Sintered ore quality improving method and system based on size fraction optimization
By classifying and quantitatively optimizing the particle size of sinter raw materials, a quantitative relationship model between particle size and metallurgical properties was established. Combined with enhanced granulation and linear programming algorithms, the dilemma of improving sinter performance was solved, and the stable improvement of sinter quality and intelligent management of the production process were achieved.
Patent Information
- Application Number
- CN202511539616.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-27
- Publication Date
- 2026-03-17
AI Technical Summary
Existing technologies lack systematic and quantitative optimization of the particle size distribution of sinter raw materials, making it difficult to improve the various metallurgical properties of sinter in a coordinated manner. Often, one property is sacrificed in exchange for the improvement of another.
By dividing the sintering mixture raw materials into fine, medium and coarse particles, a quantitative relationship model between particle size range and metallurgical properties is established, the particle size range is optimized, and ore blending calculations are performed through enhanced granulation and linear programming algorithms. Closed-loop control is achieved by combining online particle size analysis.
It significantly improved the drum strength, low-temperature reduction pulverization rate and reduction degree of sinter, achieved stable improvement in sinter quality and intelligent management of the production process, and improved the economy and reliability of production.
Smart Images
Figure CN121684115A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of sinter technology, and particularly relates to a method and system for improving the quality of sinter based on particle size optimization. Background Technology
[0002] Sinter, as an indispensable raw material for blast furnace ironmaking, has a decisive impact on the smoothness, stability, and overall technical and economic indicators of blast furnace operation due to its comprehensive quality. Key metallurgical performance indicators for evaluating sinter quality mainly include drum strength, low-temperature reduction pulverization rate (RDI), and reducibility (RI). In-depth research shows a very close correlation between these core performance parameters and the particle size distribution of the sintering mixture: excessively coarse particles (e.g., those larger than 3 mm) often fail to fully melt during the relatively short high-temperature sintering process, tending to form fragile and easily broken slag bridges, thus significantly weakening the overall mechanical strength of the sinter. On the other hand, excessively fine powder (e.g., components with a particle size less than 0.5 mm) severely deteriorates the original permeability of the sintering bed, leading to uneven heat and mass transfer during sintering, decreased vertical sintering speed, and reduced energy utilization efficiency. Simultaneously, a large amount of fine particles are easily carried away from the bed by high-speed airflow, resulting in significant raw material dust loss.
[0003] Currently, in mainstream sintering production processes, the strategies for controlling raw material particle size generally have limitations, relying heavily on constraints from single empirical indicators (such as specifying only the upper limit of particle size for fuel or flux), or considering it only as a secondary correction factor in the batching calculation stage. Industry optimization practices mostly focus on fine-tuning chemical composition (such as alkalinity balance, MgO / Al2O3 content control), while insufficient investment is made in systematic optimization research on the particle size composition and distribution of raw materials. In particular, there is a lack of mathematical models and corresponding closed-loop control methods that can quantitatively describe the complex mapping relationship between raw material particle size distribution (such as the proportion of specific particle sizes, average particle size, and uniformity coefficient) and multiple key metallurgical performance indicators of the final sinter (strength, pulverization rate, reducibility). This technological shortcoming directly leads to a dilemma in performance optimization during actual production. To improve a certain performance (such as increasing the drum strength by increasing the fuel particle size), it is often necessary to sacrifice another key performance (such as the deterioration of reducibility due to excessively large fuel particles). It is difficult to break through the bottleneck and achieve synergistic optimization and overall improvement of core metallurgical performance such as sinter strength, reducibility and low pulverization rate. Summary of the Invention
[0004] This invention proposes a method for improving the quality of sintered ore based on particle size optimization, in order to solve the problem that the lack of systematic and quantitative optimization of the particle size composition of raw materials in the existing technology leads to the difficulty in synergistic improvement of various metallurgical properties of sintered ore.
[0005] To achieve the above-mentioned technical objectives, the first objective of this invention is to provide a method for improving the quality of sintered ore based on particle size optimization, comprising: S1. Raw material particle size range classification and quantification: The sintering mixture raw material is divided into three particle size ranges: fine, medium and coarse. The mass percentage of each particle size range in various iron-containing raw materials, fluxes and fuels is determined by sieve analysis. S2. Establish the quantitative relationship between particle size range and metallurgical properties: Through sintering cup experiments, establish a quantitative regression model for the mass percentage of each particle size range with the sinter drum strength, low-temperature reduction pulverization rate RDI+3.15mm and reduction degree RI. S3. Determine the optimal particle size range control target: Based on the quantitative regression model, and to meet the constraints of the target sinter drum strength, RDI+3.15mm, and RI, the optimal range of the three particle size ranges is solved in reverse. S4. Raw material pretreatment and particle size orientation control: Based on the optimization range, the raw materials are pretreated: For iron ore powder with excessive fine particle content, granulation is carried out by strengthening the cylindrical mixing mechanism and adding sodium carboxymethyl cellulose; S5. Optimized ore blending and closed-loop control of particle size distribution: Based on the particle size distribution data of each pretreated raw material, the linear programming algorithm is used to optimize the ore blending calculation so that the particle size distribution of the final mixture falls within the optimization range determined in S3.
[0006] Preferably, S5 includes: installing an online particle size analyzer at the tail of the sintering machine to monitor the particle size of the sintered ore in real time and provide feedback to adjust the pretreatment and ore blending parameters.
[0007] Preferably, the fine particles have a diameter of less than 0.5 mm, the medium particles have a diameter range of 0.5 mm to 3 mm, and the coarse particles have a diameter greater than 3 mm.
[0008] Preferably, in S3, the constraints include: the target sinter drum strength is greater than 78%, RDI+3.15mm is greater than 72%, and RI is greater than 85%.
[0009] Preferably, in S3, the optimized range includes: fine particle content controlled at 18% to 25%, medium particle content controlled at 40% to 50%, and coarse particle content controlled at 25% to 35%.
[0010] Preferably, in step S4, sodium carboxymethyl cellulose is added at a mass of 0.03% to 0.05% by weight of the mixture.
[0011] Preferably, in S4, the parameters for granulation of the enhanced cylindrical mixer are: filling rate 14% to 18%, rotation speed 11 rpm to 13 rpm, and granulation time 4 min to 6 min.
[0012] Preferably, in S4, the moisture content of the material during enhanced granulation is controlled at 7.0% to 7.8%.
[0013] A second objective of this invention is to provide a sinter quality improvement system based on particle size optimization, for implementing the aforementioned sinter quality improvement method based on particle size optimization; the system includes: Online raw material particle size detection device; Enhanced granulation system equipped with a sodium carboxymethyl cellulose addition device; A central control system integrating a linear programming optimization ore blending model; Online particle size analyzer for sintered ore products.
[0014] Preferably, the rotational speed of the cylindrical mixer in the enhanced granulation system is adjustable from 11 rpm to 13 rpm.
[0015] The advantages and positive effects of this invention are as follows: This invention addresses common problems in sintering production, such as uneven particle size distribution and poor permeability of the mixture. It establishes a scientific model that quantitatively correlates raw material particle size classification with metallurgical properties. Based on extensive experimental data and statistical analysis, this model precisely determines the optimal control range for the particle size composition of the sintering mixture.
[0016] Based on this, the present invention effectively improves the pelleting characteristics of fine-grained materials by adding sodium carboxymethyl cellulose as a binder and combining it with enhanced granulation process (including optimized moisture addition and mixing time), significantly improving the uniformity of particle size distribution and the permeability of the material layer.
[0017] Furthermore, this invention employs a linear programming ore blending algorithm with cost optimization as its objective, combined with a closed-loop control system constructed using real-time monitoring data from an online particle size analyzer. This system can dynamically adjust the raw material ratio and process parameters, achieving intelligent control of the entire sintering production process. This not only ensures the stable improvement of sinter quality but also significantly enhances the economy and reliability of the production process.
[0018] Ultimately, this invention provides an efficient and reliable technical solution for the refined management and intelligent upgrading of sintering production, thus promoting technological progress in the industry. Attached Figure Description
[0019] To more clearly illustrate the solutions in the embodiments of the present invention or the prior art, the present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0020] Figure 1 This is a flowchart of a preferred embodiment of the present invention. Detailed Implementation
[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only some examples of the present invention, and not all examples. Obviously, the embodiments of the present invention shown in the accompanying drawings illustrate characteristic technical solutions. Based on the embodiments of the present invention, any other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.
[0022] Please see Figure 1 : A method for improving the quality of sintered ore based on particle size optimization includes: S1. Raw material particle size range classification and quantification: The sintering mixture raw material is divided into three particle size ranges: fine, medium and coarse. The mass percentage of each particle size range in various iron-containing raw materials, fluxes and fuels is determined by sieve analysis. S2. Establish the quantitative relationship between particle size range and metallurgical properties: Through sintering cup experiments, establish a quantitative regression model for the mass percentage of each particle size range with the sinter drum strength, low-temperature reduction pulverization rate RDI+3.15mm and reduction degree RI. S3. Determine the optimal particle size range control target: Based on the quantitative regression model, and to meet the constraints of the target sinter drum strength, RDI+3.15mm, and RI, the optimal range of the three particle size ranges is solved in reverse. S4. Raw material pretreatment and particle size orientation control: Based on the optimization range, the raw materials are pretreated: For iron ore powder with excessive fine particle content, granulation is carried out by strengthening the cylindrical mixing mechanism and adding sodium carboxymethyl cellulose; S5. Optimized ore blending and closed-loop control of particle size distribution: Based on the particle size distribution data of each pretreated raw material, the linear programming algorithm is used to optimize the ore blending calculation so that the particle size distribution of the final mixture falls within the optimization range determined in S3.
[0023] To better understand the technical concept of this invention, non-limiting examples are given below: Example 1: This invention provides a method for improving the quality of sintered ore based on particle size optimization, comprising the following steps: S1. Raw material particle size range division and quantification: The sintering mixture raw material is divided into three particle size ranges: fine particle size (less than 0.5 mm), medium particle size (0.5 mm to 3 mm), and coarse particle size (greater than 3 mm). Through sieve analysis, the mass percentage of each particle size range in various iron-containing raw materials, fluxes, and fuels is accurately determined. Specifically, standard sieve sets can be used for mechanical screening to ensure the accuracy and repeatability of the data. Detailed particle size profiles should be established for each raw material to provide targeted guidance for subsequent precision blending and pretreatment.
[0024] S2. Establish the quantitative relationship between particle size range and metallurgical properties: Through sintering cup experiments, establish a quantitative regression model for the mass percentage of each particle size range with the sinter drum strength, low-temperature reduction pulverization rate RDI+3.15mm and reduction degree RI. Specifically, by designing multiple sets of sintering cup experiments with different particle size compositions, a large amount of data was collected, and a precise mathematical model was constructed using multivariate statistical methods (such as multivariate linear regression). This model can quantitatively predict the approximate quality of sintered ore under a certain particle size composition. S3. Determine the optimal particle size range control target: Based on the quantitative regression model in S2, with the constraints of satisfying the target sinter drum strength greater than 78%, RDI+3.15mm greater than 72%, and RI greater than 85%, the optimal range for fine particle content to be controlled at 18% to 25%, medium particle content to be controlled at 40% to 50%, and coarse particle content to be controlled at 25% to 35% is solved in reverse. Specifically, under the premise of meeting all key metallurgical performance indicators, the "golden range" of particle size composition is derived in reverse. This range is a result of comprehensive balance, which ensures that the sintered ore maintains a high level in terms of strength, pulverization and reducibility, and solves the problem of "sacrificing one performance for another" in traditional production.
[0025] S4. Raw material pretreatment and particle size orientation control: Based on the optimization range determined in step S3, the raw materials are pretreated as follows: For iron ore powder with excessive fine particle content, granulation is carried out by strengthening the cylindrical mixer (by optimizing the tilt angle, speed and filling rate of the cylindrical mixer to extend the effective granulation time), and adding sodium carboxymethyl cellulose at a mass of 0.03% to 0.05% of the mixture (sodium carboxymethyl cellulose can significantly improve the pelleting rate and green pellet strength of fine powder, making it less likely to be blown away by airflow during sintering, thereby improving the permeability of the material layer and reducing dust loss). S5. Optimize ore blending and closed-loop control of particle size distribution: Based on the particle size distribution data of each pretreated raw material, use a linear programming algorithm to optimize the ore blending calculation so that the particle size distribution of the final mixture falls within the optimization range determined in step S3; and install an online particle size analyzer at the tail of the sintering machine to monitor the particle size of the sintered ore in real time and provide feedback to adjust the pretreatment and ore blending parameters. Specifically, the linear programming algorithm uses "lowest cost" or "optimal performance" as the objective function, and uses chemical composition indicators such as TFe, SiO2, and alkalinity, as well as the particle size range indicators obtained in step S3, as constraints to calculate the optimal ore blending scheme. Closed-loop control: When the finished product particle size deviates from the target, the system can automatically adjust the pretreatment intensity in S4 (such as the crusher roller gap) or the ore blending model parameters in S5 in reverse, thereby realizing intelligent and stable control of the production process.
[0026] As shown above, this method first scientifically classifies and quantitatively analyzes the particle size of the raw materials, establishes a quantitative relationship model between each particle size and metallurgical properties, and determines the optimal particle size control range accordingly. Through targeted raw material pretreatment processes, including enhanced granulation of fine-grained materials, the particle size distribution of the mixture is significantly improved. This optimization method steadily increases the drum strength of sinter to over 78%, significantly improves the low-temperature reduction pulverization rate (RDI+3.15mm) to over 72%, and achieves an excellent reduction rate (RI) of over 85%. Finally, by establishing a closed-loop control system, employing linear programming algorithms for ore blending optimization, and implementing online particle size monitoring, the stability and reliability of the production process were ensured. This method not only improves the metallurgical properties of sintered ore but also provides reliable technical support for the refined management of the production process.
[0027] Example 2: This example is basically the same as the previous example, except that the parameters of the enhanced cylindrical mixer granulation in step S4 are: filling rate 14% to 18%, rotation speed 11 rpm to 13 rpm, and granulation time 4 min to 6 min.
[0028] The 14%–18% filling rate ensures that the material has enough space inside the cylinder to roll and knead fully, forming dense mother balls; the 11 rpm–13 rpm rotation speed provides optimal centrifugal force, allowing the material to be carried to a suitable height for cascading rather than centrifuging and adhering to the wall, thereby maximizing granulation efficiency; the 4 min–6 min granulation time ensures that fine particles have enough time to adhere to the mother balls, forming mixed pellets with uniform particle size and qualified strength.
[0029] Specifically, in step S4, the moisture content of the material during granulation is controlled at 7.0% to 7.8%.
[0030] When the moisture content is below 7.0%, the material is not sufficiently wetted, the green pellets have poor strength, and they are prone to pulverization during sintering, which worsens the permeability of the material layer. When the moisture content is higher than 7.8%, the mixture will be over-wetted, forming a mud-like or large lump, which will also impair air permeability and increase ignition energy consumption. This method strictly controls the moisture content within a narrow range of 7.0% to 7.8%, and when combined with the above-mentioned granulation parameters, it can provide optimal liquid phase conditions for granulation, maximizing the binding effect of sodium carboxymethyl cellulose.
[0031] Specifically, the objective function of the linear programming algorithm in step S5 is to minimize the material mixing cost, and the constraints include the chemical composition requirements of the mixture and the particle size range requirements determined in step S3.
[0032] In this process, the linear programming algorithm not only seeks a ore blending scheme that meets the requirements of chemical composition and particle size, but also automatically searches for and locks the scheme with the lowest cost among all feasible solutions, thereby achieving a significant reduction in production costs while absolutely ensuring the quality of sintered ore.
[0033] Specifically, the chemical composition requirements of the mixture include: total iron content, silicon dioxide content, calcium oxide content, magnesium oxide content, and alkalinity.
[0034] Among them, the total iron (TFe) content determines the product grade, the silicon dioxide (SiO2) and calcium oxide (CaO) content determines the basicity (R=CaO / SiO2), which directly affects the metallurgical properties of sinter; and the magnesium oxide (MgO) content is used to adjust the softening and melting characteristics of sinter.
[0035] As can be seen from the above, by optimizing the granulation parameters of the cylindrical mixer, controlling the filling rate at 14% to 18%, maintaining the rotation speed at 11 rpm to 13 rpm, controlling the granulation time at 4 min to 6 min, and coordinating with a material moisture content of 7.0% to 7.8%, the granulation effect and particle size distribution uniformity of the mixture are effectively improved. In the application of linear programming algorithm, this embodiment clearly takes the lowest ingredient cost as the objective function, while comprehensively considering the requirements of chemical composition such as total iron content, silicon dioxide content, calcium oxide content, magnesium oxide content and alkalinity, as well as the predetermined particle size range requirements, to achieve the optimal balance between economic benefits and technical indicators. Through the optimization and implementation of the specific process parameters described above, this embodiment further improves the particle size optimization method for enhancing the quality of sintered ore, making the production process more stable and reliable.
[0036] Example 3: This example is basically the same as the previous example, except that a sinter quality improvement system based on particle size optimization is used to implement the above method. This system includes: Online raw material particle size detection device; Enhanced granulation system equipped with a sodium carboxymethyl cellulose addition device; A central control system integrating a linear programming optimization ore blending model; Online particle size analyzer for sintered ore products.
[0037] Specifically, the rotational speed of the cylindrical mixer in the enhanced granulation system is adjustable from 11 rpm to 13 rpm.
[0038] As can be seen from the above, a complete production process control system has been constructed by integrating an online raw material particle size detection device, an enhanced granulation system, an intelligent central control system, and a finished product particle size analyzer. The rotation speed of the cylindrical mixer can be flexibly controlled within the range of 11 rpm to 13 rpm, ensuring the optimal implementation of process parameters. This production system achieves precise control of raw material particle size and enhanced granulation process through the coordinated operation of various devices. Online monitoring devices monitor changes in raw material particle size in real time, the sodium carboxymethyl cellulose addition device significantly improves granulation efficiency, and the central control system optimizes ore blending through linear programming algorithms, ultimately ensuring a stable improvement in the quality of the sintered ore product. The systematic production equipment not only ensures the efficient implementation of particle size optimization methods, but also forms a complete closed-loop quality control system through real-time feedback from the online particle size analyzer of the finished product. This integrated solution significantly improves the automation level and process stability of sinter production, providing reliable equipment guarantees and technical support for the large-scale production of high-quality sinter.
[0039] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for improving sinter quality based on size fraction optimization, characterized by, The method comprises the following steps: S1, raw material particle size interval division and quantification: the sintering mixture raw materials are divided into three particle size intervals, i.e., fine particle size interval, medium particle size interval and coarse particle size interval; the mass percentage of each particle size interval in each iron-containing raw material, flux and fuel is determined through sieve analysis; S2, establishment of quantitative relationship between particle size interval and metallurgical properties: a quantitative regression model of the mass percentage of each particle size interval and the tumbler strength of sinter, RDI+3.15mm and RI is established through sintering cup experiments; S3, determination of the optimal particle size interval control target: according to the quantitative regression model, the optimal interval of the three particle size intervals is reversely solved under the constraint conditions of meeting the target tumbler strength of sinter, RDI+3.15mm and RI; S4, raw material pretreatment and particle size directional regulation: the raw materials are pretreated according to the optimal interval: for the fine particle size interval content of the iron ore powder exceeding the standard, the particle size is regulated through the intensified cylindrical mixer, and sodium carboxymethyl cellulose is added; S5, optimization of ore blending and closed-loop control of particle size composition: the pretreated raw materials are calculated for ore blending according to the particle size composition data thereof by using a linear programming algorithm, so that the particle size composition of the final mixture falls within the optimal interval determined in S3.
2. The particle size-based sinter quality enhancement method according to claim 1, characterized in that, S5 comprises: an online particle size analyzer is installed at the tail of the sintering machine to monitor the particle size of the sintered product in real time, and the pretreatment and ore blending parameters are adjusted and fed back.
3. The particle size based sinter quality enhancement method as claimed in claim 1, wherein, The particle size of the fine particle size interval is less than 0.5mm, the particle size of the medium particle size interval ranges from 0.5mm to 3mm, and the particle size of the coarse particle size interval is greater than 3mm.
4. The granule size optimization based sinter quality enhancement method as claimed in claim 3 wherein, In S3, the constraint conditions include: the target tumbler strength of sinter is greater than 78%, RDI+3.15mm is greater than 72%, and RI is greater than 85%.
5. The granule size optimization based sinter quality enhancement method as claimed in claim 4 wherein, In S3, the optimal interval includes: the content of the fine particle size interval is controlled to be 18% to 25%, the content of the medium particle size interval is controlled to be 40% to 50%, and the content of the coarse particle size interval is controlled to be 25% to 35%.
6. The granule size optimization based sinter quality enhancement method as claimed in claim 5 wherein, In S4, 0.03% to 0.05% of sodium carboxymethyl cellulose is added to the mixture.
7. The granule size optimization based sinter quality enhancement method as claimed in claim 5 wherein, In S4, the parameters of the intensified cylindrical mixer for particle size regulation are as follows: the filling rate is 14% to 18%, the rotating speed is 11rpm to 13rpm, and the particle size regulation time is 4min to 6min.
8. The granule size distribution based sinter quality enhancement method as claimed in claim 5 wherein, In S4, the water content of the material during the intensified particle size regulation is controlled to be 7.0% to 7.8%.
9. A sinter quality enhancement system based on size fraction optimization, characterized by, The system is used for implementing the sinter quality improvement method based on particle size optimization in any one of claims 1 to 8, and comprises: a raw material particle size online detection device; an intensified particle size regulation system provided with a sodium carboxymethyl cellulose adding device; a central control system integrated with a linear programming ore blending optimization model; a sintered product online particle size analyzer.
10. The granule size optimization based sinter quality enhancement system as claimed in claim 9, wherein, The rotating speed of the cylindrical mixer of the intensified particle size regulation system can be adjusted in the range of 11rpm to 13rpm.