CN111639800B - Method, device and storage medium for setting blast furnace process parameter range - Google Patents
Method, device and storage medium for setting blast furnace process parameter range Download PDFInfo
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Abstract
The invention discloses a method, equipment and storage medium for setting a blast furnace process parameter range, wherein the method comprises the following steps: acquiring data of one parameter and related parameters of the blast furnace process parameters; analyzing the parameters and the correlation parameters by using an interval analysis method to obtain a linear regression relation between the parameters and each correlation parameter; and according to the linear regression relation, combining one or more known target indexes of the correlation parameters to obtain a reasonable range of the parameters. Compared with the prior art, the setting method of the blast furnace process parameter range utilizes the interval analysis method to analyze the data of the blast furnace process parameter, and scientifically designates a reasonable range and an alarm range for the blast furnace process parameter. Meanwhile, according to the reasonable range and the alarm range, real-time data of the blast furnace process parameters can be monitored and alarmed, the production of the blast furnace is effectively guided, and the economic benefit of the blast furnace is improved.
Description
Technical Field
The invention relates to the technical field of blast furnace ironmaking production, in particular to a method, equipment and a storage medium for setting a blast furnace process parameter range.
Background
The blast furnace smelting is to reduce pig iron ore into iron, and is a continuous and complex production process. For the blast furnace production site, the raw materials, equipment, operation process and the like of the blast furnace directly influence the running condition of the blast furnace, but the parameters related to the raw materials, the equipment and the operation process of the blast furnace are very many, and the number of parameters for representing the running state of the blast furnace is as high as tens.
Any one parameter may affect other parameters or directly the furnace conditions of the blast furnace. Because of the complicated and irregular relationship among the blast furnace process parameters, the formulation of the reasonable range of the traditional blast furnace process parameters can only rely on the blast furnace operation experience, and lacks scientific basis and data support.
Therefore, how to set the reasonable range of the blast furnace process parameters by using a scientific method is a difficult problem which needs to be solved urgently.
Disclosure of Invention
The invention aims to provide a method, equipment and storage medium for setting a blast furnace process parameter range.
In order to achieve one of the above objects, an embodiment of the present invention provides a method for setting a parameter range of a blast furnace process, the method comprising:
acquiring data of one parameter and related parameters of the blast furnace process parameters;
analyzing the parameters and the correlation parameters by using an interval analysis method to obtain a linear regression relation between the parameters and each correlation parameter;
And according to the linear regression relation, combining one or more known target indexes of the correlation parameters to obtain a reasonable range of the parameters.
As a further improvement of an embodiment of the present invention, the method further includes:
and according to the linear regression relation, combining one or more known lower limit indexes of the correlation parameters to obtain the alarm range of the parameters.
As a further improvement of an embodiment of the present invention, the "interval analysis method" includes:
Acquiring sample data of a plurality of parameters at different time points, and dividing the fluctuation range of the sample data of the first parameter into intervals;
According to the time corresponding relation between other parameters and the first parameter, dividing the sample data of all other parameters into the same intervals, and calculating the average value of each parameter in each interval;
And respectively taking the average values of the first parameter and the other parameters in each interval as coordinate values of two coordinate axes, and respectively calculating the linear regression relation of the first parameter and the other parameters.
As a further improvement of an embodiment of the present invention, the fluctuation range of the sample data of the first parameter is divided into sections by means of average division.
As a further improvement of an embodiment of the present invention, the "dividing the fluctuation range of the sample data of the first parameter into intervals" includes:
dividing the fluctuation range of the sample data of the first parameter into a plurality of intervals, counting the total sample size of the first parameter and the sample size in each interval, and calculating the sample size ratio of each interval;
And deleting the interval with the sample size ratio less than the preset threshold value to obtain the finally divided interval.
As a further improvement of an embodiment of the present invention, the correlation parameter includes a yield of the blast furnace, and a reasonable range of the parameter is obtained by combining a known target index of the yield through a linear regression relation of the parameter and the yield;
or the correlation parameter comprises the fuel ratio of the blast furnace, and the reasonable range of the parameter is obtained by combining the known target index of the fuel ratio through the linear regression relation of the parameter and the fuel ratio.
As a further improvement of an embodiment of the present invention, the "acquiring data of one of the blast furnace process parameters and the correlation parameter thereof" includes:
Classifying blast furnace process parameters, wherein the blast furnace process parameters are divided into input parameters, process parameters and output parameters;
Establishing a time corresponding relation between the input parameters and the process parameters and between the input parameters and the output parameters;
according to the time corresponding relation, establishing a blast furnace database from the acquired data of the blast furnace process parameters;
and acquiring data of one parameter and the correlation parameter thereof from the database.
As a further improvement of an embodiment of the invention, the input parameters include a coke quality parameter, a sintering ore quality parameter, a pellet ore quality parameter, a lump ore quality parameter, a burden structure parameter and a bin space parameter, the process parameters include an operation parameter, a furnace condition characterization parameter and a furnace body management parameter, and the output parameters include a technical economic index parameter of the blast furnace.
As a further improvement of an embodiment of the present invention, the "establishing a time correspondence relationship between the input parameter and the process parameter and the output parameter" specifically includes:
The time corresponding relation of the input parameters, the process parameters and the output parameters of the blast furnace is calculated or obtained through a tracing test by dynamically monitoring the detection and test data, the time to the factory, the arrival goods quantity, the change of the finished product bin, the belt transfer speed and the transfer quantity from the finished product bin to the blast furnace raw material bin, the bin position of the blast furnace raw material bin, the transfer speed and the transfer quantity after the blast furnace raw material is fed and the smelting period of the blast furnace raw material in the blast furnace.
As a further improvement of an embodiment of the present invention, the "establishing the collected data of the blast furnace process parameters into the blast furnace database" specifically includes:
The method comprises the steps of carrying out data cleaning, data mining and data fusion on data in a blast furnace database, and carrying out data analysis, monitoring and alarming by using the fused data in the blast furnace database, wherein the data cleaning refers to removing abnormal points in the collected data, the data mining refers to obtaining indirect parameter data through calculation according to an existing formula on the basis of the data, and the data fusion refers to unifying data frequency or data period of all parameters to obtain periodic data.
In order to achieve one of the above objects, an embodiment of the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the steps in the method for setting the blast furnace process parameter range according to any one of the above when executing the program.
In order to achieve one of the above objects, an embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for setting a blast furnace process parameter range according to any one of the above.
Compared with the prior art, the setting method of the blast furnace process parameter range utilizes the interval analysis method to analyze the data of the blast furnace process parameter, and scientifically designates a reasonable range and an alarm range for the blast furnace process parameter. Meanwhile, according to the reasonable range and the alarm range, real-time data of the process parameters of the blast furnace can be monitored and alarmed, the production of the blast furnace is effectively guided, the stability of the condition of the blast furnace is facilitated, and the economic benefit of the blast furnace is improved.
Drawings
FIG. 1 is a schematic flow chart of the interval analysis method of the present invention.
Fig. 2 is a flow chart of a method for setting the parameters of the blast furnace process according to the present invention.
Fig. 3 is an example of a linear regression plot of coke M40 and production.
Fig. 4 is an example of a linear regression relationship between the blowing energy PI and the production capacity Ke.
Detailed Description
The present invention will be described in detail below with reference to specific embodiments shown in the drawings. These embodiments are not intended to limit the invention and structural, methodological, or functional modifications of these embodiments that may be made by one of ordinary skill in the art are included within the scope of the invention.
The blast furnace process parameters comprise blast furnace operation process operation parameters, blast furnace cooling system monitoring parameters, blast furnace raw material parameters, blast furnace distribution matrix parameters, blast furnace blanking parameters, furnace top gas temperature parameters, blast furnace gas composition parameters, molten iron weight, quality and temperature parameters, and slag weight and quality parameters. The operation parameters of the blast furnace in the operation process comprise theoretical combustion temperature of a tuyere zone, blast kinetic energy, furnace belly gas index, ventilation resistance coefficient, wind speed of the tuyere zone, wind quantity of the tuyere zone, wind temperature of the tuyere zone, wind pressure of the tuyere zone, humidification amount, oxygen enrichment amount, coal injection amount and the like. The blast furnace cooling system monitoring parameters comprise cooling wall temperature, cooling system flow, cooling water pressure, cooling water temperature and the like. The blast furnace raw material parameters comprise the mass, bin space, batching structure and the like of coke, sintered ore, lump ore and pellets used by the blast furnace. The furnace top gas temperature parameters comprise furnace top gas temperature, furnace top gas pressure, cross temperature measurement temperature, furnace top Z/W and the like.
From historical data, it can be seen that for so many blast furnace process parameters, there is little linear relationship between the parameters, basically nonlinear relationship, even random relationship, and analysis of these data using various statistical methods cannot simplify the relationship between the blast furnace process parameters. Therefore, the traditional blast furnace process parameter range can be established only by virtue of the blast furnace operation experience, and the scientific basis and the data support are lacked.
Through long-time researches of the inventor, the invention provides an interval analysis method which can linearize the data of the nonlinear relations of the blast furnace process parameters, even the disordered data, so that the relations among the blast furnace process parameters are simplified, and a scientific means is provided for setting the blast furnace process parameter range.
As shown in fig. 1, the interval analysis method includes the steps of:
Step S110: sample data of a plurality of parameters at different time points are obtained, and the fluctuation range of the sample data of the first parameter is divided into intervals.
For the convenience of division, it is preferable to divide the fluctuation range of the sample data of the first parameter into sections by means of average division.
The number of sections may be large or small, but since the average value of each section is linearly regressed later, the number of sections divided is preferably 6 to 8, and if the sample data amount is large, the number of sections may be divided into 8, and if the sample data amount is small, the number of sections may be divided into 6, and so on.
In addition, after the division of the sections, there may be few sample sizes in some sections, which does not contribute to the subsequent processing, and therefore, in a preferred embodiment, after dividing the fluctuation range of the sample data of the first parameter into a plurality of sections, the total sample size of the first parameter and the sample size in each section are counted, and the sample size ratio of each section is calculated. And deleting the interval with the sample size ratio less than the preset threshold value to obtain the finally divided interval. The predetermined threshold may be 5%, i.e. when the sample size of a certain interval is less than 5% of the total sample size, this interval is deleted or removed, and the data of this interval does not enter the subsequent processing.
Step S120: and according to the time corresponding relation between the other parameters and the first parameter, dividing the sample data of all the other parameters into the same intervals, and calculating the average value of each parameter in each interval.
For example, the sample data of the first parameter is divided into M intervals, the first interval includes four sample data of the first parameter at time points A, B, C and D, the sample data of the other parameter at corresponding time points A, B, C and D are also divided into the first interval according to the time correspondence between the other parameters and the first parameter, and so on. In this way, the sample data of the other parameters is also divided into M sections which are identical to the first parameter and have a correspondence relationship.
After the interval division is finished, calculating the average value of each parameter in each interval, including the average value of the first parameter in M intervals, and the average value of each other parameter in M intervals.
Step S130: and respectively taking the average values of the first parameter and the other parameters in each interval as coordinate values of two coordinate axes, and respectively calculating the linear regression relation of the first parameter and the other parameters.
The two coordinate axes may be a horizontal axis and a vertical axis, and the average value of the first parameter in each section is used as a coordinate value of the horizontal axis/the vertical axis, and the average value of one other parameter in each section is used as a coordinate value of the vertical axis/the horizontal axis, so as to calculate a linear regression relation between the first parameter and the one other parameter.
All other parameters are processed in the same way, resulting in a plurality of linear regression relations of the first parameter with all other parameters.
When analyzing the linear regression relationship between parameters by using the interval analysis method, the data of all the parameters involved in the analysis have a time correspondence relationship, but for the blast furnace process parameters, many times we cannot accurately know that the parameter data of the raw materials reacting in the blast furnace, that is, the data of the raw materials and the acquired data of the blast furnace conditions have no time correspondence relationship, so that the blast furnace process parameters need to be sorted, the sorted parameters are set up to have a time correspondence relationship, and then the acquired data is set up to a blast furnace database according to the time correspondence relationship.
Specifically, the blast furnace process parameters are sorted, and all the blast furnace process parameters are divided into input parameters, process parameters and output parameters. Wherein:
the input parameters refer to raw material parameters including quality parameters, bin space parameters, batching structure parameters and the like of coke, sintered ore, lump ore and pellets used in a blast furnace, and the following table 1.
The process parameters include operating parameters, furnace characterization parameters, and furnace management parameters, see table 2 below.
The output parameters refer to the technical and economic index parameters of the blast furnace, including yield, fuel ratio and the like, and are shown in the following table 3.
TABLE 1
TABLE 2
TABLE 3 Table 3
It can be seen from tables 1-3 that the process parameters and the output parameters are collected at the same time, or can be calculated according to the data collected at the same time, only the input parameters are not collected at the same time, and the time correspondence between the input parameters and the process parameters and the time correspondence between the input parameters and the output parameters need to be established.
The time corresponding relation of the input parameters, the process parameters and the output parameters of the blast furnace is calculated or obtained through a tracing test by dynamically monitoring the detection and test data, the time to the factory, the arrival goods quantity, the change of the finished product bin, the belt transfer speed and the transfer quantity from the finished product bin to the blast furnace raw material bin, the bin position of the blast furnace raw material bin, the transfer speed and the transfer quantity after the blast furnace raw material is fed, the smelting period of the blast furnace raw material in the blast furnace and the like.
Specifically, the raw material quality parameters (including the quality parameters of coke, sinter, pellet and ore block) of the input parameters have a time difference with the process parameters or the output parameters, wherein the time difference=the reaction time in the furnace-the sampling time of the blast furnace raw material=the belt transfer time from the finished product bin to the blast furnace raw material bin after the blast furnace raw material is sampled+the storage time of the blast furnace raw material in the blast furnace raw material bin+the transfer time of the blast furnace raw material after the blast furnace raw material is fed+the smelting period of the blast furnace raw material in the blast furnace.
In a specific embodiment, a temporal correspondence of the coke quality parameter and the process parameter of the input parameter is established. The method comprises the steps of collecting the sampling time T Taking out of coke, the belt transfer time delta T Coke from a sampling point to a blast furnace coke bin, collecting the bin storage H of the blast furnace coke bin at the time T Taking out +Δt Coke , the feeding speed V of the blast furnace coke, the feeding transfer time delta T Furnace with a heat exchanger of the blast furnace, and collecting the smelting period delta T Smelting of furnace burden in the blast furnace. The acquisition time T Furnace with a heat exchanger of the process parameters is used for determining the time corresponding relation between the coke quality parameters and the process parameters, and the time corresponding relation is as follows:
T Furnace with a heat exchanger ＝T Taking out +Δt Coke +H/V+Δt Furnace with a heat exchanger +T Smelting 。
After the time corresponding relation between the input parameters and the process parameters and the output parameters is established, the acquired data of the blast furnace process parameters are established into a blast furnace database according to the time corresponding relation. And then analyzing the data of each parameter in the blast furnace database by using an interval analysis method to obtain a linear regression relation among the blast furnace process parameters.
The data of the collected blast furnace process parameters may be all data collected for a certain period of time, for example, for the last two years. After the blast furnace database is established according to the time corresponding relation for the acquired data of the blast furnace process parameters, the data in the blast furnace database is required to be cleaned, mined and fused, and then the fused data is used for data analysis, monitoring and alarming, such as analysis by using an interval analysis method or a normalization interval analysis method, and the like, wherein the data in the blast furnace database is used in the whole text, and refer to the fused data in the blast furnace database.
The data cleaning refers to removing abnormal dead point data and supplementing missing data. For example, the data of the temperature of the thermocouple of the cooling wall is cleaned, and dead point data is removed. And (3) removing data which are not in a reasonable fluctuation range according to different heights and different materials in a furnace body of each layer of cooling wall of the blast furnace and different temperature fluctuation ranges in normal production. For example, the temperature of the cast iron cooling wall at the upper 13 sections of the furnace body is generally between 70 ℃ and 300 ℃ due to the protection of cooling water, thermocouple data outside 70 ℃ to 300 ℃ are removed firstly, and finally, if no fluctuation or change exists in the data within 70 ℃ to 300 ℃ at any point, the thermocouple at the monitoring point is considered to be damaged, the temperature data are removed, and the blast furnace thermocouple fault point is left after being removed, so that the fault of judgment of the furnace condition caused by data distortion is avoided. And for the test data, abnormal data point rejection is carried out according to whether the test data is in a normal detection range. Judging whether missing data exists according to the test frequency, automatically filling the missing data, and filling the average test data of nearly three times.
The data mining means that on the basis of collected data, statistical analysis is performed on each parameter data, and statistical average values, maximum values, minimum values, data distribution, standard deviation and the like are performed. Meanwhile, the data mining also comprises data mining indirect parameters, wherein the indirect parameters are parameter data which cannot be directly obtained through data acquisition and are obtained through calculation through an existing formula. Such as blast furnace blast kinetic energy, hearth activity index, distribution ore-coke ratio radial distribution, heat balance, theoretical combustion temperature and the like are all indirect parameters.
The data fusion refers to unifying the data frequency or the data period of all parameters to obtain period data. Because the data acquisition frequencies of the blast furnace process parameters are different, for example, some parameters are acquired every second, some parameters are acquired every minute, some parameters are acquired every hour or even every day, so that the data of the parameters with different data acquisition frequencies are required to be subjected to data fusion, and the data frequencies or data periods of all the parameters are unified to obtain period data. For example, the data frequency of unifying all parameters is one hour and one data, and the data period is one hour. Since the blast furnace has a relatively large data volume and a relatively long overall cycle, the preferred data frequency is one data per day, i.e., the data cycle is one day. The method for obtaining the periodic data of one parameter comprises the following steps: the average or latest value of all data of this parameter in the data period is obtained as one period data of this parameter. The subsequent use of data for a certain parameter in the blast furnace database refers to periodic data for that parameter.
As shown in fig. 2, the present invention provides a method for setting a range of a blast furnace process parameter, which uses an interval analysis method to analyze data of the blast furnace process parameter and make a reasonable range for the blast furnace process parameter. The method comprises the following steps:
step S210: data of one of the blast furnace process parameters and its correlation parameters are acquired.
And acquiring one parameter to be analyzed and data of parameters related to the parameter from the blast furnace database with the time corresponding relation.
The correlation parameter is a parameter having a correlation with the parameter, and may be considered as all other blast furnace process parameters than the parameter, which are all correlated because they are blast furnace process parameters. Of course, the correlation parameter may be considered to be a part of another blast furnace process parameter having a strong correlation with the parameter, and the method for obtaining the parameter having a correlation with the parameter may be determined by historical experience or may be obtained by correlation analysis.
Correlation analysis is prior art, i.e. the correlation between two parameters can be analyzed by data. The method for obtaining the correlation parameter of one parameter by using the correlation analysis comprises the following steps:
And respectively analyzing the correlation of the parameter and other blast furnace process parameters through data, and selecting the other blast furnace process parameters with the correlation of the parameter and the top N as parameters with the correlation according to the intensity of the correlation.
Since the output parameters comprise technical and economic indicators of the blast furnace, it is preferred that the correlation parameters comprise output parameters.
In a preferred embodiment, the correlation parameter comprises the yield of the blast furnace, and a reasonable range of the parameter is obtained by combining the known target index of the yield through the linear regression relation of the parameter and the yield.
In another preferred embodiment, the correlation parameter comprises the fuel ratio of the blast furnace, and a reasonable range of the parameter is obtained by combining a known target index of the fuel ratio with a linear regression relation of the parameter and the fuel ratio.
Step S220: and analyzing the parameters and the correlation parameters by using an interval analysis method to obtain a linear regression relation between the parameters and each correlation parameter.
And dividing the data of the parameters into intervals according to the fluctuation range. And then, according to the time corresponding relation between the correlation parameters and the parameters, carrying out the same interval division on the data of all the correlation parameters. And after the interval division is finished, calculating the average value of the parameters in each interval, and simultaneously calculating the average value of each correlation parameter in each interval.
And calculating the linear regression relation between the parameter and the correlation parameter by taking the average value of the parameter in each interval as a coordinate value of a coordinate axis (such as a horizontal axis) and taking the average value of the parameter in each interval as a coordinate value of another coordinate value (such as a vertical axis). Using the same method, a linear regression relationship of the parameter with other correlation parameters is calculated.
Step S230: and according to the linear regression relation, combining one or more known target indexes of the correlation parameters to obtain a reasonable range of the parameters.
The known target index refers to the existing target range or target attribute of the parameter, for example, the target range of the yield of a certain blast furnace is 13500-14500t/d, and the yield is 13500-14500t/d, namely the known target index of the yield. Also for example, in the target range of the yield, we consider that the higher the yield, the better that yield is, a target property, i.e., a known target index.
In a specific embodiment, the data in the blast furnace database is divided into M intervals according to the fluctuation range of the coke M40, the average value of all parameters in each class is calculated, the average value of the coke M40 is taken as the abscissa, the average value of other blast furnace process parameters is taken as the ordinate, the rules (linear regression relation) of the coke M40 and the other blast furnace process parameters are obtained, and the reasonable range of the coke M40 is determined according to the known target index of one or more other blast furnace process parameters. Taking the linear regression relationship of coke M40 and yield as shown in FIG. 3 for example, when M40 exceeds 89.5, the yield does not rise but decreases, and a reasonable range for coke M40 is determined to be [87.5,89.5] based on the known target index of yield (the higher the better the yield).
In another specific embodiment, the data in the blast furnace database is divided into 8 intervals according to the fluctuation range of the yield, important blast furnace process parameters (important parameters for follow-up) such as coke quality, sinter quality, blast kinetic energy and the like are selected, the data of the important parameters are divided into 8 identical intervals according to the time corresponding relation with the yield, the average value of the yield and the average value of the important parameters in each interval is calculated, then the average value of the important parameters is taken as the abscissa, the average value of the yield is taken as the ordinate, and the rule (linear regression relation) between the yield and the important parameters is obtained. Then, a reasonable range of important parameters is determined according to the known target index of the yield. As shown in fig. 4, the linear regression relationship between the blower energy PI and the yield Ke satisfies the following relationship:
Ke＝1.522×PI-10335。
as can be seen from FIG. 4, when the production is between 13500-14500t/d (a known target index of production), the reasonable range of blast energy is between 15600-16300J/s.
In a preferred embodiment, the method further comprises: and according to the linear regression relation, combining one or more known lower limit indexes of the correlation parameters to obtain the alarm range of the parameters.
The known lower limit index is a known lower limit range of parameters in which data of the parameters is determined to be abnormal or the blast furnace is determined to be abnormal. For example, for the blast furnace production, assuming that the lower limit range is less than 11000t/d, when the production is within this lower limit range, it is determined that the blast furnace is abnormal.
With continued reference to fig. 3 and 4, an alarm range for the coke M40 may be obtained by a linear regression relationship of the coke M40 and the production, in combination with a known lower limit indicator of the production. Also, by combining the known lower limit index of the yield by a linear regression relationship between the blast kinetic energy and the yield, an alarm range of the blast kinetic energy can be obtained.
According to the method for setting the blast furnace process parameter ranges of the present invention, the reasonable ranges and the alarm ranges of all blast furnace process parameters can be set, and the following table 4 shows the reasonable ranges of some important blast furnace operation parameters and raw material quality parameters obtained by the method of the present invention.
TABLE 4 Table 4
By setting the reasonable range and the alarm range of the blast furnace process parameters, the real-time data of the blast furnace process parameters can be monitored and alarmed, the production of the blast furnace is effectively guided, the stability of the condition of the blast furnace is facilitated, and the economic benefit of the blast furnace is improved.
The invention also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program which can be run on the processor, and the processor realizes any one step of the method for setting the blast furnace process parameter range, namely, realizes the step of any one technical scheme of the method for setting the blast furnace process parameter range when executing the program.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements any one of the steps of the above-described method for setting a blast furnace process parameter range, that is, implements the steps of any one of the above-described methods for setting a blast furnace process parameter range.
It should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is for clarity only, and that the skilled artisan should recognize that the embodiments may be combined as appropriate to form other embodiments that will be understood by those skilled in the art.
The above list of detailed descriptions is only specific to practical embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent embodiments or modifications that do not depart from the spirit of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for setting a blast furnace process parameter range, the method comprising:
acquiring data of one parameter and related parameters of the blast furnace process parameters;
analyzing the parameters and the correlation parameters by using an interval analysis method to obtain a linear regression relation between the parameters and each correlation parameter;
according to the linear regression relation, combining one or more known target indexes of the correlation parameters to obtain a reasonable range of the parameters;
wherein the interval analysis method comprises:
Acquiring sample data of a plurality of parameters at different time points, and dividing the fluctuation range of the sample data of the first parameter into intervals;
According to the time corresponding relation between other parameters and the first parameter, dividing the sample data of all other parameters into the same intervals, and calculating the average value of each parameter in each interval;
respectively taking the average values of the first parameter and other parameters in each interval as coordinate values of two coordinate axes, and respectively calculating the linear regression relation of the first parameter and the other parameters;
The "acquiring data of one of the blast furnace process parameters and its correlation parameters" includes:
Classifying blast furnace process parameters, wherein the blast furnace process parameters are divided into input parameters, process parameters and output parameters;
Establishing a time corresponding relation between the input parameters and the process parameters and between the input parameters and the output parameters;
according to the time corresponding relation, establishing a blast furnace database from the acquired data of the blast furnace process parameters;
and acquiring data of one parameter and the correlation parameter thereof from the database.
2. The method for setting a blast furnace process parameter range according to claim 1, wherein the method further comprises:
and according to the linear regression relation, combining one or more known lower limit indexes of the correlation parameters to obtain the alarm range of the parameters.
3. The method for setting a blast furnace process parameter range according to claim 1, wherein:
And dividing the fluctuation range of the sample data of the first parameter into sections by means of average segmentation.
4. The method for setting a range of a blast furnace process parameter according to claim 1, wherein the "dividing the fluctuation range of the sample data of the first parameter into intervals" comprises:
dividing the fluctuation range of the sample data of the first parameter into a plurality of intervals, counting the total sample size of the first parameter and the sample size in each interval, and calculating the sample size ratio of each interval;
And deleting the interval with the sample size ratio less than the preset threshold value to obtain the finally divided interval.
5. The method for setting a blast furnace process parameter range according to claim 1, wherein:
the correlation parameters comprise the yield of the blast furnace, and a reasonable range of the parameters is obtained by combining known target indexes of the yield through a linear regression relation of the parameters and the yield;
or the correlation parameter comprises the fuel ratio of the blast furnace, and the reasonable range of the parameter is obtained by combining the known target index of the fuel ratio through the linear regression relation of the parameter and the fuel ratio.
6. The method for setting a blast furnace process parameter range according to claim 1, wherein:
the input parameters comprise coke quality parameters, sintering ore quality parameters, pellet ore quality parameters, lump ore quality parameters, batching structure parameters and bin space parameters, the process parameters comprise operation parameters, furnace condition representation parameters and furnace body management parameters, and the output parameters comprise technical and economic index parameters of the blast furnace.
7. The method for setting blast furnace process parameters according to claim 1, wherein the step of establishing the time correspondence between the input parameters and the process parameters and the output parameters comprises:
The time corresponding relation of the input parameters, the process parameters and the output parameters of the blast furnace is calculated or obtained through a tracing test by dynamically monitoring the detection and test data, the time to the factory, the arrival goods quantity, the change of the finished product bin, the belt transfer speed and the transfer quantity from the finished product bin to the blast furnace raw material bin, the bin position of the blast furnace raw material bin, the transfer speed and the transfer quantity after the blast furnace raw material is fed and the smelting period of the blast furnace raw material in the blast furnace.
8. The method for setting blast furnace process parameters according to claim 1, wherein the step of establishing the collected blast furnace process parameter data into a blast furnace database comprises:
The method comprises the steps of carrying out data cleaning, data mining and data fusion on data in a blast furnace database, and carrying out data analysis, monitoring and alarming by using the fused data in the blast furnace database, wherein the data cleaning refers to removing abnormal points in the collected data, the data mining refers to obtaining indirect parameter data through calculation according to an existing formula on the basis of the data, and the data fusion refers to unifying data frequency or data period of all parameters to obtain periodic data.
9. An electronic device comprising a memory and a processor, said memory storing a computer program executable on said processor, characterized in that said processor, when executing said program, implements the steps of the method for setting the range of blast furnace process parameters according to any of claims 1-8.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method for setting a range of process parameters of a blast furnace according to any one of claims 1-8.
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