Converter smelting method for reducing unit consumption of lime

Abstract

The invention relates to the technical field of converter smelting, and discloses a converter smelting method for reducing unit consumption of lime, which comprises the following steps: step 1, before molten iron flows into a converter through a ladle, detecting phosphorus content and sulfur content in the molten iron by using a laser-induced breakdown spectroscopy analyzer, the detected molten iron component data is acquired by a data acquisition unit integrated in the spectrum analyzer and is transmitted to a central data processing system through an industrial communication protocol, and the central data processing system comprises a storage module used for receiving and storing the molten iron component data. Through an optimization model module in the central data processing system, lime demand is comprehensively calculated in combination with multiple parameters of molten iron components, slag alkalinity, fluidity and molten steel temperature, and is decomposed into specific feeding proportions of all stages, so that accurate management of lime feeding is realized, excessive and insufficient feeding is avoided, and the quality of lime is improved. The unit consumption of lime is obviously reduced; and the dephosphorization and desulfurization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of converter smelting, and specifically to a converter smelting method for reducing the unit consumption of lime. Background Art

[0002] Converter smelting is an important steel smelting process, which is widely used in the processes of dephosphorization, desulfurization and impurity removal of molten steel. In converter smelting, lime is a commonly used slag former, and its main function is to react chemically with acidic impurities (such as phosphorus and sulfur) in hot metal and slag to form a stable molten slag, and further remove phosphorus and sulfur in the molten steel. However, in the traditional converter smelting method, the feeding process of lime lacks precise control, and usually adopts a one-time and empirical feeding method, thus there are the following problems:

[0003] The compositions of hot metal and slag fluctuate between different heats. The traditional lime feeding method cannot be accurately adjusted according to the real-time states of hot metal and slag, which easily leads to insufficient and excessive lime feeding, thereby affecting the dephosphorization and desulfurization efficiency and increasing the unit consumption of lime.

[0004] The decomposition rate and reaction rate of lime are affected by factors such as slag basicity, fluidity and temperature, and the traditional feeding method fails to effectively monitor and utilize parameters for dynamic adjustment, resulting in low lime utilization rate, prolonged reaction time and reduced smelting efficiency.

[0005] Excessive and insufficient lime will directly affect the basicity and fluidity of the slag. Both too high and too low slag basicity will weaken the ability to adsorb impurities and affect the dephosphorization and desulfurization effects. In addition, the unstable slag state will increase the slag volume, resulting in an increase in the cost of waste slag treatment and having an adverse impact on environmental protection.

[0006] After the lime feeding in the traditional method is completed, there is a lack of real-time evaluation and feedback on the feeding effect, and the subsequent feeding plan cannot be optimized according to the actual reaction conditions in each stage, further leading to waste of resources and increased smelting costs.

[0007] Therefore, those skilled in the art provide a converter smelting method for reducing the unit consumption of lime to solve the above-mentioned problems. Summary of the Invention

[0008] In view of the deficiencies of the prior art, the present invention provides a converter smelting method for reducing the unit consumption of lime to solve the problems raised in the above background art.

[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: A converter smelting method for reducing the unit consumption of lime, comprising:

[0010] Step 1: Before the molten iron flows into the converter through the ladle, the phosphorus content and sulfur content in the molten iron are detected by a laser induced breakdown spectrometer. The detected molten iron composition data are collected by a data acquisition unit integrated in the spectrometer and transmitted to a central data processing system through an industrial communication protocol. The central data processing system includes a storage module for receiving and storing the molten iron composition data to provide input for subsequent slag state detection and lime demand calculation;

[0011] Step 2: The alkalinity value of the slag is detected by an electrochemical alkalinity sensor installed on the side wall of the converter, and the fluidity of the slag is measured by a rheometer. In addition, the temperature of the molten steel is detected by an infrared thermometer installed on the top of the converter. The detected slag alkalinity value, fluidity data and molten steel temperature are collected by the data acquisition unit of the sensor, and then transmitted to the storage module of the central data processing system using the industrial communication protocol, and integrated with the stored molten iron composition data to provide input for the calculation of lime demand;

[0012] Step 3, input the molten iron composition data stored in the storage module in the central data processing system in step 1 and the slag basicity value, fluidity data and molten steel temperature transmitted to the storage module in step 2 into the optimization model module in the central data processing system. The optimization model module is constructed based on the historical furnace data, and the lime demand in the converter smelting process is calculated by a multi-parameter coupling method. The lime demand refers to the total amount of lime required in the initial slag formation stage, the main slag formation stage and the final slag adjustment stage. At the same time, the optimization model module decomposes the total lime demand into specific feeding ratios for each stage, wherein the initial slag formation stage is 30% to 40%, the main slag formation stage is 50% to 60%, and the final slag adjustment stage is 10% to 20%. The calculation results are transmitted to the feeding control module through the optimization model module of the data processing system;

[0013] Step 4: According to the lime feeding ratio of each stage calculated and transmitted by the feeding control module in the central data processing system in step 3, a feeding amount distribution instruction is sent to the multi-stage screw feeding equipment through the PLC control system. The PLC control system is the upper control unit of the multi-stage screw feeding equipment. The multi-stage screw feeding equipment is used to transport the lime to the converter in stages according to a preset ratio. The feeding control module dynamically adjusts the feeding ratio of each stage in combination with the real-time monitoring data in step 2. The dynamic adjustment is based on the change in slag basicity and fluidity. At the same time, the adjusted feeding instruction is updated to the operating parameters of the multi-stage screw feeding equipment through the PLC control system;

[0014] Step 5: Using the feeding amount determined by the multi-stage spiral feeding equipment in step 4, the lime transportation and feeding operation are completed in stages. During the feeding process of each stage, the central data processing system monitors the slag basicity and fluidity data in real time through the feeding recording module, and records the completion of the feeding at each stage;

[0015] Step 6: After the staged lime feeding operation in Step 5 is completed, the PLC control system analyzes the completion status of the feeding in each stage based on the slag basicity value and fluidity data monitored in real time through the analysis module of the central data processing system. Combining with the lime demand calculated by the optimization model module in Step 3, the lime feeding amount and feeding rate in the remaining stages are optimized and adjusted. At the same time, the operating parameters of the multi-stage screw feeding equipment are updated, and the adjusted feeding instruction is sent to the multi-stage screw feeding equipment through the PLC control system, finally completing the whole process of dynamic lime feeding in each stage.

[0016] Preferably, in Step 1, the detected phosphorus content represents the concentration of phosphorus impurities in the hot metal, and the sulfur content represents the concentration of sulfur impurities in the hot metal. The phosphorus content is used to determine the lime addition amount in the converter dephosphorization stage, and the sulfur content is used to determine the lime addition amount in the converter desulfurization stage.

[0017] Preferably, in Step 2, the slag basicity value is calculated by the following formula:

[0018]

[0019] where A slag is the slag basicity value, [CaO] represents the mass fraction of calcium oxide in the slag, and [SiO2] represents the mass fraction of silicon dioxide in the slag;

[0020] In Step 2, the detected slag fluidity is inversely proportional to the slag viscosity. Among them, the slag basicity value is calculated from the torque measured by the rheometer and the slag shear rate. The slag fluidity is used to evaluate the adsorption effect of the slag on phosphorus and sulfur impurities in the molten steel.

[0021] Preferably, in Step 3, the formula for the optimization model module to calculate the lime demand is:

[0022]

[0023] where Q lime represents the total lime demand, [P iron represents the phosphorus content in the hot metal, [S iron represents the sulfur content in the hot metal, A slag is the slag basicity value, A target represents the target value of the slag basicity, V slag represents the real-time fluidity of the slag, V target represents the target value of the slag fluidity,

[0024] k1, k2, and k3 are empirical adjustment coefficients;

[0025] The value ranges of the empirical coefficients k1, k2, and k3 are:

[0026] k1 = 1.2 ± 0.2, k2 = 0.5 ± 0.1, k3 = 0.8 ± 0.1,

[0027] The optimization model module adapts the calculation results to the compositions of hot metal and slag in different heats by dynamically adjusting the empirical coefficients.

[0028] Preferably, in step 3, the optimization model module for the lime requirement superimposes and calculates the thermodynamic equilibrium relationship between the slag and lime reactions according to the dynamic reaction conditions in different stages of converter smelting, and calculates the target basicity value for each stage through the following formula:

[0029]

[0030] where A stage represents the target basicity value for different stages, [P target is the target dephosphorization efficiency, [P initial is the initial phosphorus concentration in the hot metal, [S target is the target desulfurization efficiency, [S initial is the initial sulfur concentration in the hot metal, A initial is the initial slag basicity value, and k4 and k5 are empirical adjustment coefficients.

[0031] Preferably, in step 4, the feeding control module combines the target basicity value and the real-time monitored slag basicity value, and dynamically adjusts the lime feeding rate for each stage through the following formula:

[0032]

[0033] where R lime represents the current lime feeding rate, A target represents the target basicity value, A current represents the real-time detected slag basicity value, t reaction represents the remaining reaction time, and k6 is the rate adjustment coefficient;

[0034] The feeding control module adjusts the operating parameters of the multi-stage spiral feeding equipment by calculating the feeding rate in real time.

[0035] Preferably, in step 5, the feeding record module of the central data processing system dynamically updates the feeding completion status for each stage according to the feeding rate and cumulative feeding amount for each stage, and generates time-series-based slag state change data. The change data includes the change curves of slag basicity, fluidity, and dephosphorization and desulfurization efficiencies, and the change data is used by the analysis module of the central data processing system to evaluate the reaction completion degree for each stage.

[0036] Preferably, in step 6, the analysis module optimizes and adjusts the feeding rate and lime feeding amount in the remaining stages based on the slag state change data in step 5, and recalculates the lime demand in the remaining stages through the following formula:

[0037] Q lime-remain =Q lime-total -Q lime-used ,

[0038] wherein, Q lime-remain represents the lime demand in the remaining stages, Q lime-total represents the total lime demand calculated by the optimization model module in step 3, and Q lime-total represents the cumulative feeding amount in the completed stages.

[0039] Preferably, in steps 5 and 6, the slag state change data includes the slag temperature change curve, and the slag temperature compensates the lime decomposition rate through the following formula:

[0040]

[0041] wherein, R lime-comp represents the temperature compensation value of the lime decomposition rate, T slag-current is the slag temperature detected in real time, T lime-optimal is the optimal temperature of the lime decomposition reaction, t remain is the remaining reaction time, and k7 is the temperature compensation coefficient.

[0042] Preferably, the optimization model module, feeding control module, feeding record module and analysis module of the central data processing system perform data processing based on artificial intelligence technology, and dynamically update the value ranges of various parameters through a learning model based on historical heat data. Among them, the learning model forms empirical rules adapted to different heat conditions by training the relationships between hot metal composition, slag state and lime demand of historical heats.

[0043] The present invention provides a converter smelting method for reducing lime unit consumption. It has the following beneficial effects:

[0044] 1. Through the optimization model module in the central data processing system of the present invention, the lime demand is comprehensively calculated by combining multiple parameters such as hot metal composition, slag basicity, fluidity and molten steel temperature, and decomposed into specific feeding ratios for each stage, realizing precise management of lime feeding, obtaining the effects of avoiding excessive and insufficient feeding, significantly reducing lime unit consumption and improving the dephosphorization and desulfurization efficiency.

[0045] 2. The present invention dynamically adjusts the lime feeding rate at each stage by means of the feeding control module in combination with the slag basicity and fluidity data monitored in real time, realizes the real-time matching of lime feeding with the actual reaction requirements, and obtains the effects of reducing lime waste, optimizing the slag reaction rate and reducing the smelting energy consumption.

[0046] 3. The present invention dynamically adjusts the lime decomposition rate by monitoring the slag temperature in real time and combining with the temperature compensation formula, realizes the improvement of the lime reaction depth and reaction efficiency under different temperature conditions, and obtains the effects of further reducing lime consumption, improving the lime utilization efficiency and reducing the smelting cost.

[0047] 4. The present invention generates the slag state change data based on time series by using the feeding record module of the central data processing system, and optimizes and adjusts the lime demand and feeding rate in the remaining stages in combination with the analysis module, realizes the dynamic and precise control of lime feeding at each stage, and obtains the effects of more stable slag state, more efficient impurity removal reaction and reduced waste slag amount. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] The present invention will be described in detail below with reference to the accompanying drawings:

[0051] Embodiment:

[0052] Please refer to the attached Figure 1 , the embodiment of the present invention provides a converter smelting method for reducing the unit consumption of lime, including:

[0053] Step 1. Before the molten iron flows into the converter through the ladle, the phosphorus content and sulfur content in the molten iron are detected by using a laser-induced breakdown spectroscopy analyzer. The detected molten iron composition data is collected by the data acquisition unit integrated in the spectroscopy analyzer and transmitted to the central data processing system through an industrial communication protocol. The central data processing system includes a storage module for receiving and storing the molten iron composition data, providing input for subsequent slag state detection and lime demand calculation;

[0054] Step 2: The alkalinity value of the slag is detected by an electrochemical alkalinity sensor installed on the side wall of the converter, and the fluidity of the slag is measured by a rheometer. In addition, the temperature of the molten steel is detected by an infrared thermometer installed on the top of the converter. The detected slag alkalinity value, fluidity data and molten steel temperature are collected by the data acquisition unit of the sensor, and then transmitted to the storage module of the central data processing system using the industrial communication protocol, and integrated with the stored molten iron composition data to provide input for the calculation of lime demand;

[0055] Step 3, input the molten iron composition data stored in the storage module in the central data processing system in step 1 and the slag basicity value, fluidity data and molten steel temperature transmitted to the storage module in step 2 into the optimization model module in the central data processing system. The optimization model module is constructed based on the historical furnace data, and the lime demand in the converter smelting process is calculated by a multi-parameter coupling method. The lime demand refers to the total amount of lime required in the initial slag formation stage, the main slag formation stage and the final slag adjustment stage. At the same time, the optimization model module decomposes the total lime demand into specific feeding ratios for each stage, wherein the initial slag formation stage is 30% to 40%, the main slag formation stage is 50% to 60%, and the final slag adjustment stage is 10% to 20%. The calculation results are transmitted to the feeding control module through the optimization model module of the data processing system;

[0056] Step 4: According to the lime feeding ratio of each stage calculated and transmitted by the feeding control module in the central data processing system in step 3, a feeding amount distribution instruction is sent to the multi-stage screw feeding equipment through the PLC control system. The PLC control system is the upper control unit of the multi-stage screw feeding equipment. The multi-stage screw feeding equipment is used to transport the lime to the converter in stages according to a preset ratio. The feeding control module dynamically adjusts the feeding ratio of each stage in combination with the real-time monitoring data in step 2. The dynamic adjustment is based on the change in slag basicity and fluidity. At the same time, the adjusted feeding instruction is updated to the operating parameters of the multi-stage screw feeding equipment through the PLC control system;

[0057] Step 5: Using the feeding amount determined by the multi-stage spiral feeding equipment in step 4, the lime transportation and feeding operation are completed in stages. During the feeding process of each stage, the central data processing system monitors the slag basicity and fluidity data in real time through the feeding recording module, and records the completion of the feeding at each stage;

[0058] Step 6. After the staged lime feeding operation in Step 5 is completed, the PLC control system analyzes the completion status of the feeding in each stage based on the slag basicity value and fluidity data monitored in real time through the analysis module of the central data processing system. Combining with the lime demand calculated by the optimization model module in Step 3, the lime feeding amount and feeding rate in the remaining stages are optimized and adjusted. At the same time, the operating parameters of the multi-stage screw feeding equipment are updated, and the adjusted feeding instruction is sent to the multi-stage screw feeding equipment through the PLC control system, finally completing the whole process of dynamic lime feeding in each stage.

[0059] Benefits of Step 1: Realize the real-time and accurate detection of the molten iron composition, dynamically obtain the phosphorus and sulfur concentration data, provide reliable input for calculating the lime demand in the subsequent dephosphorization and desulfurization stages, improve the efficiency and accuracy of molten iron detection, lay a foundation for the precise control of lime feeding, and avoid the problems of excessive and insufficient feeding caused by empirical estimation.

[0060] Benefits of Step 2: Dynamically monitor the parameters in the converter (basicity value, fluidity and molten steel temperature). These parameters directly affect the decomposition rate and reaction efficiency of lime, provide accurate slag and molten steel state data for the subsequent optimization model, enable the accurate calculation of lime demand, ensure the reasonable feeding ratio in each stage, help to detect and adjust abnormal slag states in time, and avoid affecting the dephosphorization and desulfurization effects due to unqualified slag performance.

[0061] Benefits of Step 3: Calculate the total lime demand by combining multiple parameters (molten iron phosphorus and sulfur concentrations, slag basicity value, fluidity and molten steel temperature), ensure the scientificity and rationality of the feeding plan. The setting of the staged feeding ratio (30% - 40% for the initial slag, 50% - 60% for the main slag, 10% - 20% for the final slag) enables the lime feeding to accurately match the actual reaction requirements in each stage, and avoid problems of resource waste and excessive feeding.

[0062] Benefits of Step 4: Realize the real-time adjustment of the lime feeding amount, dynamically optimize the feeding rate according to the change of the slag state, make the decomposition and reaction of lime efficient. The multi-stage screw feeding equipment ensures the uniformity and accuracy of feeding, avoids the reduction of reaction efficiency and resource waste caused by too fast and too slow feeding. The linkage between the feeding control module and the PLC system enables the feeding parameters to quickly respond to the change of monitoring data, improving the controllability and stability of the smelting process.

[0063] Benefits of Step 5: The feeding record module can record the data in the feeding process in real time, provide a basis for subsequent feeding adjustment and result analysis. By recording the completion status of feeding, feeding abnormalities can be detected and adjusted in time, further improving the accuracy of lime feeding and the stability of smelting, helping to accumulate furnace data, optimize the subsequent feeding plan, and form a more intelligent feeding control system.

[0064] Benefits of Step 6: By analyzing the module in combination with real-time monitoring data and historical furnace data, dynamically evaluate and adjust the feeding effect, making the lime feeding in the remaining stages more accurate, further reducing the unit consumption of lime, achieving closed-loop control, feeding the real-time data back into the feeding system, optimizing the subsequent feeding rate and parameter settings, ensuring that the slag basicity and fluidity during the smelting process are always within the optimal range, finally completing the whole process of accurate feeding, making the lime reaction in each stage more efficient, while reducing the amount of waste slag, reducing the environmental protection pressure and economic cost of smelting.

[0065] The phosphorus content detected in Step 1 represents the concentration of phosphorus impurities in the hot metal, and the sulfur content represents the concentration of sulfur impurities in the hot metal. The phosphorus content is used to determine the amount of lime added in the converter dephosphorization stage, and the sulfur content is used to determine the amount of lime added in the converter desulfurization stage.

[0066] Step 1 provides a scientific basis for calculating the lime demand in the subsequent converter dephosphorization and desulfurization stages by detecting the concentrations of phosphorus and sulfur impurities in the hot metal, solving the problem of inaccurate feeding in traditional methods. By using the phosphorus content to determine the amount of lime added in the dephosphorization stage and the sulfur content to determine the amount of lime added in the desulfurization stage, it significantly improves the lime utilization rate, reduces the smelting cost, and reduces the generation amount of waste slag, provides technical support for a green and efficient smelting process, lays the foundation for accurate feeding and dynamic adjustment, and provides guarantee for realizing the optimal control of the whole smelting process.

[0067] In Step 2, the slag basicity value is calculated by the following formula:

[0068]

[0069] where A slag is the slag basicity value, [CaO] represents the mass fraction of calcium oxide in the slag, and [SiO2] represents the mass fraction of silicon dioxide in the slag;

[0070] In Step 2, the detected slag fluidity is inversely proportional to the slag viscosity. Among them, the slag basicity value is calculated from the torque measured by the rheometer and the slag shear rate. The slag fluidity is used to evaluate the adsorption effect of the slag on phosphorus and sulfur impurities in the molten steel.

[0071] Step 2 calculates the slag basicity value through the formula and combines the rheometer to detect the fluidity of the slag in real time, providing a basis for the optimal control of the dephosphorization and desulfurization reactions during the converter smelting process. The basicity value ensures the chemical reaction ability of the slag, and the fluidity ensures the adsorption and mass transfer performance of the slag. The combined monitoring of the two makes the lime feeding more accurate and efficient, thereby improving the utilization efficiency of lime, reducing the smelting cost, and reducing the amount of waste slag. At the same time, the real-time acquisition and evaluation of data further improve the stability of the smelting operation, providing technical support for an efficient and green converter smelting process.

[0072] In Step 3, the formula for the optimization model module to calculate the lime demand is as follows:

[0073]

[0074] Where Q lime represents the total lime demand, [P iron represents the phosphorus content in the hot metal, [S iron represents the sulfur content in the hot metal, A slag is the slag basicity value, A target represents the target value of the slag basicity, V slag represents the real-time fluidity of the slag, V target represents the target value of the slag fluidity,

[0075] k1, k2, and k3 are empirical adjustment coefficients;

[0076] The value ranges of the empirical coefficients k1, k2, and k3 are:

[0077] k1 = 1.2 ± 0.2, k2 = 0.5 ± 0.1, k3 = 0.8 ± 0.1,

[0078] The optimization model module makes the calculation results adapt to the hot metal and slag compositions of different heats by dynamically adjusting the empirical coefficients.

[0079] In Step 3, the optimization model module uses the formula:

[0080]

[0081] to accurately calculate the total lime demand. This formula comprehensively considers the phosphorus and sulfur contents in the hot metal, the basicity value of the slag, and the fluidity difference, and adapts to different heat conditions by dynamically adjusting the empirical coefficients. Through the optimization model module, the utilization efficiency of lime is improved, the dephosphorization and desulfurization processes are optimized, the unit consumption of lime and the slag production are reduced, and the smelting cost and environmental burden are significantly reduced. At the same time, the dynamic matching of the feeding plan and the overall process optimization are realized, providing technical support for efficient and green converter smelting.

[0082] In Step 3, the optimization model module for lime demand superimposes and calculates the thermodynamic equilibrium relationship between the slag and lime reactions according to the dynamic reaction conditions at different stages of converter smelting, and calculates the target basicity value at each stage through the following formula:

[0083]

[0084] Where A stage represents the target basicity value at different stages, [P target is the target dephosphorization efficiency, [P initial is the initial phosphorus concentration in the hot metal, [S targetis the target desulfurization efficiency, [S initial is the initial sulfur concentration of hot metal, A initial is the initial slag basicity value, and k4 and k5 are empirical adjustment coefficients.

[0085] In step 3, the optimization model module of lime demand calculates through the formula:

[0086]

[0087] Taking the target dephosphorization efficiency, target desulfurization efficiency, initial phosphorus and sulfur concentrations, and initial basicity as variables, it accurately calculates the target basicity value at different stages of converter smelting. This formula dynamically adapts to the changing conditions of hot metal composition in different heats, combines the thermodynamic equilibrium relationship to optimize the reaction paths of dephosphorization and desulfurization processes. Through the formula, the lime feeding process is refined, the slag basicity control is stable, the smelting efficiency is significantly improved, while reducing the unit consumption of lime and the amount of waste slag, lowering the comprehensive cost and environmental protection pressure of smelting, and providing guarantee for realizing an efficient and green smelting process.

[0088] In step 4, the feeding control module combines the target basicity value and the real-time monitored slag basicity value, and dynamically adjusts the lime feeding rate at each stage through the following formula:

[0089]

[0090] Among them, R lime represents the feeding rate of lime at the current stage, A target represents the target basicity value, A current represents the real-time detected slag basicity value, t reaction represents the remaining reaction time, and k6 is the rate adjustment coefficient;

[0091] The feeding control module adjusts the operating parameters of the multi-stage spiral feeding equipment by calculating the feeding rate in real time.

[0092] In step 4, the feeding control module calculates through the formula:

[0093]

[0094] Dynamically adjusts the lime feeding rate at each stage, accurately matches the difference between the target basicity and the real-time basicity, and at the same time considers the influence of the remaining reaction time on the rate. By calculating the feeding rate in real time and optimizing the operating parameters of the multi-stage spiral feeding equipment, this method realizes the dynamic adjustment of the feeding rate, ensures the stability of the slag basicity, improves the efficiency of dephosphorization and desulfurization reactions, and significantly reduces the unit consumption of lime and smelting cost. Further, this method reduces the amount of waste slag and its impact on the environment, and provides technical support for an efficient and green converter smelting process.

[0095] In Step 5, the feeding record module of the central data processing system dynamically updates the feeding completion status of each stage according to the feeding rate and cumulative feeding amount of each stage, and generates slag state change data based on time series. The change data includes the change curves of slag basicity, fluidity, and dephosphorization and desulfurization efficiency, and the change data is used by the analysis module of the central data processing system to evaluate the reaction completion degree of each stage.

[0096] In Step 6, based on the slag state change data in Step 5, the analysis module optimizes and adjusts the feeding rate and lime feeding amount of the remaining stages, and recalculates the lime demand of the remaining stages through the following formula:

[0097] Q lime-remain =Q lime-total -Q lime-used ,

[0098] where Q lime-remain represents the lime demand of the remaining stages, Q lime-total represents the total lime demand calculated by the optimization model module in Step 3, and Q lime-total represents the cumulative feeding amount of the completed stages.

[0099] In Steps 5 and 6, the slag state change data includes the slag temperature change curve, and the slag temperature compensates the lime decomposition rate through the following formula:

[0100]

[0101] where R lime-comp represents the temperature compensation value of the lime decomposition rate, T slag-current is the slag temperature detected in real time, T lime-optimal is the optimal temperature of the lime decomposition reaction, t remain is the remaining reaction time, and k7 is the temperature compensation coefficient.

[0102] The analysis module combines the slag state change data and dynamically adjusts the lime feeding rate of the remaining stages to make the feeding rate more matching with the actual demand of the slag state. Through dynamic optimization, the lime waste in the reaction process can be reduced, the utilization efficiency of lime can be improved, and the smelting cost can be further reduced.

[0103] The feeding record module and the analysis module work together, combine the real-time recorded slag state change data with the historical data, form a dynamically optimized closed-loop control mechanism, can quickly respond to the changes of the slag state, guide the operation optimization of the remaining stages, and ensure the stability and efficiency of the smelting process.

[0104] By dynamically adjusting the lime feeding amount of the remaining stages, the increase in the amount of waste slag caused by excessive feeding can be avoided, and the waste slag treatment cost and the burden on the environment can be significantly reduced, providing support for green smelting.

[0105] The optimization model module, feeding control module, feeding record module and analysis module of the central data processing system perform data processing based on artificial intelligence technology, and dynamically update the value ranges of various parameters through a learning model based on historical heat data. Among them, the learning model forms empirical rules adapted to different heat conditions by training the relationships among hot metal composition, slag state and lime demand of historical heats.

[0106] The optimization model module, feeding control module, feeding record module and analysis module of the central data processing system perform data processing based on artificial intelligence technology, form a learning model adapted to different working conditions by training historical heat data, and realize intelligent and precise control of the smelting process. The learning model can dynamically update the value ranges of various parameters, provide a more flexible feeding scheme by combining real-time data and historical data, and significantly improve the adaptability of the system to smelting conditions.

[0107] The core advantage of the technology lies in strengthening the closed-loop optimization ability of the smelting process. By dynamically adjusting lime demand, feeding rate and slag state control, multiple effects such as precise feeding, reducing lime unit consumption, optimizing smelting efficiency and reducing the amount of waste slag are achieved. The introduction of artificial intelligence technology brings a significant intelligent upgrade to the smelting system and provides technical support for efficient and green steel production.

[0108] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A converter smelting method for reducing lime consumption, characterized in that: include: Step 1: Before the molten iron flows into the converter through the ladle, the phosphorus content and sulfur content in the molten iron are detected by a laser induced breakdown spectrometer. The detected molten iron composition data are collected by a data acquisition unit integrated in the spectrometer and transmitted to a central data processing system through an industrial communication protocol. The central data processing system includes a storage module for receiving and storing the molten iron composition data to provide input for subsequent slag state detection and lime demand calculation; Step 2: The alkalinity value of the slag is detected by an electrochemical alkalinity sensor installed on the side wall of the converter, and the fluidity of the slag is measured by a rheometer. In addition, the temperature of the molten steel is detected by an infrared thermometer installed on the top of the converter. The detected slag alkalinity value, fluidity data and molten steel temperature are collected by the data acquisition unit of the sensor, and then transmitted to the storage module of the central data processing system using the industrial communication protocol, and integrated with the stored molten iron composition data to provide input for the calculation of lime demand; Step 3, input the molten iron composition data stored in the storage module in the central data processing system in step 1 and the slag basicity value, fluidity data and molten steel temperature transmitted to the storage module in step 2 into the optimization model module in the central data processing system. The optimization model module is constructed based on the historical furnace data, and the lime demand in the converter smelting process is calculated by a multi-parameter coupling method. The lime demand refers to the total amount of lime required in the initial slag formation stage, the main slag formation stage and the final slag adjustment stage. At the same time, the optimization model module decomposes the total lime demand into specific feeding ratios for each stage, wherein the initial slag formation stage is 30% to 40%, the main slag formation stage is 50% to 60%, and the final slag adjustment stage is 10% to 20%. The calculation results are transmitted to the feeding control module through the optimization model module of the data processing system; Step 4: According to the lime feeding ratio of each stage calculated and transmitted by the feeding control module in the central data processing system in step 3, a feeding amount distribution instruction is sent to the multi-stage screw feeding equipment through the PLC control system. The PLC control system is the upper control unit of the multi-stage screw feeding equipment. The multi-stage screw feeding equipment is used to transport the lime to the converter in stages according to a preset ratio. The feeding control module dynamically adjusts the feeding ratio of each stage in combination with the real-time monitoring data in step 2. The dynamic adjustment is based on the change in slag basicity and fluidity. At the same time, the adjusted feeding instruction is updated to the operating parameters of the multi-stage screw feeding equipment through the PLC control system; Step 5: Using the feeding amount determined by the multi-stage spiral feeding equipment in step 4, the lime transportation and feeding operation are completed in stages. During the feeding process of each stage, the central data processing system monitors the slag basicity and fluidity data in real time through the feeding recording module, and records the completion of the feeding at each stage; Step 6. After completing the staged lime feeding operation in step 5, the PLC control system is used to analyze the feeding completion status of each stage through the analysis module of the central data processing system according to the real-time monitored slag alkalinity value and fluidity data, and the lime feeding amount and feeding rate of the remaining stages are optimized and adjusted in combination with the lime demand calculated by the optimization model module in step 3. At the same time, the operating parameters of the multi-stage screw feeding equipment are updated, and the adjusted feeding instructions are sent to the multi-stage screw feeding equipment through the PLC control system, and finally the whole process of dynamic lime feeding in each stage is completed.

2. A converter smelting method for reducing lime consumption according to claim 1, characterized in that: In step 1, the detected phosphorus content represents the concentration of phosphorus impurities in the molten iron, and the sulfur content represents the concentration of sulfur impurities in the molten iron. The phosphorus content is used to determine the amount of lime added in the converter dephosphorization stage, and the sulfur content is used to determine the amount of lime added in the converter desulfurization stage.

3. A converter smelting method for reducing lime consumption according to claim 1, characterized in that: In step 2, the slag basicity value is calculated by the following formula: Among them, A slag is the slag basicity value, [CaO] represents the mass fraction of calcium oxide in the slag, and [SiO2] represents the mass fraction of silicon dioxide in the slag; In the step 2, the detected slag fluidity is inversely proportional to the slag viscosity, wherein the slag basicity value is calculated by the torque measured by the rheometer and the slag shear rate, and the slag fluidity is used to evaluate the adsorption effect of the slag on phosphorus and sulfur impurities in the molten steel.

4. A converter smelting method for reducing lime consumption according to claim 1, characterized in that: In step 3, the formula for calculating the lime demand by the optimization model module is: Among them, Q lime represents the total demand for lime, [P iron ] represents the phosphorus content in molten iron, [S iron ] represents the sulfur content in molten iron, A slag is the slag basicity value, A target Indicates the target value of slag basicity, V slag Indicates the real-time fluidity of slag, V target Indicates the target value of slag fluidity, k1, k2, and k3 are empirical adjustment coefficients; The value range of the empirical coefficients k1, k2, and k3 is: k1=1.2±0.2, k2=0.5±0.1, k3=0.8±0.1, The optimization model module dynamically adjusts the empirical coefficients to adapt the calculation results to the composition of molten iron and slag of different batches.

5. A converter smelting method for reducing lime consumption according to claim 1, characterized in that: In step 3, the optimization model module of lime demand calculates the thermodynamic equilibrium relationship of slag and lime reaction according to the dynamic reaction conditions at different stages of converter smelting, and calculates the target alkalinity value at each stage by the following formula: Among them, A stage Indicates the target alkalinity value at different stages, [P target ] is the target dephosphorization efficiency, [P initial ] is the initial phosphorus concentration of molten iron, [S target ] is the target desulfurization efficiency, [S initial ] is the initial sulfur concentration of molten iron, A initial is the initial slag basicity value, k4 and k5 are empirical adjustment coefficients.

6. A converter smelting method for reducing lime consumption according to claim 1, characterized in that: In step 4, the feeding control module dynamically adjusts the lime feeding rate at each stage by combining the target alkalinity value and the real-time monitored slag alkalinity value through the following formula: Among them, R lime Indicates the lime feeding rate at the current stage, A target Indicates the target alkalinity value, A current Indicates the real-time detected slag basicity value, t reaction represents the remaining reaction time, k6 is the rate adjustment coefficient; The feeding control module adjusts the operating parameters of the multi-stage spiral feeding equipment by calculating the feeding rate in real time.

7. A converter smelting method for reducing lime consumption according to claim 1, characterized in that: In step 5, the feeding record module of the central data processing system dynamically updates the feeding completion status of each stage according to the feeding rate and cumulative feeding amount of each stage, and generates slag state change data based on time series, wherein the change data includes the change curves of slag basicity, fluidity and dephosphorization and desulfurization efficiency, and the change data is used to evaluate the reaction completion degree of each stage through the analysis module of the central data processing system.

8. A converter smelting method for reducing lime consumption according to claim 7, characterized in that: In step 6, the analysis module optimizes and adjusts the feeding rate and lime feeding amount of the remaining stage based on the slag state change data in step 5, and recalculates the lime demand of the remaining stage by the following formula: Q lime-remain =Q lime-total -Q lime-used , Among them, Q lime-remain represents the lime demand in the remaining stage, Q lime-total represents the total lime demand calculated by the optimization model module in step 3, Q lime-total Indicates the cumulative amount of material fed during the completed phase.

9. A converter smelting method for reducing lime consumption according to claim 8, characterized in that: In step 5 and step 6, the slag state change data includes a slag temperature change curve, and the slag temperature compensates the lime decomposition rate by the following formula: Among them, R lime-comp The temperature compensation value of lime decomposition rate, T slag-current is the real-time detected slag temperature, T lime-optimal is the optimum temperature for lime decomposition reaction, t remain is the remaining reaction time, and k7 is the temperature compensation coefficient.

10. A converter smelting method for reducing lime consumption according to claim 1, characterized in that: The optimization model module, feeding control module, feeding record module and analysis module of the central data processing system perform data processing based on artificial intelligence technology, and dynamically update the value range of each parameter through a learning model based on historical furnace data, wherein the learning model forms empirical rules adapted to different furnace conditions by training the relationship between the molten iron composition, slag state and lime demand of historical furnaces.

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