A combined method for resolving ultra-high stock layer mixture and sinter

Abstract

The application discloses a kind of ultra-high material layer mixture-sinter combined analysis method.The method is respectively divided into sampling point sampling to mixture car and sinter car, respectively determine mixture characteristic parameter and sinter organization parameter, after analysis, the mapping relationship of mixture and sinter is established, and the parameters of distributor are judged and optimized.The method considers the characteristic parameter in mixture and the organization parameter in sinter as two different sets respectively, by determining the correlation in two sets, the influence of mixture on sinter performance is judged from macroscopic point of view, further overall distributed optimization is carried out to sinter performance, the optimization interval of energy consumption and sinter performance is sought, and the adjustment range of distributor parameter is determined, so as to realize sinter quality improvement and consumption reduction and homogenization of different varieties of ore source.

Description

Technical Field

[0001] This invention relates to a method for analyzing sintered ore, specifically a combined analysis method for ultra-high bed material mixture and sintered ore, belonging to the field of iron and steel metallurgical technology. Background Technology

[0002] The use of high-quality feedstock in blast furnace ironmaking and the reduction of process energy consumption are key to the transformation of long-process steelmaking. Ultra-high bed sintering (ZL 200510032095.6), first proposed by Central South University at the beginning of this century, is an important development direction for the greening of blast furnace feedstock. Studies have shown that when the bed height is 500-750mm, for every 100mm increase in bed height, process energy consumption decreases by approximately 10kg / ts. Currently, the average bed height in my country is close to 800mm, and some advanced enterprises have exceeded 900mm, even reaching 1000mm. However, the effect of reducing process energy consumption during the implementation of ultra-high bed sintering in some enterprises is not significant, and even the phenomenon of increased solid fuel consumption has occurred. Furthermore, many enterprises have reported a deterioration in the low-temperature reduction and pulverization performance of sintered ore, leading to a decrease in blast furnace production efficiency. Therefore, a systematic analysis of the problems existing in ultra-high bed sintering and the proposal of targeted improvement measures are of great significance.

[0003] There are numerous case studies on sintering in China. In 2013, Long Hongming analyzed a 400m... 2 Sinter samples were taken from the sintering machine for analysis. The sinter was divided into 9 points (3×3) to focus on analyzing the unevenness of the sinter. In 2020, Ma Xianguo conducted a study on the 405m sintering machine at Ansteel Bayuquan. 2 The sintering mixture was divided into 6×7 sampling points, and the distribution of fuel and components in the mixture was analyzed in detail. However, the analytical methods in the above cases lacked systematicity, focusing only on the individual performance of the mixture or sinter, and lacking the organic connection between the two. Furthermore, in analyzing the uneven quality of sinter, they lacked a systematic connection between particle size, heat, and ore-forming components in the mixture. Therefore, it is necessary to invent a novel, scientific, and practically applicable method for analyzing the performance of ultra-high bed mixtures and sinter, which is of great significance for further reducing solid fuel consumption in ultra-high bed materials. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention aims to provide a method for analyzing ultra-high layer sinter. This method involves array-distributed sampling of the mixing trolley and the sinter trolley, respectively. By measuring and analyzing the characteristic parameters and microstructure parameters of the samples, a mapping relationship between the mixture and the sinter is established. Based on this relationship, the parameters of the material distributor are optimized and adjusted, thereby achieving quality improvement and energy saving in ultra-high layer sintering.

[0005] To achieve the above technical objectives, this invention provides a method for combined analysis of ultra-high bed weights and sintered ore, comprising the following steps:

[0006] 1) Sampling points were set up for the mixed material trolley and the sinter ore trolley, respectively;

[0007] 2) Determine the characteristic parameters of the mixture and the microstructure parameters of the sinter based on the samples taken from the sampling points;

[0008] 3) Analyze and judge the mixture and sinter according to the characteristic parameters and microstructure parameters respectively, and establish the mapping relationship between the mixture and sinter based on the analytical judgment results;

[0009] 4) The parameters of the feeder are judged and optimized based on the mapping relationship between the mixture and the sinter.

[0010] The analytical method provided by this invention is based on the above four-step method. By analyzing and judging the characteristic parameters of the mixture sample and the microstructure parameters of the sinter sample, a mapping relationship between the mixture and the sinter is established, thereby realizing the optimization and adjustment of the feeder process parameters, thus achieving quality improvement and consumption reduction in the sintering process.

[0011] As a preferred embodiment, the number of sampling points on the mixing trolley is 16 to 64, and the number of sampling points on the sintering ore trolley is 16 to 36.

[0012] As a preferred embodiment, the sampling points are distributed in a matrix. More preferably, the sampling points are distributed in a square matrix. The selection of sampling points is a key influencing factor in establishing the mapping relationship between the mixture and the sinter. The sampling points should cover all parts of the material as comprehensively as possible, reflecting the basic physicochemical properties of the material as accurately as possible, thereby improving the objectivity and confidence level of subsequent analysis processes.

[0013] As a preferred embodiment, the characteristic parameters of the mixture include: particle size distribution, composition, fuel distribution, liquid phase masterbatch composition, and the proportion of liquid phase masterbatch.

[0014] As a preferred embodiment, the sintered ore microstructure parameters include: composition, macro- and micro-structure, phase composition, strength, and metallurgical properties.

[0015] As a preferred embodiment, the particle size distribution of the mixture is the composition ratio of each particle size in the mixture, wherein the particle size is: 0.5mm, 1mm, 3mm, 5mm, 8mm.

[0016] As a preferred embodiment, the fuel distribution of the mixture is the distribution of fixed carbon and free carbon in the mixture.

[0017] As a preferred embodiment, the liquid phase masterbatch is a primary liquid phase material in an efficient mineralization mode with a particle size of -3mm.

[0018] As a preferred embodiment, the mixture comprises TFe, FeO, SiO2, CaO, MgO, Al2O3, K2O, Na2O, and TiO2.

[0019] As a preferred embodiment, the strength of the sintered ore is the drum strength.

[0020] As a preferred embodiment, the metallurgical properties include: reducibility index, low-temperature reduction pulverization performance, and load softening and dripping performance.

[0021] As a preferred embodiment, the sintered ore comprises: TFe, FeO, SiO2, CaO, MgO, Al2O3, K2O, Na2O, TiO2, and sinter loss.

[0022] As a preferred embodiment, the macroscopic and microscopic structure of the sinter includes: porosity and the crystal morphology of calcium ferrite.

[0023] As a preferred embodiment, the phase composition of the sinter includes hematite, magnetite, calcium ferrite, and silicates, and the composition ratio is obtained by calculating the area ratio using mineral phase or MLA.

[0024] As a preferred embodiment, the analytical determination process of the mixture is as follows: the segregation distribution of the mixture is determined based on the distribution changes of characteristic parameters of each sample, wherein the segregation distribution is lateral segregation, longitudinal segregation, or no segregation.

[0025] As a preferred embodiment, the analytical process of the sinter is as follows: the segregation distribution of the sinter is determined based on the changes in the distribution of the microstructure parameters of each sample, wherein the segregation distribution is lateral segregation, longitudinal segregation, or no segregation.

[0026] As a preferred embodiment, the process of establishing the mapping relationship between the mixture and the sinter includes: i) judging the error of the segregation distribution of the mixture and the sinter respectively, and determining the error difference between the two;

[0027] ii) Determine the maximum effective parameter among the characteristic parameters of the mixture and the maximum fluctuation parameter among the microstructure parameters of the sinter based on the error difference, and determine the directional correlation between the maximum effective parameter and the maximum fluctuation parameter;

[0028] iii) After removing the maximum action parameter from the characteristic parameters and the maximum fluctuation parameter from the tissue parameters, repeat steps i) to ii) to determine the action parameters and fluctuation parameters from level 2 to n, and thus obtain the results;

[0029] The directional correlation is either positive or negative; the mapping relationship between the mixture and the sinter is non-injective.

[0030] The mapping relationship between the mixture and sinter provided by this invention is not limited to the traditional functional relationship. It regards the characteristic parameters in the mixture and the microstructure parameters in the sinter as two different sets. By determining the correlation between the two sets, the influence of the mixture on the performance of the sinter can be judged from a macroscopic perspective. Through this mapping relationship, the real interaction between the mixture and the sinter can be reflected more realistically, and the influence of test errors on the analysis results can be reduced.

[0031] As a preferred embodiment, the process for determining the feeder parameters is as follows: the directional correlation between the feeder parameters and the sinter performance is determined based on the mapping relationship between the mixture and the sinter.

[0032] As a preferred embodiment, the optimization process of the feeder parameters is as follows: based on the directional correlation between the feeder parameters and the sinter performance, the adjustment range of the feeder parameters is determined through overall distributed optimization of the sinter performance.

[0033] Based on the mapping relationship between the provided mixture and sinter, this invention seeks the optimization range of energy consumption and sinter performance by performing overall distributed optimization of sinter performance, and determines the adjustment range of the feeder parameters, thereby achieving targeted improvement of sinter quality and reduction of energy consumption and homogenization of different types of mineral sources.

[0034] By corroborating the composition, fuel, and particle size distribution of the mixture with the structure, strength, and metallurgical properties of the sinter, a precise analysis of the problems existing in sintering production was achieved.

[0035] Compared with the prior art, the beneficial technical effects of the technical solution of the present invention are as follows:

[0036] 1) The analytical method provided by this invention performs array distributed sampling on the mixing trolley and the sintering trolley respectively. By measuring and analyzing the characteristic parameters and microstructure parameters of the samples, a mapping relationship between the mixture and the sinter is established. Based on this relationship, the parameters of the material distributor are optimized and adjusted, thereby achieving quality improvement and consumption reduction in ultra-high material layer sintering.

[0037] 2) In the technical solution provided by the present invention, the sampling points of the material are selected by matrix distribution, so as to achieve comprehensive coverage of all parts of the material, truly reflect the basic physicochemical properties of the material, thereby improving the objectivity and confidence of the subsequent analysis process;

[0038] 3) In the technical solution provided by the present invention, the characteristic parameters in the mixture and the microstructure parameters in the sinter are regarded as two different sets. By determining the correlation between the two sets, the influence of the mixture on the performance of the sinter is judged from a macroscopic perspective. Based on this mapping relationship, the overall distributed optimization of the sinter performance is further carried out to seek the optimization range between energy consumption and sinter performance, and to determine the adjustment range of the feeder parameters, thereby achieving targeted improvement of sinter quality and reduction of energy consumption and homogenization of different types of mineral sources. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the sampling of the mixture and sinter in Example 2 of the present invention. Detailed Implementation

[0040] The following examples are intended to further illustrate the present invention, but not to limit it.

[0041] Example 1

[0042] For a certain 265m 2 The sintering machine was analyzed, and the mixture trolley and sinter trolley were removed and divided into 16 (4×4) sampling points. The average particle size of the first layer of the mixture was 4.17 mm, the average particle size of the fourth layer was 6.49 mm, the average particle size on the left side of the trolley was 4.72 mm, and the average particle size on the right side of the trolley was 6.81 mm. The basicity of the mixture in the cross section ranged from 1.1 to 1.9, with 1.91 on the left side and 1.10 on the right side of the fourth layer. The basicity of the sinter ore in the cross section ranged from 1.81 to 2.08, with 1.81 on the left side and 1.99 on the right side of the fourth layer. The strength increased from 64% of the surface layer to 78% of the bottom layer, the reducibility index (RI) decreased from 84% of the surface layer to 68% of the bottom layer, and the porosity decreased from 20% of the surface layer to 8% of the bottom layer.

[0043] Through analytical judgment and error analysis of the mixture and sinter, the lateral particle size distribution of the mixture was identified as the maximum influencing parameter, and basicity as the secondary influencing parameter. These parameters indicate significant lateral segregation in the mixture. In contrast, the maximum fluctuation parameter of the sinter trolley was metallurgical properties, and the secondary fluctuation parameter was strength. A mapping relationship exists between the mixture and sinter. These results show that the sinter trolley did not exhibit lateral segregation, while the severe lateral segregation of the mixture trolley led to significant differences in the material layer structure between trolleys. Furthermore, the large difference in basicity of the mixture indicates poor granulation effect of the flux in the raw materials. The mixture was not properly distributed. By optimizing the residence time parameters of the shuttle feeder above the mixing silo, specifically shortening the residence time on the left and lengthening the residence time on the right, the formation of an incline within the silo was avoided, thus reducing lateral segregation. At the same time, the particle size distribution and flux structure were optimized, and the proportion of quicklime in the mixture was increased from 4% to 6%. The granulation effect of the mixture was improved. After testing, the average particle size of the first layer of the optimized mixture was 4.53 mm, the average particle size of the fourth layer was 6.25 mm, the average particle size of the left side of the trolley was 5.22 mm, and the average particle size of the right side of the trolley was 5.54 mm.

[0044] Example 2

[0045] For a certain 360m 2 The sintering machine was used for analysis, and the mixture was taken out and divided into 64 (8×8) sampling points, while the sintering ore trolley was taken out and divided into 16 (4×4) sampling points. Analysis of each sampling point revealed that the distribution difference of each particle size in the mixture along the material layer height was within 6%. The average particle size of the surface layer was approximately 2.5 mm, and the average particle size of the bottom layer was approximately 3.5 mm. The composition of the mixture showed a pattern of high content in the middle and low content on both sides in the trolley; for example, the TFe content reached 51% in columns 4-5, while the content in columns 1 and 8 was 48.5%. The fixed carbon content of the mixture also showed a pattern of low content in the middle and high content on both sides. The composition of the sinter exhibits a consistent pattern, with the fixed carbon content in columns 4 and 5 being 2.8%, while the fixed carbon content on both sides reaches 3.5%. The longitudinal distribution of the mixed fuel shows relatively small differences; for example, in column 4, the surface layer contains 3.51%, while the bottom layer contains 3.47%. However, this pattern does not exist in the transverse direction of the sinter composition. For example, in the fourth layer, the TFe content on the left, middle, and right sides is all 56.5% ± 0.5%. However, the compositional distribution of the sinter varies significantly along the height of the layer, with CaO content differing by 0.7% and MgO content by 0.3%. The strength of the sinter changes from 61% to 77%.

[0046] Through analytical judgment and error analysis of the mixture and sinter, the lateral particle size distribution of the mixture is the maximum influencing parameter, and the lateral fuel distribution is the secondary influencing parameter. These parameters indicate significant lateral segregation in the mixture. For the sinter trolley, the maximum fluctuation parameter is the compositional distribution along the material layer height, and the secondary fluctuation parameter is the longitudinal phase composition distribution. These analytical judgments show a mapping relationship between the mixture and the sinter. Although the mixture exhibits lateral segregation, the sinter shows significant compositional segregation along the material height. Therefore, it can be determined that the sintering machine has some lateral segregation, meaning the shuttle feeding parameters need optimization. Based on the average values ​​of the liquid phase masterbatch in each layer, the CaO content of the surface layer is known to be 18%, and the bottom layer 15%. The basicity of the surface layer is 3.2, and the bottom layer 2.7. This indicates a significant difference in the composition of the liquid phase masterbatch from the surface to the bottom layer, with higher basicity in the surface layer and lower basicity in the bottom layer. Simultaneously, the fuel distribution is uniform, indicating a significant difference in mineralization along the material layer height. Based on the analysis results, it can be concluded that by optimizing the residence time parameters of the feeder, specifically shortening the residence time on both sides, the segregation of the mixture on both sides can be effectively alleviated, reducing fuel waste on both sides. At the same time, a high-powered mixer can be used to homogenize the fine-particle components in the mixture, ensuring the homogenization of the liquid phase during the sintering process. After testing, the difference in CaO content was reduced to 0.3%, and the difference in MgO content was reduced to 0.1%. The strength variation of the sintered ore was 69-75%, the lateral segregation of fuel in the mixture was reduced, and the fixed carbon content in the left, middle, and right parts was 3.2±0.1%.

[0047] Example 3

[0048] For a certain 550m 2 The sintering machine was analyzed, and the mixture was divided into 64 (8×8) sampling points, and the sintering ore trolley was divided into 16 (4×4) sampling points. The analysis of each sampling point showed that the content of 1-3 mm particle size mixture had the greatest difference in the longitudinal direction, reaching 19%. The range of CaO in the height direction of the mixture was 1.35%, and the range of basicity was 0.35. At the same time, the fuel distribution of the mixture in the longitudinal direction was not ideal. The fixed carbon in the first layer was 4.58%, the fixed carbon in the second layer was 3.50%, and the fixed carbon in the third to eighth layers ranged from 3.3% to 3.5%. According to the heat storage calculation, the heat input of the first layer was 2601 MJ / ts, while the heat input of the eighth layer reached 4819 MJ / ts, and the heat storage rate was close to 90%. The compositional segregation of the sinter and the mixture showed good correspondence. The CaO content in the sinter ranged from approximately 1.30%. The drum strength of the sinter was 65% at the surface and 83% at the bottom. The low-temperature reduction pulverization index (RDI) of the sinter was also good. -0.5mm The content of hematite in the phase composition increased from 6% in the surface layer to 14% in the bottom layer, and the content of hematite in the phase composition increased from 29% in the surface layer to 40% in the bottom layer.

[0049] Through analytical judgment and error analysis of the mixture and sinter, the longitudinal particle size distribution of the mixture was identified as the maximum influencing parameter, and the longitudinal composition distribution as the secondary influencing parameter. For the sinter, the maximum fluctuation parameter of the trolley was the longitudinal intensity distribution, and the secondary fluctuation parameter was the longitudinal phase composition distribution. These analytical judgments indicate a mapping relationship between the mixture and the sinter. This suggests that the fuel segregation effect in the bed is unsatisfactory. Due to excess heat in the lower layer and insufficient flux, a significant amount of iron ore is assimilated into the liquid phase, forming secondary hematite encapsulated in the liquid phase, thus deteriorating the reduction and pulverization performance of the sinter. By optimizing the fuel particle size distribution, the proportion of coarse particles (1-3mm) in the fuel was increased from 38% to 50%, reducing the fuel distribution in the lower layer and achieving uniform heat distribution. At the same time, the proportion of coarse particles (>5mm) in the iron material was increased from 30% to 40%, reducing the proportion of fine-grained mixture (containing high fuel content) in the lower layer, which also achieved homogenized sintering. After testing, the heat gain of the first layer was 3215MJ / ts after adjusting the fuel distribution parameters, and the heat gain of the eighth layer reached 4461MJ / ts, with a heat storage rate of nearly 75%.

[0050] Furthermore, this embodiment also makes the following adjustments to the parameters of the feeder to address the component segregation of the mixture: 1) Adjust the parameters of the multi-roller feeder, reduce the number of feeder rolls by 18%, and reduce the rotation speed of the multi-roller feeder; 2) Enhance granulation by optimizing the flux structure or digestion method; After further adjustments, the range of CaO in the sinter is reduced to about 0.6%.

[0051] As can be seen from Examples 1-3 above, the technical solution provided by this invention lies in finding the mapping relationship between the mixture and the sinter, thereby judging and optimizing the parameters of the feeder. It should be noted that the mapping relationship indicated by this invention varies depending on the specific production process and is not a simple one-to-one relationship. Therefore, the technical solution provided by this invention has stronger compatibility and can guide processes from 200 to 500m... 2 Production optimization for sintering machines of different sizes.

Claims

1. A method for combined analysis of ultra-high bed material and sinter, characterized in that, Includes the following steps: 1) Sampling points were set up for the mixed material trolley and the sinter ore trolley, respectively; 2) Determine the characteristic parameters of the mixture and the microstructure parameters of the sinter based on the samples taken from the sampling points; 3) Analyze and judge the mixture and sinter according to the characteristic parameters and microstructure parameters respectively, and establish the mapping relationship between the mixture and sinter based on the analytical judgment results; 4) Determine and optimize the parameters of the feeder based on the mapping relationship between the mixture and the sinter; The characteristic parameters of the mixture include: particle size distribution, composition, fuel distribution, liquid phase masterbatch composition, and the proportion of liquid phase masterbatch; the microstructure parameters of the sinter include: composition, macro- and micro-structure, phase composition, strength, and metallurgical properties; The process of establishing the mapping relationship between the mixture and the sinter includes: i) judging the error of the segregation distribution of the mixture and the sinter respectively, and determining the error difference between the two; ii) Determine the maximum effective parameter among the characteristic parameters of the mixture and the maximum fluctuation parameter among the microstructure parameters of the sinter based on the error difference, and determine the directional correlation between the maximum effective parameter and the maximum fluctuation parameter; iii) After removing the maximum action parameter from the characteristic parameters and the maximum fluctuation parameter from the tissue parameters, repeat steps i) to ii) to determine the action parameters and fluctuation parameters from level 2 to n, and thus obtain the results; The directional correlation is either positive or negative; the mapping relationship between the mixture and the sinter is non-injective.

2. The method for combined analysis of ultra-high bed material and sinter as described in claim 1, characterized in that: The number of sampling points on the mixing trolley is 16-64, and the number of sampling points on the sintering ore trolley is 16-36; the sampling points are distributed in a matrix.

3. The method for combined analysis of ultra-high bed material and sinter as described in claim 1, characterized in that: The particle size distribution of the mixture refers to the composition ratio of each particle size in the mixture, and the particle size is: 0.5mm, 1mm, 3mm, 5mm, 8mm; the fuel distribution of the mixture refers to the distribution of fixed carbon and free carbon in the mixture; the liquid phase masterbatch is the primary liquid phase material in the high-efficiency mineralization mode, with a particle size of -3mm.

4. The method for combined analysis of ultra-high bed material and sinter as described in claim 1, characterized in that: The strength of the sintered ore is the drum strength; the metallurgical properties include: reducibility index, low-temperature reduction pulverization performance, and load softening and dripping performance.

5. The method for combined analysis of ultra-high bed material and sinter as described in claim 4, characterized in that: The analytical judgment process of the mixture is as follows: the segregation distribution of the mixture is determined based on the distribution changes of characteristic parameters of each sample. The segregation distribution is divided into transverse segregation, longitudinal segregation, and no segregation.

6. The method for combined analysis of ultra-high bed material and sinter as described in claim 5, characterized in that: The analytical process of the sinter is as follows: the segregation distribution of the sinter is determined based on the changes in the distribution of the microstructure parameters of each sample. The segregation distribution is divided into transverse segregation, longitudinal segregation, and no segregation.

7. The method for combined analysis of ultra-high bed material and sintered ore according to claim 1, characterized in that: The process for determining the parameters of the feeder is as follows: the directional correlation between the feeder parameters and the performance of the sinter is determined based on the mapping relationship between the mixture and the sinter.

8. The method for combined analysis of ultra-high bed material and sinter as described in claim 1, characterized in that: The optimization process of the feeder parameters is as follows: based on the directional correlation between the feeder parameters and the sinter performance, the adjustment range of the feeder parameters is determined through overall distributed optimization of the sinter performance.

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