CN117035531A - Method and related equipment for determining quality index and operation system of blast furnace production - Google Patents
Method and related equipment for determining quality index and operation system of blast furnace production Download PDFInfo
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Abstract
The method and the related equipment for determining the quality index and the operation system of the blast furnace production are applied to the technical field of the blast furnace production, firstly, the blast furnace production data are acquired, the production data of the blast furnace are input into a trained blast furnace decision support system prediction model, the key economic and technical index of the blast furnace is obtained, the influence of the structure and the like of the blast furnace production burden on the key economic and technical index of the blast furnace is considered, whether the key economic and technical index of the blast furnace meets the preset requirements for the blast furnace production and the cost control is judged, if the key economic and technical index of the blast furnace meets the preset requirements for the blast furnace production and the cost control, the blast furnace production data are sent to blast furnace production engineering technicians to determine the quality index and the operation system of the blast furnace production, and the proper quality index and the operation system of the blast furnace production are determined by judging the key economic and technical index of the blast furnace, so that the method is favorable for improving the economic benefit of the blast furnace production, and the method has good practicability and popularization value.
Description
Technical Field
The application belongs to the technical field of blast furnace production, and particularly relates to a method for determining quality indexes and operation systems of blast furnace production.
Background
Under the situation that the domestic iron and steel industry rapidly develops at present, the productivity of the iron and steel enterprises is seriously excessive, the production profit of the blast furnace is continuously low, and the search for a proper blast furnace production system has become an urgent need for improving the economic benefit of the iron and steel enterprises.
At present, the quality index and the operation system of the traditional blast furnace production are determined by manually calculating according to the aspects of chemical components, normal temperature performance and the like through Excel or batching calculation software.
However, the conventional method for determining the quality index and the operation system of the blast furnace production does not consider the influence of the structures and the like of various blast furnace production burden on the key economic and technical indexes of the blast furnace, so that the quality index and the operation system of the conventional blast furnace production have lower economic benefits and are not beneficial to popularization.
Disclosure of Invention
In view of the above problems, the present application provides a method for determining quality index and operation system of blast furnace production, and in order to improve economic benefit of blast furnace production, the specific scheme is as follows:
a method for determining quality index and operation system of blast furnace production comprises the following steps:
acquiring blast furnace production data, wherein the blast furnace production data comprises blast furnace burden quality indexes, blast furnace burden structures and blast furnace process parameters;
inputting the production data of the blast furnace into a trained blast furnace decision support system prediction model to obtain key economic and technical indexes of the blast furnace, wherein the key economic and technical indexes of the blast furnace comprise a blast furnace utilization coefficient, a blast furnace coke ratio and a blast furnace fuel ratio;
judging whether the key economic and technical index of the blast furnace meets the preset blast furnace production and cost control requirements;
and if the key economic and technical indexes of the blast furnace meet the preset requirements for blast furnace production and cost control, sending the blast furnace production data to blast furnace production engineering technicians so that the blast furnace production engineering technicians determine the quality indexes and the operation system of blast furnace production based on the blast furnace production data.
Optionally, after the determining whether the key economic and technical index of the blast furnace meets the preset requirements for blast furnace production and cost control, the method further includes:
and if the key economic and technical indexes of the blast furnace do not meet the preset requirements for blast furnace production and cost control, re-determining the blast furnace production data.
Optionally, the training mode of the blast furnace decision support system prediction model includes:
acquiring a plurality of historical blast furnace production data, wherein each blast furnace production data is marked with the key economic and technical indexes of the blast furnace;
dividing the plurality of historical blast furnace production data into a first historical blast furnace production data set and a second historical blast furnace production data set;
training by using the first historical blast furnace production data set, and optimizing by using the second historical blast furnace production data set to obtain the blast furnace decision support system prediction model.
Optionally, the training using the first historical blast furnace production data set and the optimizing using the second historical blast furnace production data set, to obtain the blast furnace decision support system prediction model, includes:
training by using the first historical blast furnace production data set, and optimizing by using the second historical blast furnace production data set to obtain a model training error;
judging whether the model training error is smaller than a preset threshold value or not;
and if the model training error is smaller than a preset threshold value, determining a prediction model of the blast furnace decision support system.
A quality index and operation system determining device for blast furnace production comprises:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring blast furnace production data, and the blast furnace production data comprise blast furnace burden quality indexes, blast furnace burden structures and blast furnace process parameters;
the input unit is used for inputting the production data of the blast furnace into a trained blast furnace decision support system prediction model to obtain the key economic and technical indexes of the blast furnace, wherein the key economic and technical indexes of the blast furnace comprise a blast furnace utilization coefficient, a blast furnace coke ratio and a blast furnace fuel ratio;
the judging unit is used for judging whether the key economic and technical indexes of the blast furnace meet the preset blast furnace production and cost control requirements;
and the determining unit is used for sending the blast furnace production data to blast furnace production engineering technicians if the key economic and technical indexes of the blast furnace meet the preset blast furnace production and cost control requirements so as to enable the blast furnace production engineering technicians to determine the quality index and the operation degree of blast furnace production based on the blast furnace production data.
Optionally, the apparatus further includes:
and the redetermining unit is used for redetermining the blast furnace production data if the key economic and technical indexes of the blast furnace do not meet the preset blast furnace production and cost control requirements.
Optionally, the apparatus further includes:
the data acquisition unit is used for acquiring a plurality of historical blast furnace production data, and each blast furnace production data is marked with the key economic and technical indexes of the blast furnace;
a dividing unit for dividing the plurality of historical blast furnace production data into a first historical blast furnace production data set and a second historical blast furnace production data set;
the training unit is used for training by using the first historical blast furnace production data set and optimizing by using the second historical blast furnace production data set to obtain the blast furnace decision support system prediction model.
Optionally, the training unit includes:
the training subunit is used for training by using the first historical blast furnace production data set and optimizing by using the second historical blast furnace production data set to obtain a model training error;
the judging subunit is used for judging whether the model training error is smaller than a preset threshold value or not;
and the determining subunit is used for determining the blast furnace decision support system prediction model if the model training error is smaller than a preset threshold value.
A quality index and operation system determining device for blast furnace production comprises a memory and a processor;
the memory is used for storing programs;
the processor is used for executing the program to realize the steps of the method for determining the quality index and the operation system of the blast furnace production.
A readable storage medium, wherein the computer program, when executed by a processor, implements the steps of the method for determining the quality index and the operating regime of the blast furnace production according to any one of the above.
Based on the technical scheme, the application provides a method for determining the quality index and the operation system of blast furnace production and related equipment. Firstly, obtaining blast furnace production data, wherein the blast furnace production data comprises blast furnace burden quality indexes, a blast furnace burden structure and blast furnace process parameters, inputting the blast furnace production data into a trained blast furnace decision support system prediction model to obtain blast furnace key economic and technical indexes, wherein the blast furnace key economic and technical indexes comprise blast furnace utilization coefficients, a blast furnace coke ratio and a blast furnace fuel ratio, considering the influence of the structure and the like of blast furnace production burden on the blast furnace key economic and technical indexes, judging whether the blast furnace key economic and technical indexes meet preset blast furnace production and cost control requirements, and if the blast furnace key economic and technical indexes meet the preset blast furnace production and cost control requirements, sending the blast furnace production data to blast furnace production engineering technicians so that the blast furnace production engineering technicians determine the quality indexes and the operation system of blast furnace production based on the blast furnace production data, and determine proper blast furnace production quality indexes and operation systems by judging the blast furnace key economic and technical indexes, thereby being beneficial to improving the economic benefits of blast furnace production and having better practicability and popularization value.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a schematic flow chart of a method for determining quality index and operation system of blast furnace production according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a prediction model of a decision support system for a blast furnace according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a flow file corresponding to a prediction model of a decision support system of a blast furnace according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an output table of a flow file corresponding to a prediction model of a blast furnace decision support system according to an embodiment of the present application;
FIG. 5 is a schematic flow chart of a method for training a prediction model of a blast furnace decision-support system according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a method for training with a first historical blast furnace production data set and optimizing with a second historical blast furnace production data set to obtain a prediction model of a blast furnace decision support system according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a device for determining quality index and operation degree of blast furnace production according to an embodiment of the present application;
fig. 8 is a block diagram of a hardware configuration of a quality index and operation system determining apparatus for blast furnace production according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely illustrative of the manner in which embodiments of the application have been described in connection with the description of the objects having the same attributes. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
In order to improve the economic benefit of the blast furnace production, the application provides a method for determining the quality index and the operation degree of the blast furnace production, and the method for determining the quality index and the operation degree of the blast furnace production provided by the application is further described in detail below with reference to the accompanying drawings and the specific embodiments.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for determining quality index and operation system of blast furnace production according to an embodiment of the present application. The method may comprise the steps of:
step S101: and obtaining blast furnace production data, wherein the blast furnace production data comprises blast furnace burden quality indexes, blast furnace burden structures and blast furnace process parameters.
In the application, the blast furnace production data are dependent variables influencing key economic and technical indexes of the blast furnace, wherein the quality indexes of blast furnace burden comprise sintered ore quality indexes, pellet ore quality indexes, charged block ore quality indexes, blast furnace coal injection quality indexes, blast furnace coke quality indexes and the like.
Step S102: and (3) inputting the production data of the blast furnace into a trained blast furnace decision support system prediction model to obtain the key economic and technical indexes of the blast furnace, wherein the key economic and technical indexes of the blast furnace comprise a blast furnace utilization coefficient, a blast furnace coke ratio and a blast furnace fuel ratio.
It should be noted that the decision support system is a computer application system for assisting a decision maker to make semi-structured or unstructured decisions in a man-machine interaction manner through data, models and knowledge, and can provide environments for analyzing problems, modeling, simulating decision making processes and schemes for the decision maker, and call various information resources and analysis tools to help the decision maker to improve decision level and quality.
In the application, the blast furnace production data is imported into an input data table of the stream file, the established stream file is operated, and the predicted blast furnace key economic and technical index is output into an output table, so that the prediction of the blast furnace key economic and technical index is completed.
For easy understanding, reference may be made to fig. 2, 3 and 4, fig. 2 is a schematic diagram of a prediction model of a blast furnace decision support system, fig. 3 is a schematic diagram of a flow file corresponding to the prediction model of a blast furnace decision support system, fig. 4 is a schematic diagram of an output table of a flow file corresponding to the prediction model of a blast furnace decision support system, and IBM SPSS model 18.0 software may be used to build the prediction model of a blast furnace decision support system and the corresponding flow file
Step S103: judging whether the key economic and technical indexes of the blast furnace meet the preset blast furnace production and cost control requirements.
In the application, the predicted key economic and technical index of the blast furnace is called, and whether the key economic and technical index of the blast furnace meets the production target requirement is judged.
Step S104: if the key economic and technical indexes of the blast furnace meet the preset requirements for blast furnace production and cost control, the blast furnace production data are sent to blast furnace production engineering technicians, so that the blast furnace production engineering technicians determine the quality indexes and the operation system of the blast furnace production based on the blast furnace production data.
In the application, if the key economic and technical indexes of the blast furnace meet the preset requirements of blast furnace production and cost control, the blast furnace burden structure and blast furnace process parameters are provided for blast furnace production engineering technicians, so that the blast furnace production engineering technicians determine the quality index of blast furnace production and the operation system matched with the blast furnace burden structure based on blast furnace production data.
For ease of understanding, the sintered pellet quality control index among the quality indexes of the blast furnace production may be referred to table 1, table 1 as follows:
TABLE 1
The quality control indexes of coke and coal powder in the quality indexes of the blast furnace production can be referred to in table 2, and table 2 is as follows:
| index (I) | Mt | Ad | Vdaf | Std | C | CRI | CSR | M40 | M10 |
| Coke,% | 0.2 | 12.7 | 1.15 | 0.8 | 85.15 | 22.6 | 68.03 | 88.78 | 5.7 |
| Pulverized coal of% | 1.4 | 9.75 | 18.5 | 0.6 | 84.22 |
TABLE 2
The blast furnace slag control index among the quality indexes of the blast furnace production can be referred to table 3, and table 3 is shown below:
| index (I) | Slag ratio | SiO2 | CaO | MgO | Al2O3 | R | F | S |
| Slag,% | 0.343 | 33.87 | 39.01 | 8.79 | 14.17 | 1.15 | 0.51 | 1.17 |
TABLE 3 Table 3
The blast furnace process parameters can be referred to in table 4, table 4 as follows:
| index (I) | Jacking and pressing | Air volume | Oxygen enrichment | Wind temperature | Area of air intake | Gas utilization rate | Z value |
| Blast furnace | 0.24 | 6657 | 23000 | 1217 | 0.465 | 0.432 | 7.01 |
TABLE 4 Table 4
The blast furnace burden structure can be referred to in table 5, table 5 is shown below:
| mineral seeds | Sintered ore | Pellet ore | Lump ore | Grade into furnace | Zinc loading | Alkali load | Lot weight |
| Proportioning, percent | 75 | 25 | 0 | 58.1 | 0.531 | 3.76 | 94 |
TABLE 5
In summary, according to the method for determining the quality index and the operation system of the blast furnace production, the blast furnace production data is firstly obtained, the blast furnace production data comprises the blast furnace burden quality index, the blast furnace burden structure and the blast furnace process parameters, the production data of the blast furnace is input into the trained blast furnace decision support system prediction model to obtain the blast furnace key economic technical index, the blast furnace key economic technical index comprises the blast furnace utilization coefficient, the blast furnace coke ratio and the blast furnace fuel ratio, the influence of the structure and the like of the blast furnace burden on the blast furnace key economic technical index is considered, whether the blast furnace key economic technical index meets the preset blast furnace production and cost control requirements is judged, if the blast furnace key economic technical index meets the preset blast furnace production and cost control requirements, the blast furnace production data is sent to blast furnace production engineering technicians, so that the blast furnace production engineering technicians determine the quality index and the operation system of the blast furnace production based on the blast furnace production data, and determine the proper blast furnace production quality index and the operation index by judging the blast furnace key economic technical index, the blast furnace production economic benefit and the good popularization value are facilitated to be improved.
On the basis of the embodiment disclosed in the present application, in still another embodiment of the present application, the specific implementation manner after step S103 of determining whether the key economic and technical indicators of the blast furnace meet the preset requirements for production and cost control of the blast furnace is described in detail.
As an embodiment, if the key economic and technical index of the blast furnace does not meet the preset blast furnace production and cost control requirements, the blast furnace production data can be redetermined.
In the application, if the key economic and technical indexes of the blast furnace do not meet the preset requirements for blast furnace production and cost control, the key economic and technical indexes of the blast furnace are predicted again after the quality indexes, the blast furnace burden structure and the blast furnace process parameters of the blast furnace in the blast furnace production data are adjusted until the predicted key economic and technical indexes of the blast furnace meet the preset requirements for blast furnace production and cost control.
Wherein, the key economic and technical indexes of the new blast furnace can be referred to in table 2, and table 2 is as follows:
TABLE 1
On the basis of the embodiment disclosed by the application, in still another embodiment of the application, a specific implementation manner of the training manner of the prediction model of the blast furnace decision support system is described in detail.
As an embodiment, please refer to fig. 5, which is a schematic flow chart of a method for training a prediction model of a blast furnace decision support system according to the present application. The method may comprise the steps of:
step S201: and acquiring a plurality of historical blast furnace production data, wherein each blast furnace production data is marked with a blast furnace key economic and technical index.
In the application, the historical blast furnace production data database comprises a raw material database for the blast furnace, a fuel flux database for the blast furnace, a blast furnace production process parameter database and a blast furnace economic and technical index database, and the blast furnace production data, the blast furnace burden structure, the blast furnace process parameters and the blast furnace key economic and technical index are input into the database.
Wherein, the raw material data comprises information such as physical and chemical properties, sintering properties, balling properties, market price and the like of each mineral.
Step S202: the plurality of historical blast furnace production data is divided into a first historical blast furnace production data set and a second historical blast furnace production data set.
Step S203: training by using the first historical blast furnace production data set, and optimizing by using the second historical blast furnace production data set to obtain a blast furnace decision support system prediction model.
On the basis of the embodiment disclosed in the application, in still another embodiment of the application, the specific implementation manner of training the step S203 by using the first historical blast furnace production data set and optimizing the step S by using the second historical blast furnace production data set to obtain the prediction model of the blast furnace decision support system is described in detail.
As an embodiment, please refer to fig. 6, which is a schematic flow chart of a method for training using a first historical blast furnace production data set and optimizing using a second historical blast furnace production data set to obtain a prediction model of a blast furnace decision support system. The method may comprise the steps of:
step S301: training by using the first historical blast furnace production data set, and optimizing by using the second historical blast furnace production data set to obtain model training errors.
Step S302: judging whether the model training error is smaller than a preset threshold value.
Step S303: and if the model training error is smaller than a preset threshold value, determining a prediction model of the blast furnace decision support system.
In the application, the accuracy of the prediction model of the blast furnace decision support system can be verified through the industrial test, the blast furnace production data of the industrial test is imported into the production history database, and the prediction model of the blast furnace decision support system is corrected.
In summary, according to the method for obtaining the prediction model of the blast furnace decision support system by training the first historical blast furnace production data set and optimizing the second historical blast furnace production data set, the model can be continuously corrected through accumulation analysis of the production data, and the method is beneficial to more accurately determining the quality index of blast furnace burden, the structure of blast furnace burden and the process parameters of blast furnace.
The method is described in detail in the embodiment disclosed by the application, and the method can be realized by adopting various devices, so the application also discloses a device for determining the quality index and the operation degree of the blast furnace production, and specific embodiments are given below for detail.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a device for determining quality index and operation degree of blast furnace production according to an embodiment of the present application, the device includes:
an acquisition unit 11 for acquiring blast furnace production data including blast furnace burden quality index, blast furnace burden structure, blast furnace process parameters.
And the input unit 12 is used for inputting the production data of the blast furnace into the trained blast furnace decision support system prediction model to obtain the key economic and technical indexes of the blast furnace, wherein the key economic and technical indexes of the blast furnace comprise a blast furnace utilization coefficient, a blast furnace coke ratio and a blast furnace fuel ratio.
And the judging unit 13 is used for judging whether the key economic and technical indexes of the blast furnace meet the preset blast furnace production and cost control requirements.
And the determining unit 14 is configured to send the blast furnace production data to a blast furnace production engineering technician if the key economic and technical indexes of the blast furnace meet the preset blast furnace production and cost control requirements, so that the blast furnace production engineering technician determines a quality index and an operation system of blast furnace production based on the blast furnace production data.
As an embodiment, the apparatus further comprises:
and the redetermining unit is used for redetermining the blast furnace production data if the key economic and technical indexes of the blast furnace do not meet the preset blast furnace production and cost control requirements.
As an embodiment, the apparatus further comprises:
the data acquisition unit is used for acquiring a plurality of historical blast furnace production data, and each blast furnace production data is marked with the key economic and technical indexes of the blast furnace.
A dividing unit for dividing the plurality of historical blast furnace production data into a first historical blast furnace production data set and a second historical blast furnace production data set.
The training unit is used for training by using the first historical blast furnace production data set and optimizing by using the second historical blast furnace production data set to obtain the blast furnace decision support system prediction model.
As an embodiment, the training unit includes:
and the training subunit is used for training by using the first historical blast furnace production data set and optimizing by using the second historical blast furnace production data set to obtain a model training error.
And the judging subunit is used for judging whether the model training error is smaller than a preset threshold value.
And the determining subunit is used for determining the blast furnace decision support system prediction model if the model training error is smaller than a preset threshold value.
Referring to fig. 8, fig. 8 is a block diagram of a hardware structure of a quality index and operation degree determining apparatus for blast furnace production according to an embodiment of the present application, the hardware structure of the quality index and operation degree determining apparatus for blast furnace production may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.
In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3 and the communication bus 4 is at least one, and the processor 1, the communication interface 2 and the memory 3 complete communication with each other through the communication bus 4.
The processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present application, etc.
The memory 3 may comprise a high-speed RAM memory, and may also comprise a non-volatile memory (non-volatile memory) or the like, such as at least one disk memory.
Wherein the memory stores a program, and the processor is operable to invoke the program stored in the memory, the program being operable to:
acquiring blast furnace production data, wherein the blast furnace production data comprises blast furnace burden quality indexes, blast furnace burden structures and blast furnace process parameters;
inputting the production data of the blast furnace into a trained blast furnace decision support system prediction model to obtain key economic and technical indexes of the blast furnace, wherein the key economic and technical indexes of the blast furnace comprise a blast furnace utilization coefficient, a blast furnace coke ratio and a blast furnace fuel ratio;
judging whether the key economic and technical index of the blast furnace meets the preset blast furnace production and cost control requirements;
and if the key economic and technical indexes of the blast furnace meet the preset requirements for blast furnace production and cost control, sending the blast furnace production data to blast furnace production engineering technicians so that the blast furnace production engineering technicians determine the quality indexes and the operation system of blast furnace production based on the blast furnace production data.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
The embodiment of the present application also provides a readable storage medium storing a program adapted to be executed by a processor, the program being configured to:
acquiring blast furnace production data, wherein the blast furnace production data comprises blast furnace burden quality indexes, blast furnace burden structures and blast furnace process parameters;
inputting the production data of the blast furnace into a trained blast furnace decision support system prediction model to obtain key economic and technical indexes of the blast furnace, wherein the key economic and technical indexes of the blast furnace comprise a blast furnace utilization coefficient, a blast furnace coke ratio and a blast furnace fuel ratio;
judging whether the key economic and technical index of the blast furnace meets the preset blast furnace production and cost control requirements;
and if the key economic and technical indexes of the blast furnace meet the preset requirements for blast furnace production and cost control, sending the blast furnace production data to blast furnace production engineering technicians so that the blast furnace production engineering technicians determine the quality indexes and the operation system of blast furnace production based on the blast furnace production data.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
It should be further noted that the above-described apparatus embodiments are merely illustrative, where elements described as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the application, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present application without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general purpose hardware, or of course by means of special purpose hardware including application specific integrated circuits, special purpose CPUs, special purpose memories, special purpose components, etc. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions can be varied, such as analog circuits, digital circuits, or dedicated circuits. However, a software program implementation is a preferred embodiment for many more of the cases of the present application. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random-access Memory (RAM, random Access Memory), a magnetic disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method of the embodiments of the present application.
In summary, the above embodiments are only for illustrating the technical solution of the present application, and are not limiting. Although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the above embodiments can be modified or some of the technical features can be replaced equivalently. Such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method for determining the quality index and the operation system of blast furnace production is characterized by comprising the following steps:
acquiring blast furnace production data, wherein the blast furnace production data comprises blast furnace burden quality indexes, blast furnace burden structures and blast furnace process parameters;
inputting the production data of the blast furnace into a trained blast furnace decision support system prediction model to obtain key economic and technical indexes of the blast furnace, wherein the key economic and technical indexes of the blast furnace comprise a blast furnace utilization coefficient, a blast furnace coke ratio and a blast furnace fuel ratio;
judging whether the key economic and technical index of the blast furnace meets the preset blast furnace production and cost control requirements;
and if the key economic and technical indexes of the blast furnace meet the preset requirements for blast furnace production and cost control, sending the blast furnace production data to blast furnace production engineering technicians so that the blast furnace production engineering technicians determine the quality indexes and the operation system of blast furnace production based on the blast furnace production data.
2. The method according to claim 1, wherein after said determining whether said blast furnace critical economic-technical-indicators meet preset blast-furnace production and cost-control requirements, said method further comprises:
and if the key economic and technical indexes of the blast furnace do not meet the preset requirements for blast furnace production and cost control, re-determining the blast furnace production data.
3. The method of claim 1, wherein the training mode of the blast furnace decision-support system prediction model comprises:
acquiring a plurality of historical blast furnace production data, wherein each blast furnace production data is marked with the key economic and technical indexes of the blast furnace;
dividing the plurality of historical blast furnace production data into a first historical blast furnace production data set and a second historical blast furnace production data set;
training by using the first historical blast furnace production data set, and optimizing by using the second historical blast furnace production data set to obtain the blast furnace decision support system prediction model.
4. A method according to claim 3, wherein said training with said first historical blast furnace production data set and optimizing with said second historical blast furnace production data set results in said blast furnace decision-support system predictive model comprising:
training by using the first historical blast furnace production data set, and optimizing by using the second historical blast furnace production data set to obtain a model training error;
judging whether the model training error is smaller than a preset threshold value or not;
and if the model training error is smaller than a preset threshold value, determining a prediction model of the blast furnace decision support system.
5. A quality index and operation degree determining device for blast furnace production, characterized by comprising:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring blast furnace production data, and the blast furnace production data comprise blast furnace burden quality indexes, blast furnace burden structures and blast furnace process parameters;
the input unit is used for inputting the production data of the blast furnace into a trained blast furnace decision support system prediction model to obtain the key economic and technical indexes of the blast furnace, wherein the key economic and technical indexes of the blast furnace comprise a blast furnace utilization coefficient, a blast furnace coke ratio and a blast furnace fuel ratio;
the judging unit is used for judging whether the key economic and technical indexes of the blast furnace meet the preset blast furnace production and cost control requirements;
and the determining unit is used for sending the blast furnace production data to blast furnace production engineering technicians if the key economic and technical indexes of the blast furnace meet the preset blast furnace production and cost control requirements so as to enable the blast furnace production engineering technicians to determine the quality index and the operation degree of blast furnace production based on the blast furnace production data.
6. The apparatus of claim 5, wherein the apparatus further comprises:
and the redetermining unit is used for redetermining the blast furnace production data if the key economic and technical indexes of the blast furnace do not meet the preset blast furnace production and cost control requirements.
7. The apparatus of claim 5, wherein the apparatus further comprises:
the data acquisition unit is used for acquiring a plurality of historical blast furnace production data, and each blast furnace production data is marked with the key economic and technical indexes of the blast furnace;
a dividing unit for dividing the plurality of historical blast furnace production data into a first historical blast furnace production data set and a second historical blast furnace production data set;
the training unit is used for training by using the first historical blast furnace production data set and optimizing by using the second historical blast furnace production data set to obtain the blast furnace decision support system prediction model.
8. The apparatus of claim 7, wherein the training unit comprises:
the training subunit is used for training by using the first historical blast furnace production data set and optimizing by using the second historical blast furnace production data set to obtain a model training error;
the judging subunit is used for judging whether the model training error is smaller than a preset threshold value or not;
and the determining subunit is used for determining the blast furnace decision support system prediction model if the model training error is smaller than a preset threshold value.
9. A quality index and operation system determining device for blast furnace production is characterized by comprising a memory and a processor;
the memory is used for storing programs;
the processor is used for executing the program to realize the steps of the method for determining the quality index and the operation system of the blast furnace production according to any one of claims 1 to 4.
10. A readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, realizes the steps of the method for determining a quality indicator and an operating regime of a blast furnace production according to any one of claims 1 to 4.
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| CN202311044420.5A CN117035531A (en) | 2023-08-17 | 2023-08-17 | Method and related equipment for determining quality index and operation system of blast furnace production |
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| CN202311044420.5A CN117035531A (en) | 2023-08-17 | 2023-08-17 | Method and related equipment for determining quality index and operation system of blast furnace production |
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