CN109047024B - Iron ore material classification judgment method - Google Patents
Iron ore material classification judgment method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
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Abstract
The application provides an iron ore material classification judgment method, which comprises the following steps of: averagely segmenting the detected iron ore material, and sampling in each segmentation interval to obtain a plurality of first samples; and (3) grading the particle size: mixing the first samples, taking a plurality of mixed samples to obtain second samples, and screening the second samples according to the selected size fraction to obtain classified samples; and (3) grading detection: respectively taking a plurality of samples from the classified samples, and detecting and analyzing the chemical components of each sample; and (3) analysis and judgment: and counting the chemical components of the sample, respectively carrying out intra-granular judgment and inter-granular judgment, and comparing the intra-granular judgment and the inter-granular judgment to obtain a comprehensive judgment result. The iron ore material classification judgment method overcomes the defects of component size fraction parallel analysis, numerous and complicated data and the like of the existing iron ore material classification judgment method, can accurately classify and judge the mixed ore powder, provides decision basis for ore material purchase and settlement, and provides scientific data for scientific production of enterprises.
Description
Technical Field
The application relates to the technical field of iron ore metallurgy, in particular to a method for classifying and judging iron ore materials.
Background
Whether the ironmaking raw materials (ore materials) can be stably supplied is always a bottleneck factor for restricting production enterprises. In recent years, the steel industry is becoming more and more intense, depending on various factors such as the supply of iron-making raw materials (ore materials) and market conditions. In order to stabilize the tension situation of the iron and steel industry, at present, various iron-making production enterprises cancel the 'long-term co-mineral' supply mechanism and use the 'trade mineral' supply mechanism instead. The use of trade mine, on one hand, because of the dynamic management that adapts to the sharp change in market, has compressed the mineral aggregate cost as far as possible, has striven for the living space for manufacturing enterprise.
However, the "trade mine" supply mechanism brings more technical problems of procurement due to the continuous change of suppliers, such as: mixed ore powder, large water fluctuation, high powder content, impurity doping and the like. In order to ensure the rationality of the purchasing cost, the iron ore materials need to be classified and judged in the purchasing process. At present, the classification and judgment of iron ore materials are mainly carried out through component detection, specifically, the average components of the ore powder are detected after the ore powder is uniformly mixed as much as possible, and the ore powder belonging to the average components is judged according to the comparison of the average components and empirical data.
The method for classifying and judging iron ore materials through component detection is to classify and judge the iron ore materials on the premise of classifying the detected iron ore materials into one ore material, so that the detected iron ore materials cannot be identified to be composed of one ore material or be mixed by several ore materials. However, if the mixed ore powder cannot be accurately identified, the ore powder purchasing cost is greatly increased, and the production stability is adversely affected. Therefore, the accurate identification of the mixed mineral powder becomes a technical problem to be solved urgently in the technical field.
Disclosure of Invention
The application provides a method for classifying and judging iron ore materials, which is used for classifying and judging the iron ore materials and accurately identifying the doping condition of the iron ore materials.
The application provides a method for classifying and judging iron ore materials, which comprises the following steps:
segmented sampling: averagely segmenting the detected iron ore material, and sampling in each segmentation interval to obtain a plurality of first samples;
and (3) grading the particle size: mixing the first samples, taking a plurality of mixed samples to obtain second samples, and screening the second samples according to the selected size fraction to obtain classified samples;
and (3) grading detection: respectively taking a plurality of samples from the classified samples, and detecting and analyzing the chemical components of each sample;
and (3) analysis and judgment: and counting the chemical components of the sample, respectively carrying out intra-granular judgment and inter-granular judgment, and comparing the intra-granular judgment and the inter-granular judgment to obtain a comprehensive judgment result.
Optionally, in the method for classifying and determining an iron ore material, the step of averagely segmenting the detected iron ore material, and sampling in each segmentation interval to obtain a plurality of first samples includes:
respectively carrying out equidistant segmentation in the length direction, the width direction and the height direction according to the material pile of the detected iron ore material;
sampling in each subsection and sampling at the extreme of the subsection at the end side of the pile to obtain a plurality of first samples.
Optionally, in the method for classifying and determining an iron ore material, the number of samples N1 of the first sample is greater than or equal to 30, and the weight G > (standard quantity of assay samples/N1) of the first sample is 5%.
Optionally, in the method for classifying and determining an iron ore material, the classifying the particle size further includes:
when the fraction sample obtained by screening is less than 5% of the total amount of the second sample, the fraction sample is incorporated into an adjacent fraction sample of a larger proportion.
Optionally, in the method for classifying and determining an iron ore material, taking a plurality of samples from the classified samples, and detecting and analyzing chemical components of each sample includes:
respectively taking a plurality of equal samples from each size fraction sample, and carrying out labeling;
detecting and analyzing the chemical components of each sample;
the chemical components detected in the sample and their corresponding labels are compiled.
Optionally, in the method for classifying and determining an iron ore material, the intra-fraction determination includes:
determining a limit interval value;
counting the frequency number corresponding to each interval value in each grade of the same component according to the limit interval value, and determining inflection points, the number of the inflection points, the distance between the inflection points and the frequency ratio, wherein the inflection points refer to the interval value corresponding to the frequency number greater than the frequency number before and after the inflection points, the number of the inflection points is the number of the inflection points, the distance between the inflection points refers to the interval distance between the maximum inflection point and the minimum inflection point, and the frequency ratio is the ratio of the frequency number corresponding to each inflection point to the total frequency number of each inflection point;
determining a judgment value according to the number of inflection points, the inflection point interval and the frequency ratio;
and determining the judgment category in the iron ore material size fraction according to the judgment value.
Optionally, in the method for classifying and determining an iron ore material, the inter-fraction determining includes:
determining a limit interval value;
counting the frequency number corresponding to each interval value between each grain level of the same component according to the limit interval value, and determining inflection points, the number of the inflection points, the interval value of the inflection points and the frequency ratio, wherein the inflection points refer to the interval value corresponding to the frequency number greater than the frequency number before and after the inflection points, the number of the inflection points is the number of the inflection points, the interval value of the inflection points refers to the interval value between the maximum inflection point and the minimum inflection point, and the frequency ratio is the ratio of the frequency number corresponding to each inflection point to the total frequency number of each inflection point;
determining a judgment value according to the number of inflection points, the inflection point interval and the frequency ratio;
and determining the grade judgment category of the iron ore material particles according to the judgment value.
Optionally, in the method for classifying and determining an iron ore material, comparing the intra-fraction determination and the inter-fraction determination to obtain a comprehensive determination result includes:
and comparing the intra-grade judgment and the inter-grade judgment by combining the grading measurement results to obtain a comprehensive judgment result.
According to the iron ore material classification judgment method, classification judgment of the iron ore material to be detected is achieved through segmented sampling, particle size classification, classification detection and analysis judgment, and the doping condition of the iron ore material is accurately identified. The iron ore material classification judgment method overcomes the defects of component size fraction parallel analysis, numerous and complicated data and the like of the existing iron ore material classification judgment method, can accurately classify and judge the mixed ore powder, provides decision basis for ore material purchase and settlement, and provides scientific data for scientific production of enterprises. The iron ore material classification judgment method is flexible and simple to operate.
Drawings
In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a structural flow chart of a method for determining iron ore classification according to an embodiment of the present disclosure;
FIG. 2 is an intra-granular decision TFe assay 1 provided in an example of the present application;
FIG. 3 is an intra-granular decision TFe analysis 2 provided by an embodiment of the present application;
FIG. 4 is an intra-granular decision TFe analysis 3 provided by an embodiment of the present application;
FIG. 5 is a graph showing the in-fraction SiO determination provided in the examples of the present application2Analysis 1;
FIG. 6 shows the determination of SiO in the fraction provided in the examples of the present application2Analysis 2;
FIG. 7 is a graph showing the in-fraction SiO determination provided in the examples of the present application2Analysis 3;
FIG. 8 is a TFe analysis of inter-fraction decision provided in an embodiment of the present application;
FIG. 9 is a graph showing the determination of SiO between particle sizes according to the examples of the present application2And (6) analyzing.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 shows specific process steps of a method for determining iron ore classification provided by an embodiment of the present application. As shown in fig. 1, a method for classifying and determining iron ore provided in an embodiment of the present application includes:
s100, segmented sampling: the detected iron ore material is averagely segmented, and a sample is taken in each segmentation interval to obtain a plurality of first samples.
The sectional sampling realizes the comprehensive coverage of the detection sampling, and is beneficial to ensuring that the sample taken in the detection process can comprehensively represent the detected iron ore material. The segmentation can adopt three modes of transverse, longitudinal and horizontal, the iron-containing ore powder is reasonably segmented at equal intervals, and the iron-containing ore powder with extreme values in stockpiles and the like is also provided with an extreme section, so that the sampling representativeness and the convenient operation are ensured, and the purpose of collecting the iron-containing ore powder of each segment including the extreme segment and the middle segment is met.
In the specific implementation mode of the application, the length, the width and the height are respectively segmented equidistantly according to the material pile of the detected iron ore material; sampling in each subsection and sampling at the extreme of the subsection at the end side of the pile to obtain a plurality of first samples. The number of the first samples is usually not less than 30, that is, the number of the first samples N1 is not less than 30, and the weight G > (standard amount of assay samples/N1) per 5% of each sample.
S200, grading the particle size: and mixing the first samples, taking a plurality of mixed samples to obtain second samples, and screening the second samples according to the selected size fraction to obtain classified samples.
Mixing the first samples obtained in the sectional sampling, taking a plurality of mixed samples as second samples, selecting screening size fractions, and screening the second samples according to the selected size fractions to obtain classified samples. Size fraction refers to the screening criteria selected prior to screening, such as: the size fraction "≦ 0.08mm, 0.08-3mm, ≧ 3 mm" was selected, i.e., the second sample was screened into 3 grades according to the 0.08mm and 3mm standards. In the present application, the size fraction is not limited to 0.08mm and 3mm, and may be selected from 0.08, 1mm, 3mm, 5mm, 10mm, etc., according to actual needs. In the present embodiment, since in actual production, there is no species of absolutely precise single size fraction, with slightly critical size fractions for each species fraction present, the fractionated sample is incorporated into an adjacent larger proportion of fractionated samples when the screening yields a fractional sample proportion of less than 5% of the total second sample.
S300, hierarchical detection: and respectively taking a plurality of samples from the classified samples, and detecting and analyzing the chemical components of each sample.
Respectively extracting equivalent samples from iron-containing mineral aggregates with different size fractions, marking and compiling the samples; detecting main chemical components of each equivalent sample by adopting a universal detection mode, wherein the main chemical components can be selected from TFe and SiO2、CaO、Al2O3Etc.; and summarizing and editing the chemical components detected by the sample and the corresponding labels thereof, and inputting the sample numbers and the detection results into a statistical table for facilitating checking and subsequent data processing.
The universal chemical component detection refers to the detection of TFe and SiO in experimental test samples according to LQB95-2004 ' chemical analysis method for iron and steel ' and LQB99-2004 ' chemical analysis method for iron ore and ilmenite2、CaO、Al2O3Quantitative analysis of the equivalent content.
S400, analysis and judgment: and counting the chemical components of the sample, respectively carrying out intra-granular judgment and inter-granular judgment, and comparing the intra-granular judgment and the inter-granular judgment to obtain a comprehensive judgment result.
The intra-size fraction judgment and the inter-size fraction judgment are performed based on the sample components obtained by the classification detection. Specifically, the method comprises the following steps:
within the size fraction, the judgment is as follows: setting reasonable limit values of different components according to industry experience by using common office software of a computer, such as Mintab, Excel and the like, and determining limit interval values, such as TFe +/-0.5 percent and SiO2Plus or minus 0.3 percent and the like, taking the frequency as a vertical axis and taking a limit interval as a horizontal axis interval, processing data in different size fractions with the same component, drawing a histogram, determining inflection points, calculating the number of the inflection points, the inflection point interval and the frequency ratio in different size fractions with the same component, determining values according to industry experience and judgment of the number of the inflection points, the inflection point interval and the frequency ratio, and determining judgment categories in the iron ore material size fractions according to the determination values.
In the application, a judgment value principle of the number of inflection points, the inflection point distance and the frequency ratio is established. Specifically, the number of inflection points is determined to be T1, the inflection point distance is determined to be T2, and the frequency ratio is determined to be T3:
when the number of the inflection points is less than or equal to 1, taking T1 as 0 to represent normal;
when the number of the inflection points is 2, taking T1 as 0.5 to indicate doubt;
when the number of the inflection points is more than 2, taking T1 as 1 to represent abnormity;
when the inflection point interval is less than or equal to 3 × limit interval value, taking T2 as 0 to represent normal;
when the 3-point limit interval value is less than the inflection point interval and is less than or equal to 5-point limit interval value, taking T2 as 0.5 to represent doubt;
when the inflection point spacing is larger than 5 × limit interval value, taking T2 as 1 to represent abnormality;
when the number of inflection points with the frequency ratio value of more than 15 is 1, taking T3 as 0 to indicate normal;
when the number of inflection points with the frequency ratio of more than 15 is 2, taking T3 as 0.5 to indicate doubt;
when the number of inflection points with the frequency ratio value of more than 15 is more than or equal to 3, T3 is equal to 1, which indicates an abnormality.
Establishing a judgment principle as follows:
the number of inflection points is equal to 0 or 1, and the judgment is 'one type', which indicates normal;
the sum of the values of the inflection point distance and the frequency ratio is equal to 0, and is judged as 'one class', which indicates normal;
the sum of the values of the inflection point distance and the frequency ratio is equal to 0.5, and is judged as doping, which indicates slight abnormality;
the sum of the values of the inflection point distance and the frequency ratio is 1.0, and is judged as 'second class', which represents abnormity;
the sum of the values of the inflection point distance and the frequency ratio is 1.5, and the doping is judged to be 'doping type II', which indicates obvious abnormality;
and the sum of the values of the number of inflection points, the inflection point distance and the frequency ratio is equal to 2.0, and the abnormal condition is judged to be more than two types, which represents a remarkable abnormal condition.
And calculating the number of inflection points corresponding to the judgment value or the sum of the number of inflection points, the inflection point distance and the frequency ratio corresponding to the judgment value to obtain the judgment category in the iron ore material grain size.
The grain grade judgment method comprises establishing reasonable limit values of different components according to industry experience by using common office software of computer, such as Mintab, Excel, etc., and determining limit interval values, such as TFe + -0.5% and SiO2Plus or minus 0.3%, etc., taking frequency as vertical axis and limit interval as horizontal axis, processing all data of the same component, drawing histogram and determining inflection point, calculating the number of inflection points in different size fractions of the same componentAnd determining the judgment category in the iron ore material size fraction according to the judgment values of the number of inflection points, the inflection point interval and the frequency ratio and the judgment values according to the industry experience. The judgment value principle and the judgment principle refer to the judgment value principle and the judgment principle of the intra-size judgment, and are not described herein again.
And comparing the judgment conclusions of intra-grade judgment and inter-grade judgment to form a final judgment conclusion of the iron-containing mineral aggregate. In the present embodiment, the specific comparison and determination method is as follows:
first see if there is doping within the fraction: generally, each grade is normally classified or respectively doped, and each grade is initially explained as a normal 'one material seed'; if the above conclusion is reached for each fraction, the preliminary explanation may be a poor dopant species. And then whether different component particle fractions are doped or not: generally, each component grade is in a 'class' or is in a 'doping' state, and is further explained as a 'material seed' state; if the above conclusion of inter-particle "doping" occurs, the preliminary explanation may be a poor doping species or a multi-species doping. So finally, comprehensive analysis: if the 'one material seed' is judged in the grain grade and between the grain grades, the 'one material seed' can be comprehensively judged; the occurrence of doping can be comprehensively judged as one material seed doping; if the judgment in the size fraction is 'one material seed or doping', the judgment between the size fractions is 'two types' or more, and the comprehensive judgment is mainly based on the judgment conclusion between the size fractions.
The method for determining the classification of iron ore provided in the embodiments of the present application is described in detail below with reference to specific examples:
at 265m2For example, the production system of a sintering machine includes the following common iron-containing mineral powder: the invention relates to a method for preparing iron ore concentrate, which comprises the following steps of coarse raw materials, Australian raw materials, domestic mineral powder and the like, wherein the actual batch materials take stacks as distinguishing units, doping and mixing are occasionally generated in the used material seeds, and a stack of domestic iron concentrate powder with the stack length of 10 meters, the stack height of 8 meters and the stack width of 12 meters and the stack weight of 16000 tons is taken as an example, and the specific implementation mode of the invention is further explained by combining the attached drawings.
Firstly, material is taken in sections. The iron-containing mineral aggregate is reasonably segmented at equal intervals in three modes of transversely segmenting every 2 meters, longitudinally segmenting every 4 meters and horizontally segmenting every 6 meters, two sides and two ends of the aggregate pile are also provided with extreme values, and total sampling is carried out for (10/2+2) × 2 ([ 12/6+2) ] -56. Selecting and determining the grade number of the granularity to be 3 by referring to the actual granularity distribution condition of the iron-containing ore material; according to the total amount of the iron-containing mineral aggregate of 16000 tons, 30 detection frequencies in the grade are determined; according to the segmented setting, the material taking amount > (3 x 30 x 1 g/56)/5% of each segment is required (the factors such as intermediate loss are considered, and about 250g can be controlled in actual execution) so as to ensure the sampling representativeness and the convenient operation. A total of 56 samples were taken, approximately 250g each, for a total of 14.5kg, according to the established sampling scheme.
And secondly, grading the granularity. Firstly, referring to the distribution situation of the particle sizes of the actual iron-containing ores, selecting the particle size fraction of less than or equal to 0.08mm, 0.08-3mm and more than or equal to 3 mm; and mixing 56 samples, taking 14.00kg of mixed sample, and screening and grading the collected iron-containing mineral aggregate in a screening mode. 10.15kg of a ". ltoreq.0.08 mm" mixed sample, 2.82kg of a "0.08-3 mm" mixed sample, and 1.03kg of a ". gtoreq.3 mm" mixed sample were obtained. No ore powder with the size fraction less than 5 percent exists, so that the ore powder is determined to be three-grade ore powder and is included in the grading detection.
And thirdly, carrying out grading detection. Firstly, preparing samples, and respectively extracting 30 samples of 1g from iron-containing mineral aggregates with three size fractions of less than or equal to 0.08mm, 0.08-3mm and more than or equal to 3mm to obtain 90 samples and marking; the same component detection is carried out on the obtained samples, and a general detection mode is adopted, namely, the main chemical components 'TFe and SiO' of each sample are detected2"detecting; and finally, collecting data, and recording the sample number and the detection result into a statistical form, such as table 1.
Table 1:
wherein: data 101-130 particles less than 0.08Composition of mm specimen TFe and SiO2The detection data of (1); data 201-230 are the components TFe and SiO of a sample with particles of 0.08-3mm2The detection data of (1); data 301-330 for compositions TFe and SiO for samples with particles larger than 3mm2The detected data of (1).
And fourthly, analyzing and judging. Setting reasonable limit values of different components according to industry experience by using common office software of a computer, such as Mintab, Excel and the like, and determining limit interval values, TFe +/-0.5 percent and SiO2Plus or minus 0.25 percent, respectively processing data in different size fractions with the same component and processing all data with the same component by taking the frequency as a vertical axis and taking a limit interval as a horizontal axis, drawing a histogram, determining inflection points, calculating the number of the inflection points, the inflection point interval and the frequency ratio in different size fractions with the same component, determining values according to the number of the inflection points, the inflection point interval and the frequency ratio according to industrial experience, and determining the judgment types in and among the size fractions of the iron ore material according to the determined values.
FIG. 2 is a graph showing the results of TFe analysis in a sample having a size fraction of ≦ 0.08mm, FIG. 3 is a graph showing the results of TFe analysis in a sample having a size fraction of "0.08-3 mm", FIG. 4 is a graph showing the results of TFe analysis in a sample having a size fraction of ≧ 3mm ", and FIG. 5 is a graph showing the results of SiO analysis in a sample having a size fraction of ≦ 0.08mm2FIG. 6 is a graph showing the analysis results, and is a graph showing SiO in a sample having a particle size fraction of "0.08 to 3mm2FIG. 7 is a graph showing the results of analysis, in which FIG. 4 is a graph showing the particle size ". gtoreq.3 mm" of SiO in the sample2Analysis results figures, FIGS. 2-7 show intra-granular decision making; FIG. 8 is a graph showing the results of TFe analysis in all samples, and FIG. 9 is a graph showing the results of SiO analysis in all samples2The results of the analysis are shown in FIGS. 8-9, which show the inter-granular decision. The present application is further described below with reference to the accompanying drawings.
As can be seen from FIG. 2, TFe in the samples with the size fraction of ≦ 0.08mm has no inflection point, and the samples with the size fraction of ≦ 0.08mm are judged as "class I"; as can be seen from FIG. 3, the number of inflection points of TFe in the sample having a size fraction of "0.08 to 3 mm" was 1, and the sample was judged as "first class" within the size fraction; as is clear from FIG. 4, the number of inflection points of TFe in the sample having a size fraction of "≧ 3 mm" was 1, and the sample was judged as "one type" within the size fraction. As can be seen from FIG. 5, SiO in the sample having a size fraction of ≦ 0.08mm2The number of turning points is 1, and the grain size is judged as 'one class'; as can be seen from FIG. 6, SiO in the sample having a particle size fraction of "0.08 to 3mm2The number of turning points is 1, and the grain size is judged as 'one class'; as can be seen from FIG. 7, SiO in the sample having a particle size of ". gtoreq.3 mm2The number of the inflection points is 2, the inflection point distance is 2, the frequency ratio is 1, the sum of the values of the inflection point distance and the frequency ratio is 1, and the doping is judged in the size fraction. As can be seen from FIG. 8, the number of TFe inflection points in all samples is 1, and the samples are classified into "one class"; as can be seen from FIG. 9, SiO was contained in all the samples2The number of the inflection points is 3, the inflection point distance is 6, and the frequency ratio is 2, the sum of the values of the inflection point distance and the frequency ratio is 1.5, and the grain size is judged to be the second-class doping.
From the above analysis, it can be seen that: according to SiO2Judging and analyzing in the particle size fraction, wherein the particle size fraction 301-330 (not less than 3mm) is judged as doped particles, and the other types are judged; according to SiO2And judging and analyzing the grain level to judge the second doping type. Combining the grading measurement data:
| ≤0.08mm | 10.15 | 72.50％ |
| 0.08-3mm | 2.82 | 20.14％ |
| ≥3mm | 1.03 | 7.36％ |
comprehensive judgment can be formed: the iron concentrate powder in the pile is formed by doping two types of mineral powder, TFe is 66.5 +/-1.0%, the main difference is that the silicon content of the doped mineral powder is higher, and SiO is contained in the main body mineral powder23.0 +/-0.5%, about 92.64% and SiO doped mineral powder27.0±0.5% and accounts for about 7.36%.
According to the iron ore material classification judgment method, classification judgment of the iron ore material to be detected is achieved through segmented sampling, particle size classification, classification detection and analysis judgment, and the doping condition of the iron ore material is accurately identified. The iron ore material classification judgment method overcomes the defects of component size fraction parallel analysis, numerous and complicated data and the like of the existing iron ore material classification judgment method, can accurately classify and judge the mixed ore powder, provides decision basis for ore material purchase and settlement, and provides scientific data for scientific production of enterprises. The iron ore material classification judgment method is flexible and simple to operate.
It should be noted that, in the present specification, all the embodiments are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment is mainly described as a difference from the other embodiments, and related parts may be referred to the part of the description of the method embodiment. Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (7)
1. A method for classifying and determining an iron ore, the method comprising:
segmented sampling: averagely segmenting the detected iron ore material, and sampling in each segmentation interval to obtain a plurality of first samples;
and (3) grading the particle size: mixing the first samples, taking a plurality of mixed samples to obtain second samples, and screening the second samples according to the selected size fraction to obtain classified samples;
and (3) grading detection: respectively taking a plurality of samples from the classified samples, and detecting and analyzing the chemical components of each sample;
and (3) analysis and judgment: counting the chemical components of the sample, respectively carrying out intra-grade judgment and inter-grade judgment, and comparing the intra-grade judgment and the inter-grade judgment to obtain a comprehensive judgment result; wherein:
the intra-fraction determining comprises:
determining a limit interval value;
counting the frequency number corresponding to each interval value in each grade of the same component according to the limit interval value, and determining inflection points, the number of the inflection points, the distance between the inflection points and the frequency ratio, wherein the inflection points refer to the interval value corresponding to the frequency number greater than the frequency number before and after the inflection points, the number of the inflection points is the number of the inflection points, the distance between the inflection points refers to the interval distance between the maximum inflection point and the minimum inflection point, and the frequency ratio is the ratio of the frequency number corresponding to each inflection point to the total frequency number of each inflection point;
determining a judgment value according to the number of inflection points, the inflection point interval and the frequency ratio;
and determining the judgment category in the iron ore material size fraction according to the judgment value.
2. The method according to claim 1, wherein the step of averagely segmenting the iron ore material to be detected, and sampling the iron ore material in each segment interval to obtain a plurality of first samples comprises:
respectively carrying out equidistant segmentation in the length direction, the width direction and the height direction according to the material pile of the detected iron ore material;
sampling in each subsection and sampling at the extreme of the subsection at the end side of the pile to obtain a plurality of first samples.
3. The method according to claim 1 or 2, wherein the number of samples N1 of the first sample is greater than or equal to 30, and the weight G > (standard quantity of in-grain detection frequency assay samples/N1)/5% of the first sample.
4. The method of classifying and determining an iron ore according to claim 1, wherein the classifying of the particle size further includes:
when the fraction sample obtained by screening is less than 5% of the total amount of the second sample, the fraction sample is incorporated into an adjacent fraction sample of a larger proportion.
5. The method according to claim 1, wherein the step of taking a plurality of samples from the classification samples and analyzing a chemical composition of each sample comprises:
respectively taking a plurality of equal samples from each size fraction sample, and carrying out labeling;
detecting and analyzing the chemical components of each sample;
the chemical components detected in the sample and their corresponding labels are compiled.
6. The iron ore classification determination method according to claim 1, wherein the inter-granular determination includes:
determining a limit interval value;
counting the frequency number corresponding to each interval value between each grain level of the same component according to the limit interval value, and determining inflection points, the number of the inflection points, the interval value of the inflection points and the frequency ratio, wherein the inflection points refer to the interval value corresponding to the frequency number greater than the frequency number before and after the inflection points, the number of the inflection points is the number of the inflection points, the interval value of the inflection points refers to the interval value between the maximum inflection point and the minimum inflection point, and the frequency ratio is the ratio of the frequency number corresponding to each inflection point to the total frequency number of each inflection point;
determining a judgment value according to the number of inflection points, the inflection point interval and the frequency ratio;
and determining the grade judgment category of the iron ore material particles according to the judgment value.
7. The method of classifying and judging iron ore according to claim 1, wherein comparing the intra-fraction judgment and the inter-fraction judgment to obtain the comprehensive judgment result includes:
and comparing the intra-grade judgment and the inter-grade judgment by combining the grading measurement results to obtain a comprehensive judgment result.
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