analysis, the initial contents of each chemical element in the raw materials are: the iron element content is 50g, the silicon element content is 20g, the calcium element content is 15g, and the aluminum element content is 15g. Perform normalization processing on these element contents: the total content is 100g. Then the proportion of the iron element is 0.5, the proportion of the silicon element is 0.2, the proportion of the calcium element is 0.15, and the proportion of the aluminum element is 0.15.

[0035] Define the membership function of the fuzzy subset for each chemical element: Define the fuzzy subsets of the iron element as "high", "medium", and "low", and its membership function is set as "high": when the content is greater than or equal to 0.4, the membership degree is 1, when it is less than 0.3, the membership degree is 0, and it linearly changes between 0.3 and 0.4. At this time, the membership degree of the iron element in the "high" fuzzy subset is 1.

[0036] "medium": The membership degree linearly changes from 0 to 1 between 0.2 and 0.3, and linearly changes from 1 to 0 between 0.3 and 0.4. The membership degree of the iron element in the "medium" fuzzy subset is 0.

[0037] "low": When the content is less than or equal to 0.2, the membership degree is 1, when it is greater than 0.3, the membership degree is 0, and it linearly changes between 0.2 and 0.3. The membership degree of the iron element in the "low" fuzzy subset is 0.

[0038] Similarly, define the corresponding fuzzy subsets and membership functions for silicon, calcium, and aluminum elements respectively.

[0039] Fuzzy inference calculation: In the preset fuzzy inference rule base, for example, there is a rule: If the iron element belongs to the "high content" fuzzy subset and the silicon element belongs to the "medium content" fuzzy subset, then a certain related compound component in the slag will be at a relatively high level. According to the membership degree of each element belonging to the fuzzy subset and the functional relationship between the antecedent and consequent of the rules in the rule base for reasoning calculation. If the fuzzy inference result of the iron element for the expected chemical component A in the slag is 0.8, the fuzzy inference result of the silicon element for the chemical component A is 0.3, the calcium element is 0.1, and the aluminum element is 0.2.

[0040] Generate the expected matrix of slag composition: In the preset chemical composition A, the weights of iron, silicon, calcium, and aluminum elements are 0.5, 0.3, 0.1, and 0.1 respectively. Then the expected content of chemical composition A = 0.8×0.5 + 0.3×0.3 + 0.1×0.1 + 0.2×0.1 = 0.52.

[0041] In the same way, calculate other chemical components in the slag, and finally obtain the expected matrix of slag composition. For example: [A: 0.52, B: 0.28, C: 0.15, C: 0.05].

[0042] S1-4. According to the fuzzy algorithm, analyze and obtain the expected viscosity value and expected output range of the slag in the same way as the analysis steps of the expected matrix of slag composition.

[0043] In the embodiment of the present invention, by inputting the content of each chemical element and the dynamic viscosity in the raw materials into the Takagi-Sugeno fuzzy inference metallurgical model, the expected composition matrix, expected viscosity value, and expected output range of the slag are obtained, accurately predicting the composition parameters of the slag, thereby improving the accuracy of the subsequent preset control of the slag pump, and thus realizing the improvement of energy efficiency.

[0044] S2. Obtain the temperatures of the temperature measurement points, and calculate the preset control command of the slag pump through the adaptive control algorithm.

[0045] It should be added that the temperatures of the temperature measurement points are obtained through the distributed optical fiber temperature measurement system installed on the wall of the submerged arc furnace. The advantages of choosing the distributed optical fiber temperature measurement system to obtain temperatures are as follows: 1. Full distributed continuous temperature measurement ability: A single optical fiber can achieve kilometer-level continuous temperature monitoring, breaking through the space limitation of traditional point sensors, completely covering the complex space temperature field of the submerged arc furnace, and at the same time supporting the real-time generation of three-dimensional temperature cloud maps to accurately locate local high-temperature areas and temperature abnormal points. 2. Extreme environment adaptability: Using quartz optical fiber as the sensing medium, it can withstand high-temperature environments above 1000°C. It has an intrinsically safe design, no electrical signal transmission, strong anti-electromagnetic interference ability, and excellent anti-corrosion performance, and can resist chemical erosion during the slag treatment process. 3. High-precision dynamic monitoring: The temperature resolution can reach 0.1°C, the spatial resolution is better than 10 cm, the response time < 1 second, meeting the monitoring requirements of the rapidly changing slag treatment process, supporting the detection of μW-level weak optical signals, and realizing reliable temperature measurement in an extremely low-loss environment.

[0046] It should be added that the adaptive control algorithm is a control method that can automatically adjust the control strategy and parameters according to changes in the environment, parameters, etc. during the operation of the system, so that the system remains in an optimal or satisfactory operating state. It does not require precise knowledge of the mathematical model of the controlled object in advance, but adjusts the parameters of the controller according to certain adaptive laws by real-time monitoring the input and output information of the system to adapt to changes in the system characteristics.

[0047] Please refer to Figure 2 shown. Exemplarily, calculating the preset control instruction of the slag flushing pump by the adaptive control algorithm includes: S2-1. Based on the temperatures of temperature measurement points, statistically calculate the preset control water volume of the slag flushing pump

[0048] Further, the statistical calculation of the preset control water volume of the slag flushing pump includes: S2-1-1. Denote the temperatures of the temperature measurement points as , where is the temperature measurement point number.

[0049] S2-1-2. Calculate the average value of the temperatures of each temperature measurement point to obtain the average temperature in the submerged arc furnace, denoted as , and denote the expected viscosity value of the slag as , where

[0050] S2-1-3. Calculate the preset control water volume of the slag flushing pump, , , , and are respectively the set reference proportional coefficient, integral coefficient, slag flushing start time, and slag flushing end time, is the number of temperature measurement points.

[0051] It should be added that is a measure of the temperature dispersion degree of each temperature measurement point in the submerged arc furnace. By calculating the square root of the average value of the sum of the squares of the differences between the temperatures of each temperature measurement point and the average temperature, the temperature dispersion degree is obtained, reflecting the non-uniformity of the temperature distribution in the furnace. denotes the integral of the product of the expected viscosity of the slag and the integral coefficient from the slag flushing start time to the slag flushing end time . This is to consider the influence of the cumulative change of the slag viscosity over time on the required slag flushing water volume during the entire slag flushing process.

[0052] It should be added that Setting process: When studying the influence of the temperature gradient difference on the water flow rate, we take some factors related to the temperature gradient difference as the factors of the orthogonal experiment. For example, the pressure in the furnace, the raw material ratio, the smelting time, etc. These factors may indirectly affect the temperature distribution, and then affect the relationship between the temperature gradient difference and the water flow rate. Then for The setting process is as follows: (1) Level setting: Different levels are set for each factor. For example, the pressure in the furnace can be set to three levels: low, medium, and high. The raw material ratio can be set according to different proportion ranges, and the smelting time can be set to several different duration levels.

[0053] (2) Experimental design and implementation: Arrange the experiments according to the orthogonal table, and record the corresponding temperature gradient difference and water flow rate data for each experiment.

[0054] (3) Data analysis: By analyzing the experimental data, such as methods like variance analysis, determine the significance of the influence of each factor on the water flow rate. Thus, find the best correlation relationship between the temperature gradient difference and the water flow rate under different conditions, and determine the appropriate proportionality coefficient , for example, when the slag content > 0.45, determine integral coefficient Obtained through the principle of the Arrhenius equation: The general form of the Arrhenius equation is , where is the reaction rate constant, is the pre-exponential factor, is the activation energy, is the gas constant, is the absolute temperature. It shows the exponential relationship between the chemical reaction rate and the temperature. As the temperature increases, the reaction rate constant increases and the reaction rate accelerates. In the application of correcting : In the water flow rate adjustment formula, the viscous compensation term is related to the viscosity of the slag , and the viscosity of the slag is closely related to the physical and chemical properties of the slag. Among them, the melting point of the slag is an important factor.

[0055] is corrected according to the Arrhenius equation, and is expressed as , where is similar to the pre-exponential factor, takes 60 - 80 J / mol, which reflects the energy required to overcome the resistance when the internal structure of the slag changes or flows. When the melting point of the slag changes, the value of the exponential term will also change, thereby adjusting its size, corrected in this way , the melting point of the slag can be more accurately considered for the real-time expected viscosity value the influence of the water flow rate.

[0056] It should be added that and are obtained by extracting the content range of the submerged arc furnace slag corresponding to each slag flushing time from the submerged arc furnace slag treatment control cloud platform, and then matching and comparing the intermediate value of the expected output range of the submerged arc furnace slag with the content range of the submerged arc furnace slag corresponding to each slag flushing time to obtain the slag flushing time period corresponding to the expected output of the submerged arc furnace slag. Then, the time to start the slag flushing pump is set as , and the time after the slag flushing time period corresponding to the expected output of the submerged arc furnace slag is set as .

[0057] S2-2: Extract the length and diameter of the pipeline between the submerged arc furnace and the water quenching tank from the submerged arc furnace slag treatment control cloud platform, and then count the preset control water pressure of the slag flushing pump .

[0058] Furthermore, the counting of the preset control water pressure of the slag flushing pump includes: S2-2-1: Denote the length and diameter of the pipeline between the submerged arc furnace and the water quenching tank as and .

[0059] S2-2-2: Extract the local resistance coefficients of each part of the pipeline between the submerged arc furnace and the water quenching tank from the submerged arc furnace slag treatment control cloud platform, and sum them up to obtain the sum of the local resistance coefficients of the pipeline, denoted as .

[0060] S2-2-3: Count the preset control water pressure of the slag flushing pump , , where , , and are the safety factor, the friction factor, the flow velocity of the slag, and the density of the slag for setting reference respectively.

[0061] It should be added that in is the safety factor for setting reference. Adding 1 is to increase a certain safety margin on the basis of the calculation result to cope with possible unconsidered factors or working condition fluctuations and ensure the safety of system operation. in is the friction factor, which reflects the friction factor characteristics during the flow of the slag in the pipeline due to factors such as friction with the inner wall of the pipeline. is the length of the pipeline between the submerged arc furnace and the water quenching tank. is the diameter of the pipeline, which reflects the influence of the geometric size of the pipeline on the frictional resistance along the way. The longer the length and the smaller the diameter, the greater the frictional resistance along the way. is the sum of the local resistance coefficients of the pipeline, which is obtained by extracting and summing up the local resistance coefficients from the slag treatment control cloud platform of the submerged arc furnace, representing the total resistance generated by local components such as elbows and valves in the pipeline. This part overall describes the resistance characteristics of the pipeline system. in is the slag density, is the slag flow velocity. These two parameters reflect the physical characteristics of the slag itself. Similar to the expression related to kinetic energy in fluid dynamics, here it reflects the influence of the kinetic energy of the slag flow on the required water pressure. The formula multiplies the above several parts and comprehensively considers various factors to calculate the preset control water pressure of the slag flushing pump. .

[0062] It should be added that the preset control water pressure of the slag flushing pump needs to overcome the total pressure loss and also consider a certain safety margin. Generally, the safety margin is 10%-20% of the total pressure loss, that is, the safety factor takes values from 0.1 to 0.2.

[0063] It should be added that The setting method: Build a simulation device similar to the actual submerged arc furnace slag treatment system in the laboratory, use slag or simulation medium similar to the actual situation, and conduct flow experiments under different flow velocities and pipeline conditions. By measuring parameters such as the pressure difference and flow rate at both ends of the pipeline, use the Darcy-Weisbach formula to inversely deduce the frictional resistance coefficient , and after multiple experiments, comprehensively analyze the experimental data and select the intermediate value of the frictional resistance coefficient as the reference frictional resistance coefficient for setting.

[0064] It should be added that The acquisition method: Extract the intermediate value from the expected output range of the slag as the expected output of the slag , and according to the density of the slag, convert the flow rate of the slag into volume flow rate , , the cross-sectional area of the pipeline is , then the slag flow velocity is: .

[0065] It should be added that The acquisition method: Estimate the density of the slag according to the composition matrix of the slag. A commonly used method is the extended form of Kopp's law. Assume that the slag consists of n components, and the particle size of each component is , and the density of each component is , then the density of the slag can be approximately expressed as: .

[0066] S2-3. Take the preset control water volume and preset control water pressure of the slag flushing pump as the preset control instruction of the slag flushing pump.

[0067] In the embodiment of the present invention, by calculating the control instruction of the slag flushing pump according to the expected composition matrix, viscosity, production range and temperature of the slag, accurately adapting to the complex characteristics of the slag and temperature fluctuations, dynamically optimizing the water quenching parameters, the water quenching effect is improved, and the wear of equipment and blockage of pipelines are reduced.

[0068] S3. Execute the preset control instruction of the slag flushing pump, detect the water quenching data when the high-temperature liquid slag contacts with water for water quenching, and dynamically adjust the control instruction of the slag flushing pump.

[0069] It should be added that the water quenching data includes: the actual output of the slag from the submerged arc furnace, the central position coordinates of the slag pile in the water quenching tank, the distribution uniformity of the water quenched slag, the reflux speed of the slag flushing water, the real-time temperature and real-time steam pressure, and the diameter of each water quenched slag in the water quenched slag pool.

[0070] It should be added that the actual output of the slag from the submerged arc furnace is obtained by a thermal imager. Since the slag has a high temperature, the thermal imager can capture the thermal radiation image of the slag, and according to the temperature distribution and shape of the slag in the thermal image, the actual output of the slag is estimated by using image analysis technology.

[0071] It should be added that the central position coordinates of the slag pile are obtained by a three-dimensional laser scanner installed on the top of the water quenched slag pool, and the distribution uniformity of the water quenched slag is obtained by ultrasonic sensors arranged annularly along the inner wall of the water quenching tank, and the thickness distribution of the slag layer is analyzed through the echo signal.

[0072] It should be added that the reflux speed of the slag flushing water is obtained by an electromagnetic flowmeter installed on the reflux pipeline between the cold water tank and the water quenching tank, the interval time between each acquisition in the water quenching tank is obtained by an industrial timer, the real-time temperature of the water quenching tank is obtained by an infrared thermometer installed inside the water quenching tank, and the real-time steam pressure of the water quenching tank is obtained by a pressure sensor installed inside the water quenching tank.

[0073] It should be added that the diameter of each water quenched slag in the water quenched slag pool is obtained by a laser particle size analyzer.

[0074] Exemplarily, the dynamically adjusting the control instruction of the slag flushing pump includes: S3-1. Extract the central position coordinates of the slag pile, the distribution uniformity of the water quenched slag and the reflux speed of the slag flushing water from the water quenching data, and count the angle regulation demand coefficient of the slag flushing pump 。

[0075] Furthermore, the statistical angle regulation demand coefficient of the slag flushing pump includes: S3-1-1. Based on the position coordinates of the center of the slag pile, the deviation coefficient of the slag pile is statistically calculated. 。

[0076] Furthermore, the statistical calculation of the deviation coefficient of the slag pile includes: S3-1-1-1. Extract the position coordinates of the center of the slag groove in the water quenching tank from the submerged arc furnace slag treatment control cloud platform.

[0077] S3-1-1-2. Based on the position coordinates of the center of the slag pile and the center of the slag groove, the deviation distance of the slag pile is obtained through the Euclidean distance formula, denoted as 。

[0078] It should be added that the explanation of the Euclidean distance formula: Suppose there are two points on a two-dimensional plane and , then the formula for the Euclidean distance between points A and B is: 。

[0079] S3-1-1-3. Denote the stacking volume of the slag pile as 。

[0080] S3-1-1-4. Statistically calculate the deviation coefficient of the slag pile , , where and are the permitted deviation distance and the permitted stacking volume for setting reference respectively.

[0081] It should be added that in the submerged arc furnace slag treatment, the ideal state is that the slag is evenly stacked at a suitable position near the slag groove, while in actual production, the slag pile may deviate from the ideal position. The larger the value, the farther the slag pile deviates from the ideal position. Dividing by the permitted deviation distance

[0082] for setting reference, the obtained value is the relative value of the deviation distance, which is used to measure the degree of the actual deviation distance compared with the maximum permitted deviation distance, that is, the deviation situation in terms of position. Considering the factor of stacking volume represents the stacking volume of the slag pile. The amount of stacking volume will also affect the slag treatment process. For example, too much stacking volume may affect the effect of subsequent slag flushing and other operations.

[0083] Dividing , and then comprehensively consider the differences between the slag pile in terms of the two key aspects of position deviation and accumulation amount and the set permitted situation. The The larger the value, the more serious the deviation of the slag pile from the permitted state in terms of position and accumulation amount, that is, the larger the deviation coefficient, which also means that the current state of the slag pile requires more attention and adjustment for better subsequent slag treatment work.

[0084] S3-1-2. Denote the distribution uniformity of the granulated slag and the interval of the set reference distribution uniformity of the granulated slag as and .

[0085] S3-1-3. Statistically calculate the uniformity deviation coefficient , .

[0086] S3-1-4. Based on the reflux velocity of the slag flushing water, statistically calculate the reflux velocity deviation coefficient of the slag flushing water in the same statistical manner as , and denote it as .

[0087] S3-1-5. Select the maximum value from the slag pile deviation coefficient, the granulated slag uniformity deviation coefficient, and the reflux velocity deviation coefficient of the slag flushing water as the angle control demand coefficient of the slag flushing pump, and denote it as .

[0088] S3-2. Obtain the actual output of the submerged arc furnace and the real-time temperature and real-time steam pressure of the granulation tank in the granulated slag data, calculate the temperature deviation ratio and steam pressure deviation ratio of the granulation tank, and simultaneously calculate the water volume control demand coefficient of the slag flushing pump in combination with the expected output of the submerged arc furnace.

[0089] Furthermore, the calculating the temperature deviation ratio and steam pressure deviation ratio of the granulation tank, and simultaneously calculating the water volume control demand coefficient of the slag flushing pump in combination with the expected output of the submerged arc furnace includes: S3-2-1. Extract the reference temperature interval of the granulation tank from the submerged arc furnace slag treatment control cloud platform, and extract the intermediate value as the reference temperature of the granulation tank.

[0090] S3-2-2. Subtract the real-time temperature of the granulation tank from the reference temperature, and take the absolute value of the difference as the real-time temperature difference of the granulation tank.

[0091] S3-2-3. Take the difference between the maximum value and the minimum value in the reference temperature interval of the granulation tank as the reference temperature difference of the granulation tank.

[0092] S3-2-4. Take the ratio of the real-time temperature difference of the granulation tank to the reference temperature difference as the temperature deviation ratio of the granulation tank, and denote it as .

[0093] S3-2-5. Based on the real-time steam pressure of the water quenching tank, the steam pressure deviation ratio of the water quenching tank is statistically obtained in the same way as the statistical method of the temperature deviation ratio of the water quenching tank .

[0094] S3-2-6. Extract the median from the expected output range of the submerged arc furnace as the expected output of the submerged arc furnace, and denote the expected output and the actual output of the submerged arc furnace as and .

[0095] S3-2-7. Statistically obtain the water volume regulation demand coefficient of the slag flushing pump , , and are the temperature deviation ratio and the steam pressure deviation ratio set as references respectively, , and are the weights of the temperature deviation coefficient, the steam pressure deviation coefficient and the output deviation coefficient set as references respectively, , .

[0096] It should be added that for the part related to temperature deviation: is the temperature deviation ratio of the water quenching tank, which is obtained by the ratio of the real-time temperature difference of the water quenching tank to the reference temperature difference, and reflects the degree to which the current temperature of the water quenching tank deviates from the reference temperature range, is the temperature deviation ratio set as a reference, representing the reference value of the temperature deviation under ideal or normal working conditions, calculates the relative change of the real-time temperature deviation ratio relative to the reference temperature deviation ratio, reflecting the influence degree of the current temperature condition on the water volume regulation demand. For the part related to steam pressure deviation: is the steam pressure deviation ratio of the water quenching tank, and its calculation method is similar to that of the temperature deviation ratio, reflecting the degree to which the current steam pressure of the water quenching tank deviates from the reference range, is the steam pressure deviation ratio set as a reference, which is the reference standard for steam pressure deviation, calculates the relative change of the real-time steam pressure deviation ratio relative to the reference steam pressure deviation ratio, indicating the magnitude of the effect of the steam pressure condition on the water volume regulation demand. For the part related to the output deviation of the submerged arc furnace: and are the expected outputs of the submerged arc furnace, obtained by extracting the median from the expected output range, is the actual output of the slag of the submerged arc furnace, reflects the proportional relationship between the expected output and the actual output, and reflects the influence of the output change on the water volume regulation demand of the slag flushing pump. Different outputs result in different amounts of slag produced and different required slag flushing water volumes.

[0097] It should be added that the temperature of the water quenching tank has a significant impact on the physical state of the slag. If the temperature is too high or too low, the properties of the slag such as viscosity will change. For example, when the temperature is too high, the viscosity of the slag decreases but the fluidity is too strong, which may cause problems such as splashing. Therefore, it is necessary to adjust the water volume of the slag sluicing pump in time to maintain a proper water quenching effect to ensure the smooth treatment of the slag. So the weight corresponding to the temperature deviation has the greatest impact on the water volume regulation requirement. The steam pressure in the water quenching tank will affect the efficiency and stability of the water quenching process. When the steam pressure is abnormal, the heat exchange and other links in the water quenching process will be disturbed, which will affect the cooling and treatment effect of the slag. Although it does not directly change the physical properties of the slag like temperature, it is also necessary to adjust the water volume of the slag sluicing pump to balance the operation state of the system to ensure the normal operation of the entire slag sluicing process. Therefore, the weight of the steam pressure is in the middle. The production deviation of the submerged arc furnace will indeed affect the amount of slag generated, and thus affect the water volume requirement of the slag sluicing pump. However, the production change is relatively slow and predictable. The production plan usually has a certain plan and expectation for the production volume. Moreover, in actual production, other auxiliary measures (such as adjusting the production rhythm, etc.) can be used to jointly cope with the production change, rather than simply relying on the adjustment of the water volume of the slag sluicing pump. Compared with the immediate impact of temperature and steam pressure, the impact of production deviation on the water volume regulation requirement coefficient is relatively small. Therefore, its weight is the smallest. Therefore, set , for the convenience of analysis, it can be specifically taken as 0.5, it can be specifically taken as 0.3, it can be specifically taken as 0.2.

[0098] S3-3. Extract the diameters of the water quenched slag in the water quenching slag pool from the water quenching data and sort them, and take a specific particle size value after sorting. At the same time, calculate the average diameter and the standard deviation of the particle size distribution, and then statistically calculate the water pressure regulation requirement coefficient of the slag sluicing pump .

[0099] Furthermore, the statistical calculation of the water pressure regulation requirement coefficient of the slag sluicing pump includes: S4-3-1. Sort the diameters of the water quenched slag in the water quenching slag pool from large to small, and respectively extract the particle size values corresponding to when the particle size reaches 10%, 50% and 90%, and respectively record them as , and .

[0100] It should be added that in the statistics of the particle size distribution, , and are parameters used to describe the particle size characteristics of the particles, represents the particle size value corresponding to when the cumulative particle size reaches 10% after sorting all the water quenched slag particle sizes from small to large, that is, 10% of the water quenched slag particle sizes are less than or equal to , Also known as the median particle size, it refers to the particle size value corresponding to when the cumulative particle size reaches 50%, meaning that half (50%) of the water-quenched slag particle sizes are less than or equal to , which reflects the intermediate level of the particle sizes, represents the particle size value corresponding to when the cumulative particle size reaches 90%, that is, 90% of the water-quenched slag particle sizes are less than or equal to , and the remaining 10% of the slag particle sizes are greater than , and this value reflects the situation of larger particle size particles.

[0101] S3-3-2. Calculate the average of the diameters of each water-quenched slag to obtain the average diameter, denoted as , and at the same time, statistically calculate the standard deviation of the particle size distribution of the water-quenched slag through the standard deviation formula, denoted as .

[0102] S3-3-3. Statistically calculate the water pressure regulation demand coefficient of the slag flushing pump , , is the median particle size of the slag set as a reference, , and are respectively the weights of the set reference particle size deviation coefficient, distribution uniformity coefficient, and proportion of coarse particles, , .

[0103] It should be added that is set as follows: Extract the particle size data of each water-quenched slag in the historical treatment process of the submerged arc furnace slag from the submerged arc furnace slag treatment control cloud platform, calculate the median particle size of the particle size data, and after statistically analyzing multiple groups of particle size data, extract the mode as the set reference median particle size.

[0104] It should be added that calculates the relative deviation between the current median particle size of the water-quenched slag and the median particle size of the reference slag, this item represents the influence of the particle size deviation on the water pressure regulation demand coefficient. The greater the deviation, the greater the difference between the current slag particle size characteristics and the expectation, and it may be necessary to adjust the water pressure, and the contribution to the water pressure regulation demand coefficient is also greater. is the standard deviation of the particle size distribution of the water-quenched slag, reflecting the degree of dispersion of the particle sizes; is the average diameter, reflecting the uniformity of the particle size distribution. The larger this ratio, the more uneven the particle size distribution, this item represents the influence of the particle size distribution uniformity on the water pressure regulation demand coefficient. The more uneven the distribution, the more necessary it is to adjust the water pressure to adapt to the slag with different particle sizes, and the greater the influence on the water pressure regulation demand coefficient. The ratio of ​ It can reflect the proportion of coarse particles (particles with larger particle sizes) among all particles. The larger this ratio is, the higher the proportion of coarse particles. This item represents the influence of the proportion of coarse particles on the demand coefficient for water pressure regulation. The higher the proportion of coarse particles, the greater the water pressure required to flush the slag, and the greater the contribution to the demand coefficient for water pressure regulation.

[0105] It should be added that the median particle size of water-quenched slag reflects the overall level of the slag particle size. The change in the median particle size directly affects the fluidity of the slag and the frictional resistance to the pipeline. For example, when the median particle size increases, the slag particles become relatively coarser, and a greater water pressure is required to push them during flow in the pipeline, which has a more direct and significant impact on the demand for water pressure regulation. Therefore, the particle size deviation factor plays a key role in determining the water pressure of the slag flushing pump, so its weight is the largest. The uniformity of the slag particle size distribution will affect its flow state in the pipeline. When the particle size distribution is uneven, particles of different sizes are prone to stratification, blockage, etc., which in turn affects the slag flushing effect, and the water pressure needs to be adjusted to ensure smooth slag flushing. Although it is not as direct as the influence of the change in the median particle size, it also plays an important role in water pressure regulation, so its weight is in the middle. The proportion of coarse particles has a certain impact on the water pressure. A higher proportion of coarse particles may require a greater water pressure. However, in the actual slag flushing process, the influence of the proportion of coarse particles is relatively more indirect, and it can be assisted by other means (such as adjusting the slag flushing time, etc.). Compared with the median particle size and the uniformity of the particle size distribution, its influence degree on the demand coefficient for water pressure regulation is relatively small, so its weight is the smallest. Therefore, set , for the convenience of analysis, it can be specifically taken as 0.5, it can be specifically taken as 0.3, it can be specifically taken as 0.2.

[0106] In the embodiment of the present invention, by statistically calculating the demand coefficient for water pressure regulation of the slag flushing pump according to the diameters of the water-quenched slags in the water-quenched slag pool, the slag flushing requirements of slags with different particle sizes are accurately adapted, avoiding problems such as insufficient slag flushing or excessive erosion of equipment due to improper water pressure, thereby improving the slag flushing efficiency and quality.

[0107] S3-4. Take the condition that the angle regulation demand coefficient of the slag flushing pump is greater than the set reference angle regulation demand coefficient as condition 1, and take the condition that the water volume regulation demand coefficient of the slag flushing pump is greater than the set reference water volume regulation demand coefficient as condition 2, and at the same time take the condition that the water pressure regulation demand coefficient of the slag flushing pump is greater than the set reference water pressure regulation demand coefficient as condition 3.

[0108] S3-5. When condition 1 is satisfied, take the angle regulation as the current control instruction of the slag flushing pump.

[0109] S3-6. When condition 2 is satisfied, take the water volume regulation as the current control instruction of the slag flushing pump.

[0110] S3-7. When condition 3 is satisfied, the water pressure regulation will be carried out as the current control instruction for the slag scouring pump.

[0111] S3-8. When none of conditions 1, 2, and 3 are satisfied, the current regulation will be maintained as the current control instruction for the slag scouring pump.

[0112] In the embodiment of the present invention, by detecting the water quenching data, analyzing the angle regulation demand coefficient, water volume regulation demand coefficient, and water pressure regulation demand coefficient of the slag scouring pump, and determining the current control instruction of the slag scouring pump, a dynamic response relationship between the water quenching data and the adjustment instruction is established, effectively ensuring the consistency of the slag water quenching effect and realizing the refined control of slag water quenching.

[0113] S4. After the water quenching is completed, the cold water is separated through precipitation, cooling tower cooling, and inclined plate sedimentation device and recycled.

[0114] Embodiment Two

[0115] As an implementation manner of the present invention, referring to Figure 3 , the flow chart of the water quenching treatment of the submerged arc furnace slag, wherein the submerged arc furnace is the core production equipment, and liquid slag is generated by high-temperature smelting of ores (such as ferroalloys, calcium carbide, etc.).

[0116] The function of the water quenching tank is rapid cooling: the high-temperature liquid slag is directly contacted with high-pressure water, and the heat is carried away by the latent heat of vaporization of water, so that the slag is instantly cooled and broken.

[0117] The function of the water quenched slag pond is slag-water separation: the quenched slag particles precipitate at the bottom of the pond, and the hot water flows to the hot water pond through the hot water pump.

[0118] The function of the hot water pond is to store hot water: collect the hot water overflowing from the water quenching tank (temperature about 60 - 90 °C), and at the same time have a heat buffering function to balance the water temperature fluctuation of the system and avoid directly affecting the load of the cooling tower.

[0119] The function of the cooling tower is cooling and circulation: cool the hot water in the hot water pond to 20 - 30 °C through evaporation cooling or forced ventilation for recycling in the cold water pond.

[0120] The function of the cold water pond is to store cold water: store the cooling water cooled by the cooling tower to provide a stable low-temperature water source for the water quenching tank.

[0121] The function of the makeup water tank is to supplement water source: supplement the water volume evaporated, leaked, or taken away by the slag to maintain the water volume balance of the system.

[0122] In the water quenching operation process of the submerged arc furnace, first, the submerged arc furnace smelts ore at high temperature to produce liquid slag. These high-temperature liquid slags flow out of the submerged arc furnace and directly enter the connected water quenching tank.

[0123] There are slag flushing pumps arranged in the water quenching tank. When the high-temperature slag enters the water quenching tank, a large amount of high-pressure water jets out from the nozzles of the slag flushing pumps and comes into instant contact with the slag. The latent heat of vaporization of water quickly takes away the heat of the slag, causing the slag to cool rapidly and break into small particles, realizing the granulation of the slag.

[0124] The slag-water mixture after water quenching flows from the water quenching tank into the water quenched slag pond. In the water quenched slag pond, the slag and water are separated. The slag particles settle to the bottom of the pond due to gravity, while the upper-layer hot water flows into the hot water pond through the hot water pump.

[0125] The hot water collected in the hot water pond has a relatively high temperature and is then transported to the cooling tower. The cooling tower reduces the temperature of the hot water through methods such as evaporative heat dissipation and forced ventilation. The cooled water flows into the cold water pond from the cooling tower after filtration operation.

[0126] The cold water pond plays a role in storage and buffering, continuously providing low-temperature circulating water for the water quenching tank to ensure the stable progress of the water quenching process.

[0127] With the operation of the entire water quenching system, part of the water will be lost due to evaporation, being carried away with the slag, etc. At this time, the makeup water tank comes into play. The makeup water tank replenishes water to the cold water pond to maintain the water volume balance in the system and ensure that the water quenching operation can run continuously for a long time.

[0128] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A control method for treating the slag of a submerged arc furnace based on the water quenching method, characterized in that: The method includes: S1. Obtain the contents of various chemical elements and the dynamic viscosity in the raw materials, and input the raw material data into the Takagi-Sugeno fuzzy inference metallurgical model to obtain the expected composition matrix of the slag, the expected viscosity value, and the expected output range; S2. Obtain the temperatures of the temperature measurement points, and calculate the preset control instruction of the slag flushing pump through an adaptive control algorithm; Calculate the preset control instruction of the slag flushing pump through an adaptive control algorithm, including: Based on the temperatures of the temperature measurement points, the preset control water volume of the slag flushing pump is statistically calculated ; Extract the length and diameter of the pipeline between the submerged arc furnace and the water quenching tank from the submerged arc furnace slag treatment control cloud platform, and then count the preset control water pressure of the slag flushing pump ; Take the preset control water volume and the preset control water pressure of the slag flushing pump as the preset control instruction of the slag flushing pump; Statistics , including: Denote the temperature of temperature measurement points as , being the temperature measurement point number, ; Calculate the average temperature of each temperature measurement point to obtain the average temperature inside the submerged arc furnace, denoted as , and denote the expected viscosity value of the slag as , represents time; , , , and are respectively the set reference proportionality coefficient, integral coefficient, slag flushing start time, and slag flushing end time, is the number of temperature measurement points; Statistics , including: The length and diameter of the pipeline between the submerged arc furnace and the water quenching tank are respectively denoted as and ; Extract the local resistance coefficients of the pipeline between the submerged arc furnace and the water quenching tank from the submerged arc furnace slag treatment control cloud platform, sum them up, and obtain the sum of the local resistance coefficients of the pipeline, denoted as ; , where , , and are respectively the safety factor, the friction factor along the way, the slag flow rate and the slag density set as references S3. Execute the preset control instruction of the slag flushing pump, detect the water quenching data when the high-temperature liquid slag contacts with water for water quenching, and dynamically adjust the control instruction of the slag flushing pump; The dynamically adjusting the control instruction of the slag flushing pump includes: Extract the central position coordinates of the slag pile, the distribution uniformity of the granulated slag, and the reflux velocity of the slag flushing water from the water quenching data, and count the angle regulation demand coefficient of the slag flushing pump ; Obtain the actual output of the submerged arc furnace and the real-time temperature and real-time steam pressure of the water quenching tank in the water quenching data, calculate the temperature deviation ratio and steam pressure deviation ratio of the water quenching tank, and at the same time calculate the water volume regulation demand coefficient of the slag sluicing pump in combination with the expected output of the submerged arc furnace ; Extract the diameters of the granulated slag in the granulation slag pond from the water quenching data, sort them, and take a specific particle size value after sorting. At the same time, calculate the average diameter and the standard deviation of the particle size distribution, and then statistically calculate the water pressure regulation demand coefficient of the slag flushing pump ; Take the angle regulation demand coefficient greater than the set reference as Condition 1, and take the water volume regulation demand coefficient greater than the set reference as Condition 2, and at the same time take the water pressure regulation demand coefficient greater than the set reference as Condition 3; When condition 1 is satisfied, take the angle regulation as the current control instruction of the slag flushing pump; When condition 2 is satisfied, take the water volume regulation as the current control instruction of the slag flushing pump; When condition 3 is satisfied, take the water pressure regulation as the current control instruction of the slag flushing pump; When none of conditions 1, 2, and 3 are satisfied, take the maintenance of the current regulation as the current control instruction of the slag flushing pump; S4. After the water quenching is completed, separate the cold water through precipitation, cooling tower cooling, and inclined plate precipitation device and recycle it.

2. The control method for treating the slag of a submerged arc furnace based on the water quenching method according to claim 1, wherein: The inputting the raw material data into the Takagi-Sugeno fuzzy inference metallurgical model to obtain the expected composition matrix of the slag, the expected viscosity value, and the expected output range includes: Normalize the contents of various chemical elements to obtain the proportion of each chemical element, and define the membership function of its fuzzy subset; Based on the membership degrees of the chemical elements belonging to the fuzzy subsets, match the corresponding rules in the pre-set fuzzy inference rule base, and perform inference calculations according to the membership degrees of the antecedents and the functional relationships of the consequents to obtain the fuzzy inference results of each chemical component; Multiply the fuzzy inference results of each chemical component by the preset weights of each chemical component to obtain the expected contents of each chemical component, and then generate the composition expected matrix of the slag.

3. A control method for treating slag of submerged arc furnace based on water quenching method according to claim 1, characterized in that: The statistical angle regulation demand coefficient of the slag flushing pump includes: Based on the position coordinates of the center of the slag heap, the deviation coefficient of the slag heap is statistically calculated ; The distribution uniformity of the water-quenched slag and the distribution uniformity interval of the water-quenched slag set as a reference are respectively denoted as and ; Statistical uniformity deviation coefficient of water-quenched slag , ; Based on the return flow rate of the slag flushing water, in accordance with the same statistical method, the return flow rate deviation coefficient of the slag flushing water is statistically obtained and denoted as ; Select the maximum value from the slag heap deviation coefficient, the uniformity deviation coefficient of granulated slag, and the backflow speed deviation coefficient of slag flushing water as the angle control demand coefficient of the slag flushing pump, denoted as .

4. A control method for treating slag of submerged arc furnace based on water quenching method according to claim 3, characterized in that: The statistical deviation coefficient of the slag heap includes: Extract the position coordinates of the center of the slag groove in the water quenching tank from the ore thermal furnace slag treatment control cloud platform; Based on the position coordinates of the center of the slag heap and the center of the slag ditch, the deviation distance of the slag heap is obtained through the Euclidean distance formula, denoted as ; Record the stacking amount of the slag pile as ; Statistical deviation coefficient of the slag heap , , where and are the permitted deviation distance and permitted stacking volume of the set reference respectively.

5. A control method for treating ferromanganese slag based on the water quenching method according to claim 1, characterized in that: Calculate the water volume regulation demand coefficient of the slag flushing pump, including: Extract the reference temperature range of the water quenching tank from the ore thermal furnace slag treatment control cloud platform, and extract the intermediate value as the reference temperature of the water quenching tank; Subtract the real-time temperature of the water quenching tank from the reference temperature, and take the absolute value of the difference as the real-time temperature difference of the water quenching tank; Take the difference between the maximum value and the minimum value in the reference temperature range of the water quenching tank as the reference temperature difference of the water quenching tank; The ratio of the real-time temperature difference of the water quenching tank to the reference temperature difference is taken as the temperature deviation ratio of the water quenching tank, denoted as ; Based on the real-time steam pressure of the water quenching tank, the steam pressure deviation ratio of the water quenching tank is statistically obtained in the same way as the statistical method of the temperature deviation ratio of the water quenching tank ; Extract the median value from the expected output range of the submerged arc furnace as the expected output of the submerged arc furnace, and denote the expected output and the actual output of the submerged arc furnace as and ; Calculate the water volume regulation demand coefficient of the slag flushing pump , , and are the temperature deviation ratio and steam pressure deviation ratio set as references respectively, , and are the weights of the temperature deviation coefficient, steam pressure deviation coefficient and output deviation coefficient set as references respectively, , .

6. The control method for treating the slag of a submerged arc furnace based on the water quenching method according to claim 1, wherein: The statistical water pressure regulation demand coefficient of the slag flushing pump includes: Sort the diameters of the water-quenched slag in the water-quenched slag pool from largest to smallest, and extract the corresponding particle size values when the particle sizes reach 10%, 50% and 90% respectively, and record them as 、 and ; Calculate the mean value of the diameters of each water-quenched slag to obtain the average diameter, denoted as , and at the same time, use the standard deviation formula to calculate the standard deviation of the particle size distribution of the water-quenched slag, denoted as ; Statistical coefficient of water pressure regulation demand for slag flushing pump , , is the median particle size of slag for setting reference, , and are respectively the weight of particle size deviation coefficient, distribution uniformity coefficient and proportion of coarse particles for setting reference, , .

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