CN105136623A - Potential energy change based method for quantitatively characterizing packing segregation state of particles after falling - Google Patents

Abstract

The invention discloses a potential energy change based method for quantitatively characterizing the packing segregation state of particles after falling. The method comprises the following steps: (1), establishing a batching model with SolidWorks; (2), introducing the batching model into LIGGGHTS; (3), establishing particle models with two particle diameters in the LIGGGHTS; (4), performing an analog experiment; (5), substituting recorded data into a segregation index calculation model after the analog experiment is finished to obtain a segregation index. The method can be used for quantitatively characterizing the mixing uniformity degree of particles with different particle diameters and the segregation degree of the particles after falling, thereby reducing the energy consumption effectively and prompting energy conservation and emission reduction.

Description

Quantitative Characterization Method of Accumulation Segregation State of Particles Falling Based on Potential Energy Change

technical field

The invention relates to the technical field of metallurgical engineering, in particular to a method for quantitatively characterizing the accumulation and segregation state of particles after falling based on potential energy changes.

Background technique

In recent years, the iron and steel situation has become increasingly severe. How to further reduce smelting costs and reduce energy consumption is of great significance to iron and steel enterprises. Blast furnace ironmaking is a major energy consumer in the entire process, and iron and steel enterprises pay more attention to saving blast furnace raw materials. In actual production in the past, in order to ensure smooth operation of the furnace, taking sinter as an example, it is usually required that the particle size of the sinter into the furnace should be greater than 5mm, so the sinter under the screen must be returned to the sinter plant for re-sintering, a large amount of The returned materials not only increase the cost of raw materials, but also increase the transportation cost. Therefore, in recent years, many iron and steel enterprises have begun to try to use small particle size charge in production practice. Reducing the lower limit of the particle size of the blast furnace charge will greatly improve the heating and reduction conditions of the iron-containing raw materials, so that the "preprocessing" process in the furnace body can be fully carried out. But on the other hand, due to the expansion of the particle size range of the furnace charge, the phenomenon of particle size segregation and distribution is more likely to occur during the blast furnace charge distribution process, which will easily cause the porosity of the local material layer at the furnace throat to decrease and the pressure difference to increase, directly affecting the gas flow. The uniform distribution, and then indirectly affect the anterograde furnace conditions.

For particle size segregation, predecessors have carried out a lot of research work, the focus of which is to measure the degree of particle size segregation by analyzing the penetration between particles. At the same time, with the continuous improvement of the level of computational simulation technology, among them, the discrete element method (DEM) takes single particles as the object, and based on Newton's second law, can directly simulate the translation and rotation states of particles. The penetration between them plays an important role. Especially with the continuous enhancement of computer performance, DEM is more and more widely used in the field of simulating particle flow.

Rahman used the DEM numerical simulation method to study the particle infiltration phenomenon in the packed bed, and investigated the distribution laws of the infiltration velocity, residence time and radial dissipation degree under different conditions. Based on the DEM numerical simulation method, Zhu further studied the influence of the particle's own characteristics on the infiltration. The results showed that the damping coefficient of the particle and the particle size ratio are two important factors affecting the inter-particle interaction.

On the basis of the study of inter-particle penetration, combined with the actual production of blast furnaces, the researchers began to conduct research on particle size segregation. Inada developed a mathematical model based on a bell blast furnace, and investigated the distribution of radial particle size under fixed parameters. It proposed that the particle size ratio between large particles and small particles and the velocity gradient of particles on the slope are the factors that affect the particle size segregation distribution. The two most important factors. Li Qiang studied the material distribution process of the COREX-3000 shaft furnace roof, and found that the change of the inclination angle of the chute has a significant impact on the radial distribution of the bulk density. When the inclination angle increases, the minimum value of the bulk density moves to the side of the furnace wall. Therefore, the inclination angle of the chute also has an important influence on the segregation distribution of particles in the radial direction. Mio also used the DEM method to observe that when the inclination angle of the chute changes, the small particles and large particles move on the chute to be stratified, and the former is close to the chute wall while the latter is away from the bottom of the chute.

At present, the particle size segregation phenomenon caused by the use of small particle size furnace materials in China mainly analyzes the particle size segregation law from the final particle distribution state by changing the material distribution conditions. To a certain extent, it lacks the collision of the combined particles in the entire flow process. The behavior and the key process parameters are used to analyze the general law of particle size segregation of multivariate particles. Therefore, the study of the particle size segregation behavior during the distribution process, especially the segregation distribution law of the small particle size charge during the charge distribution process, has important guiding significance for the use of blast furnaces and the advantages of small particle size charge and energy saving and consumption reduction.

Contents of the invention

In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is how to solve the problem of lack of general rules for particle segregation in existing fabrics, and provide a method for quantitatively characterizing the accumulation and segregation state of particles after falling based on potential energy changes, which can effectively reduce Energy consumption, promote energy conservation and emission reduction.

In order to solve the above-mentioned technical problems, the technical solution adopted in the present invention is as follows: a method for quantitatively characterizing the accumulation and segregation state of particles after falling based on potential energy changes, which is characterized in that it includes the following steps:

1) Use the 3D drawing software SolidWorks to establish a batching model consisting of a distributor and a charging tank;

2) Import the batching model established in step 1) into the simulation software LIGGGHTS;

3) Establish particle models of two particle sizes in the simulation software LIGGGHTS, and set the basic parameters of the batching model and the basic parameters of the particle model, so that the batching model forms a simulated batching device, and the particle model forms simulated particles;

4) Simulation experiment, mix the simulated particles uniformly in different proportions, put them into the dispenser, record the state parameters of each simulated particle at this time; after the simulated particles fall to the charging tank for redistribution, record the state parameters of each simulated particle again state parameter;

5) After the simulation experiment is completed, the recorded data are substituted into the segregation index calculation model to obtain the segregation index, wherein the segregation index calculation model is:

K=| _{beginning of} A- _end of A|/ _beginning of A*100%;

In the formula, K is the segregation index, _Abegin is the ratio of the potential energy of particle A to the total potential energy in the _initial state, and Amo is the ratio of the potential energy of particle A to the total potential energy in the final state.

Further, the basic parameters of the ingredient model are the shape and size of the distributor, the shape and size of the charging tank, and the heights of the distributor and the charging tank.

Further, the basic parameters of the particle model include particle diameter, particle Young's modulus, particle Poisson's ratio, particle density, friction coefficient between particles and walls, restitution coefficient between particles and walls, friction coefficient between particles , the restitution coefficient between particles, and the ratio between different particles.

Further, the recorded state parameters of the simulated particle include the serial number of the simulated particle, the coordinates of the simulated particle in the three-dimensional coordinate system, the speed of the X axis, the Y axis and the Z axis, and the friction force.

Compared with prior art, the present invention has following advantage:

1. The present invention can quantitatively characterize the particle segregation law, and the degree of uniformity of mixing between particles is high, the experimental method is easy to operate, the reading error is small, and a large amount of experimental data can be obtained, so that the characterization of the segregation state is more accurate.

2. The present invention utilizes numerical simulation, adopts the discrete element method, and utilizes the precise tracking of numerical simulation to record the state of each particle, and analyze the data to quantitatively characterize the state of particle distribution; calculate the potential energy change situation under the state of the particle before and after, so as to Accurate analysis of segregation index; in order to effectively reduce energy consumption and promote energy saving and emission reduction in the actual production process.

Description of drawings

Fig. 1 is the schematic diagram of batching model of the present invention.

Figure 2 is a diagram of the particle distribution state before the start of the simulation experiment.

Fig. 3 is a particle distribution state diagram after the simulation test is completed.

In the figure: 1—distributor, 2—feeding port, 3—charging tank.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

Embodiment: Referring to Fig. 1, Fig. 2 and Fig. 3, a method for quantitatively characterizing the accumulation and segregation state of particles after falling based on the change of potential energy comprises the following steps:

1) Use the 3D drawing software SolidWorks to establish a batching model consisting of the distributor 1 and the charging tank 3 . Wherein, the distributor 1 is located above the charging tank 3, its upper end is a feeding port, its lower end is a feeding port 2, and its lower part is a closing structure; the section of the inner chamber of the charging tank 3 is a rectangular structure, Its upper end is open, and the opening degree of the upper end of the charging tank 3 is greater than the opening degree of the feed opening 2 of the distributor 1 .

2) Import the batching model established in step 1) into the (computer numerical) simulation software LIGGGHTS.

3) Establish particle models of two particle sizes in the simulation software LIGGGHTS, and set the basic parameters of the batching model and the basic parameters of the particle model. Among them, the particle models of the two particle sizes have the same factors except for the particle size. And the batching model forms a simulated batching device, and the particle model forms simulated particles. Among them, the basic parameters of the batching model are the shape and size of the distributor 1, the shape and size of the charging tank 3, and the heights of the distributor 1 and the charging tank 3, and the specific parameters depend on the test plan. The basic parameters of the particle model include particle diameter, particle Young's modulus, particle Poisson's ratio, particle density, friction coefficient between particles and walls, restitution coefficient between particles and walls, friction coefficient between particles, The restitution coefficient between them, and the ratio between different particles (particles of different particle sizes are mixed according to different mass ratios under a certain total mass).

4) Simulation experiment, after mixing the simulated particles uniformly in different proportions, put them into the dispenser 1, and record the state parameters of each simulated particle at this time; after the simulated particles fall to the charging tank 3 for redistribution, record each simulated particle again The state parameters of the particles; the recorded state parameters of the simulated particles include the number of the simulated particles, the coordinates of the simulated particles in the three-dimensional coordinate system, the speed of the X axis, the Y axis and the Z axis, and the friction force.

5) After the simulation experiment is completed, the recorded data are substituted into the segregation index calculation model to obtain the segregation index, wherein the segregation index calculation model is:

K=| _{beginning of} A- _end of A|/ _beginning of A*100%;

In the formula, K is the segregation index, _Abegin is the ratio of the potential energy of particle A to the total potential energy in the _initial state, and Amo is the ratio of the potential energy of particle A to the total potential energy in the final state.

The present invention is based on the discrete element method in the numerical simulation, at first utilizes SolidWorks software to establish a batching model that a distributing device and charging tank are formed; Set up corresponding experimental parameter according to experimental requirement; Run LIGGGHTS software, simulate the state after the mixed filling of particle , and the state of the particles after falling, number each particle, and through tracking, generate relevant state data at each step at the same time; after the experiment, analyze and process the data. The present invention utilizes precise tracking of numerical simulation to record the state of each particle, and analyzes the data to quantitatively characterize the state of particle distribution; thus, quantitative segregation state characterization can be performed on the state of particle distribution.

Embodiment 1 :

1) Establish a batching model consisting of the distributor 1 and the charging tank 3 to simulate the falling process of 300g of aluminum oxide, in which the length L1=200mm, the height L2=250mm, and the width L3=28mm of the charging tank 3, The length L4 of the discharge opening 2 of the distributor 1 is 28 mm, and the width L5 is 14 mm, and the opposite side walls of the discharge opening 2 of the distributor 1 form an angle of 50° with the horizontal direction.

2) Establish particle models with two particle sizes, where the two particle diameters are set to 3mm and 6mm respectively, the Young's modulus of the particle is set to 375GPa, the Poisson's ratio of the particle is set to 0.22, and the particle density is set to 2099kg/m ³ , The friction coefficient between particles and the wall is set to 0.4, the restitution coefficient between particles and the wall is set to 0.7, the friction coefficient between particles is set to 0.5, the restitution coefficient between particles is set to 0.6, 3mm particle quality: 6mm particles Quality = 2:8.

3) Run the simulation software LIGGGHTS, simulate the experiment, that is: first mix the simulated particles evenly, then put the mixed simulated particles into the distributor 1, record the state parameters of each simulated particle at this time, and then simulate the falling state of the particles, to be simulated The redistribution of particles after falling into the charging tank 3, and record the state parameters of each simulated particle at this time.

4) After the simulation experiment is completed, substitute the recorded data into the segregation index calculation model: K=| _Abeginning - _Aend |/ _Abeginning *100%.

5) Get the segregation index: K=20.29%.

Embodiment 2 :

1) Establish a model consisting of the distributor 1 and the charging tank 3 to simulate the falling process of 300g of aluminum oxide, wherein the length L1=200mm, the height L2=250mm, and the width L3=28mm of the charging tank 3, The length L4 of the discharge opening 2 of the distributor 1 is 28 mm, and the width L5 is 14 mm, and the opposite side walls of the discharge opening 2 of the distributor 1 form an angle of 50° with the horizontal direction.

2) Establish particle models with two particle sizes, in which particle diameters are set to 3mm and 6mm respectively, particle Young’s modulus is set to 375GPa, particle Poisson’s ratio is set to 0.22, particle density is set to 2099kg/m ³ , particle and The friction coefficient between the walls is set to 0.4, the restitution coefficient between the particles and the wall is set to 0.7, the friction coefficient between the particles is set to 0.5, the restitution coefficient between the particles is set to 0.6, the mass of 3mm particles: the mass of 6mm particles =4:6.

3) Run the simulation software LIGGGHTS, simulate the experiment, that is: first mix the simulated particles evenly, then put the mixed simulated particles into the distributor 1, record the state parameters of each simulated particle at this time, and then simulate the falling state of the particles, to be simulated The redistribution of particles after falling into the charging tank 3, and record the state parameters of each simulated particle at this time.

4) After the simulation experiment is completed, substitute the recorded data into the segregation index calculation model: K=| _Abeginning - _Aend |/ _Abeginning *100%.

5) Get the segregation index: K=6.07%.

Example 3:

1) Establish a model consisting of distributor 1 and charging tank 3 to simulate the falling process of 300g of aluminum oxide, where the length L1=200mm, height L2=250mm, width L3=28mm of charging tank 3, The length L4=28mm of the feed port 2, the width L5=14mm, and the opposite side walls of the feed port 2 of the distributor 1 form an angle of 50 degrees with the horizontal direction.

2) Establish particle models with two particle sizes, in which particle diameters are set to 3mm and 6mm respectively, particle Young’s modulus is set to 375GPa, particle Poisson’s ratio is set to 0.22, particle density is set to 2099kg/m ³ , particle and The friction coefficient between the walls is set to 0.4, the restitution coefficient between the particles and the wall is set to 0.7, the friction coefficient between the particles is set to 0.5, the restitution coefficient between the particles is set to 0.6, the mass of 3mm particles: the mass of 6mm particles =9:1.

3) Run the simulation software LIGGGHTS, simulate the experiment, that is: first mix the simulated particles evenly, then put the mixed simulated particles into the distributor 1, record the state parameters of each simulated particle at this time, and then simulate the falling state of the particles, to be simulated The redistribution of particles after falling into the charging tank 3, and record the state parameters of each simulated particle at this time.

4) After the simulation experiment is completed, substitute the recorded data into the segregation index calculation model: K=| _Abeginning - _Aend |/ _Abeginning *100%.

5) Get the segregation index: K=0.43%.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than limit the technical solutions. Those skilled in the art should understand that those who modify or replace the technical solutions of the present invention without departing from the present technology The purpose and scope of the scheme should be included in the scope of the claims of the present invention.

Claims (4)

1., based on a method of piling up segregation status after potential variation quantitatively characterizing particles fall, it is characterized in that: comprise the following steps:

1) three-dimensional drawing software SolidWorks is utilized to set up an Alloying Ingredient Model be made up of distributing device and charge can;

2) Alloying Ingredient Model that step 1) is set up is imported in simulation software LIGGGHTS;

3) in simulation software LIGGGHTS, set up the granular model of two kinds of particle diameters, and arrange the basic parameter of Alloying Ingredient Model and the basic parameter of granular model, make Alloying Ingredient Model form analog ligand materials device, granular model forms simulation particle;

4) simulated experiment, after being mixed by simulation particle, loads distributing device, record the state parameter of now each simulation particle by different proportion; After treating simulation particles fall to charge can redistribution, again record the state parameter of each simulation particle;

5) after simulated experiment completes, recorded data is substituted into segregation index and resolve model, obtain segregation index, wherein, described segregation index resolves model and is:

K=|A _just-A _end|/A _just* 100%;

In formula, K is segregation index, A _justfor A particle potential energy under original state and the ratio of total potential energy, A _endfor A particle potential energy under last current state and the ratio of total potential energy.

2. the method based on piling up segregation status after potential variation quantitatively characterizing particles fall according to claim 1, is characterized in that: the height of the geomery of basic parameter distributing device of described Alloying Ingredient Model, the geomery of charge can and distributing device and charge can.

3. the method based on piling up segregation status after potential variation quantitatively characterizing particles fall according to claim 1, is characterized in that: the basic parameter of granular model comprises the ratio between coefficient of restitution between friction factor between particle diameter, particle Young modulus, particle Poisson ratio, particle density, the friction factor between particle and wall, the coefficient of restitution between particle and wall, particle, particle and variable grain.

4. the method based on piling up segregation status after potential variation quantitatively characterizing particles fall according to claim 1, is characterized in that: the simulation graininess parameter recorded comprises speed and the friction force of the simulation numbering of particle, the coordinate of three-dimensional system of coordinate Imitating particle, X-axis, Y-axis and Z axis.