CN116577478A - Coal identification and sorting selectivity evaluation method and device and electronic equipment - Google Patents
Coal identification and sorting selectivity evaluation method and device and electronic equipment Download PDFInfo
- Publication number
- CN116577478A CN116577478A CN202310308755.7A CN202310308755A CN116577478A CN 116577478 A CN116577478 A CN 116577478A CN 202310308755 A CN202310308755 A CN 202310308755A CN 116577478 A CN116577478 A CN 116577478A
- Authority
- CN
- China
- Prior art keywords
- coal
- ash
- coal sample
- value
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000003245 coal Substances 0.000 title claims abstract description 352
- 238000011156 evaluation Methods 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 claims abstract description 73
- 238000004458 analytical method Methods 0.000 claims abstract description 28
- 238000000926 separation method Methods 0.000 claims abstract description 23
- 238000012216 screening Methods 0.000 claims abstract description 17
- 238000003860 storage Methods 0.000 claims abstract description 10
- 238000005070 sampling Methods 0.000 claims abstract description 8
- 239000002956 ash Substances 0.000 claims description 143
- 239000010883 coal ash Substances 0.000 claims description 41
- 235000019580 granularity Nutrition 0.000 claims description 32
- 238000009826 distribution Methods 0.000 claims description 20
- 238000005259 measurement Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 13
- 238000009825 accumulation Methods 0.000 claims description 12
- 238000010304 firing Methods 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 abstract description 10
- 238000012797 qualification Methods 0.000 abstract description 7
- 239000012535 impurity Substances 0.000 abstract description 6
- 230000008569 process Effects 0.000 description 31
- 238000004364 calculation method Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 239000002245 particle Substances 0.000 description 7
- 238000012545 processing Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000002360 preparation method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 229910052500 inorganic mineral Inorganic materials 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000011707 mineral Substances 0.000 description 2
- 235000010755 mineral Nutrition 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- JIAARYAFYJHUJI-UHFFFAOYSA-L zinc dichloride Chemical compound [Cl-].[Cl-].[Zn+2] JIAARYAFYJHUJI-UHFFFAOYSA-L 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000002864 coal component Substances 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003670 easy-to-clean Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007667 floating Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000001678 irradiating effect Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
- 239000011592 zinc chloride Substances 0.000 description 1
- 235000005074 zinc chloride Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/22—Fuels; Explosives
- G01N33/222—Solid fuels, e.g. coal
-
- 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
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/361—Processing or control devices therefor, e.g. escort memory
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Engineering & Computer Science (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The embodiment of the application provides a method and a device for evaluating the selectivity of coal identification and separation, electronic equipment and a storage medium, wherein the method comprises the following steps: sampling the coal to obtain an initial raw coal sample; screening the initial raw coal sample to obtain a raw coal sample; carrying out ash analysis on the raw coal sample to obtain an ash coal sample sequence; obtaining an average relative deviation value according to the ash coal sample sequence; and carrying out the selectivity evaluation on the raw coal sample according to the average relative deviation value to obtain a selectivity evaluation result. By implementing the embodiment of the application, the screening mode of coal can be simplified, the screening efficiency of coal can be improved, impurities in the coal can be conveniently separated from raw coal, the qualification rate of the coal can be improved, the quality of products can be ensured, and the production cost can be reduced.
Description
Technical Field
The application relates to the technical field of coal processing, in particular to a coal identification and separation selectivity evaluation method, a coal identification and separation selectivity evaluation device, electronic equipment and a computer storage medium.
Background
At present, an optional curve adopted in the optional analysis in the coal dressing process of the coal industry is an H-R curve, and a sorting density + -0.1 content method is also a main method for evaluating the coal optional in China. Different curves may show the relation between the respective ash and the clean coal yield, mineral density, particle size distribution.
However, along with the progress of social science and technology and coal processing and utilization technology, the intellectualization of coal preparation plants is rapidly developed, the current coal preparation mode cannot meet the requirements, and some defects are gradually exposed. For example, separation of coal and gangue cannot be realized directly by judging ash content of an input coal sample or establishing relation between ash content and other physical indexes, and products in the production process are difficult to regulate and control, so that the quality requirements of the products cannot be met, the difficulty of selecting qualified products is greatly improved, and the production cost is increased intangibly.
Disclosure of Invention
The embodiment of the application aims to provide a coal identification and separation selectivity evaluation method, a device, electronic equipment and a storage medium, which can simplify the selectivity evaluation mode of coal, improve the selectivity evaluation efficiency of coal, facilitate the separation of impurities in coal from raw coal, improve the qualification rate of screened coal, ensure the quality of products and reduce the production cost.
In a first aspect, an embodiment of the present application provides a method for evaluating the selectivity of coal identification and separation, the method comprising:
sampling the coal to obtain an initial raw coal sample;
screening the initial raw coal sample to obtain a raw coal sample;
carrying out ash analysis on the raw coal sample to obtain an ash coal sample sequence;
obtaining an average relative deviation value according to the ash coal sample sequence;
and carrying out the selectivity evaluation on the raw coal sample according to the average relative deviation value to obtain a selectivity evaluation result.
In the implementation process, the coal is sampled, screened and ash analyzed, so that the coal can be gradually refined and screened, the selectivity evaluation mode of the coal is simplified, the selectivity evaluation efficiency of the coal is improved, impurities in the coal are conveniently separated from raw coal, the qualification rate of the coal subjected to the selectivity evaluation can be improved, the product quality is ensured, and the production cost is reduced.
Further, the step of performing ash analysis on the raw coal sample to obtain an ash coal sample sequence comprises the following steps:
carrying out ash analysis on the raw coal sample to obtain ash mass fraction;
and constructing the ash coal sample sequence according to the mass fraction of the ash according to the granularity information.
In the implementation process, ash analysis is carried out on raw coal samples, more data of the raw coal samples can be obtained, the constructed ash coal sample sequence is ensured to be more accurate, and errors are reduced.
Further, the step of obtaining an average relative deviation value from the sequence of ash coal samples comprises:
obtaining an average ash value of a raw coal sample of the raw coal sample;
selecting coal ash values corresponding to different granularities in the ash coal sample sequence according to the granularity information;
and obtaining the average relative deviation value according to the raw coal sample average ash value and the coal ash value.
In the implementation process, the ash content value of the coal is divided according to different granularities, so that the obtained average relative deviation value is more accurate, larger errors caused by different granularities are avoided, and further optional evaluation of the raw coal sample is facilitated.
Further, ash analysis is carried out on the raw coal sample according to the following formula, and the ash mass fraction is obtained:
wherein A is ad Is the ash mass fraction, M 0 Is the weight of raw coal sample, M 1 Is the weight of the remainder of the raw coal sample after being burned.
In the implementation process, the ash mass fraction is obtained according to the weight of the raw coal sample and the weight of the burnt raw coal sample, so that the accuracy of the ash mass fraction can be improved, and the error is reduced.
Further, the average relative deviation value is obtained according to the raw coal sample average ash value and the coal ash value according to the following formula:
wherein alpha is the average ash value of the raw coal sample, beta i Is the ash value of the coal, gamma i The mass ratio of the raw coal sample to the coal is M, and the average relative deviation value is M.
In the implementation process, the average relative deviation value is obtained according to the average ash value of the raw coal sample, the coal ash value and the mass ratio of the raw coal sample to the coal, so that the robustness of the average relative deviation value in the calculation process can be improved, and the calculation difficulty is reduced.
Further, the step of selectively evaluating the raw coal sample according to the average relative deviation value to obtain a selectively evaluating result comprises the following steps:
selecting a target coal sample in the raw coal samples according to the average relative deviation value;
performing quality measurement on the target coal sample according to the sorting characteristics to obtain a measurement result;
classifying the measurement results according to the granularity information to obtain a plurality of groups;
and carrying out the selectivity evaluation on the target coal samples in the groups respectively to obtain the selectivity evaluation result.
In the implementation process, the quality measurement is carried out on the target coal sample according to the sorting characteristics, and then the classification is carried out according to the granularity information, so that the raw coal sample can be further refined, the screening range of the target coal sample is more accurate, and the accuracy degree of the selectivity evaluation result is improved.
Further, the step of performing the selectivity evaluation on the target coal samples in the plurality of groups to obtain the selectivity evaluation result includes:
respectively obtaining weighted average ash values of target coal samples in each group;
respectively obtaining the percentage yield of the coal sample mass of the target coal sample in each group;
respectively obtaining the refined coal accumulation yield of the target coal samples in each group;
respectively obtaining the clean coal ash value of the target coal sample in each group;
respectively obtaining the refined coal ash distribution value of the target coal sample in each group;
and obtaining the selectivity evaluation result according to the weighted average ash value, the percentage yield, the clean coal accumulated yield, the clean coal ash value and the clean coal ash distribution value.
In the implementation process, the selectivity evaluation result is obtained according to the weighted average ash value, the percentage yield, the clean coal accumulation yield, the clean coal ash value and the clean coal ash distribution value, so that the selectivity evaluation of coal can be realized from multiple dimensions, and the more comprehensive selectivity evaluation result is ensured.
In a second aspect, the embodiment of the application also provides an optional evaluation device for identifying and sorting coal, which comprises:
the acquisition module is used for sampling the coal to obtain an initial raw coal sample;
the screening module is used for screening the initial raw coal sample to obtain a raw coal sample;
the ash analysis module is used for carrying out ash analysis on the raw coal samples to obtain ash coal sample sequences;
the data acquisition module is used for acquiring an average relative deviation value according to the ash coal sample sequence;
and the selectivity evaluation module is used for carrying out selectivity evaluation on the raw coal samples according to the average relative deviation value to obtain a selectivity evaluation result.
In the implementation process, the coal is sampled, screened and ash analyzed, so that the coal can be gradually refined and screened, the selectivity evaluation mode of the coal is simplified, the selectivity evaluation efficiency of the coal is improved, impurities in the coal are conveniently separated from raw coal, the qualification rate of the screened coal can be improved, the product quality is ensured, and the production cost is reduced.
In a third aspect, an electronic device provided in an embodiment of the present application includes: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any one of the first aspects when the computer program is executed.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where instructions are stored, when the instructions are executed on a computer, to cause the computer to perform the method according to any one of the first aspects.
In a fifth aspect, embodiments of the present application provide a computer program product, which when run on a computer causes the computer to perform the method according to any of the first aspects.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part will be obvious from the description, or may be learned by practice of the techniques of the disclosure.
And can be implemented in accordance with the teachings of the specification, the following detailed description of the preferred embodiments of the application, taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be construed as limiting the scope values, and other related drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an alternative assessment method for coal identification and separation provided by an embodiment of the application;
FIG. 2 is a graph of ash characteristics of the easy-to-clean coal provided by the embodiment of the application;
FIG. 3 is a graph of ash characteristics of refractory coal provided by an embodiment of the application;
FIG. 4 is a schematic structural diagram of an alternative evaluation device for coal identification and separation according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
The following describes in further detail the embodiments of the present application with reference to the drawings and examples. The following examples are illustrative of the application and are not intended to limit the scope of the application.
Example 1
Fig. 1 is a schematic flow chart of an alternative evaluation method for coal identification and separation according to an embodiment of the present application, as shown in fig. 1, the method includes:
s1, sampling coal to obtain an initial raw coal sample;
s2, screening an initial raw coal sample to obtain a raw coal sample;
s3, carrying out ash analysis on raw coal samples to obtain ash coal sample sequences;
s4, obtaining an average relative deviation value according to the ash coal sample sequence;
and S5, carrying out the selectivity evaluation on the raw coal samples according to the average relative deviation value to obtain a selectivity evaluation result.
In the implementation process, the coal is sampled, screened and ash analyzed, so that the coal can be gradually refined and screened, the selectivity evaluation mode of the coal is simplified, the selectivity evaluation efficiency of the coal is improved, impurities in the coal are conveniently separated from raw coal, the qualification rate of the screened coal can be improved, the product quality is ensured, and the production cost is reduced.
Because the content of the coal components is between different coal particles, according to the differences in the physical and chemical properties of the coal particles, and also based on the requirements of users on the quality of products, the different coals need to be subjected to selective assessment.
The difficulty of selecting qualified products in the traditional coal processing industry is high, in the eighteenth century, an expert first utilizes zinc chloride solution to carry out a coal floating and sinking test, and a selectable curve is drawn. At present, the coal selectivity evaluation methods at home and abroad are all based on the curve. The proper optional evaluation mode can determine reasonable methods, processes and product structures, so that full utilization of coal resources is realized, coal can be influenced by the attribute of the coal, and different optional properties can be shown under different ash conditions, therefore, research on the coal optional properties is enhanced, and the production efficiency and qualification rate of coal products can be improved.
The current selectivity curve adopted by the selectivity analysis in the coal preparation industry is an H-R curve. It includes 5 curves, ash characteristics, float accumulation, sink accumulation, density and density + -0.1 curves, respectively. Sorting density + -0.1 content method is also the main method for evaluating coal selectivity at present. The different curves show the relation between the respective ash and the clean coal yield, mineral density, particle size distribution. The curve is drawn directly related to the coal volume, density and ash content.
The embodiment of the application provides a novel coal identification and separation selectable evaluation mode, which is a coal selectable evaluation method suitable for identification and separation of coal sensors, and can select high-quality coal resources by combining with a novel reasonable evaluation method, and can provide more support for production management, scientific research and the like.
The coal identification and separation selectivity assessment method comprises raw coal sample sampling, separation method feasibility research, coal sortability test and selectivity curve establishment.
Specifically, in S1 and S2, tens of initial raw coal samples conforming to the sorting granularity are collected on a down-hole raw coal belt or a raw coal bin lower belt, and the collected initial raw coal samples are screened according to the grain grades of 6mm, 13mm, 25mm, 50mm, 100mm, 150mm and 200mm, and weighing is carried out after screening, so that the raw coal samples are obtained.
Further, S3 includes:
carrying out ash analysis on raw coal samples to obtain ash mass fractions;
and constructing an ash coal sample sequence according to the particle size information and the ash mass fraction.
In the implementation process, ash analysis is carried out on raw coal samples, more data of the raw coal samples can be obtained, the constructed ash coal sample sequence is ensured to be more accurate, and errors are reduced.
The embodiment of the application adopts the average relative deviation value of the clean coal ash and the raw coal average ash in coal to evaluate the selectivity in the screening process. Therefore, ash analysis is carried out on raw coal samples with various particle sizes after screening is completed.
Further, ash analysis is carried out on raw coal samples according to the following formula to obtain ash mass fractions:
wherein A is ad Is ash mass fraction, M 0 Is the weight of raw coal sample, M 1 Is the weight of the remainder of the raw coal sample after being burned. Note that a ad Is the mass fraction of dry base ash in air.
In the implementation process, the ash mass fraction is obtained according to the weight of the raw coal sample and the weight of the burnt raw coal sample, so that the accuracy of the ash mass fraction can be improved, and the error is reduced.
Further, S4 includes:
obtaining an average ash value of a raw coal sample of the raw coal sample;
selecting coal ash values corresponding to different granularities in an ash coal sample sequence according to granularity information;
and obtaining an average relative deviation value according to the average ash value and the coal ash value of the raw coal sample.
In the implementation process, the ash content value of the coal is divided according to different granularities, so that the obtained average relative deviation value is more accurate, larger errors caused by different granularities are avoided, and further optional evaluation of the raw coal sample is facilitated.
After the ash determination is completed, a sequence of ash coal samples is established for different grades. And finally, evaluating the selectivity of coal sorting according to the average relative deviation value of the ash content of the coal in the samples with different granularity and the average ash content of the raw coal sample.
Specifically, an average relative deviation value is obtained according to the average ash value and the coal ash value of the raw coal sample according to the following formula:
wherein alpha is the average ash value of the raw coal sample, beta i Is the ash value of coal, gamma i The mass ratio of raw coal samples to coal is represented by M, and the average relative deviation value is represented by M.
In the implementation process, the average relative deviation value is obtained according to the average ash value of the raw coal sample, the coal ash value and the mass ratio of the raw coal sample to the coal, so that the robustness of the average relative deviation value in the calculation process can be improved, and the calculation difficulty is reduced.
The magnitude of the average relative deviation value M is very closely related to the coal selectivity curve, so the average relative deviation value M is also referred to as the selectivity coefficient.
The value of M is in the range of 0-1, and if the ash content distribution of the coal is very uniform, the calculated M value is approaching to 0; if the ash distribution of the coal is uneven, the M value approaches 1. The magnitude of the M value reflects the distribution condition of ash content of coal, and the larger the M value is, the larger the change of ash content of the coal is, and the separation is easier; the smaller the M value, the smaller the change of coal ash, the more uniform the ash distribution, and the harder the separation. For example, if the M value is less than 0.5, there is generally no economical advantage in sorting, so that there is no need for sorting. Table 1 shows the relationship between M value and sorting difficulty.
Table 1M value and sorting difficulty relationship table
| M value | 0-0.5 | 0.5-1 |
| Degree of separation | Difficult to select | Easy to select |
Further, S5 includes:
selecting a target coal sample in the raw coal samples according to the average relative deviation value;
performing quality measurement on the target coal sample according to the sorting characteristics to obtain a measurement result;
classifying the measurement results according to the granularity information to obtain a plurality of groups;
and carrying out the selectivity evaluation on the target coal samples in the groups respectively to obtain a selectivity evaluation result.
In the implementation process, the quality measurement is carried out on the target coal sample according to the sorting characteristics, and then the classification is carried out according to the granularity information, so that the raw coal sample can be further refined, the selectivity evaluation range of the target coal sample is more accurate, and the accuracy degree of the selectivity evaluation result is improved.
On the basis of the necessity of sorting, 200-500 representative raw coal samples are selected as target coal samples, and the quality and the clean coal content of the selected target coal samples are determined according to sorting characteristics (such as reflected visible light, fluorescence, radioactivity intensity, images and the like). Dividing the measurement result into a plurality of groups according to the granularity sequence, calculating the weight of each group of target coal samples and the ash content of each group of target coal samples, and carrying out the selectivity research of the whole coal samples.
Further, the step of selectively evaluating the target coal samples in the plurality of groups to obtain a selectively evaluating result includes:
respectively obtaining weighted average ash values of target coal samples in each group;
respectively obtaining the percentage yield of the coal sample mass of the target coal sample in each group;
respectively obtaining the refined coal accumulation yield of the target coal samples in each group;
respectively obtaining the clean coal ash value of the target coal sample in each group;
respectively obtaining the refined coal ash distribution value of the target coal sample in each group;
the selectivity evaluation result is obtained according to the weighted average ash value, percentage yield, clean coal accumulation yield, clean coal ash value and clean coal ash distribution value.
In the implementation process, the selectivity evaluation result is obtained according to the weighted average ash value, the percentage yield, the clean coal accumulation yield, the clean coal ash value and the clean coal ash distribution value, so that the selectivity evaluation of coal can be realized from multiple dimensions, and the more comprehensive selectivity evaluation result is ensured.
The embodiment of the application takes the X-ray sorting result of the optional evaluation result as an example. The method comprises the following steps:
1) Confirming the granularity range of the coal sample group, wherein the granularity range is large and small;
2) Calculating a weighted average ash value beta i of the target coal sample in each granularity range;
3) Calculating the percentage yield delta gamma i of the mass of the target coal sample in each granularity range accounting for all the coal samples selected;
4) The cumulative yield of the clean coal is calculated and is accumulated from each group delta gamma i from bottom to top.
5) The value of the clean coal ash is calculated from the accumulation from bottom to top, namely the sum of the clean coal quality of each group from bottom to top is divided by the sum of the yield of each corresponding granularity level.
6) And calculating lambda value, wherein the lambda value represents the distribution condition of clean coal ash in each granularity range of the target coal sample.
λ 1 ＝Δγ 1 /2，λ 2 ＝Δγ 1 +Δγ 2 /2，λ 3 ＝Δγ 1 +Δγ 2 +Δγ 3 /2，λ n
＝Δγ 1 +Δγ 2 +......+Δγ n-1 +Δγ n /2
According to the data, the table is drawn, as shown in table 2 and table 3, and the data in the table is input into a calculation formula of M, so that the value of M can be obtained, meanwhile, an optional curve can be drawn, the sorting difficulty degree can be qualitatively evaluated according to the shape of the curve, the ash characteristic drawing curve of the easy-to-sort coal is shown in fig. 2, and the ash characteristic drawing curve of the difficult-to-sort coal is shown in fig. 3.
Table 2 data distribution table for target coal samples
TABLE 3 data distribution table for target coal samples
According to the actually measured selectivity curve, besides the difficulty of the selectivity of the coal can be seen from the shape of the coal, the index M value of the ash non-uniformity degree of the coal sample can be obtained, and the M value has direct guiding significance for evaluating the possibility of coal sorting in mining areas.
The above example is to directly measure ash index of coal by irradiating the coal with X-rays and calculate the ore selectivity coefficient M. Alternatively, other sorting methods, such as image recognition, are ash indexes that are converted by establishing correspondence between sorting characteristics (coal image characteristics) and ash, and an optional curve is drawn therefrom.
Example two
In order to perform a corresponding method of the above embodiment to achieve the corresponding functions and technical effects, an alternative evaluation device for identifying and sorting coal is provided below, as shown in fig. 4, the device includes:
the acquisition module 1 is used for sampling coal to obtain an initial raw coal sample;
the screening module 2 is used for screening the initial raw coal sample to obtain a raw coal sample;
the ash analysis module 3 is used for carrying out ash analysis on raw coal samples to obtain an ash coal sample sequence;
the data acquisition module 4 is used for acquiring an average relative deviation value according to the ash coal sample sequence;
and the selectivity evaluation module 5 is used for carrying out selectivity evaluation on the raw coal samples according to the average relative deviation value to obtain a selectivity evaluation result.
In the implementation process, the coal is sampled, screened and ash analyzed, so that the coal can be gradually refined and screened, the selectivity evaluation mode of the coal is simplified, the selectivity evaluation efficiency of the coal is improved, impurities in the coal are conveniently separated from raw coal, the qualification rate of the coal subjected to the selectivity evaluation can be improved, the product quality is ensured, and the production cost is reduced.
Further, ash analysis module 3 is also configured to:
carrying out ash analysis on raw coal samples to obtain ash mass fractions;
and constructing an ash coal sample sequence according to the particle size information and the ash mass fraction.
In the implementation process, ash analysis is carried out on raw coal samples, more data of the raw coal samples can be obtained, the constructed ash coal sample sequence is ensured to be more accurate, and errors are reduced.
Further, the data obtaining module 4 is further configured to:
obtaining an average ash value of a raw coal sample of the raw coal sample;
selecting coal ash values corresponding to different granularities in an ash coal sample sequence according to granularity information;
and obtaining an average relative deviation value according to the average ash value and the coal ash value of the raw coal sample.
In the implementation process, the ash content value of the coal is divided according to different granularities, so that the obtained average relative deviation value is more accurate, larger errors caused by different granularities are avoided, and further optional evaluation of the raw coal sample is facilitated.
Further, the ash analysis module 3 is further configured to perform ash analysis on the raw coal sample according to the following formula, so as to obtain the ash mass fraction:
wherein A is ad Is ash mass fraction, M 0 Is the weight of raw coal sample, M 1 Is the weight of the remainder of the raw coal sample after being burned.
In the implementation process, the ash mass fraction is obtained according to the weight of the raw coal sample and the weight of the burnt raw coal sample, so that the accuracy of the ash mass fraction can be improved, and the error is reduced.
Further, the data obtaining module 4 is further configured to obtain an average relative deviation value according to the average ash value and the coal ash value of the raw coal sample according to the following formula:
wherein alpha is the average ash value of the raw coal sample, beta i Is the ash value of coal, gamma i The mass ratio of raw coal samples to coal is represented by M, and the average relative deviation value is represented by M.
In the implementation process, the average relative deviation value is obtained according to the average ash value of the raw coal sample, the coal ash value and the mass ratio of the raw coal sample to the coal, so that the robustness of the average relative deviation value in the calculation process can be improved, and the calculation difficulty is reduced.
Further, the selectivity evaluation module 5 is further configured to:
selecting a target coal sample in the raw coal samples according to the average relative deviation value;
performing quality measurement on the target coal sample according to the sorting characteristics to obtain a measurement result;
classifying the measurement results according to the granularity information to obtain a plurality of groups;
and carrying out the selectivity evaluation on the target coal samples in the groups respectively to obtain a selectivity evaluation result.
In the implementation process, the quality measurement is carried out on the target coal sample according to the sorting characteristics, and then the classification is carried out according to the granularity information, so that the raw coal sample can be further refined, the screening range of the target coal sample is more accurate, and the accuracy degree of the selectivity evaluation result is improved.
Further, the selectivity evaluation module 5 is further configured to:
respectively obtaining weighted average ash values of target coal samples in each group;
respectively obtaining the percentage yield of the coal sample mass of the target coal sample in each group;
respectively obtaining the refined coal accumulation yield of the target coal samples in each group;
respectively obtaining the clean coal ash value of the target coal sample in each group;
respectively obtaining the refined coal ash distribution value of the target coal sample in each group;
the selectivity evaluation result is obtained according to the weighted average ash value, percentage yield, clean coal accumulation yield, clean coal ash value and clean coal ash distribution value.
In the implementation process, the selectivity evaluation result is obtained according to the weighted average ash value, the percentage yield, the clean coal accumulation yield, the clean coal ash value and the clean coal ash distribution value, so that the selectivity evaluation of coal can be realized from multiple dimensions, and the more comprehensive selectivity evaluation result is ensured.
The above-described coal identification and sorting selectivity evaluation device can implement the method of the above-described embodiment one. The options in the first embodiment described above also apply to this embodiment, and are not described in detail here.
The rest of the embodiments of the present application may refer to the content of the first embodiment, and in this embodiment, no further description is given.
Example III
The embodiment of the application provides electronic equipment, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic equipment to execute the coal identification and sorting selectable assessment method in the embodiment I.
Alternatively, the electronic device may be a server.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include a processor 51, a communication interface 52, a memory 53, and at least one communication bus 54. Wherein the communication bus 54 is used to enable direct connection communication of these components. Wherein the communication interface 52 of the device in the embodiment of the present application is used for signaling or data communication with other node devices. The processor 51 may be an integrated circuit chip with signal processing capabilities.
The processor 51 may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. The general purpose processor may be a microprocessor or the processor 51 may be any conventional processor or the like.
The Memory 53 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The memory 53 has stored therein computer readable instructions which, when executed by the processor 51, enable the apparatus to perform the steps described above in relation to the embodiment of the method of fig. 1.
Optionally, the electronic device may further include a storage controller, an input-output unit. The memory 53, the memory controller, the processor 51, the peripheral interface, and the input/output unit are electrically connected directly or indirectly to each other, so as to realize data transmission or interaction. For example, the components may be electrically coupled to each other via one or more communication buses 54. The processor 51 is arranged to execute executable modules stored in the memory 53, such as software functional modules or computer programs comprised by the device.
The input-output unit is used for providing the user with the creation task and creating the starting selectable period or the preset execution time for the task so as to realize the interaction between the user and the server. The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative, and that the electronic device may also include more or fewer components than shown in fig. 5, or have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
In addition, the embodiment of the application also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the coal identification and sorting selectivity assessment method in the first embodiment when being executed by a processor.
The present application also provides a computer program product which, when run on a computer, causes the computer to perform the method described in the method embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The above description is merely illustrative of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the scope of the present application, and the application is intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be defined by the protection scope of the claims.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for assessing the selectivity of coal identification and separation, the method comprising:
sampling the coal to obtain an initial raw coal sample;
screening the initial raw coal sample to obtain a raw coal sample;
carrying out ash analysis on the raw coal sample to obtain an ash coal sample sequence;
obtaining an average relative deviation value according to the ash coal sample sequence;
and carrying out the selectivity evaluation on the raw coal sample according to the average relative deviation value to obtain a selectivity evaluation result.
2. The method for selectively assessing the coal identification and separation selectivity of claim 1, wherein the step of performing ash analysis on the raw coal sample to obtain a sequence of ash coal samples comprises:
carrying out ash analysis on the raw coal sample to obtain ash mass fraction;
and constructing the ash coal sample sequence according to the mass fraction of the ash according to the granularity information.
3. The method of claim 1, wherein the step of obtaining average relative bias values from the sequence of ash coal samples comprises:
obtaining an average ash value of a raw coal sample of the raw coal sample;
selecting coal ash values corresponding to different granularities in the ash coal sample sequence according to the granularity information;
and obtaining the average relative deviation value according to the raw coal sample average ash value and the coal ash value.
4. The method for selectively assessing the coal identification and separation according to claim 2, wherein ash analysis is performed on the raw coal sample according to the following formula to obtain ash mass fractions:
wherein A is ad Is the ash mass fraction, M 0 Is the weight of raw coal sample, M 1 Is raw coalThe weight of the remainder of the sample after firing.
5. The method for evaluating the coal identification and sorting selectivity according to claim 3, wherein the average relative deviation value is obtained from the raw coal sample average ash value and the coal ash value according to the following formula:
wherein alpha is the average ash value of the raw coal sample, beta i Is the ash value of the coal, gamma i The mass ratio of the raw coal sample to the coal is M, and the average relative deviation value is M.
6. The method for selectively evaluating the coal identification and separation according to claim 5, wherein the step of selectively evaluating the raw coal sample according to the average relative deviation value to obtain a selectively evaluated result comprises the following steps:
selecting a target coal sample in the raw coal samples according to the average relative deviation value;
performing quality measurement on the target coal sample according to the sorting characteristics to obtain a measurement result;
classifying the measurement results according to the granularity information to obtain a plurality of groups;
and carrying out the selectivity evaluation on the target coal samples in the groups respectively to obtain the selectivity evaluation result.
7. The method for selectively evaluating the coal identification and separation according to claim 6, wherein the step of selectively evaluating the target coal samples in the plurality of groups to obtain the result of the selectively evaluating comprises:
respectively obtaining weighted average ash values of target coal samples in each group;
respectively obtaining the percentage yield of the coal sample mass of the target coal sample in each group;
respectively obtaining the refined coal accumulation yield of the target coal samples in each group;
respectively obtaining the clean coal ash value of the target coal sample in each group;
respectively obtaining the refined coal ash distribution value of the target coal sample in each group;
and obtaining the selectivity evaluation result according to the weighted average ash value, the percentage yield, the clean coal accumulated yield, the clean coal ash value and the clean coal ash distribution value.
8. An apparatus for assessing the selectivity of coal identification and separation, the apparatus comprising:
the acquisition module is used for sampling the coal to obtain an initial raw coal sample;
the screening module is used for screening the initial raw coal sample to obtain a raw coal sample;
the ash analysis module is used for carrying out ash analysis on the raw coal samples to obtain ash coal sample sequences;
the data acquisition module is used for acquiring an average relative deviation value according to the ash coal sample sequence;
and the selectivity evaluation module is used for carrying out selectivity evaluation on the raw coal samples according to the average relative deviation value to obtain a selectivity evaluation result.
9. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the coal identification sorting option assessment method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the method for evaluating the selectivity of coal identification sorting according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310308755.7A CN116577478A (en) | 2023-03-27 | 2023-03-27 | Coal identification and sorting selectivity evaluation method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310308755.7A CN116577478A (en) | 2023-03-27 | 2023-03-27 | Coal identification and sorting selectivity evaluation method and device and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116577478A true CN116577478A (en) | 2023-08-11 |
Family
ID=87538356
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310308755.7A Pending CN116577478A (en) | 2023-03-27 | 2023-03-27 | Coal identification and sorting selectivity evaluation method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116577478A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882849A (en) * | 2023-09-07 | 2023-10-13 | 天津美腾科技股份有限公司 | Method and device for measuring and calculating yield of clean coal |
-
2023
- 2023-03-27 CN CN202310308755.7A patent/CN116577478A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882849A (en) * | 2023-09-07 | 2023-10-13 | 天津美腾科技股份有限公司 | Method and device for measuring and calculating yield of clean coal |
| CN116882849B (en) * | 2023-09-07 | 2023-12-19 | 天津美腾科技股份有限公司 | Method and device for measuring and calculating yield of clean coal |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gorsky et al. | Digital zooplankton image analysis using the ZooScan integrated system | |
| Belis et al. | A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises | |
| CN106802977A (en) | One kind is used for sintering performance index prediction and Quality evaluation method | |
| CN116577478A (en) | Coal identification and sorting selectivity evaluation method and device and electronic equipment | |
| KR20100048738A (en) | Method for classification and forecast of remote measuring power load patterns | |
| CN111080356A (en) | Method for calculating residence price influence factors by using machine learning regression model | |
| CN114707120A (en) | Statistical modeling method for measuring and calculating carbon emission scale | |
| CN115188434A (en) | Watershed-scale water body non-point source pollution classification and partition identification method | |
| CN117637080B (en) | Yield stress prediction method based on filling slurry differential analysis | |
| CN115471000A (en) | Method for evaluating uncertainty of deterministic graded rainfall forecast | |
| CN105223140A (en) | The method for quickly identifying of homology material | |
| CN113919729A (en) | Regional three-generation space influence and cooperation level evaluation method and system | |
| CN117541095A (en) | Agricultural land soil environment quality classification method | |
| CN117435949A (en) | Industrial wastewater treatment effect detection and evaluation method based on artificial intelligence | |
| CN115808378B (en) | Sea sand thickness degree classification method | |
| CN115186776B (en) | Method, device and storage medium for classifying ruby producing areas | |
| CN110826917A (en) | Construction waste recycling mode fuzzy evaluation method | |
| CN116304948A (en) | Unsupervised electricity consumption anomaly detection method integrating multi-scale fuzzy information particles | |
| CN115392561A (en) | Coal blending method and system | |
| CN114627979A (en) | Method and system for determining biomass material characteristic probability distribution information | |
| CN112461780B (en) | Evaluation and calculation method for original total organic carbon content of hydrocarbon source rock | |
| CN114298877A (en) | Index evaluation system based on social improvement | |
| Aliaga et al. | Nanoparticle ranking analysis: determining new particle formation (NPF) event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particles | |
| CN113205274A (en) | Quantitative ranking method for construction quality | |
| CN117191172B (en) | Method and device for mass detection and saturation early warning of balance weighing cover |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |