CN116497166A - Intelligent control method for blast furnace top material distribution process flow - Google Patents
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- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
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Abstract
The invention provides an intelligent control method of a blast furnace top material distribution process flow, which comprises the following steps: establishing a blast furnace top material distribution geometric model, a blast furnace body geometric model and a blast furnace top material distribution layer structure model; processing a blast furnace top burden distribution layer structure model to obtain a result as an input condition of a blast furnace body geometric model; establishing a blast furnace body model through the blast furnace body geometric model to obtain key economic and technical indexes of blast furnace ironmaking; obtaining data through a blast furnace body model, solving various blast furnace ironmaking key economic and technical index functions, and performing multi-objective optimization on the blast furnace ironmaking key economic and technical index functions to obtain optimal blast furnace ironmaking key economic and technical indexes; and (3) evaluating the blast furnace top burden distribution layer structure model by adopting a standard mean square error, judging whether the standard mean square error is greater than 5%, and if not, obtaining the optimal blast furnace top burden distribution layer structure model. According to the invention, the description of the internal state of the blast furnace is realized from the control angle, and the visualization of the internal state of the blast furnace is realized.
Description
Technical Field
The invention belongs to the technical field of blast furnace numerical simulation, and particularly relates to an intelligent control method of a blast furnace top material distribution process flow.
Background
The blast furnace is an important device in the iron-making process, with the capacity structure adjustment of the iron and steel industry, the market competition is increasingly strong, the smelting efficiency is improved, and the high-quality development has become the future direction of blast furnace iron-making. However, the sealing performance and the complexity of the furnace condition of the blast furnace are very difficult to obtain information in the blast furnace, and blast furnace ironmaking is a typical "black box" production mode, although the production automation level is increasingly high, the existing detection means mainly use thermocouples or sensors arranged at relevant parts of blast furnace equipment to detect the temperature of the furnace body and the pressure information of the furnace top of the blast furnace.
Chinese invention patent name: a simulation method for movement of furnace charge particles in a blast furnace rotating chute comprises the following steps: CN110348156B discloses a simulation method for the movement of furnace charge particles inside a blast furnace rotating chute, comprising the following steps: step one, determining parameters used for simulation; step two, using ANSYS to obtain the mass and moment of inertia tensors of the rigid body in the rotary chute; step three, performing pretreatment by using ANSYS to generate a k file; step four, calculating the k file generated in the step three, and generating a calculation result after calculation; and fifthly, submitting the calculation result of the k file to graphic interface software for observing the calculation result. The patent 201710804565.4 and 2017410804565.9 also cannot describe the internal state of the blast furnace from a control point of view, and cannot visualize the internal state of the entire blast furnace.
Disclosure of Invention
Aiming at the problems in the prior art, the invention realizes the description of the internal state of the blast furnace from the control angle, realizes the visualization of the internal state of the blast furnace, and provides an intelligent control method for the blast furnace top material distribution process flow.
The technical scheme adopted by the invention is as follows:
an intelligent control method for a blast furnace top material distribution process flow comprises the following steps:
establishing a blast furnace top distribution geometric model and a blast furnace body geometric model, setting size parameters of the two geometric models, and carrying out grid division on the two geometric models;
the geometrical model of the blast furnace top burden distribution adopts a discrete unit method to establish a structural model of the blast furnace top burden distribution layer;
adopting a two-dimensional sheet method to process a blast furnace top burden distribution layer structure model, and obtaining a result as an input condition of a blast furnace body geometric model;
the geometric model of the blast furnace body adopts computational fluid dynamics technology to establish the model of the blast furnace body, and key economic and technical indexes of blast furnace ironmaking are obtained;
solving various blast furnace ironmaking key economic and technical index functional formulas based on a curve fitting method, and carrying out multi-objective optimization on the blast furnace ironmaking key economic and technical index functional formulas by adopting a multi-objective particle swarm algorithm to obtain an optimal blast furnace ironmaking key economic and technical index;
calculating the standard mean square error between the key economic and technical indexes of the blast furnace ironmaking and the optimal key economic and technical indexes of the blast furnace ironmaking, and evaluating a blast furnace top burden distribution layer structure model by adopting the standard mean square error; the structural model for evaluating the material layer structure of the blast furnace top material by adopting the standard mean square error is specifically as follows: judging whether the standard mean square error is greater than 5%, if so, controlling and adjusting parameters of a blast furnace top burden distribution layer structure model; if not, selecting the model with the minimum standard mean square error as the optimal blast furnace top burden material layer structure model. A computer storage medium, on which a computer program is stored, which computer program, when being processed by a processor, is adapted to carry out the intelligent control method of the blast furnace top burden distribution process.
The beneficial effects of the invention are as follows: according to the invention, data obtained by the blast furnace body model is used for solving various blast furnace ironmaking key economic and technical index functions based on a curve fitting method, and various blast furnace ironmaking key economic and technical index functions are subjected to multi-objective optimization based on a multi-objective particle swarm algorithm, so that an optimal blast furnace top material layer structure model is determined; the method controls the mean square error to be within 5%, and can be used in blast furnaces with different volumes through changing parameters, so that the method has high calculation precision and strong applicability, compared with the existing method for searching furnace top burden on site, which mainly or through trial and error, the method has lower trial and error cost, and can obtain the influence on smelting state in the furnace (the furnace top burden distribution mode of the blast furnace can change internal smelting of the blast furnace, different burden distribution modes can cause different shapes of soft smelting belts in the blast furnace), which is not easy to obtain through a physical experiment method, and finally realize the effects of improving productivity and reducing emission with low carbon.
Drawings
Fig. 1 is a flow chart of a control method of a blast furnace ironmaking process.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings: in order to clearly illustrate the technical features of the present solution, the present invention will be described in detail below with reference to the following detailed description and the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different structures of the invention. In order to simplify the present disclosure, components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and processes are omitted so as to not unnecessarily obscure the present invention.
As shown in fig. 1, the invention provides an intelligent control method for a blast furnace top material distribution process flow, which comprises the following steps:
step one: establishing a blast furnace top distribution geometric model and a blast furnace body geometric model through three-dimensional modeling software, setting dimension parameters of the two geometric models, and carrying out grid division on the two geometric models; the geometrical model of the blast furnace top distribution is built according to the physical parameters of the blast furnace (namely, the blast furnace top distribution is built by using 3D modeling software through the data of the height, diameter, volume and the like of an actual blast furnace), and key parameters at least comprise the diameter of the blast furnace throat and the inclination angle of a rotary chute; the blast furnace body of the blast furnace body geometric model is divided into the following parts from top to bottom: five parts of furnace throat, furnace body, furnace waist, furnace abdomen and furnace hearth.
Step two: establishing a blast furnace top burden distribution layer structure model based on a discrete unit method through a blast furnace top burden distribution geometric model; the structural model of the blast furnace roof burden distribution layer is built according to the burden distribution process parameters and the physical properties of furnace burden particles (the physical parameters such as the particle diameter, friction coefficient, shear modulus and the like of particles are input through discrete element simulation software, and then the structural model is imported into the blast furnace roof geometric model built in the upper stage, and the software can automatically generate the model), and the burden distribution process parameters and the physical properties of the furnace burden particles at least comprise: rotation speed of a rotating chute, ore coke ratio, particle diameter of furnace burden, elastic recovery coefficient of the particle of the furnace burden, sliding friction coefficient of the particle of the furnace burden and rolling friction coefficient of the particle of the furnace burden.
Step three: optimizing parameters of a blast furnace top burden material layer structure model based on a coarse granulation method, and solving the blast furnace top burden material layer structure model.
Step four: and processing the blast furnace top burden distribution layer structure model based on a two-dimensional sheet method to obtain a result as an input condition of the blast furnace body model.
Step five: establishing a blast furnace body model based on computational fluid dynamics through the blast furnace body geometric model, taking the result obtained in the step four as an input condition of the blast furnace body model, coupling a blast furnace top material distribution layer structure model and the blast furnace body model, and solving a blast furnace ironmaking process through the blast furnace body model to obtain key economic and technical indexes of the blast furnace ironmaking; the key economic and technical indexes of blast furnace ironmaking comprise a temperature field, a pressure field, a speed field, an iron yield, a furnace top gas utilization rate, a furnace top gas temperature, a furnace top gas pressure, a molten iron temperature, a fuel ratio and a blast furnace utilization coefficient in the blast furnace; solving the blast furnace ironmaking process through the blast furnace body model (namely, establishing the blast furnace body model through the blast furnace body geometric model based on computational fluid dynamics to obtain key economic and technical indexes of the blast furnace ironmaking) comprises the following steps: solving an in-furnace temperature field, a pressure field, a speed field, an iron yield, a top gas utilization rate, a top gas temperature, a top gas pressure, a molten iron temperature, a fuel ratio and a blast furnace utilization coefficient in the blast furnace ironmaking process.
Step six: the method comprises the steps of obtaining data through a blast furnace body model (the obtained data are some result data through model simulation, relevant blast furnace ironmaking key economic and technical indexes can be obtained through calculation of the simulation data), solving various blast furnace ironmaking key economic and technical index functional formulas based on a curve fitting method, and carrying out multi-objective optimization on the blast furnace ironmaking key economic and technical index functional formulas based on a multi-objective particle swarm algorithm to obtain the optimal blast furnace ironmaking key economic and technical index.
Step seven: and evaluating a material layer structure model according to the standard mean square error between the step five blast furnace ironmaking key economic technical index and the step six optimal blast furnace ironmaking key economic technical index.
Step eight: according to the evaluation result of the step seven, changing the blast furnace top burden distribution layer structure model in the step two, repeating the step three, the step four, the step five and the step seven, and determining the optimal blast furnace top burden distribution layer structure model by comparing standard mean square deviations of all models, wherein the method specifically comprises the following steps: judging whether the standard mean square error is greater than 5%, if so, controlling and adjusting parameters of a blast furnace top burden distribution layer structure model; if not, selecting the model with the minimum standard mean square error as the optimal blast furnace top burden material layer structure model.
Example 1
Step one: with an effective volume of 1880m 3 Is the subject of investigation. Establishing a blast furnace bell-less top distribution geometric model and a blast furnace body geometric model through SolidWorks three-dimensional modeling software, and dividing grids; the geometrical model of the distribution of the blast furnace top is simplified to establish a quarter blast furnace throat geometrical model, so that the calculation rate of simulation can be improved on the premise of not influencing the accuracy of the result; the inclination angles of the rotating chute are respectively selected from five gears of 30 degrees, 32 degrees, 34 degrees, 36 degrees and 38 degrees.
Step two: simulating the blast furnace top distribution process based on a discrete unit method through a blast furnace top distribution geometric model, and establishing a furnace charge material layer structure model. The furnace burden is coke and ore, the ore is composed of a large amount of sinter and a small amount of pellet and lump ore, in order to improve the calculation efficiency, simplify the material composition and the particle diameter, the particle diameter of the coke is 45mm, and the particle diameter of the ore is 20mm. Bulk density of the furnace charge is obtained through a physical experiment, and the sliding friction coefficient of the coke is 0.7, the rolling friction coefficient of the coke is 0.1, the sliding friction coefficient of the ore is 0.7, the rolling friction coefficient of the ore is 0.1, and the particle elastic recovery coefficient of the coke and the ore is 0.1.
Step three: the coke particle size and the ore particle size are amplified by a coarse granulating method, the amplified coke particle size is 135mm, the amplified ore particle size is 60mm, the quantity of burden distribution particles can be reduced to about 100 ten thousand on the premise of not influencing the result precision, and the calculation rate of simulation is improved.
Step four: and simulating the material distribution process of the blast furnace top, discharging the furnace burden from the hopper to form a particle flow, passing through the rotary chute, and enabling the particles to freely fall down to the furnace throat under the action of gravity after passing through the chute. The rotating speed of the rotating chute is 6, 7.5 and 8RPM respectively under the selected inclination angle gear to carry out the simulation of the blast furnace top material distribution process, and a furnace burden layer structure model is obtained.
Step five: and processing a furnace burden layer structure model by a two-dimensional sheet method, and analyzing the cross-sectional structure of the furnace burden layer to obtain the grain position, the radial ratio of furnace burden grains, the segregation degree of the furnace burden layer, the grain porosity of the furnace burden layer and the grain-fluid interaction force.
Step six: and establishing a blast furnace body model based on computational fluid dynamics through the blast furnace body geometric model, and according to the particle position, the radial ratio of furnace charge particles, the segregation degree of the material layer, the particle porosity of the material layer, the particle-fluid interaction force and the like in the fifth step, as input conditions of the blast furnace body model, coupling the material layer structure model at the top of the blast furnace and the blast furnace body model. In the simulation process, single particle positions, speeds, particle radial ratios and the like of the material layers in each time step are obtained through simulation by a discrete unit method, the porosity and the fluid resistance among particles are calculated, and the fluid resistance acting on the single particles is solved through computational fluid mechanics by using the data. The generated force is integrated into the discrete unit method simulation, and the next time step single particle movement information can be generated, wherein the information comprises the position of furnace charge particles, the particle speed, the radial distribution of ore coke particles, the segregation of particle separation and the particle-fluid interaction force. Based on computational fluid dynamics, the information of the blast furnace body model including a chemical reaction model, a heat transfer model, a mass transfer model, a soft melting zone model, a coal powder injection model and a gas-solid two-phase model at each time step can be obtained, and the key economic and technical indexes of blast furnace ironmaking are obtained by solving.
Step seven, a step of performing a step of; and solving various blast furnace ironmaking key economic and technical index functions based on curve fitting methods by using the obtained data of the blast furnace body model, wherein the functions comprise a molten iron temperature function, a furnace top gas pressure function, a fuel ratio function and a blast furnace utilization coefficient function, and performing multi-objective optimization on various blast furnace key economic and technical index functions based on a multi-objective particle swarm algorithm to obtain the optimal blast furnace ironmaking key economic and technical index.
Step eight: calculating the error of the key economic and technical index of the blast furnace ironmaking and the key economic and technical index of the optimal blast furnace ironmaking, and evaluating the blast furnace top material distribution layer structure model through standard mean square error.
Step nine: the standard mean square error is less than or equal to 5 percent, and the blast furnace top burden material layer structure model is preferable. The standard mean square error is larger than 5%, the rotating speed of the rotating chute or the inclination angle of the rotating chute is changed, and the steps four, five, six and eight are repeated.
Step ten: and comparing all the preferred results, and selecting a model with the minimum standard mean square error as the optimal.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (9)
1. An intelligent control method for a blast furnace top material distribution process flow is characterized by comprising the following steps:
establishing a blast furnace top distribution geometric model and a blast furnace body geometric model, setting size parameters of the two geometric models, and carrying out grid division on the two geometric models;
the geometrical model of the blast furnace top burden distribution adopts a discrete unit method to establish a structural model of the blast furnace top burden distribution layer;
adopting a two-dimensional sheet method to process a blast furnace top burden distribution layer structure model, and obtaining a result as an input condition of a blast furnace body geometric model;
the geometric model of the blast furnace body adopts computational fluid dynamics technology to establish the model of the blast furnace body, and key economic and technical indexes of blast furnace ironmaking are obtained;
solving various blast furnace ironmaking key economic and technical index functional formulas based on a curve fitting method, and carrying out multi-objective optimization on the blast furnace ironmaking key economic and technical index functional formulas by adopting a multi-objective particle swarm algorithm to obtain an optimal blast furnace ironmaking key economic and technical index;
calculating the standard mean square error between the key economic and technical indexes of the blast furnace ironmaking and the optimal key economic and technical indexes of the blast furnace ironmaking, and evaluating a blast furnace top burden distribution layer structure model by adopting the standard mean square error; the structural model for evaluating the material layer structure of the blast furnace top material by adopting the standard mean square error is specifically as follows: judging whether the standard mean square error is greater than 5%, if so, controlling and adjusting parameters of a blast furnace top burden distribution layer structure model; if not, selecting the model with the minimum standard mean square error as the optimal blast furnace top burden material layer structure model.
2. The intelligent control method of a blast furnace roof burden distribution process flow according to claim 1, wherein the method further comprises: optimizing parameters of a blast furnace top burden material layer structure model based on a coarse granulation method, and solving the blast furnace top burden material layer structure model.
3. The intelligent control method of blast furnace top burden distribution process flow according to claim 1, wherein a blast furnace top burden distribution geometric model is established according to blast furnace physical parameters, and key parameters of the blast furnace top burden distribution geometric model comprise blast furnace throat diameter and rotary chute inclination angle.
4. The intelligent control method of blast furnace roof burden distribution process flow according to claim 1, wherein the blast furnace roof burden distribution material layer structure model is established according to burden distribution process parameters and physical properties of burden particles, and the burden distribution process parameters and the physical properties of the burden particles comprise: rotation speed of a rotating chute, ore coke ratio, particle diameter of furnace burden, elastic recovery coefficient of the particle of the furnace burden, sliding friction coefficient of the particle of the furnace burden and rolling friction coefficient of the particle of the furnace burden.
5. The intelligent control method for blast furnace top burden distribution process flow according to claim 1, wherein the blast furnace body model respectively establishes a chemical reaction model, a heat transfer model, a mass transfer model, a reflow zone model, a pulverized coal injection model and a gas-solid two-phase model according to particle positions, particle radial ratios, porosities and particle-fluid interaction forces obtained through the blast furnace top burden distribution layer structure model.
6. The intelligent control method for blast furnace top burden distribution process flow according to claim 1, wherein the step of establishing a blast furnace body model by adopting computational fluid dynamics technology through the blast furnace body geometric model and obtaining key economic and technical indexes of blast furnace ironmaking comprises the following steps: and (3) taking a result obtained by processing the blast furnace top burden distribution layer structure model by a two-dimensional sheet method as an input condition of a local blast furnace model, coupling the blast furnace top burden distribution layer structure model and the blast furnace body model, and solving a blast furnace iron making process through the blast furnace body model to obtain key economic and technical indexes of blast furnace iron making.
7. The intelligent control method for blast furnace top burden distribution process flow according to claim 6, wherein the key economic and technical indexes of blast furnace iron making comprise a temperature field, a pressure field, a speed field, an iron yield, a top gas utilization rate, a top gas temperature, a top gas pressure, a molten iron temperature, a fuel ratio and a blast furnace utilization coefficient in the blast furnace.
8. The intelligent control method for the blast furnace top burden distribution process flow according to claim 1, wherein the judging whether the standard mean square error is more than 5%, if so, the controlling and adjusting the blast furnace top burden distribution material layer structure model parameters specifically comprises: and if the standard mean square error is greater than 5%, changing the rotating speed or the inclination angle of the rotating chute.
9. A computer storage medium having stored thereon a computer program, which, when processed by a processor, is adapted to perform the intelligent control method of a blast furnace roof burden distribution process according to any of claims 1-8.
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| CN113139275A (en) * | 2021-03-22 | 2021-07-20 | 浙江大学 | Blast furnace throat temperature estimation method based on multilayer ore-coke ratio distribution model |
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