A system and method for detecting and analyzing the composition of the slag produced in the production of high-carbon ferrochrome

Abstract

The present invention belongs to the technical field of slag composition detection, and particularly relates to a system and method for detecting and analyzing the composition of high-carbon ferrochrome production slag. By implementing multi-point temperature acquisition on the slag discharge flow path, and then optimizing the slag sampling strategy based on the temperature distribution uniformity, precise sampling for different regions is achieved, greatly improving the flexibility and representativeness of sampling, thereby significantly enhancing the accuracy and reliability of subsequent composition detection. At the same time, after stratified sampling of the slag, stratified composition detection is first carried out to realize the quantitative analysis of the slag composition distribution. Based on this analysis result, the selection ratio of each stratified sample is dynamically adjusted, a mixed sample is prepared and its composition is detected, which fully reflects the overall analysis ability of the overall mixed slag, ensures that the mixed sample can comprehensively reflect the overall composition characteristics of the slag, and maximally improves the accuracy and reliability of the final detection result.

Description

Technical Field

[0001] The present invention belongs to the technical field of slag composition detection, and particularly relates to a detection and analysis system and method for the composition of high-carbon ferrochrome production slag. Background Art

[0002] High-carbon ferrochrome is an iron alloy mainly containing chromium with a carbon content of 4%-8%, which is obtained by high-temperature smelting of chromium ore and a reducing agent and is widely used in the steel industry. During production, chromium oxide is reduced to metallic chromium and a large amount of slag is generated. To meet the product performance requirements, a slag-forming agent needs to be added to regulate the composition and performance of the slag. Since the slag composition directly affects the smelting stability and product quality, it is crucial to frequently analyze the slag composition to adjust the type and dosage of the slag-forming agent. Therefore, accurate slag composition analysis is the core link of process control.

[0003] In the prior art, there have been some technical solutions for slag composition detection. For example, the Chinese invention patent with the publication number CN114910467B proposes a method and system for on-line monitoring and analysis of metallurgical slag. This method first completes the debugging preparation of the detection device by establishing a standard working curve for the elements to be analyzed, and then uses the normal-temperature digestion method or the high-temperature melting digestion method to digest the metallurgical slag to generate a slag digestion solution. Finally, at least one of the ICP spectrometry method, the electrode method, or the titration analysis method is used to detect the element content in the slag digestion solution, aiming to balance timeliness and accuracy and meet the requirements of real-time monitoring of the slag composition during the smelting process.

[0004] In addition, the Chinese invention patent with the publication number CN109507172A proposes an on-line detection and analysis device for the composition of high-temperature liquid slag in steelmaking. This device consists of a slag composition rapid analyzer and an optical probe. During detection, the optical probe is inserted into the liquid slag, and efficient detection is achieved through the slag composition rapid analyzer.

[0005] Although the above two technical solutions have improved the efficiency of slag composition analysis to a certain extent, there are still deficiencies in the analysis accuracy, which are specifically manifested in the following aspects: 1. The first solution does not fully consider the composition distribution differences caused by uneven temperature distribution during slag discharge, thus lacking an optimized setting for sampling uniformity. During the slag discharge process, due to the existence of a temperature gradient, a composition stratification phenomenon may occur: the slag in the high-temperature area has stronger fluidity and a more uniform composition distribution, while the slag in the low-temperature area may show local enrichment or depletion due to an increase in viscosity. If the sampling method is too fixed and fails to dynamically adjust the sampling strategy according to the characteristics of different temperature regions, it may lead to insufficient representativeness of the collected samples, thereby affecting the accuracy of the final detection results.

[0006] 2. The second solution, although it recognizes that slag is a molten mixture between solid and liquid states and inserts an optical probe into the liquid slag to obtain data (based on the judgment that the composition of liquid slag is relatively more uniform), this judgment is subjective. It does not analyze the actual distribution characteristics of the slag composition through rational data analysis, which will weaken the reliability of the detection results. Additionally, the detection method of the optical probe is essentially limited to a local area of the slag and cannot achieve a global analysis of the overall mixed slag. This locality may cause the information of some key components to be omitted. Especially when there is spatial heterogeneity or local enrichment in the slag, the comprehensiveness and accuracy of the detection results will be significantly affected. Summary of the Invention

[0007] The present invention aims to overcome the deficiencies in the prior art and proposes a system and method for detecting and analyzing the composition of high-carbon ferrochrome production slag. By optimizing the sampling method and the composition distribution analysis strategy during the detection of the high-carbon ferrochrome slag composition, high-precision and global detection of the slag composition can be achieved.

[0008] The object of the present invention can be achieved through the following technical solutions: In the first aspect of the present invention, a system for detecting and analyzing the composition of high-carbon ferrochrome production slag is provided, including the following modules: Temperature distribution acquisition module: A plurality of temperature measurement points are arranged on the layer regions divided by the flow path at the slag discharge outlet to collect slag temperature data and evaluate the uniformity of the temperature distribution.

[0009] Slag sampling module: When the temperature distribution is evaluated to be uniform, single-point sampling is started and direct composition detection is carried out. Otherwise, stratified sampling is started.

[0010] Sampling stratified processing module: Take a part of the stratified sampling slag sample as the detection sample.

[0011] Stratified composition detection module: Perform composition detection on each layer of detection samples to obtain the content data of each component, and accordingly evaluate the uniformity of the slag composition distribution.

[0012] Preliminary mixing ratio determination module: Determine the preliminary mixing ratio of the remaining slag samples of each layer according to the uniformity of the slag composition distribution, and extract samples according to the ratio and then perform homogenization treatment to obtain a preliminary mixed sample.

[0013] Mixed ratio dynamic adjustment module: Perform composition detection on the preliminary mixed sample, and dynamically adjust the preliminary mixing ratio based on the deviation of the composition detection to complete the composition detection of the mixed sample.

[0014] In the second aspect of the present invention, a method for detecting and analyzing the composition of high-carbon ferrochrome production slag is proposed, including the following steps: S1: Arrange a plurality of temperature measurement points on the layer regions divided by the flow path at the slag discharge outlet to collect slag temperature data and evaluate the uniformity of the temperature distribution.

[0015] S2: Start single-point sampling and execute S3 when the evaluated temperature distribution is uniform; otherwise, start stratified sampling and execute S4 - S7.

[0016] S3: Conduct component detection on the slag samples obtained by single-point sampling.

[0017] S4: Take a part of the slag samples obtained by stratified sampling as the detection samples.

[0018] S5: Conduct component detection on each layer of detection samples to obtain the content data of each component, and evaluate the uniformity of the slag component distribution based on this.

[0019] S6: Determine the preliminary mixing ratio of the remaining slag samples of each layer according to the uniformity of the slag component distribution, extract samples according to the ratio, and conduct homogenization treatment to obtain the preliminary mixed samples.

[0020] S7: Conduct component detection on the preliminary mixed samples, and dynamically adjust the preliminary mixing ratio based on the deviation of the component detection to complete the component detection of the mixed samples.

[0021] Combining all the above technical solutions, the positive effects of the present invention are as follows: 1. By implementing multi-point temperature acquisition on the slag discharge flow path, and then optimizing the slag sampling strategy based on the temperature distribution uniformity, the present invention realizes precise sampling for different regions, greatly improves the flexibility and representativeness of sampling, and thus significantly improves the accuracy and reliability of subsequent component detection, providing a solid guarantee for the refined control of the high-carbon ferrochrome production process.

[0022] 2. By first conducting stratified component detection after stratified sampling of the slag, the present invention realizes the quantitative analysis of the slag component distribution. Based on this analysis result, the mixing ratio of each stratified sample is dynamically adjusted to prepare the mixed samples and conduct component detection on them, which fully reflects the overall analysis ability of the overall mixed slag, ensures that the mixed samples can comprehensively reflect the overall component characteristics of the slag, and maximally improves the accuracy and reliability of the final detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the following drawings without creative efforts.

[0024] Figure 1 It is a schematic diagram of the module cooperation relationship of a high-carbon ferrochrome production slag component detection and analysis system provided in Embodiment 1 of the present invention.

[0025] Figure 2 It is a schematic diagram of the composition of the slag sampling module in the present invention.

[0026] Figure 3It is a step diagram of a method for detecting and analyzing the composition of high-carbon ferrochrome production slag provided in Embodiment 2 of the present invention.

[0027] Figure 4 It is a schematic diagram of the computer hardware structure provided in Embodiment 3 of the present invention.

[0028] Reference numerals: 100 - bus, 101 - processor, 102 - memory, 103 - communication interface. Specific embodiments

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] Embodiment 1

[0031] The present invention provides a system for detecting and analyzing the composition of high-carbon ferrochrome production slag, including a temperature distribution acquisition module, a slag sampling module, a sampling layering processing module, a layering composition detection module, a preliminary mixing ratio determination module, and a mixing ratio dynamic adjustment module.

[0032] As Figure 1 shown, the overall cooperation relationship between the above-mentioned modules is as follows: The temperature distribution acquisition module, as the starting point, guides the slag sampling strategy by evaluating the temperature distribution uniformity.

[0033] Intermediate links: The slag sampling module, the sampling layering processing module, and the layering composition detection module sequentially complete sampling, layering processing, and composition detection, gradually refining the analysis.

[0034] Output end: The preliminary mixing ratio determination module and the mixing ratio dynamic adjustment module work together to finally achieve global high-precision detection of the overall mixed slag through multiple iterations and optimizations.

[0035] The temperature distribution acquisition module is used to arrange multiple temperature measurement points on the hierarchical areas divided by the flow path of the slag discharge port, collect the temperature data of the slag during discharge in real time, and evaluate the temperature distribution uniformity.

[0036] It is important to understand that slag is a high-temperature molten mixture with a certain viscosity and fluidity. Its flow behavior is affected by gravity, temperature distribution, and the structure of the discharge port, and a continuous flow path will naturally form during the discharge process. The slag discharge path is usually in a columnar or conical flow form with a certain height. This is because during the discharge process, the slag changes temperature due to contact with the external environment, the surface cools faster, and the fluidity decreases; while the internal temperature is higher, and the fluidity is stronger. This temperature gradient causes the slag to be layered along the flow path.

[0037] As a method for implementing the above scheme, multiple temperature measuring points are arranged on the hierarchical areas divided by the flow path of the slag discharge outlet. See the following process: the flow path is divided into a surface area, a middle area, and a bottom area according to the geometric height of the slag discharge path.

[0038] The above-mentioned mid-surface area is close to the air side, has a lower temperature, a faster cooling rate, and a higher viscosity, and may be enriched with low-melting-point substances.

[0039] The middle area has moderate temperature and good fluidity, and is the main flow area of the slag.

[0040] The bottom area is close to the high-temperature heat source or the molten steel side, has the highest temperature and the strongest fluidity, and may be enriched with high-melting-point substances.

[0041] The above division of the surface, middle and bottom layers according to the geometric shape of the flow path is to better reflect the stratified characteristics of the slag caused by the temperature gradient and uneven distribution of components. This division method not only helps with accurate sampling, but also provides basic support for achieving high-precision global detection.

[0042] After the area is divided, temperature measuring points are usually arranged on the surface, middle and bottom layers respectively to cover the entire slag flow area.

[0043] When arranging specific temperature measurement points, the flow rate and flow velocity of slag discharge can be collected, and the total number of temperature measurement points can be estimated using empirical formulas ,in Indicates the total number of temperature measurement points. Indicates the slag capacity, represents the slag flow path length, represents the slag flow velocity, Indicates the correction factor, which is determined based on actual equipment and process conditions.

[0044] It should be emphasized that although the flow rate, flow velocity and flow path length during slag discharge have different dimensions in the above empirical formula, the final result can be explained by physical meaning and applied to the layout of temperature measurement points.

[0045] In the above example, suppose , , , , substituting into the above empirical formula, the total number of temperature measurement points estimated is 20.

[0046] It should be added that when arranging the temperature measurement points, the number of temperature measurement points needs to be determined first. Specifically, it should be determined according to factors such as slag flow rate and flow velocity. This is because the slag flow rate and flow velocity directly affect the residence time, temperature distribution, and composition stratification of the slag on the flow path at the discharge port. A larger flow rate or a faster flow velocity will cause the slag to cool rapidly during the discharge process, forming a significant temperature gradient. Therefore, it is necessary to reasonably arrange the temperature measurement points according to these parameters. Specifically, if the slag flow rate is large, the flow path is long and the composition distribution is complex, then the number of temperature measurement points needs to be increased to capture more details. If the slag flow rate is small, the flow path is short and the composition distribution is relatively uniform, then the number of temperature measurement points can be reduced. In the present invention, the number of temperature measurement points on the slag flow path is determined based on the empirical formula because the empirical formula is usually summarized based on a large amount of experimental data and practical applications, and can more accurately estimate the number of temperature measurement points to be arranged, so as to ensure that the temperature distribution on the entire flow path is fully covered. In addition, it is possible to reasonably allocate limited resources (such as sensors, data acquisition equipment, etc.) on the premise of ensuring the detection accuracy, avoid the cost increase caused by over-configuration, and at the same time ensure that key areas are sufficiently monitored.

[0047] Arrange the temperature measurement points at equal intervals in proportion in the divided surface layer area, middle layer area, and bottom layer area according to the estimated total number of temperature measurement points.

[0048] In the specific implementation of the above scheme, arranging the temperature measurement points at equal intervals in proportion in each layer area is considered because the temperature distribution states of each layer area are different, and thus the proportion of temperature measurement points in each layer area is determined. Among them, in the surface layer area, due to the relatively thin thickness and fast cooling rate, the number of temperature measurement points can be relatively small, but it is necessary to ensure that the entire surface layer is covered. The middle layer area is the main flow area, and the number of temperature measurement points should account for a relatively large proportion to comprehensively reflect the temperature distribution. The bottom layer area has the highest temperature and complex composition, and the number of temperature measurement points should also be relatively large to capture local temperature changes.

[0049] As an example operation, the number of temperature measurement points in each layer area can be allocated according to the empirical distribution ratio (such as 20% for the surface layer, 50% for the middle layer, and 30% for the bottom layer).

[0050] Furthermore, when arranging the temperature measurement points in each layer area, the equal interval method is adopted to meet the average arrangement of temperature measurement points in each layer area.

[0051] It should be noted that when selecting a temperature measurement device, it is necessary to have good high-temperature resistance performance to adapt to the extreme environment during the slag discharge. Exemplarily, a high-precision thermocouple can be selected because of its fast response speed, high precision, and high-temperature resistance. At the same time, it is also necessary to ensure that the data acquisition time of all temperature measurement points is consistent to avoid errors caused by time deviation.

[0052] As a further implementable way of the above solution, the process of evaluating the temperature distribution uniformity is as follows: Calculate the average temperature and temperature standard deviation of the slag temperature at each temperature measurement point in each layer area on the flow path of the slag discharge port.

[0053] Define the temperature distribution uniformity formula , where represents the temperature distribution uniformity index, represents the temperature standard deviation, represents the average temperature.

[0054] It should be understood that in the above temperature distribution uniformity formula reflects the temperature coefficient of variation, which is used to measure the relative dispersion degree of the temperature distribution. The temperature distribution uniformity index is defined by subtracting the temperature coefficient of variation from the value 1. When the temperature variation is larger, the temperature distribution uniformity is smaller. The closer the temperature distribution uniformity index is to 1, the more uniform the temperature distribution is.

[0055] The above expression formula of the temperature distribution uniformity is convenient for explanation and practical application. In addition, in the field of engineering optimization, usually, a high temperature distribution uniformity is pursued to ensure process stability and product quality. Compared with directly minimizing the temperature coefficient of variation, by defining the value 1 minus the temperature coefficient of variation as the temperature distribution uniformity index and maximizing this index as the objective function, the optimization goal can be more clearly reflected, that is, to pursue the maximum uniformity of the temperature distribution. This expression not only simplifies the mathematical expression of the optimization problem but also improves its operability in engineering practice.

[0056] Substitute the average temperature and temperature standard deviation calculated for each layer area into the temperature distribution uniformity formula to obtain the temperature distribution uniformity index for each area.

[0057] Merge the temperature data of all temperature measurement points in all layer areas to calculate the overall average temperature and temperature standard deviation and import them into the temperature distribution uniformity formula to obtain the total temperature distribution uniformity index.

[0058] As a data example of the above temperature distribution uniformity analysis, assume that we have three layer areas (surface layer, middle layer, bottom layer), and the slag temperature data of the temperature measurement points are shown in Table 1 (unit: °C).

[0059] Table 1: Slag temperature data of some temperature measurement points in the layer area

[0060]

[0061] Calculate the average temperature and temperature standard deviation of each layer area from the data in the above table, and substitute them into the temperature distribution uniformity formula to obtain , , .

[0062] Combine the slag temperatures at all temperature measurement points to obtain the overall average temperature and temperature standard deviation , and substitute them into the temperature distribution uniformity formula to obtain .

[0063] Compare the temperature distribution uniformity indices of each layer area and the total temperature distribution uniformity index with the set threshold. If both reach the set threshold, it is evaluated that the slag temperature distribution on the flow path of the slag discharge port is uniform; otherwise, it is evaluated that the temperature distribution is non-uniform.

[0064] Continue to apply the above example. Assume that the set threshold of the temperature distribution uniformity index is 0.98. In the above example, the temperature distribution uniformity indices of all layer areas and the total temperature distribution uniformity index are both greater than 0.98, so it is evaluated that the slag temperature distribution on the flow path of the slag discharge port is uniform.

[0065] The above temperature distribution uniformity analysis based on the slag temperatures at different temperature measurement points in each layer area on the slag flow path not only analyzes the temperature distribution uniformity of each area, but also comprehensively considers the overall situation of all temperature measurement points. This "local + global" analysis method can more comprehensively reflect the characteristics of the slag temperature distribution.

[0066] When the temperature distribution is evaluated to be uniform, the slag sampling module starts single-point sampling and directly conducts component detection; otherwise, it starts stratified sampling.

[0067] It should be explained that when the temperature distribution on the slag flow path has been fully studied or is expected to be very uniform, the results of single-point sampling can represent the overall situation. At this time, there is no need to conduct comprehensive sampling to save time and resources.

[0068] It should be added that in the single-point sampling mode, since only one slag sample is collected, when conducting component detection, this sample will be directly used for the final component analysis. However, in practical applications, assuming that the temperature distribution on the slag flow path is completely uniform is an overly idealized condition. In fact, the slag temperature distribution is usually non-uniform due to various factors during the flow process. This non-uniformity may have a significant impact on the physical and chemical properties of the slag components, thus affecting the accuracy of the detection results. Therefore, in the actual sampling process, in order to more comprehensively reflect the overall characteristics and spatial heterogeneity of the slag, the stratified sampling method is widely used to ensure that the collected samples are more representative and reliable.

[0069] In the improvement operation of the above solution, single-point sampling is implemented as follows: Calculate the deviation between the slag temperature of each temperature measurement point in all layer regions and the overall average temperature, and then select the temperature measurement point with the smallest deviation as the single-point sampling position. This can ensure that the single-point sampling position can accurately reflect the overall characteristics of the slag, thereby improving the detection efficiency and the reliability of the results.

[0070] In the further improvement operation of the above solution, stratified sampling is implemented as follows: Compare the temperature distribution uniformity index of each layer region with the set threshold.

[0071] For the layer region where the temperature distribution uniformity index reaches the set threshold, calculate the deviation between the slag temperature of each temperature measurement point in this region and the average temperature of this region, and select the temperature measurement point with the smallest deviation as the sampling position of this region.

[0072] In the above stratified sampling, for the layer region where the temperature distribution uniformity index reaches the set threshold, it indicates that the temperature distribution in this layer region is relatively uniform, and the setting of its sampling points is equivalent to the aforementioned single-point sampling.

[0073] For the layer region where the temperature distribution uniformity index does not reach the set threshold, screen the temperature measurement points corresponding to the highest temperature and the lowest temperature in the region as the sampling positions of this region. At the same time, calculate the temperature gradient of adjacent temperature measurement points for the temperature measurement points arranged in sequence according to the position in the region. Specifically, , where represents the temperature gradient between two adjacent temperature measurement points and , and both represent the temperature measurement point numbers, , respectively represent the temperature difference and distance between two adjacent temperature measurement points and .

[0074] By comparing the temperature gradient of adjacent two temperature measurement points with the configured critical value, if the temperature gradient of a certain adjacent two temperature measurement points is greater than the critical value, an auxiliary sampling point is added at the middle position between these two adjacent temperature measurement points.

[0075] It should be noted that the temperature measurement points corresponding to the highest temperature and the lowest temperature can reflect the extreme situation of the temperature distribution in this region. The temperature gradient calculation is used to evaluate the severity of the temperature change. If the gradient is too large, it indicates that there may be significant heat flow or temperature anomalies in this region, and sampling points need to be added to capture these changes. The addition of auxiliary sampling points can more comprehensively reflect the details of the temperature distribution and avoid missing important information.

[0076] In the example implementation of the above operation, assume that the slag temperature data of the temperature measurement points in a certain layer region on the slag flow path is as shown in Table 2.

[0077] Table 2: Slag Temperature Data at Temperature Measuring Points

[0078]

[0079] From the data in the table, the temperature distribution uniformity index of this layer area is calculated to be 0.97, which does not reach the threshold of 0.98. At this time, the temperature measuring points corresponding to the highest temperature (1250) and the lowest temperature (1150) are selected as sampling positions 1 and 5 respectively, and the temperature gradients between adjacent temperature measuring points are calculated 、 、 、 , and when comparing with the critical value of the temperature gradient, it is assumed that and are greater than the critical value, then auxiliary sampling points are added between the corresponding temperature measuring points.

[0080] In the present invention, by selecting the temperature measuring point with the smallest deviation from the average temperature as the sampling position in the area with uniform temperature distribution, the number of redundant sampling points is effectively reduced, and the sampling efficiency is significantly improved. In the area with non-uniform temperature distribution, by screening the extreme temperature points and calculating the temperature gradient, the sampling point density is increased in a targeted manner, avoiding the resource waste caused by global dense sampling. This method comprehensively considers the local characteristics and global characteristics, and flexibly adjusts the sampling strategy according to the temperature distribution characteristics of different areas, so as to be able to obtain more representative slag samples more accurately.

[0081] In the supplementary implementation of the above solution, the slag sampling module further includes a sampling device composed of a sampling terminal, a data interface, a positioning unit, and a control unit.

[0082] The above-mentioned sampling terminal is the part directly in contact with the slag, responsible for performing specific sampling operations, and different sampling terminals (such as drill bit type, scraper type or straw type) can be selected according to the slag state. Among them, the drill bit type is suitable for harder or solidified slag, the scraper type is suitable for slag with a relatively smooth surface or semi-solid state, and the straw type is suitable for liquid or highly fluid slag.

[0083] The above-mentioned data interface receives the temperature data of the temperature measuring points in real time and feeds back the sampling results to the control unit, realizing data acquisition, transmission and feedback, and providing support for subsequent analysis and control.

[0084] The above-mentioned positioning unit is used to locate the specific position of the sampling terminal on the slag flow path to ensure the accuracy and repeatability of the sampling points.

[0085] The above-mentioned control unit generates sampling instructions according to the temperature distribution data and other input information (such as process parameters, historical data, etc.) to control the actions of the sampling terminal.

[0086] The connection between the above components is as Figure 2 shown.

[0087] It should be noted that in Figure 2 , the sampling terminal is fixed on the positioning unit through mechanical connection, and this can drive the sampling terminal to move to the specified position by the positioning unit.

[0088] It should be noted that the sampling device needs to rely on the positioning unit to accurately determine the sampling position. In specific positioning implementations, optical positioning can be exemplarily adopted. For example, optical marking points or reflectors are arranged on the slag flow path, and the relative position between the sampling terminal and the marking points is measured by a laser scanner. The advantage of using this positioning method is non-contact measurement and it is suitable for high-temperature environments, but the limitation is that it is greatly affected by soot and light.

[0089] Another example is to use lidar for positioning. By emitting laser and receiving the reflected signal, the distance and angle between the sampling terminal and the target point are calculated. The advantages of this positioning operation are high precision and strong anti-interference ability, and the limitation is that the equipment cost is relatively high.

[0090] Another example is to use electromagnetic positioning. Electromagnetic sensors are arranged near the slag flow path, and positioning is carried out by detecting the electromagnetic signal generated by the sampling head. Its advantage is that it is not affected by light or soot, and the limitation is that additional electromagnetic sensors need to be arranged, increasing the system complexity and hardware requirements.

[0091] In actual positioning operations, a suitable positioning method can be selected according to specific requirements (such as environmental conditions, accuracy requirements, cost limitations, etc.) to ensure the efficient and reliable operation of the positioning function of the sampling device.

[0092] The sampling stratification processing module takes a part of the stratified sampled slag sample as the detection sample.

[0093] It should be noted that the above-mentioned taking a part of the stratified sampled slag sample as the detection sample means that in the stratified sampling process, a part is respectively selected from the slag samples collected at each sampling point to form the detection sample corresponding to the sampling point. This means that the detection samples of each sampling point are independently extracted, ensuring the representativeness of each layer and each point.

[0094] The stratified component detection module performs component detection on the detection samples of each layer, obtains the content data of each component, and evaluates the uniformity of the slag component distribution based on this.

[0095] As a preferred embodiment, for the component detection of the detection samples of each layer, X-ray fluorescence spectrometry, atomic absorption spectrometry, etc. can be used.

[0096] In the above solution, the specific implementation of evaluating the uniformity of slag composition distribution is as follows: Calculate the average content and content standard deviation of the same component in all layer regions for the component content data of each layer of detection samples.

[0097] The above-mentioned component content data can be understood as the component proportion.

[0098] It should be noted that the component content data of each layer of detection samples mentioned above refers to the average value of the content of each component at the sampling point obtained by analyzing the detection samples of each sampling point in each layer. Specifically, this data is statistically averaged based on the content of each component in the detection samples of all sampling points in each layer, aiming to quantify and characterize the component characteristics of the slag in each layer.

[0099] Calculate the coefficient of variation of the content distribution of each component by using the average content and content standard deviation of each component in all layer regions.

[0100] Compare the coefficient of variation of the content distribution of each component with a preset allowable threshold. The allowable threshold can be preset according to the requirements of the production process for the slag composition distribution. If the coefficient of variation of the content distribution of all components meets the allowable threshold, it is evaluated that the slag composition distribution is uniform; otherwise, it is evaluated that the slag composition distribution is non-uniform.

[0101] It should be pointed out that the above analysis of the slag composition distribution uniformity based on the component content data of each layer of detection samples uses statistical methods to transform qualitative problems into quantitative analysis, making the evaluation results more scientific and objective.

[0102] The preliminary mixing ratio determination module determines the preliminary mixing ratio of the remaining slag samples in each layer according to the slag composition distribution uniformity, and extracts samples according to the ratio and then performs homogenization treatment to obtain a preliminary mixed sample.

[0103] It should be known that the above-mentioned homogenization treatment refers to the operation of fully mixing and homogenizing the slag samples extracted from each layer region by physical or chemical means to reduce the component differences between samples and ensure that the finally obtained mixed sample can accurately reflect the characteristics of the overall slag. The commonly used method in specific operations is stirring and mixing.

[0104] The process for determining the preliminary mixing ratio of the remaining slag samples in each layer according to the slag composition distribution uniformity is as follows: When it is evaluated that the slag composition distribution is uniform, the mixing ratio of the remaining slag samples in each layer is determined to be the same ratio.

[0105] As an explanation of the above operation, when the slag composition is evenly distributed, it means that there is no significant difference in the composition content between layers. In this case, the contributions of each layer to the overall properties are equivalent. Therefore, there is no need to specifically distinguish or adjust the mixing ratio, and samples can be selected in equal proportions to comprehensively reflect the characteristics of the entire slag system.

[0106] When evaluating the uneven distribution of slag composition, calculate the theoretical proportion of each component in the overall slag by combining the content of each component in each layer region with the volume of each layer region. , where represents the content of the -th component in the -th layer region, represents the slag component number, , represents the number of layers. In the present invention, the number of layers is 3, where represents the bottom layer, represents the middle layer, represents the top layer, represents the -th layer region volume.

[0107] It should be noted that the core idea of the above theoretical proportion calculation is to quantify the relative contribution ratio of each component in the entire slag system through volume-weighted averaging of each layer region. This ratio reflects the absolute contribution degree of each component based on its content and volume distribution, rather than the normalized proportion presented directly in percentage form. Therefore, without normalization, the cumulative value of the proportion of each component calculated may not be equal to 100%. If it is necessary to ensure that the sum of the proportions of all components is exactly 100%, the original proportion of each component can be adjusted by the normalization method, where the normalization formula is , represents the theoretical proportion of the -th component in the overall slag after normalization. This process can ensure that the final result meets the normalization requirements in probability statistics and accurately reflects the actual distribution characteristics of each component in the overall slag system.

[0108] As a data example of the above theoretical proportion, assume there is a three-layer slag sample (bottom layer, middle layer, top layer), and there are three components (component 1, component 2, component 3), and the content data of each component in each layer region is shown in Table 3.

[0109] Table 3: Content data of each component in each layer region

[0110]

[0111] Among them, the volumes of the bottom layer, middle layer, and surface layer are 100 cubic meters, 150 cubic meters, and 200 cubic meters respectively. When substituting the data in the table into the theoretical proportion calculation, the results after normalizing the theoretical proportion of each component in the overall slag are , , .

[0112] Using the formula to determine the preliminary mixing ratio of the remaining slag samples in each layer .

[0113] In the above formula reflects the total volume weighted content of each layer in the slag flow path, reflects the relative weight of each layer, representing the importance of each layer area. The larger the weight, the larger the preliminary mixing ratio of the layer area.

[0114] Continuing to apply the example data in Table 3 above, the preliminary mixing ratios of the remaining slag samples in each layer are , , .

[0115] The mixing ratio dynamic adjustment module detects the components of the preliminary mixing sample and dynamically adjusts the preliminary mixing ratio based on the deviation of the component detection to complete the component detection of the mixing sample.

[0116] In the preferred implementation of the above solution, the process of dynamically adjusting the preliminary mixing ratio based on the deviation of the component detection is as follows: Detect the components of the preliminary mixing sample, and conduct a deviation analysis on the detection result and the theoretical proportion of each component in the overall slag. If there is a deviation in the analysis, adjust the preliminary mixing ratio of each layer based on the component deviation amount.

[0117] In the improved implementation of the above solution, the deviation analysis can calculate the deviation between the component detection result of the preliminary mixing sample and the theoretical proportion of each component in the overall slag, that is, take the difference, and compare the difference result with the preset allowable deviation amount. If the deviation amount of a certain component is greater than the allowable deviation amount, it is considered that there is a deviation.

[0118] In the further improved implementation of the above solution, the process of adjusting the preliminary mixing ratio of each layer based on the component deviation amount is as follows: Define the deviation correction factor of each component , , represents the deviation amount of the th component, which is the component detection proportion of the preliminary mixing sample minus the theoretical proportion.

[0119] According to the deviation correction factor of each component and its content in each layer area Calculate the adjusted mixing ratio for each layer .

[0120] As an example of the above implementation, the theoretical proportion of components 1, 2, and 3 and the detected proportion data in the preliminary mixed sample are shown in Table 4.

[0121] Table 4: Comparison data of slag components

[0122]

[0123] Calculate the deviation correction factor for each component based on the data in the table , , , and thus the adjusted mixing ratio for each layer is calculated as , , .

[0124] Prepare a mixed sample again according to the adjusted mixing ratio for each layer and conduct component detection. If there is still a deviation between the detection result and the theoretical value, repeat the above steps to further optimize the mixing ratio until the components of the mixed sample meet the theoretical value.

[0125] In the case of evaluating the uneven distribution of slag components, the present invention prepares a mixed sample by mixing different equal proportions of the remaining slag samples of each layer. The core reason is that when the slag component distribution shows unevenness, certain specific layers may contain more key information. By reasonably adjusting the mixing ratio, it is possible to effectively highlight the layers with significant component differences, thereby more accurately capturing the characteristics of these key regions. This method can avoid the dilution of important information caused by equal proportion selection and is beneficial to improving the representativeness of the mixed sample for the overall slag characteristics.

[0126] Furthermore, based on the deviation between the component detection result of the preliminary mixed sample and the theoretical data under the condition of uneven slag component distribution, and combined with the importance of each layer region, dynamically adjust the mixing ratio of the remaining slag samples of each layer. The mixed sample prepared in this way can more accurately and comprehensively reflect the overall component distribution characteristics and spatial heterogeneity of the slag, thereby significantly improving the quality of the mixed sample and the reliability of the analysis result.

[0127] It should be emphasized that in the present invention, whether it is the component detection of the stratified sampling sample or the component detection of the mixed sample, it is assumed that the detection equipment has high precision and operates in an ideal detection environment. Therefore, the detection result is not affected by the performance fluctuation of the detection equipment, the change of the detection environment or other external factors, ensuring that the obtained data fully reflects the true component characteristics of the sample.

[0128] Example 2

[0129] SeeFigure 3 As shown, the present invention provides a method for detecting and analyzing the composition of high-carbon ferrochrome production slag, including the following steps: S1: Arrange a plurality of temperature measurement points on the layer areas divided by the flow path of the slag discharge port to collect slag temperature data and evaluate the uniformity of temperature distribution.

[0130] S2: When the temperature distribution is evaluated to be uniform, start single-point sampling and execute S3; otherwise, start stratified sampling and execute S4 - S7.

[0131] S3: Detect the composition of the slag sample obtained by single-point sampling.

[0132] S4: Take a part of the stratified sampling slag sample as the detection sample.

[0133] S5: Detect the composition of each layer of detection samples to obtain the content data of each component, and evaluate the uniformity of slag composition distribution based on this.

[0134] S6: Determine the preliminary mixing ratio of the remaining slag samples of each layer according to the uniformity of slag composition distribution, extract samples according to the ratio, and perform homogenization treatment to obtain the preliminary mixed sample.

[0135] S7: Detect the composition of the preliminary mixed sample, and dynamically adjust the preliminary mixing ratio based on the deviation of the composition detection to complete the composition detection of the mixed sample.

[0136] The temperature measurement equipment of the present invention uses a high-precision thermocouple with a response speed of milliseconds (<10 ms), which can capture the temperature distribution data in real time during the flow of the slag. At the same time, the flow rate of the slag is usually low (for example, the flow rate range is about 0.1 - 1.0 m / s). Under the detection time of milliseconds, the displacement of the slag is extremely small (for example, when the flow rate is 1 m / s, the displacement within 10 ms is only 1 cm). Therefore, the temperature data and the sampling position can still maintain a high degree of correspondence, and the influence of hysteresis on the sampling representativeness can be ignored. In addition, in the layer areas with uneven temperature distribution, the present invention adopts a multi-sampling point coverage strategy, and the component content data of each layer is obtained by independently detecting the samples at different sampling points of each layer of samples and taking the average value. This method effectively offsets the possible local deviation of single-point sampling through spatial redundant sampling. Even if individual sampling points have a small offset due to flow displacement, the overall accuracy of the component data of the hierarchical area can be ensured through the equalization processing of multiple data points.

[0137] Example 3

[0138] As Figure 4As shown, the embodiments of the present invention provide the following technical solutions: A computer includes a memory 102, a processor 101, and a computer program stored on the memory 102 and executable on the processor 101. When the processor 101 executes the computer program, it implements a high-carbon ferrochrome production slag composition detection and analysis system as described above.

[0139] Specifically, the above-mentioned processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits implementing the embodiments of the present application.

[0140] Among them, the memory 102 may include a mass storage for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 102 may include removable or non-removable (or fixed) media. In a suitable case, the memory 102 may be inside or outside the data processing device. In a specific embodiment, the memory 102 is a non-volatile memory. In a specific embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). In a suitable case, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. In a suitable case, the RAM may be a static random-access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random-access memory (SDRAM), etc.

[0141] The memory 102 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 101.

[0142] The processor 101 reads and executes the computer program instructions stored in the memory 102 to implement the above-mentioned high-carbon ferrochrome production slag composition detection and analysis system.

[0143] In some of the embodiments, the computer may further include a communication interface 103 and a bus 100. Among them, as Figure 4 shown, the processor 101, the memory 102, and the communication interface 103 are connected through the bus 100 to complete communication with each other.

[0144] The communication interface 103 is used to implement communication between the various modules, devices, units, and / or equipment in the embodiments of the present application.

[0145] Bus 100 includes hardware, software, or both, and couples components of a computer together. Bus 100 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, Bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus or a combination of two or more of these. In suitable cases, Bus 100 may include one or more buses. Although embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0146] Embodiment 4

[0147] Combined with the above high-carbon ferrochrome production slag composition detection and analysis system, embodiments of the present invention provide the following technical solution: a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above high-carbon ferrochrome production slag composition detection and analysis system.

[0148] Those skilled in the art can understand that in the flowchart, the data and / or steps described herein or in other ways, for example, can be considered as a predefined sequence data table of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0149] More specific examples (nonexhaustive list) of readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing when necessary, and then stored in a computer memory.

[0150] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0151] The above content is merely an example and illustration of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar ways to substitute them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A composition detection and analysis system for high-carbon ferrochrome production slag, characterized in that, It includes the following modules: Temperature distribution acquisition module: A plurality of temperature measurement points are arranged on the layer areas divided by the flow path at the slag discharge outlet to collect slag temperature data and evaluate the uniformity of temperature distribution; Slag sampling module: When the temperature distribution is evaluated to be uniform, single-point sampling is started and component detection is directly carried out. Otherwise, layered sampling is started; Sampling layered processing module: Take a part of the layered sampling slag sample as the test sample; Layered component detection module: Perform component detection on each layer of test samples to obtain the content data of each component, and evaluate the uniformity of slag component distribution based on this; Preliminary mixing ratio determination module: Determine the preliminary mixing ratio of the remaining slag samples of each layer according to the uniformity of slag component distribution, and extract samples according to the ratio and then perform homogenization treatment to obtain the preliminary mixed sample. The method for determining the preliminary mixing ratio is as follows: Calculate the average content and content standard deviation of the same component in all layer areas for the component data of each layer of test samples, and analyze to obtain the content distribution variation coefficient of each component; then evaluate whether the slag component distribution is uniform; when the slag component distribution is evaluated to be uniform, determine the mixing ratio of the remaining slag samples of each layer to be the same ratio; when the slag component distribution is evaluated to be non-uniform, calculate the theoretical proportion of each component in the overall slag by combining the content of each component in each layer area with the volume of each layer area, and determine the preliminary mixing ratio of the remaining slag samples of each layer; Mixing ratio dynamic adjustment module: Perform component detection on the preliminary mixed sample, and dynamically adjust the preliminary mixing ratio based on the deviation of the component detection to complete the component detection of the mixed sample.

2. The composition detection and analysis system for the slag of a high-carbon ferrochrome production furnace according to claim 1, characterized in that: The process of arranging a plurality of temperature measurement points on the layer areas divided by the flow path at the slag discharge outlet is as follows: According to the geometric shape of the slag discharge path, the flow path is divided into a surface layer area, a middle layer area, and a bottom layer area; Estimate the total number of temperature measurement points by using the empirical formula for the flow rate and flow velocity when collecting the slag discharge , where represents the total number of temperature measurement points, represents the slag capacity, represents the length of the slag flow path, represents the slag flow velocity, represents the correction coefficient, which is determined according to the actual equipment and process conditions; According to the estimated total number of temperature measurement points, the temperature measurement points are arranged at equal intervals in proportion in the divided surface layer area, middle layer area, and bottom layer area.

3. The composition detection and analysis system for high-carbon ferrochrome production slag according to claim 1, characterized in that: The process of evaluating the temperature distribution uniformity is as follows: Calculate the average temperature and temperature standard deviation of the slag temperature at each temperature measurement point on each layer area of the flow path at the slag discharge outlet; Define the formula for uniform temperature distribution , where represents the uniform temperature distribution index, represents the temperature standard deviation, represents the average temperature; Substitute the average temperature and temperature standard deviation calculated for each layer area into the temperature distribution uniformity formula to obtain the temperature distribution uniformity index of each area; Merge and calculate the overall average temperature and temperature standard deviation of the temperature data of all temperature measurement points in all layer areas and import them into the temperature distribution uniformity formula to obtain the total temperature distribution uniformity index; Compare the temperature distribution uniformity index of each layer area and the total temperature distribution uniformity index with the set threshold. If both reach the set threshold, it is evaluated that the slag temperature distribution on the flow path at the slag discharge outlet is uniform. Otherwise, it is evaluated that the temperature distribution is non-uniform.

4. The high-carbon ferrochrome production slag composition detection and analysis system according to claim 3, wherein: The implementation of the single-point sampling is as follows: Calculate the deviation between the slag temperature at each temperature measurement point in all layer areas and the overall average temperature, and then take the temperature measurement point with the minimum deviation as the single-point sampling position.

5. The high-carbon ferrochrome production slag composition detection and analysis system according to claim 3, characterized in that: The implementation of the layered sampling is as follows: Compare the temperature distribution uniformity index of each layer area with the set threshold; For the layer areas where the temperature distribution uniformity index reaches the set threshold, calculate the deviation between the slag temperature at each temperature measurement point in this area and the average temperature of this area, and select the temperature measurement point with the minimum deviation as the sampling position of this area; For the layer area where the temperature distribution uniformity index does not reach the set threshold, screen the temperature measurement points corresponding to the highest and lowest temperatures in the screening area as the sampling positions of this area. At the same time, calculate the temperature gradient between adjacent temperature measurement points for the temperature measurement points arranged in sequence according to the position in the area. Compare the temperature gradient between two adjacent temperature measurement points with the configured critical value. If the temperature gradient between a certain two adjacent temperature measurement points is greater than the critical value, add an auxiliary sampling point at the middle position between these two adjacent temperature measurement points.

6. The composition detection and analysis system for the slag of high-carbon ferrochrome production furnace according to claim 1, wherein: The slag sampling module further includes a sampling device composed of a sampling terminal, a data interface, a positioning unit, and a control unit. The sampling terminal is the part that directly contacts the slag and is responsible for executing the sampling instruction issued by the control unit. The data interface receives the temperature data of the temperature measurement points in real time and feeds back the sampling result to the control unit. The positioning unit is used to locate the specific position of the sampling terminal on the slag flow path. The control unit generates a sampling instruction according to the temperature distribution data and other input information.

7. The composition detection and analysis system for the slag of high-carbon ferrochrome production furnace according to claim 1, wherein: The specific implementation of evaluating the uniformity of slag component distribution is as follows: Calculate the coefficient of variation of the content distribution of various components by using the average content and the standard deviation of the content of various components in all layer areas to obtain the coefficient of variation of the content distribution of various components. Compare the coefficient of variation of the content distribution of various components with the preset allowable threshold. If the coefficient of variation of the content distribution of all components meets the allowable threshold, it is evaluated that the slag component distribution is uniform; otherwise, it is evaluated that the slag component distribution is non-uniform.

8. The composition detection and analysis system for the slag of high-carbon ferrochrome production furnace according to claim 1, characterized in that: The preliminary mixing ratio of the remaining slag samples in each layer determined according to the uniformity of slag component distribution is shown in the following process: The theoretical proportion analysis formula of each component in the overall slag is as follows: , where represents the content of the th component in the th layer area, represents the slag component number, , represents the number of layers, , represents the th layer area volume; Using the formula to determine the preliminary mixing ratio of the remaining slag samples for each layer .

9. The composition detection and analysis system for high-carbon ferrochrome production slag according to claim 8, wherein: The dynamic adjustment of the preliminary mixing ratio based on the deviation of component detection is shown in the following process: Conduct component detection on the preliminary mixed sample, and conduct deviation analysis on the detection result and the theoretical proportion of each component in the overall slag. If there is a deviation in the analysis, adjust the preliminary mixing ratio of each layer based on the component deviation amount. Prepare a mixed sample again according to the adjusted mixing ratio of each layer, and conduct component detection. If the detection result still deviates from the theoretical value, repeat the above steps to further optimize the mixing ratio until the components of the mixed sample meet the theoretical value.

10. A method for detecting and analyzing the composition of the slag in the production of high-carbon ferrochrome, characterized in that: It includes the following steps: S1: Arrange multiple temperature measurement points on the layer areas divided on the slag discharge port flow path to collect slag temperature data and evaluate the temperature distribution uniformity. S2: When the temperature distribution is evaluated to be uniform, start single-point sampling and execute S3; otherwise, start stratified sampling and execute S4 - S7. S3: Conduct component detection on the slag sample of single-point sampling. S4: Take a part of the stratified sampling slag sample as the detection sample. S5: Conduct component detection on the detection samples of each layer to obtain the content data of each component, and evaluate the uniformity of slag component distribution based on this. S6: Determine the preliminary mixing ratio of the remaining slag samples in each layer according to the uniformity of slag component distribution, and extract samples according to the ratio and then conduct homogenization treatment to obtain the preliminary mixed sample. S7: Conduct component detection on the preliminary mixed sample, and dynamically adjust the preliminary mixing ratio based on the deviation of component detection to complete the component detection of the mixed sample.

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