Optimization method, device, equipment and medium for sintering ingredient control

Abstract

The present invention provides an optimization method, device, equipment, and medium for sintering ingredient control. The optimization method includes obtaining target control data and initial mixture ratio data, breakdown spectrum parameters, and fluorescence spectrum parameters; calculating initial alkalinity data and initial magnesium oxide data based on the breakdown spectrum parameters, fluorescence spectrum parameters, and target control data; calculating ratio change data using an objective function based on the breakdown spectrum parameters, fluorescence spectrum parameters, initial alkalinity data, and initial magnesium oxide data; and adjusting the initial ratio data based on the ratio change data to obtain adjusted target ratio data. The optimization method, device, equipment, and medium for sintering ingredient control provided by the present invention can be combined with LIBS and XRF test results to perform sintering ingredient control.

Description

Technical Field

[0001] The present invention relates to the industrial field, and in particular to an optimization method, device, equipment and medium for controlling sintering ingredients. Background Art

[0002] The sintering process is a critical step in the steel production chain. Its primary task is to mix and heat various ore powders, fuel, and flux in specific proportions to form sintered ore with a defined strength and chemical composition, providing high-quality raw material for the blast furnace. The quality of the sintered ore directly impacts the efficiency and cost of subsequent blast furnace ironmaking, making precise control of the sintering batch composition crucial.

[0003] Currently, the control of sintering batch composition primarily relies on offline analysis using laboratory instruments such as X-ray Fluorescence (XRF) spectrometers. Traditional testing methods typically obtain compositional results every three hours. This low-frequency testing fails to reflect real-time changes in batch composition. Operators can only make rough adjustments based on experience, which affects the stability and yield of the finished ore composition. Furthermore, the lag in offline testing increases uncertainty and risk in the production process.

[0004] With the development of online detection technology, laser-induced breakdown spectroscopy (LIBS) has begun to be applied to the sintering batching process. LIBS equipment can significantly shorten the composition detection cycle, enable real-time monitoring and feedback, and significantly improve the accuracy and efficiency of batching control. However, with long-term use, the detection results of LIBS equipment may deviate from those of XRF equipment. How to effectively utilize LIBS and XRF detection results to control sintering batching has become an urgent problem. Summary of the Invention

[0005] The object of the present invention is to provide an optimization method, device, equipment and medium for sintering ingredient control, which can combine the detection results of LIBS and XRF to control the sintering ingredients.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] The present invention provides an optimization method for sintering ingredient control, comprising:

[0008] Obtaining target control data and corresponding initial ratio data of each material in the mixture, and analyzing the mixture to obtain corresponding breakdown spectrum parameters and fluorescence spectrum parameters;

[0009] Calculating initial alkalinity data according to the first alkalinity data in the breakdown spectrum parameters, the second alkalinity data in the fluorescence spectrum parameters, and the target alkalinity data in the target control data;

[0010] Calculating initial magnesium oxide data according to the first magnesium oxide data in the breakdown spectrum parameters, the second magnesium oxide data in the fluorescence spectrum parameters, and the target magnesium oxide data in the target control data;

[0011] Calculating the ratio change data using an objective function according to the breakdown spectrum parameters, the fluorescence spectrum parameters, the initial alkalinity data, and the initial magnesium oxide data; the objective function represents a functional relationship between the initial alkalinity data, the initial magnesium oxide data, the breakdown spectrum parameters, or the fluorescence spectrum parameters;

[0012] The initial ratio data is adjusted according to the ratio change data to obtain adjusted target ratio data.

[0013] The present invention also provides an optimization device for sintering ingredient control, comprising:

[0014] A data acquisition module is used to obtain target control data and the corresponding initial ratio data of each material in the mixture;

[0015] A data analysis module is used to analyze the mixture to obtain corresponding breakdown spectrum parameters and fluorescence spectrum parameters;

[0016] a first processing module, configured to calculate initial alkalinity data according to the first alkalinity data in the breakdown spectrum parameters, the second alkalinity data in the fluorescence spectrum parameters, and the target alkalinity data in the target control data;

[0017] a second processing module, configured to calculate initial magnesium oxide data according to the first magnesium oxide data in the breakdown spectrum parameters, the second magnesium oxide data in the fluorescence spectrum parameters, and the target magnesium oxide data in the target control data;

[0018] a third processing module, configured to calculate the ratio change data using an objective function based on the breakdown spectrum parameters, the fluorescence spectrum parameters, the initial alkalinity data, and the initial magnesium oxide data; the objective function representing a functional relationship between the initial alkalinity data, the initial magnesium oxide data, the breakdown spectrum parameters, or the fluorescence spectrum parameters;

[0019] The ratio adjustment module is used to adjust the initial ratio data according to the ratio change data to obtain adjusted target ratio data.

[0020] The present invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the optimization method for sintering ingredient control are implemented.

[0021] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the optimization method for sintering ingredient control are implemented.

[0022] As described above, the present invention provides an optimization method, device, equipment, and medium for sintering batch control. Using a multi-objective optimization approach, constraints such as raw material ratios, quality indicators (alkalinity, MgO content), cost, and inventory management are integrated into a unified model. A progressive adjustment strategy dynamically balances the priorities of these objectives. The synergistic use of LIBS and XRF addresses the challenges of traditional methods in balancing real-time response and detection accuracy, significantly improving the agility and accuracy of batch control.

[0023] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 Flowchart of an optimization method for sintering ingredient control in one embodiment of the present invention;

[0026] Figure 2 Schematic diagram of an optimization device for controlling sintering ingredients in one embodiment of the present invention;

[0027] Figure 3 FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present invention.

[0028] In the figure: 100, data acquisition module; 200, data analysis module; 300, first processing module; 400, second processing module; 500, third processing module; 600, ratio adjustment module; 10, electronic device; 11, memory; 12, processor. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] See also Figure 1 The present invention discloses an optimization method for sintering ingredient control, which utilizes LIBS and XRF detection results to control sintering ingredients. The optimization method may include the following steps: Step S10: Obtaining target control data and initial ratio data for each material in the corresponding mixture, and analyzing the mixture to obtain corresponding breakdown spectrum parameters and fluorescence spectrum parameters.

[0031] In some embodiments, the target control data refers to the control indicators issued by the laboratory, which may include target alkalinity data and target magnesium oxide data. The initial ratio data of each material refers to the proportion of each material in the mixture. For example, if there are three materials in the mixture, the proportions of these three materials may be 30%:30%:40%. Of course, in actual situations, the type, quantity and proportion of materials need to be set according to actual production needs.

[0032] In some embodiments, laser-induced breakdown spectroscopy (LIBS) can generate plasma on the surface of a mixture using high-energy laser pulses and then analyze the spectrum emitted by the plasma to determine the elemental composition of the sample. Laboratory fluorescence spectrometers (XRF) can determine elemental composition by irradiating the sample and measuring the characteristic X-rays emitted.

[0033] In some embodiments, the LIBS and XRF are installed at different locations. For example, the XRF is installed at location a and the LIBS is installed at location b. The time required to transport the mixed material from location a to location b is m. To ensure that the LIBS and XRF analyze the same batch of mixed material, the LIBS analysis can be performed after waiting for m after the XRF analysis.

[0034] In some embodiments, when the mixture is analyzed by a laser-induced breakdown spectrometer and a laboratory fluorescence spectrometer, the analysis results can be similar. The analysis of the mixture by a laboratory fluorescence spectrometer is used as an example for illustration. The analysis process of the laboratory fluorescence spectrometer is not described here. After analyzing the mixture, the laser-induced breakdown spectrometer can obtain calcium oxide C, silicon oxide S, and magnesium oxide M of all the mixtures. Calcium oxide C can be expressed as: ; Silicon oxide S can be expressed as: ; Magnesium oxide M can be expressed as: ;in, 、 、 It can be expressed as the content of calcium oxide, silicon oxide and magnesium oxide in the i-th material in the mixture, and the unit can be percentage; It can be expressed as the ratio of the i-th material in the mixture.

[0035] In some embodiments, alkalinity data (R2) refers to a measure of the ratio of basic oxides to acidic oxides in the mixture, which can be obtained by and The magnesium oxide data can be calculated by the ratio of magnesium oxide content to total components.

[0036] In some embodiments, after analyzing the mixture using a laser-induced breakdown spectrometer, breakdown spectrum parameters of the mixture can be obtained, which can include first alkalinity data and first magnesium oxide data. After analyzing the mixture using a laboratory fluorescence spectrometer, fluorescence spectrum parameters of the mixture can be obtained, which can include second alkalinity data and second magnesium oxide data.

[0037] In some embodiments, the optimization method may further include the following steps: Step S20: Calculating initial alkalinity data based on the first alkalinity data in the breakdown spectrum parameters, the second alkalinity data in the fluorescence spectrum parameters, and the target alkalinity data in the target control data. Step S20 may include the following steps: Step S21: Calculating the difference between the first alkalinity data in the breakdown spectrum parameters and the second alkalinity data in the fluorescence spectrum parameters, representing the difference as a alkalinity deviation value.

[0038] In some embodiments, by calculating the difference between the first alkalinity data detected by the laser induced breakdown spectrometer and the second alkalinity data detected by the laboratory fluorescence spectrometer, the alkalinity deviation of the same batch of mixed materials detected by the laser induced breakdown spectrometer and the laboratory fluorescence spectrometer can be obtained.

[0039] In some embodiments, step S20 may further include the following steps: step S22, determining the alkalinity deviation value and the corresponding threshold value.

[0040] In some embodiments, by determining the magnitude of the alkalinity deviation, different initial alkalinity data can be obtained through different processing steps. Specifically, the magnitude of the threshold corresponding to the alkalinity deviation value is not limited and can be set according to actual needs. For example, it can be set in the range of 0.03 to 0.07, such as 0.03, 0.05, 0.07, etc.

[0041] In some embodiments, step S20 may further include the following steps: step S23, when the alkalinity deviation value is less than the corresponding threshold, calculating initial alkalinity data according to the first alkalinity data, the second alkalinity data, and the target alkalinity data in the target control data.

[0042] In some embodiments, when the alkalinity deviation value is less than the corresponding threshold, it can be considered that the deviation between the detection results of the laser-induced breakdown spectrometer and the laboratory fluorescence spectrometer is small. At this time, since the detection principles and sampling methods of the laser-induced breakdown spectrometer and the laboratory fluorescence spectrometer are different, the target alkalinity data deviation issued by the laboratory is based on the detection results of the laboratory fluorescence spectrometer. The laser-induced breakdown spectrometer cannot directly use the target alkalinity data issued by the laboratory for calculation. Therefore, the detection results of the laser-induced breakdown spectrometer also need to be recalibrated. At this time, the initial data of the mixture can be obtained first. The initial data may include the initial ratio data of each material in the mixture, the inventory of each material and the maximum capacity of the corresponding silo, the cost data of each material, etc.

[0043] In some embodiments, after obtaining the initial target data, the difference between the target alkalinity data and the first alkalinity data may be calculated, expressed as: ,in, It can be expressed as a difference, Can be expressed as target alkalinity data, It can be expressed as the first alkalinity data. Then, the difference and the sum of the second alkalinity data can be calculated, which is expressed as: ,in, It can be expressed as the initial alkalinity data, It can be expressed as the second alkalinity data.

[0044] In some embodiments, step S20 may further include the following steps: Step S24, otherwise, within a preset time period, initial alkalinity data is calculated based on the acquired multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters.

[0045] In some embodiments, when the alkalinity deviation value is greater than or equal to a corresponding threshold, it can be considered that the deviation between the detection results of the laser-induced breakdown spectrometer and the laboratory fluorescence spectrometer is large, and the first alkalinity data and the second alkalinity data cannot be used for calculation, and further processing is required. Step S24 may include the following steps: obtaining multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters within a preset time period.

[0046] In some embodiments, since the laboratory fluorescence spectrometer detects the mixture at regular intervals, in order to obtain multiple sets of fluorescence spectrum parameters of the laboratory fluorescence spectrometer, it is necessary to obtain multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters within a preset time period. For example, when the preset time period can be set to 12 hours, the laboratory fluorescence spectrometer can detect the mixture at 10:00, 13:00, 16:00, and 19:00. The time required for the mixture to be transported from position a to position b is 2 hours. The laser induced breakdown spectrometer can detect the mixture at 12:00, 15:00, 18:00, and 21:00. At this time, the multiple sets of breakdown spectrum parameters obtained by the laser induced breakdown spectrometer can correspond to the multiple sets of fluorescence spectrum parameters obtained by the laboratory fluorescence spectrometer.

[0047] In some embodiments, step S24 may further include the following steps: calculating the mean of the first alkalinity data in all breakdown spectrum parameters, expressed as the first alkalinity mean; calculating the mean of the second alkalinity data in all fluorescence spectrum parameters, expressed as the second alkalinity mean.

[0048] In some embodiments, since there are multiple breakdown spectrum parameters and fluorescence spectrum parameters, the i-th first alkalinity data can be expressed as , the first alkalinity mean can be expressed as , the i-th second alkalinity data can be expressed as , the second alkalinity mean can be expressed as .

[0049] In some embodiments, step S24 may further include the following step: calculating an alkalinity correlation coefficient based on all first alkalinity data, the first alkalinity mean, all second alkalinity data, and the second alkalinity mean.

[0050] In some embodiments, the basicity correlation coefficient It can be expressed as: , where n is the number of breakdown spectrum parameters or fluorescence spectrum parameters.

[0051] In some embodiments, step S24 may further include the following steps: determining the alkalinity correlation coefficient and the corresponding threshold value: when the alkalinity correlation coefficient is greater than the corresponding threshold value, setting the initial alkalinity data to 0; otherwise, generating an alarm message.

[0052] In some embodiments, the size of the corresponding threshold value of the alkalinity correlation coefficient may not be restricted and may be set according to actual needs, for example, it may be set in the range of 0.4 to 0.8, for example, it may be 0.4, 0.6, 0.8, etc. When the alkalinity correlation coefficient is greater than the corresponding threshold value, it may indicate that although the detection results of the laboratory fluorescence spectrometer and the laser induced breakdown spectrometer have a large deviation, the detection results of the two have a certain trend. The alkalinity trend data may be adjusted by adjusting the ratio data of each material in the mixture, and the initial alkalinity data may be set to 0 at this time. When the alkalinity correlation coefficient is less than or equal to the corresponding threshold value, it may indicate that the detection results of the laboratory fluorescence spectrometer and the laser induced breakdown spectrometer have a large deviation, and the alkalinity data cannot be adjusted by adjusting the ratio data of each material in the mixture. At this time, the current ratio data of each material in the mixture may be maintained, and an alarm message may be generated for subsequent inspection by the staff.

[0053] In some embodiments, the optimization method may further include the following steps: Step S30, calculating initial MgO data based on the first MgO data in the breakdown spectrum parameters, the second MgO data in the fluorescence spectrum parameters, and the target MgO data in the target control data. Step S30 may include the following steps: Step S31, calculating the difference between the first MgO data in the breakdown spectrum parameters and the second MgO data in the fluorescence spectrum parameters, representing the difference as a MgO deviation value.

[0054] In some embodiments, by calculating the difference between the first magnesium oxide data detected by the laser induced breakdown spectrometer and the second magnesium oxide data detected by the laboratory fluorescence spectrometer, the magnesium oxide deviation value detected by the laser induced breakdown spectrometer and the laboratory fluorescence spectrometer for the same batch of mixed materials can be obtained.

[0055] In some embodiments, step S30 may further include the following steps: Step S32: Determine the magnesium oxide deviation value and the corresponding threshold value. By determining the magnitude of the magnesium oxide deviation value, different initial magnesium oxide data can be obtained through different processing processes. Specifically, the magnitude of the corresponding threshold value of the magnesium oxide deviation value is not limited and can be set according to actual needs, for example, it can be set in the range of 0.05 to 0.15, such as 0.05, 0.1, 0.15, etc.

[0056] In some embodiments, step S30 may further include the following steps: step S33, when the MgO deviation value is less than the corresponding threshold, calculating the initial MgO data according to the first MgO data, the second MgO data, and the target MgO data in the target control data.

[0057] In some embodiments, when the MgO deviation value is less than a corresponding threshold, the deviation between the LIBS and laboratory fluorescence spectrometer results can be considered small. This is because the deviation in the target MgO data provided by the laboratory is based on the laboratory fluorescence spectrometer's results. The LIBS cannot directly use the laboratory's target MgO data for calculations, so the LIBS results also need to be recalibrated.

[0058] In some embodiments, after obtaining the initial target data, the difference between the target MgO data and the first MgO data may be calculated, which is expressed as: ,in, It can be expressed as a difference, It can be expressed as target MgO data, It can be expressed as the first MgO data. Then, the sum of the difference and the second MgO data can be calculated, which is expressed as: ,in, It can be expressed as the initial MgO data, It can be expressed as the second magnesium oxide data.

[0059] In some embodiments, step S30 may further include the following steps: Step S34: Otherwise, within a preset time period, initial magnesium oxide data is calculated based on the acquired multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters.

[0060] In some embodiments, when the MgO deviation value is greater than or equal to a corresponding threshold, it can be considered that the deviation between the detection results of the laser-induced breakdown spectrometer and the laboratory fluorescence spectrometer is large, and the first MgO data and the second MgO data cannot be used for calculation, and further processing is required. Step S53 may include the following steps: obtaining multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters within a preset time period.

[0061] In some embodiments, since the laboratory fluorescence spectrometer periodically detects the mixture, in order to obtain multiple sets of laboratory fluorescence spectrometer fluorescence spectrum parameters, it is necessary to obtain multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters within a preset time period. The method for obtaining multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters can be similar to the method for obtaining the multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters in the above embodiment and is not further described here.

[0062] In some embodiments, step S34 may further include the following steps: calculating the mean of the first MgO data in all breakdown spectrum parameters, expressed as the first MgO mean; calculating the mean of the second MgO data in all fluorescence spectrum parameters, expressed as the second MgO mean.

[0063] In some embodiments, since there are multiple breakdown spectrum parameters and fluorescence spectrum parameters, the i-th first magnesium oxide data can be expressed as , the first MgO mean can be expressed as , the i-th second MgO data can be expressed as , the second MgO mean can be expressed as .

[0064] In some embodiments, step S34 may further include the following step: calculating a magnesium oxide correlation coefficient based on all first magnesium oxide data, the first magnesium oxide mean, all second magnesium oxide data, and the second magnesium oxide mean.

[0065] In some embodiments, the magnesium oxide correlation coefficient It can be expressed as: .

[0066] In some embodiments, step S34 may further include the following steps: determining the magnesium oxide correlation coefficient and the corresponding threshold value: when the magnesium oxide correlation coefficient is greater than the corresponding threshold value, setting the initial magnesium oxide data to 0; otherwise, generating an alarm message.

[0067] In some embodiments, the corresponding threshold value of the magnesium oxide correlation coefficient may not be restricted and may be set according to actual needs, for example, it may be set in the range of 0.4 to 0.8, such as 0.4, 0.6, 0.8, etc. When the magnesium oxide correlation coefficient is greater than the corresponding threshold value, it may indicate that although the detection results of the laboratory fluorescence spectrometer and the laser induced breakdown spectrometer deviate significantly, the magnesium oxide data can be adjusted by adjusting the ratio data of each material in the mixture. In this case, the initial magnesium oxide data may be set to 0. When the magnesium oxide correlation coefficient is less than or equal to the corresponding threshold value, it may indicate that the detection results of the laboratory fluorescence spectrometer and the laser induced breakdown spectrometer deviate significantly and the magnesium oxide data cannot be adjusted by adjusting the ratio data of each material in the mixture. In this case, the current ratio data of each material in the mixture may be maintained, and an alarm message may be generated for subsequent inspection by staff.

[0068] In some embodiments, the optimization method may further include the following steps: step S40, calculating the ratio change data through the objective function based on the breakdown spectrum parameters, fluorescence spectrum parameters, initial alkalinity data, and initial magnesium oxide data; the objective function represents the functional relationship between the initial alkalinity data, initial magnesium oxide data, breakdown spectrum parameters or fluorescence spectrum parameters.

[0069] In some embodiments, multi-objective optimization problems often require simultaneous optimization of multiple objectives (e.g., minimizing alkalinity data deviation, minimizing magnesium oxide data deviation, reducing cost data, optimizing inventory, etc.). However, these objectives may conflict with each other. For example, reducing alkalinity data deviation may increase cost data. Therefore, directly optimizing multiple objectives increases computational complexity and solution difficulty. By combining these multiple objectives into a single objective function through weighted summation, the multi-objective optimization problem can be transformed into a single-objective optimization problem, thereby reducing computational complexity and being suitable for real-time control or resource-constrained scenarios.

[0070] In some embodiments, the objective function f can be expressed as: .in, Can be expressed as the first alkalinity data or the second alkalinity data, It can be expressed as the first MgO data or the second MgO data, It can be expressed as the cost data of the i-th material in the mixture, It can be expressed as the ratio data of the i-th material in the mixture, It can be expressed as the inventory of the i-th material in the mixture, It can be expressed as the maximum capacity of the corresponding silo of the i-th material in the mixture, It can be expressed as the ratio change data of the i-th material in the mixture, It can be expressed as the ratio change data of all materials in the mixture. It can be expressed as a weight coefficient, It can be expressed as a regularization coefficient. By minimizing the objective function min(f), the ratio change data can be obtained.

[0071] In some embodiments, if alkalinity data bias is required to take precedence over magnesium oxide data bias, this can be achieved by giving alkalinity data bias a higher weight ( If an objective (such as alkalinity data deviation or magnesium oxide data deviation) falls outside a preset range, the weight of that objective can be dynamically increased, ensuring that the optimization process focuses on the failing indicator.

[0072] In some embodiments, for each material in the mixture, multiple silos may store the same material. Different silos may have different inventory levels of the same material. When adding a certain material's mix ratio, the silo with the highest inventory level can be prioritized to output that material, ensuring that all silos storing the same material maintain roughly the same inventory level.

[0073] In some embodiments, the objective function is composed of multiple objective sub-functions, each of which may have different dimensions (units) and numerical ranges (for example, the cost may be in the millions, while the alkalinity deviation may be several decimal places). Direct weighted summation may cause certain objectives to dominate the objective function. Through Z-Score normalization, the value of each objective sub-function can be standardized to a distribution with a mean of 0 and a standard deviation of 1, eliminating the effects of dimensions and numerical ranges. Z-Score normalization can be expressed as: ,in, It can be expressed as the i-th objective subfunction, It can be expressed as the mean of the i-th target sub-function, It can be expressed as the standard deviation of the i-th objective sub-function. It can be expressed as the normalized function of the i-th objective sub-function. The objective sub-function refers to each item in the objective function.

[0074] In some embodiments, by converting a multi-objective optimization problem into a single objective function and combining it with normalization and dynamic weight adjustment, computational complexity can be effectively reduced while flexibly reflecting user priorities and real-time needs. When fitting and optimizing the objective function, the NSGAII multi-objective algorithm or the SQP quadratic sequential optimization algorithm can be used to calculate the corresponding ratio change data when the objective function reaches its minimum value.

[0075] In some embodiments, when performing fitting optimization on the objective function, the following constraints need to be met: 、 、 、 、 、 .in, , , Refers to the ratio data of the i-th material in the adjusted target ratio data. , , Indicates the minimum value of a single adjustment. , 、 、 They refer to the calcium oxide content, silicon oxide content, and magnesium oxide content calculated based on the adjusted target ratio data. , , , , , .

[0076] In some embodiments, step S40 may include the following steps: step S41, fitting and optimizing the objective function based on the first magnesium oxide data and the first alkalinity data, the initial alkalinity data, and the initial magnesium oxide data in the breakdown spectrum parameters, and calculating the first ratio change data corresponding to the breakdown spectrum parameters when the objective function reaches the minimum value.

[0077] In some embodiments, when the breakdown spectrum parameters are obtained, the first ratio change data corresponding to the breakdown spectrum parameters can be calculated. In this case, the first ratio change data in the objective function f is Can be expressed as the first alkalinity data, It can be represented as first magnesium oxide data. Subsequently, the corresponding data can be substituted into the aforementioned objective function, and the objective function can be minimized using an optimization algorithm (such as gradient descent, genetic algorithm, etc.) to calculate first ratio change data corresponding to the breakdown spectrum parameters. The first ratio change data can be represented as change data from the initial ratio data of each material in the mixture. After obtaining the first ratio change data, it is necessary to limit the change data from the initial ratio data of each material to within a preset range.

[0078] In some embodiments, when the difference between the first alkalinity data and the target alkalinity data is less than the corresponding threshold, it can be considered that there is no need to solve the target sub-function corresponding to the alkalinity data deviation. In this case, the corresponding weight coefficient can be Adjust to 0, wherein the corresponding threshold value can be unlimited, for example, it can be within the range of 0.02~0.04. When the difference between the first magnesium oxide data and the target magnesium oxide data is less than the corresponding threshold value, it can be considered that there is no need to solve the target sub-function corresponding to the magnesium oxide data deviation. At this time, the corresponding weight coefficient can be Adjust to 0, wherein the size of the corresponding threshold may not be limited, for example, it may be within the range of 0.06~0.1.

[0079] In some embodiments, if the difference between the first alkalinity data and the target alkalinity data is greater than or equal to the corresponding threshold, the corresponding weight coefficient may be If the difference between the first magnesium oxide data and the target magnesium oxide data is greater than or equal to the corresponding threshold, the corresponding weight coefficient can be adjusted to a larger value. If the difference between the first alkalinity data and the target alkalinity data is greater than or equal to the corresponding threshold, and the difference between the first magnesium oxide data and the target magnesium oxide data is greater than or equal to the corresponding threshold, the weight coefficient can be given priority. Adjust to a larger value.

[0080] In some embodiments, step S40 may include the following steps: step S42, fitting and optimizing the objective function based on the second magnesium oxide data and the second alkalinity data, the initial alkalinity data, and the initial magnesium oxide data in the fluorescence spectrum parameters, and when the objective function reaches the minimum value, calculating the second ratio change data corresponding to the fluorescence spectrum parameters.

[0081] In some embodiments, when the fluorescence spectrum parameters are obtained, the corresponding second ratio change data can be calculated for the fluorescence spectrum parameters. At this time, the above objective function f Can be expressed as the second alkalinity data, It can be expressed as the second magnesium oxide data. The specific calculation process can be similar to the calculation process of substituting the breakdown spectrum parameters into the objective function, and will not be described in detail here.

[0082] In some embodiments, step S40 may include the following steps: Step S43, calculating the ratio change data according to the first ratio change data and the second ratio change data. It can be expressed as: ,in, It can be expressed as the first ratio change data, Can be expressed as the second ratio change data, It can be expressed as a mixing weight coefficient, It can be in the range of 0.2~0.8.

[0083] In some embodiments, the optimization method may further include the following steps: Step S50, adjusting the initial ratio data according to the ratio change data to obtain adjusted target ratio data.

[0084] In some embodiments, in industrial production, initial mix ratios are set based on theoretical calculations or historical experience. However, in actual production, fluctuations in mix composition, changes in equipment status, or environmental factors may cause the initial mix ratio to not meet requirements. In such cases, the initial mix ratios need to be dynamically adjusted based on mix ratio change data to generate more optimal target mix ratios.

[0085] In some embodiments, step S50 may include the following steps: Step S51, obtaining multiple sets of ratio change data within a preset time period; the ratio change data includes the ratio change value of each mixture.

[0086] In some embodiments, multiple sets of ratio change data are acquired over a preset period (e.g., one day) through real-time monitoring or historical data. Each set of ratio change data may include the ratio change value of each material in the mixture (e.g., a 2% increase in limestone, a 1% decrease in dolomite, etc.). Using multiple sets of ratio change data can eliminate random errors and ensure the stability and reliability of adjustments.

[0087] In some embodiments, step S50 may include the following steps: Step S52, calculating the cumulative impact value of each material in the mixture according to the ratio change values of multiple groups of ratio change data.

[0088] In some embodiments, by calculating the ratio change value in each set of ratio change data, the cumulative impact value of each material can be obtained to reflect its overall adjustment trend. It can be expressed as: , where t can be represented as the current time, and n can be represented as the time for obtaining the nth group of ratio change data. Can be a coefficient. Among them, n and The size of can be unlimited, for example, n can be 5, It can be 0.7.

[0089] In some embodiments, step S50 may include the following steps: Step S53, determine whether all cumulative impact values are less than the corresponding threshold: when all cumulative impact values are less than the corresponding threshold, output the ratio change data; otherwise, adjust the ratio change data and output the adjusted ratio change data.

[0090] In some embodiments, a threshold value can be set for each material (e.g., limestone variation ≤ ±3%). If all cumulative impact values do not exceed the threshold, the ratio change data is directly output. If any cumulative impact value exceeds the threshold, the ratio change data is adjusted. For example, all ratio change data can be multiplied by a coefficient to proportionally reduce the ratio change data, and the adjusted ratio change data is output. The value of this coefficient is not limited and can be, for example, 0.6.

[0091] In some embodiments, the number of changes in the ratio change data of each material in the mixture can also be counted based on the change value of the ratio change data of the material, wherein the number of changes is expressed as when adjacent ratio change data are one positive and one negative, the number of changes is increased by 1. Subsequently, the ratio of the number of changes to the number of groups of the obtained ratio change data can be calculated, and it can be determined whether the ratio is greater than the corresponding threshold: if so, the ratio change data is adjusted and the adjusted ratio change data is output; otherwise, the ratio change data is output. In particular, when adjusting the ratio change data, all the ratio change data can be multiplied by a coefficient so that all the ratio change data are proportionally reduced, and the adjusted ratio change data is output. In particular, the size of the coefficient can be unlimited, for example, it can be 0.3.

[0092] In some embodiments, step S50 may include the following steps: Step S54: adjusting the initial ratio data based on the output ratio change data or the adjusted ratio change data to obtain adjusted target ratio data. For example, if the initial ratio data of material a, material b, and material c is 30%:30%:40%, and the ratio change data is -10%:20%:-10%, then the adjusted target ratio data is 20%:50%:30%.

[0093] As can be seen, in the above scheme, constraints such as raw material ratio, quality indicators (alkalinity, MgO content), cost, and inventory management are integrated into a unified model through a multi-objective optimization approach, and the priorities of various objectives are dynamically balanced through a progressive adjustment strategy. LIBS and XRF work together to solve the difficult problem of balancing real-time response and detection accuracy in traditional methods, significantly improving the agility and accuracy of ingredient control. Based on raw material inventory status, cost fluctuations, and quality requirements, multiple sets of feasible ratio schemes can be generated in real time, prioritizing low-cost, high-inventory raw material combinations while ensuring that process indicators meet requirements. This dynamic resource allocation mechanism not only reduces raw material waste and procurement costs, but also improves the production line's adaptability to market fluctuations and supply chain disruptions, achieving a dual improvement in economic efficiency and production flexibility.

[0094] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0095] See also Figure 2 The present invention also discloses an optimization device for sintering batching control, which can apply the above-mentioned optimization method. The optimization device can include a data acquisition module 100, a data analysis module 200, a first processing module 300, a second processing module 400, a third processing module 500, and a batching ratio adjustment module 600.

[0096] In some embodiments, the data acquisition module 100 may be used to acquire target control data and corresponding initial ratio data of each material in the mixture.

[0097] In some embodiments, the data analysis module 200 may be used to analyze the mixture to obtain corresponding breakdown spectrum parameters and fluorescence spectrum parameters.

[0098] In some embodiments, the first processing module 300 may be configured to calculate initial alkalinity data based on first alkalinity data in the breakdown spectrum parameters, second alkalinity data in the fluorescence spectrum parameters, and target alkalinity data in the target control data.

[0099] In some embodiments, the second processing module 400 may be configured to calculate initial MgO data based on the first MgO data in the breakdown spectrum parameters, the second MgO data in the fluorescence spectrum parameters, and the target MgO data in the target control data.

[0100] In some embodiments, the third processing module 500 can be used to calculate the ratio change data through the objective function based on the breakdown spectrum parameters, fluorescence spectrum parameters, initial alkalinity data, and initial magnesium oxide data; the objective function represents the functional relationship between the initial alkalinity data, initial magnesium oxide data, breakdown spectrum parameters or fluorescence spectrum parameters.

[0101] In some embodiments, the ratio adjustment module 600 may be configured to adjust the initial ratio data according to the ratio change data to obtain adjusted target ratio data.

[0102] The specific definition of the optimization device can be found in the definition of the optimization method above and will not be repeated here. Each module in the above-mentioned optimization device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the memory of the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the memory can call and execute the operations corresponding to each of the above modules.

[0103] See also Figure 3 In one embodiment, the electronic device 10 may include a memory 11, a processor 12 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 12, such as a program for optimizing sintering ingredient control.

[0104] In one embodiment, the memory 11 includes at least one type of readable storage medium, including flash memory, a removable hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 10, such as a removable hard disk of the electronic device 10. In other embodiments, the memory 11 may also be an external storage device of the electronic device 10, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 11 may include both an internal storage unit of the electronic device 10 and an external storage device. The memory 11 can be used not only to store application software installed in the electronic device 10 and various data, such as optimized code for sintering ingredient control, but also to temporarily store data that has been output or is about to be output.

[0105] In one embodiment, the processor 12 may be comprised of an integrated circuit, such as a single packaged integrated circuit or multiple packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 12 is the control core (Control Unit) of the electronic device 10, connecting the various components of the electronic device 10 using various interfaces and circuits. It executes programs or modules stored in the memory 11 (e.g., a program for optimizing sintering ingredient control) and accesses data stored in the memory 11 to perform various functions and process data.

[0106] In one embodiment, the processor 12 executes the operating system and various installed application programs of the electronic device 10. The processor 12 executes the application programs to implement the steps in the above-mentioned optimization method for sintering ingredient control.

[0107] In one embodiment, the computer program may be divided into one or more modules, one or more of which are stored in the memory 11 and executed by the processor 12 to complete the present application. One or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 10. For example, the computer program may be divided into a data acquisition module 100, a data analysis module 200, a first processing module 300, a second processing module 400, a third processing module 500, a ratio adjustment module 600, etc.

[0108] The embodiments of the present invention disclosed above are intended only to illustrate the present invention. They do not describe all details in detail, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An optimization method for sintering ingredient control, characterized in that: include: Obtaining target control data and corresponding initial ratio data of each material in the mixture, and analyzing the mixture to obtain corresponding breakdown spectrum parameters and fluorescence spectrum parameters; Calculating initial alkalinity data according to the first alkalinity data in the breakdown spectrum parameters, the second alkalinity data in the fluorescence spectrum parameters, and the target alkalinity data in the target control data; Calculating initial magnesium oxide data according to the first magnesium oxide data in the breakdown spectrum parameters, the second magnesium oxide data in the fluorescence spectrum parameters, and the target magnesium oxide data in the target control data; Calculating the ratio change data using an objective function according to the breakdown spectrum parameters, the fluorescence spectrum parameters, the initial alkalinity data, and the initial magnesium oxide data; the objective function represents a functional relationship between the initial alkalinity data, the initial magnesium oxide data, the breakdown spectrum parameters, or the fluorescence spectrum parameters; The initial ratio data is adjusted according to the ratio change data to obtain adjusted target ratio data.

2. The optimization method for sintering ingredient control according to claim 1, characterized in that: The step of calculating the initial alkalinity data according to the first alkalinity data in the breakdown spectrum parameters, the second alkalinity data in the fluorescence spectrum parameters, and the target alkalinity data in the target control data comprises: Calculating a difference between the first alkalinity data in the breakdown spectrum parameter and the second alkalinity data in the fluorescence spectrum parameter, and expressing the difference as an alkalinity deviation value; Determine the alkalinity deviation value and the corresponding threshold value: When the alkalinity deviation value is less than the corresponding threshold value, calculating initial alkalinity data according to the first alkalinity data, the second alkalinity data, and the target alkalinity data in the target control data; Otherwise, within a preset time period, initial alkalinity data is calculated based on the acquired multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters.

3. The optimization method for sintering ingredient control according to claim 2, characterized in that: The step of calculating the initial alkalinity data according to the acquired multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters within the preset time period includes: Within a preset time period, multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters are obtained; Calculate the mean of the first basicity data in all breakdown spectrum parameters, expressed as the first basicity mean; Calculate the mean of the second alkalinity data in all fluorescence spectrum parameters, expressed as the second alkalinity mean; Calculating a basicity correlation coefficient based on all first basicity data, the first basicity mean, all second basicity data, and the second basicity mean; Determine the alkalinity correlation coefficient and the corresponding threshold: when the alkalinity correlation coefficient is greater than the corresponding threshold, set the initial alkalinity data to 0; otherwise, generate an alarm message.

4. The optimization method for sintering ingredient control according to claim 1, characterized in that: The step of calculating the initial magnesium oxide data according to the first magnesium oxide data in the breakdown spectrum parameters, the second magnesium oxide data in the fluorescence spectrum parameters, and the target magnesium oxide data in the target control data comprises: Calculating a difference between the first magnesium oxide data in the breakdown spectrum parameter and the second magnesium oxide data in the fluorescence spectrum parameter, and expressing the difference as a magnesium oxide deviation value; Determine the magnesium oxide deviation value and the corresponding threshold value: When the magnesium oxide deviation value is less than a corresponding threshold value, calculating initial magnesium oxide data according to the first magnesium oxide data, the second magnesium oxide data, and target magnesium oxide data in the target control data; Otherwise, within a preset time period, initial magnesium oxide data is calculated based on the acquired multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters.

5. The optimization method for sintering ingredient control according to claim 4, characterized in that: The step of calculating the initial magnesium oxide data according to the acquired multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters within the preset time period includes: Within a preset time period, multiple sets of breakdown spectrum parameters and corresponding fluorescence spectrum parameters are obtained; Calculate the mean of the first MgO data in all breakdown spectrum parameters, expressed as the first MgO mean; Calculate the mean of the second MgO data in all fluorescence spectrum parameters, expressed as the second MgO mean; Calculating a magnesium oxide correlation coefficient based on all first magnesium oxide data, the first magnesium oxide mean, all second magnesium oxide data, and the second magnesium oxide mean; Determine the magnesium oxide correlation coefficient and the corresponding threshold: when the magnesium oxide correlation coefficient is greater than the corresponding threshold, set the initial magnesium oxide data to 0; otherwise, generate an alarm message.

6. The optimization method for sintering ingredient control according to claim 1, characterized in that: The step of calculating the ratio change data by using an objective function according to the breakdown spectrum parameters, the fluorescence spectrum parameters, the initial alkalinity data, and the initial magnesium oxide data comprises: Performing fitting optimization on an objective function based on the first magnesium oxide data and the first alkalinity data in the breakdown spectrum parameters, the initial alkalinity data, and the initial magnesium oxide data, and calculating first ratio change data corresponding to the breakdown spectrum parameters when the objective function obtains a minimum value; performing fitting optimization on an objective function according to the second magnesium oxide data and the second alkalinity data in the fluorescence spectrum parameters, the initial alkalinity data, and the initial magnesium oxide data, and calculating second ratio change data corresponding to the fluorescence spectrum parameters when the objective function obtains a minimum value; The ratio change data is calculated according to the first ratio change data and the second ratio change data.

7. The optimization method for sintering ingredient control according to claim 1, characterized in that: The step of adjusting the initial ratio data according to the ratio change data to obtain adjusted target ratio data includes: Acquire multiple sets of ratio change data within a preset time period; the ratio change data includes the ratio change value of each material in the mixture; Calculating the cumulative impact value of each material in the mixture according to the ratio change values of the multiple groups of ratio change data; Determining whether all cumulative impact values are less than a corresponding threshold: if all the cumulative impact values are less than the corresponding threshold, outputting the ratio change data; otherwise, adjusting the ratio change data and outputting the adjusted ratio change data; The initial ratio data is adjusted according to the output ratio change data or the adjusted ratio change data to obtain the adjusted target ratio data.

8. An optimization device for sintering ingredient control, characterized in that: include: A data acquisition module is used to obtain target control data and the corresponding initial ratio data of each material in the mixture; A data analysis module, used to analyze the mixture to obtain corresponding breakdown spectrum parameters and fluorescence spectrum parameters; a first processing module, configured to calculate initial alkalinity data according to the first alkalinity data in the breakdown spectrum parameters, the second alkalinity data in the fluorescence spectrum parameters, and the target alkalinity data in the target control data; a second processing module, configured to calculate initial magnesium oxide data according to the first magnesium oxide data in the breakdown spectrum parameters, the second magnesium oxide data in the fluorescence spectrum parameters, and the target magnesium oxide data in the target control data; a third processing module, configured to calculate the ratio change data using an objective function based on the breakdown spectrum parameters, the fluorescence spectrum parameters, the initial alkalinity data, and the initial magnesium oxide data; the objective function representing a functional relationship between the initial alkalinity data, the initial magnesium oxide data, the breakdown spectrum parameters, or the fluorescence spectrum parameters; The ratio adjustment module is used to adjust the initial ratio data according to the ratio change data to obtain adjusted target ratio data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the optimization method for sintering ingredient control according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the optimization method for sintering ingredient control according to any one of claims 1 to 7 are implemented.

Images