CN112308296A - Method and system for predicting distribution of furnace burden in blast furnace - Google Patents
Method and system for predicting distribution of furnace burden in blast furnace Download PDFInfo
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Abstract
The invention provides a method and a system for predicting burden distribution in a blast furnace, wherein the method comprises the following steps: collecting physical property parameter data and charge level geometric data of furnace burden in a blast furnace; according to the physical property parameter data and the charge level geometric data, obtaining a charge level shape prediction curve, and further obtaining furnace charge flow data; acquiring a first boundary coordinate of each batch of furnace charge according to the furnace charge flow data; sequencing and correcting the first boundary coordinates according to the actual contour of the blast furnace so as to obtain second boundary coordinates; determining the boundary of each material layer according to the second boundary coordinates; determining the ore-coke boundary of each material layer according to the ore-coke ratio of the furnace burden and the second boundary coordinate; determining the material layer structure distribution of furnace burden according to the boundaries of the material layers and the boundaries of ore coke; the method for predicting the distribution of the furnace burden in the blast furnace can determine the distribution state of the furnace burden in the blast furnace and the structural information of all material layers in the blast furnace, realizes the real-time prediction of the material layer structure in the blast furnace, and has strong operability and higher accuracy.
Description
Technical Field
The invention relates to the field of metallurgical industry, in particular to a method and a system for predicting burden distribution in a blast furnace.
Background
In iron and steel enterprises, a blast furnace is a very common steel-making device, people pay more attention to the environmental protection and high efficiency of the blast furnace along with the development of the steel-making industry, the material bed structure distribution of the furnace burden of the blast furnace is more and more paid more and more attention by people due to important indexes such as smelting efficiency, hearth corrosion and the like, however, the blast furnace is a closed container related to high temperature and high pressure, and people are difficult to know the distribution state of the furnace burden in the blast furnace in the actual production.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention provides a method and a system for predicting the distribution of burden in a blast furnace, so as to solve the problems in the prior art that it is difficult to know the distribution of burden in the blast furnace and how to improve the prediction accuracy.
The invention provides a method for predicting burden distribution in a blast furnace, which comprises the following steps:
collecting physical property parameter data and charge level geometric data of furnace burden in a blast furnace;
acquiring a charge level shape prediction curve according to the physical property parameter data and the charge level geometric data, and further acquiring furnace charge flow data;
acquiring a first boundary coordinate of each batch of furnace charge according to the furnace charge flow data;
sequencing and correcting the first boundary coordinates according to the actual contour of the blast furnace so as to obtain second boundary coordinates;
determining the boundary of each material layer according to the second boundary coordinates;
determining the ore-coke boundary of each material layer according to the ore-coke ratio of the furnace burden and the second boundary coordinate;
and determining the material layer structure distribution of the furnace burden according to the boundaries of the material layers and the boundaries of the ore coke.
Optionally, the charge flow data includes: the material flow line number, the residence time and the position coordinate information, wherein the position coordinate information comprises: and the position coordinates of the charge level shape prediction curve.
Optionally, the step of obtaining the first boundary coordinate of each batch of furnace burden according to the furnace burden flow data includes:
judging whether the charge surface shape prediction curve is positioned in a furnace belly area or a dead charge column area or not according to the position coordinate of the charge surface shape prediction curve;
if yes, directly removing furnace charge flow data corresponding to the charge level shape prediction in advance; if not, grouping the furnace charge flow data according to the material flow line number;
sorting the grouped furnace charge flow data in a group according to the residence time;
calculating the target time of each batch of furnace charge according to the distribution period of each batch of furnace charge in a reverse order;
and acquiring the first boundary coordinate according to the target time and the furnace charge flow data after sequencing in the group.
Optionally, the step of sorting and correcting the first boundary coordinate to obtain a second boundary coordinate includes:
sequencing the first boundary coordinates of each layer of furnace burden, wherein the sequencing direction is the width direction of the blast furnace;
performing abnormity detection on the sorted first boundary coordinates, judging whether the first boundary coordinates contain abnormal coordinate points, and if so, correcting by utilizing peripheral point alignment;
reordering the corrected first boundary coordinates, wherein the ordering direction is the height direction of the blast furnace;
and judging whether the reordered first boundary coordinates are positioned below a furnace belly and a dead charge column, if so, determining invalid data and deleting the first boundary coordinates, and if not, determining the second boundary coordinates according to the reordered first boundary coordinates.
Optionally, the step of determining the ore-coke boundary of each material layer according to the burden ore-coke ratio and the second boundary coordinate includes:
determining the ore-coke ratio of the furnace burden according to the geometrical data of the charge level, wherein the geometrical data of the charge level comprise: coordinate data of the ore layer and coordinate data of the coke layer;
and acquiring the ore-coke boundary of each material layer according to the second boundary coordinate and the furnace charge ore-coke ratio.
Optionally, the step of determining the distribution of the material layer structure of the furnace burden according to the boundaries of the material layers and the boundaries of the ore coke comprises:
judging whether the ore coke boundary and the boundaries of all material layers are aligned with the contour line of the blast furnace, and if so, determining the material layer structure distribution of the furnace burden according to the ore coke boundary and the boundaries of all material layers;
if the furnace burden materials are not uniformly aligned, carrying out translation or scaling treatment on boundary data, wherein a scaling factor is the ratio of the contour radius of the blast furnace to the radius length of the blast furnace in the width direction, and further determining the burden layer structure distribution of the furnace burden materials, wherein the boundary data comprises: the coordinate data of the ore coke boundary and the boundary coordinate data of each material layer.
Optionally, the step of obtaining the charge level shape prediction curve according to the physical property parameter data and the charge level geometric data includes:
acquiring physical property parameter data and charge level geometric data of furnace burden in the blast furnace from the data container module;
acquiring flow velocity distribution data of the furnace burden along the width direction of the blast furnace according to the physical property parameter data and the charge level geometric data;
and acquiring a charge level shape prediction curve according to the flow velocity distribution data, and further acquiring charge flow data.
Optionally, the step of obtaining physical parameter data and burden surface geometric data of the burden in the blast furnace comprises:
obtaining the equivalent mass and the equivalent density of the furnace burden according to the physical property parameter data;
correcting the charge level geometric data according to a preset correction standard so as to obtain new charge level geometric data;
and acquiring flow velocity distribution data of the furnace burden along the width direction of the blast furnace according to the equivalent mass, the equivalent density and the new charge level geometric data.
Optionally, the step of correcting the charge level geometric data according to a preset correction standard includes:
judging whether the charge level geometric data are missing or not according to a preset judgment principle, and if so, completing the charge level geometric data according to an in-situ principle;
judging whether the alignment reference point of the burden surface geometric data is coincident with the alignment reference point of the blast furnace contour line, if not, carrying out translation and alignment treatment on the burden surface geometric data;
and judging whether the position coordinates in the charge level geometric data are aligned with the position coordinates of the blast furnace contour line or not, and if not, zooming the charge level geometric data, wherein the zooming factor is the ratio of the radius length of the blast furnace throat to the radius length of the blast furnace in the charge level geometric data in the width direction.
The invention also provides a system for predicting the distribution of furnace burden in the blast furnace, which comprises:
the data acquisition module is used for acquiring physical property parameter data and charge level geometric data of furnace burden in the blast furnace;
the flow data acquisition module is used for acquiring a charge level shape prediction curve according to the physical property parameter data and the charge level geometric data so as to acquire charge flow data;
the material layer boundary acquisition module is used for acquiring first boundary coordinates of each batch of furnace burden according to the furnace burden flow data;
the material bed boundary correction module is used for sequencing and correcting the first boundary coordinates according to the actual contour of the blast furnace so as to obtain second boundary coordinates;
the material layer boundary processing module is used for determining the boundary of each material layer according to the second boundary coordinates;
the ore-coke boundary acquisition module is used for determining the ore-coke boundary of each material layer according to the furnace burden ore-coke ratio and the second boundary coordinate;
and the material layer structure acquisition module is used for determining the material layer structure distribution of the furnace burden according to the boundaries of the material layers and the boundaries of the ore coke.
The invention has the beneficial effects that: the method for predicting the distribution of the furnace burden in the blast furnace can determine the distribution state of the furnace burden in the blast furnace and the structural information of all material layers in the blast furnace, realizes the real-time prediction of the material layer structure in the blast furnace, and has the advantages of strong operability, high accuracy and convenient implementation.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for predicting charge distribution in a blast furnace according to an embodiment of the present invention;
FIG. 2 is another schematic flow chart illustrating a method for predicting charge distribution in a blast furnace according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a system for predicting the distribution of charge in a blast furnace according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The inventor finds that a blast furnace is a very common steelmaking device in steel enterprises, people pay more attention to the environmental protection and high efficiency of the blast furnace along with the development of the steelmaking industry, the distribution of the material layer structure of the charging material of the blast furnace is more and more paid more attention to important indexes such as smelting efficiency, hearth corrosion and the like, however, because the blast furnace is a closed container which relates to high temperature and high pressure, in the actual production, people are difficult to know the distribution state of the furnace burden in the blast furnace, the furnace burden distribution is difficult to predict, the prediction accuracy and the rationality are low, therefore, the inventor provides a method and a system for predicting the distribution of the burden in the blast furnace to determine the distribution state of the burden in the blast furnace, and the structural information of all material layers in the blast furnace, the real-time prediction of the material layer structure in the blast furnace is realized, the operability is strong, the accuracy is high, the implementation is convenient, and the prediction is reasonable.
As shown in fig. 1, the method for predicting the distribution of charge in a blast furnace in the present embodiment includes:
s101: the physical property parameter data and the charge level geometric data of the furnace burden in the blast furnace are collected and stored in the data container module, and the physical property parameter data and the charge level geometric data of the furnace burden in the blast furnace are obtained at the same time, so that the comprehensiveness of a data coverage range is improved, errors generated in the distribution prediction of the furnace burden in the furnace are avoided, and the reasonableness is strong;
s102: acquiring a charge level shape prediction curve according to the physical property parameter data and the charge level geometric data, and further acquiring furnace charge flow data; for example: building a prediction model for predicting the shape curve of the furnace burden, inputting the real-time collected physical property parameter data and charge level geometric data of the furnace burden into the prediction model, obtaining the charge level shape prediction curve of the furnace burden, further obtaining the flow data of the furnace burden, and having strong stability; in some embodiments, the charge level shape prediction curves are a plurality of charge level shape prediction curves, and the plurality of charge level shape prediction curves are predicted positions of the batch of charge materials at various time points, for example: assuming that a batch of furnace charge needs 8 hours from the furnace top to the furnace bottom, recording every 5 seconds, and then obtaining 5760 charge surface shape prediction curves, wherein different charge surface shape prediction curves are prediction positions of the batch of furnace charge at different time points; the charge flow data comprises associated data for each point in the charge level shape prediction curve, the associated data comprising: the number of each point, the number of a material flow line, the residence time and the position coordinate information;
s103: acquiring a first boundary coordinate of each batch of furnace charge according to the furnace charge flow data;
s104: sequencing and correcting the first boundary coordinates according to the actual contour of the blast furnace so as to obtain second boundary coordinates; by sequencing and correcting the first boundary coordinates, the boundary prediction of the furnace burden in the blast furnace is more standardized, and the prediction reasonability is improved;
s105: determining the boundary of each material layer according to the second boundary coordinates; determining the ore-coke boundary of each material layer according to the ore-coke ratio of the furnace burden and the second boundary coordinate;
s106: and determining the material layer structure distribution of the furnace burden according to the boundaries of the material layers and the boundaries of the ore coke. For example: and determining the boundary of each material layer according to the second boundary coordinate, further judging whether the boundary of the ore coke and the boundary of each material layer are aligned with the contour line of the blast furnace, carrying out standardized processing on the boundary coordinate data, further determining the material layer structure distribution of the furnace burden, and simultaneously acquiring the distribution state of the furnace burden in the blast furnace and the structural information of all material layers in the blast furnace in real time, thereby realizing the real-time prediction of the material layer structure in the blast furnace, having stronger operability, higher accuracy and more convenient implementation.
As shown in fig. 2, the method for predicting the distribution of the burden in the blast furnace in the present embodiment includes:
s201: acquiring physical property parameter data and charge level geometric data of furnace burden in the blast furnace from the data container module; the data container module is used for storing various parameter data, and when physical property parameter data and charge level geometric data of furnace burden in the blast furnace are required to be acquired from the data container module, the physical property parameter data and the charge level geometric data of the furnace burden are read from a local cache file into the data container module through deserialization operation; and when the physical property parameter data and the charge level geometric data are processed, the processed data are synchronously updated into the data container module, wherein the updating strategy of the data container module is first-in first-out, namely, new data are added to the data container module according to a specified format, and meanwhile, the foremost data of the container are moved out of the container. For example: when each batch of charging materials reaches the predicted distribution position of the batch of charging materials, updating the data in the data container module, namely: the corresponding data of the batch of furnace burden is moved out, the capacity of the data container module is variable and can be adjusted according to the distribution period of the furnace burden; when the furnace burden distribution prediction is finished, exporting data in the data container module to a local file according to a fixed format through a serialization operation for the next calculation; the data is extracted, updated and stored by using the data container module, a relatively complete data base is provided for the furnace burden distribution prediction in the blast furnace, the integral physical property parameter data and the charge level geometric data in the blast furnace can be integrated, the furnace burden distribution in the blast furnace is accurately predicted, and the prediction reasonability is improved;
s202: obtaining the equivalent mass and the equivalent density of the furnace burden according to the physical property parameter data;
s203: correcting the charge level geometric data according to a preset correction standard so as to obtain new charge level geometric data; in some embodiments, the step of correcting the burden surface geometric data according to a preset correction standard comprises: judging whether the charge level geometric data are missing or not according to a preset judgment principle, and if so, completing the charge level geometric data according to an in-situ principle; judging whether the alignment reference point of the burden surface geometric data is coincident with the alignment reference point of the blast furnace contour line, if not, carrying out translation and alignment treatment on the burden surface geometric data; judging whether the position coordinates in the charge level geometric data are aligned with the position coordinates of the blast furnace contour line or not, if not, carrying out zooming processing on the charge level geometric data, wherein a zooming factor is the ratio of the radius length of the blast furnace throat to the radius length of the blast furnace in the charge level geometric data in the width direction of the blast furnace;
s204: obtaining flow velocity distribution data of the furnace burden along the width direction of the blast furnace according to the equivalent mass, the equivalent density and the new charge level geometric data;
s205: and acquiring a charge level shape prediction curve according to the flow velocity distribution data, and further acquiring charge flow data.
When the data in the data container module is updated, removing the foremost data in the data container module according to a first-in first-out principle;
in some embodiments, the charge flow data comprises: the material flow line number, the residence time and the position coordinate information, wherein the position coordinate information comprises: and the position coordinates of the charge level shape prediction curve.
S206: preprocessing the flow data in the furnace, namely judging whether the charge surface shape prediction curve is positioned in a furnace belly area or a dead charge column area according to the position coordinates of the charge surface shape prediction curve;
if yes, directly removing furnace charge flow data corresponding to the charge level shape prediction in advance; if not, grouping the furnace charge flow data according to the material flow line number;
sorting the grouped furnace charge flow data in a group according to the residence time;
s207: acquiring target time of each batch of furnace charge, further acquiring the first boundary coordinate, namely calculating the target time of each batch of furnace charge according to the material distribution period of each batch of furnace charge in a reverse order; acquiring the first boundary coordinate according to the target time and the furnace charge flow data after sequencing in the group;
s208: sequencing and correcting the first boundary coordinates of each layer of furnace burden to determine second boundary coordinates, namely sequencing the first boundary coordinates of each layer of furnace burden, wherein the sequencing direction is the width direction of the blast furnace;
performing abnormity detection on the sorted first boundary coordinates, judging whether the first boundary coordinates contain abnormal coordinate points, and if so, correcting by utilizing peripheral point alignment, so as to improve the reasonability of the boundary coordinates of the material layer;
reordering the corrected first boundary coordinates, wherein the ordering direction is the height direction of the blast furnace, so as to correct the material layer boundary and avoid the crossing condition of furnace materials of each layer;
judging whether the reordered first boundary coordinates are positioned below a furnace belly and a dead charge column or not, if so, regarding the first boundary coordinates as invalid data and deleting the first boundary coordinates, and if not, determining second boundary coordinates according to the reordered first boundary coordinates; by sequencing and correcting the first boundary coordinates of the furnace burden of each layer and judging the rationality of the coordinate positions, the accuracy of the second boundary coordinates is improved, the accuracy of predicting the distribution of the furnace burden in the blast furnace is improved, the actual condition of the blast furnace is better met, and the rationality is stronger;
s209: determining the boundary of each material layer according to the second boundary coordinates;
s210: determining the ore-coke ratio of the furnace burden according to the geometrical data of the charge level, wherein the geometrical data of the charge level comprise: coordinate data of the ore layer and coordinate data of the coke layer;
acquiring the ore-coke boundary of each material layer according to the boundary of each material layer and the ore-coke ratio of the furnace burden;
s211: judging whether the ore coke boundary and the boundaries of all material layers are aligned with the contour line of the blast furnace, and if so, determining the material layer structure distribution of the furnace burden according to the ore coke boundary and the boundaries of all material layers;
if the furnace burden materials are not uniformly aligned, carrying out translation or scaling treatment on boundary data, wherein a scaling factor is the ratio of the contour radius of the blast furnace to the radius length of the blast furnace in the width direction, and further determining the burden layer structure distribution of the furnace burden materials, wherein the boundary data comprises: the coordinate data of the ore coke boundary and the boundary coordinate data of each material layer. By judging whether the boundary of the ore coke and the boundary of each material layer are aligned with the contour line of the blast furnace or not, the translation or scaling processing is further carried out on the boundary data, so that the distribution prediction of each layer of furnace burden is more accurate, the prediction coordinate is closer to the actual condition of the blast furnace, and the distribution prediction of the furnace burden is more reasonable.
In some embodiments, the charge physical parameters comprise: ore density, coke density, ore porosity, coke porosity, ore mass, coke mass, and material distribution period, wherein the geometrical data of the charge level comprise: coordinate data of the ore layer and coordinate data of the coke layer;
as shown in fig. 3, the present embodiment further provides a system for predicting burden distribution in a blast furnace, including:
the data acquisition module is used for acquiring physical property parameter data and charge level geometric data of furnace burden in the blast furnace;
the flow data acquisition module is used for acquiring a charge level shape prediction curve according to the physical property parameter data and the charge level geometric data so as to acquire charge flow data;
the material layer boundary acquisition module is used for acquiring first boundary coordinates of each batch of furnace burden according to the furnace burden flow data;
the material bed boundary correction module is used for sequencing and correcting the first boundary coordinates according to the actual contour of the blast furnace so as to obtain second boundary coordinates;
the material layer boundary processing module is used for determining the boundary of each material layer according to the second boundary coordinates;
the ore-coke boundary acquisition module is used for determining the ore-coke boundary of each material layer according to the furnace burden ore-coke ratio and the second boundary coordinate;
the material layer structure acquisition module is used for determining the material layer structure distribution of the furnace burden according to the boundary ore coke boundary of each material layer;
the data acquisition module, the flow data acquisition module, the material layer boundary correction module, the material layer boundary processing module, the ore coke boundary acquisition module and the material layer structure acquisition module are sequentially connected, so that the distribution state of furnace burden in the blast furnace and the structural information of all material layers in the blast furnace can be acquired in real time, the real-time prediction of the material layer structure in the blast furnace is realized, the maneuverability is strong, the accuracy is high, and the implementation is convenient.
In some embodiments, when a control instruction for predicting burden distribution in the blast furnace transmitted by an upper control system is received, the acquisition module starts to acquire physical parameter data and burden surface geometric data of the burden in the blast furnace.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A method for predicting the distribution of burden in a blast furnace is characterized by comprising the following steps:
collecting physical property parameter data and charge level geometric data of furnace burden in a blast furnace;
acquiring a charge level shape prediction curve according to the physical property parameter data and the charge level geometric data, and further acquiring furnace charge flow data;
acquiring a first boundary coordinate of each batch of furnace charge according to the furnace charge flow data;
sequencing and correcting the first boundary coordinates according to the actual contour of the blast furnace so as to obtain second boundary coordinates;
determining the boundary of each material layer according to the second boundary coordinates;
determining the ore-coke boundary of each material layer according to the ore-coke ratio of the furnace burden and the second boundary coordinate; and determining the material layer structure distribution of the furnace burden according to the boundaries of the material layers and the boundaries of the ore coke.
2. The method of claim 1, wherein the charge flow data includes: the material flow line number, the residence time and the position coordinate information, wherein the position coordinate information comprises: and the position coordinates of the charge level shape prediction curve.
3. The method of claim 2, wherein the step of obtaining the first boundary coordinates of each batch of charge material based on the charge flow data comprises:
judging whether the charge surface shape prediction curve is positioned in a furnace belly area or a dead charge column area or not according to the position coordinate of the charge surface shape prediction curve;
if yes, directly removing furnace charge flow data corresponding to the charge level shape prediction in advance; if not, grouping the furnace charge flow data according to the material flow line number;
sorting the grouped furnace charge flow data in a group according to the residence time;
calculating the target time of each batch of furnace charge according to the distribution period of each batch of furnace charge in a reverse order;
and acquiring the first boundary coordinate according to the target time and the furnace charge flow data after sequencing in the group.
4. The method of claim 3, wherein the step of sorting and correcting the first boundary coordinates to obtain second boundary coordinates comprises:
sequencing the first boundary coordinates of each layer of furnace burden, wherein the sequencing direction is the width direction of the blast furnace;
performing abnormity detection on the sorted first boundary coordinates, judging whether the first boundary coordinates contain abnormal coordinate points, and if so, correcting by utilizing peripheral point alignment;
reordering the corrected first boundary coordinates, wherein the ordering direction is the height direction of the blast furnace;
and judging whether the reordered first boundary coordinates are positioned below a furnace belly and a dead charge column, if so, determining invalid data and deleting the first boundary coordinates, and if not, determining the second boundary coordinates according to the reordered first boundary coordinates.
5. The method of predicting the distribution of charge material in a blast furnace according to claim 4, wherein the step of determining the boundaries of the charge and coke for each burden layer based on the charge to charge ratio and the second boundary coordinates comprises:
determining the ore-coke ratio of the furnace burden according to the geometrical data of the charge level, wherein the geometrical data of the charge level comprise: coordinate data of the ore layer and coordinate data of the coke layer;
and acquiring the ore-coke boundary of each material layer according to the second boundary coordinate and the furnace charge ore-coke ratio.
6. The method of claim 5, wherein the step of determining the burden distribution of the burden structure of the burden according to the boundaries of the burden layers and the boundaries of the coke comprises:
judging whether the ore coke boundary and the boundaries of all material layers are aligned with the contour line of the blast furnace, and if so, determining the material layer structure distribution of the furnace burden according to the ore coke boundary and the boundaries of all material layers;
if the furnace burden materials are not uniformly aligned, carrying out translation or scaling treatment on boundary data, wherein a scaling factor is the ratio of the contour radius of the blast furnace to the radius length of the blast furnace in the width direction, and further determining the burden layer structure distribution of the furnace burden materials, wherein the boundary data comprises: the coordinate data of the ore coke boundary and the boundary coordinate data of each material layer.
7. The method for predicting the charge distribution in the blast furnace according to claim 1, wherein the step of obtaining a charge level shape prediction curve according to the physical property parameter data and the charge level geometric data comprises:
acquiring physical property parameter data and charge level geometric data of furnace burden in the blast furnace from the data container module;
acquiring flow velocity distribution data of the furnace burden along the width direction of the blast furnace according to the physical property parameter data and the charge level geometric data;
and acquiring a charge level shape prediction curve according to the flow velocity distribution data, and further acquiring charge flow data.
8. The method of claim 7, wherein the step of obtaining physical parameter data and charge level geometry data of the charge in the blast furnace is followed by the steps of:
obtaining the equivalent mass and the equivalent density of the furnace burden according to the physical property parameter data;
correcting the charge level geometric data according to a preset correction standard so as to obtain new charge level geometric data;
and acquiring flow velocity distribution data of the furnace burden along the width direction of the blast furnace according to the equivalent mass, the equivalent density and the new charge level geometric data.
9. The method of claim 8, wherein the step of modifying the burden surface geometry data according to a preset modification criterion comprises:
judging whether the charge level geometric data are missing or not according to a preset judgment principle, and if so, completing the charge level geometric data according to an in-situ principle;
judging whether the alignment reference point of the burden surface geometric data is coincident with the alignment reference point of the blast furnace contour line, if not, carrying out translation and alignment treatment on the burden surface geometric data;
and judging whether the position coordinates in the charge level geometric data are aligned with the position coordinates of the blast furnace contour line or not, and if not, zooming the charge level geometric data, wherein the zooming factor is the ratio of the radius length of the blast furnace throat to the radius length of the blast furnace in the charge level geometric data in the width direction.
10. A system for predicting distribution of burden material in a blast furnace, comprising:
the data acquisition module is used for acquiring physical property parameter data and charge level geometric data of furnace burden in the blast furnace;
the flow data acquisition module is used for acquiring a charge level shape prediction curve according to the physical property parameter data and the charge level geometric data so as to acquire charge flow data;
the material layer boundary acquisition module is used for acquiring first boundary coordinates of each batch of furnace burden according to the furnace burden flow data;
the material bed boundary correction module is used for sequencing and correcting the first boundary coordinates according to the actual contour of the blast furnace so as to obtain second boundary coordinates;
the material layer boundary processing module is used for determining the boundary of each material layer according to the second boundary coordinates;
the ore-coke boundary acquisition module is used for determining the ore-coke boundary of each material layer according to the furnace burden ore-coke ratio and the second boundary coordinate;
and the material layer structure acquisition module is used for determining the material layer structure distribution of the furnace burden according to the boundaries of the material layers and the boundaries of the ore coke.
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