CN112378946B - Efficient detection method for particle steel - Google Patents
Efficient detection method for particle steel Download PDFInfo
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- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 284
- 239000010959 steel Substances 0.000 title claims abstract description 284
- 239000002245 particle Substances 0.000 title claims abstract description 174
- 238000001514 detection method Methods 0.000 title claims abstract description 50
- 239000000463 material Substances 0.000 claims abstract description 102
- 238000002844 melting Methods 0.000 claims abstract description 94
- 230000008018 melting Effects 0.000 claims abstract description 91
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 68
- 230000001788 irregular Effects 0.000 claims abstract description 21
- 238000006243 chemical reaction Methods 0.000 claims abstract description 17
- 238000005070 sampling Methods 0.000 claims abstract description 13
- 238000007873 sieving Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 105
- 239000002893 slag Substances 0.000 claims description 31
- 230000006698 induction Effects 0.000 claims description 24
- 238000010438 heat treatment Methods 0.000 claims description 22
- 238000000034 method Methods 0.000 claims description 18
- 238000012216 screening Methods 0.000 claims description 9
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 8
- 239000013589 supplement Substances 0.000 claims description 6
- 238000005303 weighing Methods 0.000 claims description 6
- 229910052742 iron Inorganic materials 0.000 claims description 4
- 239000000203 mixture Substances 0.000 claims description 4
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 239000008188 pellet Substances 0.000 claims 2
- 238000010309 melting process Methods 0.000 abstract description 6
- 230000000694 effects Effects 0.000 abstract description 5
- BRPQOXSCLDDYGP-UHFFFAOYSA-N calcium oxide Chemical compound [O-2].[Ca+2] BRPQOXSCLDDYGP-UHFFFAOYSA-N 0.000 description 2
- 239000000292 calcium oxide Substances 0.000 description 2
- ODINCKMPIJJUCX-UHFFFAOYSA-N calcium oxide Inorganic materials [Ca]=O ODINCKMPIJJUCX-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- AMWRITDGCCNYAT-UHFFFAOYSA-L hydroxy(oxo)manganese;manganese Chemical compound [Mn].O[Mn]=O.O[Mn]=O AMWRITDGCCNYAT-UHFFFAOYSA-L 0.000 description 2
- 239000012071 phase Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- 229910000805 Pig iron Inorganic materials 0.000 description 1
- 238000003723 Smelting Methods 0.000 description 1
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 1
- 239000006227 byproduct Substances 0.000 description 1
- 229910052918 calcium silicate Inorganic materials 0.000 description 1
- 235000012241 calcium silicate Nutrition 0.000 description 1
- JHLNERQLKQQLRZ-UHFFFAOYSA-N calcium silicate Chemical compound [Ca+2].[Ca+2].[O-][Si]([O-])([O-])[O-] JHLNERQLKQQLRZ-UHFFFAOYSA-N 0.000 description 1
- BCAARMUWIRURQS-UHFFFAOYSA-N dicalcium;oxocalcium;silicate Chemical compound [Ca+2].[Ca+2].[Ca]=O.[O-][Si]([O-])([O-])[O-] BCAARMUWIRURQS-UHFFFAOYSA-N 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009851 ferrous metallurgy Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000007791 liquid phase Substances 0.000 description 1
- 239000000395 magnesium oxide Substances 0.000 description 1
- CPLXHLVBOLITMK-UHFFFAOYSA-N magnesium oxide Inorganic materials [Mg]=O CPLXHLVBOLITMK-UHFFFAOYSA-N 0.000 description 1
- AXZKOIWUVFPNLO-UHFFFAOYSA-N magnesium;oxygen(2-) Chemical compound [O-2].[Mg+2] AXZKOIWUVFPNLO-UHFFFAOYSA-N 0.000 description 1
- WPBNNNQJVZRUHP-UHFFFAOYSA-L manganese(2+);methyl n-[[2-(methoxycarbonylcarbamothioylamino)phenyl]carbamothioyl]carbamate;n-[2-(sulfidocarbothioylamino)ethyl]carbamodithioate Chemical compound [Mn+2].[S-]C(=S)NCCNC([S-])=S.COC(=O)NC(=S)NC1=CC=CC=C1NC(=S)NC(=O)OC WPBNNNQJVZRUHP-UHFFFAOYSA-L 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 230000001590 oxidative effect Effects 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 239000010703 silicon Substances 0.000 description 1
- 229910052710 silicon Inorganic materials 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000009628 steelmaking Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000011593 sulfur Substances 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 229910021534 tricalcium silicate Inorganic materials 0.000 description 1
- 235000019976 tricalcium silicate Nutrition 0.000 description 1
- 230000007306 turnover Effects 0.000 description 1
- 229910000859 α-Fe Inorganic materials 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N25/00—Investigating or analyzing materials by the use of thermal means
- G01N25/02—Investigating or analyzing materials by the use of thermal means by investigating changes of state or changes of phase; by investigating sintering
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N5/00—Analysing materials by weighing, e.g. weighing small particles separated from a gas or liquid
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- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating And Analyzing Materials By Characteristic Methods (AREA)
Abstract
The invention relates to a high-efficiency detection method of particle steel, which comprises the following steps: sampling the particle steel for three times, determining a mass difference coefficient Z according to a sample ratio, processing the sample to ensure the representativeness of the sample, sieving the sample to be divided into a coarse material group, an oversize material group and an undersize material group, measuring the sample groups, determining an irregular coefficient G of the particle steel, estimating an estimated particle steel water yield Y0 according to a preset rule, judging whether the estimated water yield Y0 can be used as a final total water yield, if not, melting the particle steel, accurately adjusting the current and the melting time of a high-frequency reaction furnace in the melting process, ensuring the melting effect and determining the particle steel water yield.
Description
Technical Field
The invention belongs to the field of detection methods, and particularly relates to a high-efficiency detection method for particle steel.
Background
The steel grain is a by-product in the steel-making process, it is made up of various oxides formed by oxidizing impurities in pig iron, such as silicon, manganese, phosphorus and sulfur, etc. in the smelting process and salts produced by the reaction of these oxides with solvent, and the steel slag contains several useful components: 2 to 8 percent of metallic iron, 40 to 60 percent of calcium oxide, 3 to 10 percent of magnesium oxide and 1 to 8 percent of manganese oxide, so the steel can be used as a ferrous metallurgy raw material, the mineral composition of the particle steel is mainly tricalcium silicate, and then dicalcium silicate, RO phase, dicalcium ferrite and free calcium oxide are added, the steel slag is clinker and is remelted phase, the melting temperature is low, the liquid phase is formed early and has good fluidity when being remelted, therefore, the recycling problem of the particle steel is more and more emphasized, wherein, the detection of the components of the particle steel is more and more important, the water yield and the components of the particle steel are important parameters representing the properties of the particle steel, and the traditional technology has the following problems on the water yield and the component detection of the particle steel,
1. in the traditional method, samples in the particle steel detection process are distinguished, so that the water yield of the obtained particle steel has errors;
2. the traditional method does not provide a method for quickly estimating the water yield of the particle steel;
3. in the traditional method, the melting process of the particle steel is not adjusted in the melting process, so that the final detection result has errors.
Disclosure of Invention
The present invention is directed to solving the above problems, and therefore the present invention provides a method for efficiently detecting a particle steel, comprising:
sampling every 50cm in a granular steel material pile by using the same sampling tool for three times, respectively measuring the weight of the sampled samples of three times, marking as m1, m2 and m3, simultaneously, uniformly mixing the granular steel sampled for three times, pouring the mixture into a detection box, flattening the upper surface of the granular steel stacked in the detection box, measuring the stacking height H of the granular steel, calculating the irregular coefficient G of the granular steel,
wherein:
representing preset parameters, S representing the bottom area of the detection box, M representing the weight of the particle steel, removing a certain amount of particle steel according to the irregularity coefficient G to form a final particle steel sample group to be detected, judging whether a particle steel sample is removed according to the irregularity coefficient G, removing an upper layer sample or/and a lower layer sample in the detection box according to a preset mode, and forming a final particle steel sample group to be detected;
step two, sieving the final particle steel sample group to be detected formed in the step one, and screening and grouping the sieved particle steel into a coarse material group, an oversize material group and an undersize material group;
step three, respectively putting the granulated steels grouped in the step one into a detection box for weighing, flattening the upper surface of the granulated steel stacked in the detection box, recording the weighed weight M1, M2 and M3 of each group of granulated steel, wherein M1 represents the weighed weight of a coarse material group, M2 represents the weighed weight of an oversize material group, M3 represents the weighed weight of an undersize material group, and recording the stacking heights H1, H2 and H3 of each group of granulated steel in the detection box, wherein H1 represents the stacking height of the coarse material group, H2 represents the stacking height of the oversize material group, and H3 represents the stacking height of the undersize material group; estimating and calculating the water yield of the particle steel to be detected according to the weighed weight and the stacking height to obtain an estimated water yield Y0, and judging whether the estimated water yield can be used as the formal total water yield YGeneral assembly；
Step four, heating the particle steel in the detection box body in a segmented manner by using the high-audio induction furnace, dividing a heating time period, and adjusting the current of the high-audio induction furnace in the heating time period in real time to prevent splashing in the heating process;
step five, after the segmented heating is finished, closing the high-audio frequency reaction furnace, and calculating the water yield of the material screening group, the material screening group and the material screening group according to the following formulas;
wherein MT represents the weight of the iron block left after heating, and M represents the weighing weight; and calculating the total water yield according to the following formula, and sending the molten particle steel residues to a laboratory for testing.
Wherein: m1 represents the grain steel mass of the coarse material group, M2 represents the grain steel mass of the oversize material group, M3 represents the grain steel mass of the undersize material group, Y1 represents the water yield of the coarse material group, Y2 represents the water yield of the oversize material group, and Y3 represents the water yield of the undersize material group.
Further, when a preset amount of particle steel is removed according to the irregular coefficient G, a mass difference coefficient Z needs to be calculated in advance according to the sample mass of the three times of sampling,
before the first step is carried out, a particle steel adjusting matrix T (T1, T2, T3 and T4) is required to be preset, wherein T1 represents the adjusting height of the first-grade particle steel, T2 represents the adjusting height of the second-grade particle steel, T3 represents the adjusting height of the third-grade particle steel, T4 represents the adjusting height of the fourth-grade particle steel, T4> T3> T2> T1, and meanwhile, preset detection parameters Z01, Z02 and Z03 are set, whether the particle steel sample to be detected needs to be removed and the removed height are judged according to the mass difference Z and preset detection parameters Z01, ZO2 and Z03, and during adjustment:
when Z is less than or equal to Z01, removing the height of the sample to be detected, and selecting the first-grade particle steel to adjust the height T1;
when Z01 is larger than or equal to Z02, removing the height of the sample to be detected, and selecting the second-grade grain steel to adjust the height T2;
when Z02 is more than or equal to Z03, the height of the sample to be detected is removed, and the third grade particle steel is selected to adjust the height T3
And when the Z is more than or equal to Z03, removing the height of the sample to be detected, and selecting fourth grade particle steel to adjust the height T4.
Further, before the third step, an irregular coefficient standard parameter G0 needs to be preset, the standard parameter G0 is an average value obtained by sampling and calculating the irregular coefficient G of the particulate steel for multiple times in advance, irregular coefficient comparison parameters G01 and G02 are preset, G01 is less than 0, G02 is greater than 0, an irregular coefficient difference value GZ is calculated, GZ is G-G0, when the sample to be detected is removed, after the height is selected to be removed, according to the irregular coefficient G of the particulate steel, when the sample to be detected is determined to be removed, the upper layer or/and the lower layer of the sample to be removed are removed to determine the final sample to be detected, and when the determination:
when GZ is less than or equal to G01, judging that the upper layer sample of the sample to be detected needs to be removed;
g01 is more than or equal to G02 and the sample to be detected does not need to be removed;
and when the GZ is larger than or equal to G02, judging that the lower layer sample needs to be removed from the sample to be detected.
Further, before the third step, an estimated parameter matrix K (K1, K2, K3, K4) is preset, wherein K1 represents 70% of the first estimated parameter value, K2 represents 77% of the second estimated parameter value, K3 represents 84% of the third estimated parameter value, K4 represents 94% of the fourth estimated parameter value, the proportion of each group is measured after the grouping is completed, the initial parameter value is determined, first, the proportion Y1 of the coarse material group is calculated,
if Y1 is not more than YO1, selecting a first parameter value K1 as an initial parameter value, and further selecting a compensation parameter;
if Y01 is more than Y1 and less than or equal to YO2, selecting a first parameter value K2 as an initial parameter value, and further selecting a compensation parameter;
if Y02 is more than Y1 and less than or equal to YO3, selecting a first parameter value K3 as an initial parameter value, and further selecting a compensation parameter;
if Y1 is larger than or equal to Y03, the first parameter value K3 is selected as the initial parameter value, and further a compensation parameter is selected.
Further, before the fourth step, an i-th compensation parameter matrix Bi (Bi1, Bi2, Bi3) is preset, i is 1,2,3,
wherein Bi1 represents the 1 st parameter of the ith grade, Bi2 represents the 2 nd parameter of the ith grade, and Bi3 represents the 3 rd parameter of the ith grade, the weight ratio of the material feeding on the sieve group to the material feeding off the sieve group is firstly calculated
And the weight ratio is
Comparing the parameters with the parameters in the compensation parameter matrix B to determine compensation parameters, and when determining the compensation parameters:
when the initial parameter value is the first parameter value K1, a first compensation parameter matrix B1 is selected as a comparison matrix, wherein:
when in use
When the compensation parameter is selected, the 1 st grade 1 parameter B11 is used as the compensation parameter;
when in use
When the compensation parameter is selected, the 1 st level 2 parameter B12 is used as the compensation parameter;
when in use
When the 1 st grade 3 parameter B13 is selected as the compensation parameterCounting;
by analogy, when the initial parameter value is the ith parameter value Ki, i is 1,2,3, 4, the ith compensation parameter matrix Bi is selected as the contrast matrix, wherein:
when in use
When the current is measured, the 1 st parameter Bi1 of the ith grade is selected as a compensation parameter;
when in use
When the current is measured, the ith grade 2 parameter Bi1 is selected as a compensation parameter;
when in use
When the current is measured, the ith grade 3 parameter Bi2 is selected as a compensation parameter;
calculating the final estimated coefficient D, D equal to the compensation parameter plus the initial parameter value, and calculating the estimated water yield Y0 according to the following formula,
wherein: h1 represents the stacking height of the coarse material group, H2 represents the stacking height of the oversize material group, H3 represents the stacking height of the undersize material group, M1 represents the weighed weight of the coarse material group, M2 represents the weighed weight of the oversize material group, M3 represents the weighed weight of the undersize material group, G0 represents a standard parameter which is a preset value, S represents the bottom area of the detection box, and D represents a final estimation coefficient.
Further, when determining whether the estimated water yield Y0 can be regarded as a formal water yield in the third step, a comparison parameter Z00 is preset, and a water yield comparison parameter matrix F (F1, F2... Fn) is preset, wherein F4 represents a 1 st comparison parameter matrix, F2 represents a 2 nd comparison parameter matrix, Fn represents an nth comparison parameter matrix, and for an ith comparison parameter matrix Fi (Fi1, Fi2), i1, 2.. n, Fi1 represents an irregular coefficient G fluctuation range, Fi2 represents a particle steel quality m fluctuation range, when determining whether the estimated water yield Y0 can be regarded as a formal water yield in the third step, the particle steel quality m is compared with a particle steel quality m fluctuation range Fi2 in the ith comparison parameter matrix Fi1, Fi2, and if the particle steel quality m belongs to any particle steel quality m range Fi2, the irregular coefficient G of the particle steel belongs to the fluctuation range Fi1 of the irregular coefficient G, and the quality difference coefficient Z is smaller than the preset contrast parameter Z00, so that the estimated water yield Y0 can be used as the formal water yield.
Further, in the fourth step, when the heating period is divided, a preheating period and a melting period are divided, the preheating period is used for preheating the granular steel in the crucible, a preheating current matrix Ii (Ii1, Ii2, Ii3, Ii4) and an ith preheating current on-time matrix Ei are required to be set before the fourth step is carried out,
for the ith preheating current intensity matrix Ii, Ii (I1, I2, I3, I4), wherein Ii1 is a preset preheating first current intensity, Ii2 is a preset preheating second current intensity, Ii3 is a preset preheating third current intensity, and Ii4 is a preset preheating fourth current intensity, each of the preset preheating current intensities is increased in a descending order;
for an ith preheating current starting time matrix Ei, Ei (Ei1, Ei2, Ei3, Ei4), wherein Ei1 presets a first preheating current starting time, Ei2 presets a second preheating current starting time, Ei3 presets a third preheating current starting time, Ei4 presets a fourth preheating current starting time, and all the preset preheating current starting times are sequentially and progressively decreased;
when the particle steel is preheated, for a coarse material group, determining a preheating parameter of a high-frequency reaction furnace according to data in a 1 st preheating current intensity matrix I1 and a 1 st preheating current starting time matrix E1, for an oversize material group, determining a preheating parameter of the high-frequency reaction furnace according to data in a 2 nd preheating current intensity matrix I2 and a 2 nd preheating current starting time matrix E2, for an undersize material group, determining a preheating parameter of the high-frequency reaction furnace according to data in a 3 rd preheating current intensity matrix I3 and a 3 rd preheating current starting time matrix E3, comparing the quality of the particle steel of different groups with preset parameters M01, MO2 and M03 to obtain preheating current intensity and preheating current starting time:
when M is less than or equal to M1, selecting Ii1 from an Ii matrix as the intensity of the preheating current and selecting Ei1 from an Ei matrix as the starting duration of the preheating current;
when M1 is larger than M and is not larger than M2, selecting Ii2 from the Ii matrix as the intensity of the preheating current and selecting Ei2 from the Ei matrix as the starting duration of the preheating current;
when M2 is larger than M and is not larger than M3, selecting Ii3 from the Ii matrix as the intensity of the preheating current and selecting Ei3 from the Ei matrix as the starting duration of the preheating current;
when M3 is larger than M and is not larger than M4, selecting Ii4 from the Ii matrix as the intensity of the preheating current and selecting Ei4 from the Ei matrix as the starting duration of the preheating current;
and when the selection of the preheating current intensity and the preheating current starting time length is finished respectively, corresponding operation is executed on the high-audio induction furnace.
Further, in the fourth step, during the melting period, when melting the particulate steel, a minimum surface slag-steel area ratio s0 and an ith melting time matrix Ti (Ti1, Ti2, Ti3, Ti4) with i ═ 1,2,3 is established, where Ti1 is a preset first melting time, Ti2 is a preset second melting time, Ti3 is a preset third melting time, Ti4 is a preset fourth melting time, Ti4> Ti3> Ti2> Ti1, after completion of the establishment, the melting time of the particulate steel is determined by comparing the weight M of the particulate steel with preset parameters M01, MO2, M03 and selecting data in the ith melting time matrix Ti (Ti1, Ti2, Ti3, Ti4), for a coarse material group, the melting time of the particulate steel is determined by using data in a 1 st melting time matrix T1, for a screen-on-screen material group, the melting time of the particulate steel is determined by using data in a T2, for the sieve group, the duration of the melting of the particle steel is determined from the data in the 3 rd melting time matrix T3, when:
when M is less than or equal to M1, setting the melting time of the high-audio-frequency induction furnace to the particle steel as Ti 1;
when M1 is more than M and less than or equal to M2, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 2;
when M2 is more than M and less than or equal to M3, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 3;
when M3 is more than M and less than or equal to M4, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 4.
Further, when the fourth step is carried out and whether the molten state of the granular steel meets the preset requirement is determined, a camera device is arranged to detect the melting condition of the granular steel in the crucible and the area ratio s of the surface slag steel of the granular steel in real time:
when the area ratio s of the slag steel on the surface of the particle steel is more than or equal to s0, judging that the particle steel is completely melted until the slag steel is separated, and closing the high-frequency induction furnace;
when the surface slag steel area ratio s of the particle steel is smaller than s0, judging that the particle steel is not completely melted until the slag steel is separated, and calculating the supplement melting time t according to the surface slag steel area ratio s of the particle steel:
wherein gamma is a compensation parameter of the re-melting time T, M represents the weight of the particle steel during melting, S represents the area ratio of slag steel, and T represents the melting time of the particle steel.
When the melting time passes t, the surface slag steel area ratio s 'of the particle steel in the crucible is detected again, and when s' < s0, the steps are repeated to supplement the melting of the particle steel until the particle steel is completely melted until the slag steel is separated.
Compared with the prior art, the method has the technical effects that the granular steel is sampled for three times and divided into a coarse material group, an oversize material group and an undersize material group, the mass difference coefficient Z is determined according to the sample ratio, then the sample is processed to ensure the representativeness of the sample, meanwhile, the sample group is measured to determine the granular steel irregularity coefficient G, the estimated granular steel water yield Y0 is estimated according to the preset rule, whether the estimated water yield Y0 can be used as the final total water yield is judged, if not, the granular steel is melted, the current and melting time of a high-frequency reaction furnace in the melting process are accurately adjusted to ensure the melting effect to determine the granular steel water yield, the method provided by the invention ensures the representativeness of the granular steel sample, and provides the granular steel irregularity coefficient G combined with the granular steel quality to obtain the estimated granular steel water yield, the estimation result is reliable and quick, meanwhile, different currents and melting times of the high-frequency reaction furnace are adopted according to the quality of the particle steel and the different particle steel sample groups according to the heating temperature of the particle steel in the melting process, the particle steel is guaranteed not to be over-melted or less melted, the accurate detection of the water yield of the particle steel is guaranteed, the follow-up chemical detection of the melted particle steel is guaranteed to be more accurate, and the far sample property of the particle steel is better kept.
Particularly, in the first step of the invention, a mass difference coefficient Z is calculated, a particle steel adjustment matrix T (T1, T2, T3 and T4) is preset, the fused sample is adjusted again through the mass difference of the three groups of samples, and samples with a certain height on the upper layer or the lower layer are removed.
Particularly, in the third step of the invention, the grouped rough material group, the oversize material group and the undersize material group are respectively processed, the irregularity coefficient G of the sample group is calculated, the estimated water yield is determined according to the estimated parameter matrix K (K1, K2, K3 and K4), and the influence of the irregularity degree and the size of the sample on the water yield of the particle steel is generally large.
Particularly, in the fourth step of the invention, for the condition that the estimated result is not in accordance with the expectation, the granular steel is melted, the preheating current matrix Ii (Ii1, Ii2, Ii3, Ii4) and the ith preheating current on-time matrix Ei are used, and the currents of the high-frequency reaction furnace, the melting time period and the time period of the melting time period in the melting process are adjusted according to the quality of the granular steel and the difference of the sample groups, so that the melting effect is ensured, the excessive melting or the insufficient melting is avoided, the sample property of the granular steel is maintained, and the final detection is more accurate.
Particularly, in the fourth step of the invention, the melting condition of the particle steel is detected, because the melting condition has more variables, the melting effect is ensured, the over-melting or the less-melting is avoided, the property of the particle steel sample is reserved, and the final detection is more accurate.
Drawings
FIG. 1 is a step diagram of a method for efficiently detecting particle steel according to an embodiment of the present invention;
FIG. 2 is a schematic view of a detection box according to an embodiment of the present invention.
Detailed Description
The above and further features and advantages of the present invention are described in more detail below with reference to the accompanying drawings.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, which is a step diagram of a method for efficiently detecting a particle steel according to an embodiment of the present invention, the method for efficiently detecting a particle steel according to the embodiment includes:
sampling every 50cm in a granular steel material pile by using the same sampling tool for three times, respectively measuring the weight of the sampled samples of three times, marking as m1, m2 and m3, simultaneously, uniformly mixing the granular steel sampled for three times, pouring the mixture into a detection box, flattening the upper surface of the granular steel stacked in the detection box, measuring the stacking height H of the granular steel, calculating the irregular coefficient G of the granular steel,
wherein:
representing preset parameters, S representing the bottom area of the detection box, M representing the weight of the particle steel, removing a certain amount of particle steel according to the irregularity coefficient G to form a final particle steel sample group to be detected, judging whether a particle steel sample is removed according to the irregularity coefficient G, removing an upper layer sample or/and a lower layer sample in the detection box according to a preset mode, and forming a final particle steel sample group to be detected;
step two, sieving the final particle steel sample group to be detected formed in the step one, and screening and grouping the sieved particle steel into a coarse material group, an oversize material group and an undersize material group;
step three, respectively putting the granulated steels grouped in the step one into a detection box for weighing, flattening the upper surface of the granulated steel stacked in the detection box, recording the weighed weight M1, M2 and M3 of each group of granulated steel, wherein M1 represents the weighed weight of a coarse material group, M2 represents the weighed weight of an oversize material group, M3 represents the weighed weight of an undersize material group, and recording the stacking heights H1, H2 and H3 of each group of granulated steel in the detection box, wherein H1 represents the stacking height of the coarse material group, H2 represents the stacking height of the oversize material group, and H3 represents the stacking height of the undersize material group; estimating and calculating the water yield of the particle steel to be detected according to the weighed weight and the stacking height to obtain an estimated water yield Y0, and judging whether the estimated water yield can be used as a formal water yield;
step four, heating the particle steel in the detection box body in a segmented manner by using the high-audio induction furnace, dividing a heating time period, and adjusting the current of the high-audio induction furnace in the heating time period in real time to prevent splashing in the heating process;
step five, after the segmented heating is finished, closing the high-audio frequency reaction furnace, and calculating the water yield of the material screening group, the material screening group and the material screening group according to the following formulas;
wherein MT represents the weight of the iron block left after heating, and M represents the weighing weight; and the total water yield is calculated according to the following formula,
wherein: m1 represents the grain steel mass of the coarse material group, M2 represents the grain steel mass of the oversize material group, M3 represents the grain steel mass of the undersize material group, Y1 represents the water yield of the coarse material group, Y2 represents the water yield of the oversize material group, and Y3 represents the water yield of the undersize material group.
Specifically, referring to fig. 2, which is a schematic view of a structure of a detection box according to an embodiment of the present invention, in the first step, when a predetermined amount of grain steel is removed according to the irregularity coefficient G, a mass difference coefficient Z needs to be calculated in advance according to the mass of the three sampling samples,
before the first step is carried out, a particle steel adjusting matrix T (T1, T2, T3 and T4) is required to be preset, wherein T1 represents the adjusting height of the first-grade particle steel, T2 represents the adjusting height of the second-grade particle steel, T3 represents the adjusting height of the third-grade particle steel, T4 represents the adjusting height of the fourth-grade particle steel, T4> T3> T2> T1, and meanwhile, preset detection parameters Z01, Z02 and Z03 are set, whether the particle steel sample to be detected needs to be removed and the removed height are judged according to the mass difference Z and preset detection parameters Z01, ZO2 and Z03, and during adjustment:
when Z is less than or equal to Z01, removing the height of the sample to be detected, and selecting the first-grade particle steel to adjust the height T1;
when Z01 is larger than or equal to Z02, removing the height of the sample to be detected, and selecting the second-grade grain steel to adjust the height T2;
when Z02 is more than or equal to Z03, the height of the sample to be detected is removed, and the third grade particle steel is selected to adjust the height T3
And when the Z is more than or equal to Z03, removing the height of the sample to be detected, and selecting fourth grade particle steel to adjust the height T4.
Specifically, an irregular coefficient standard parameter G0 needs to be preset before the first step is performed, the standard parameter G0 is an average value obtained by sampling and calculating the irregular coefficient G of the particulate steel for multiple times in advance, irregular coefficient comparison parameters G01 and G02 are preset, G01 is less than 0, G02 is greater than 0, an irregular coefficient difference value GZ is calculated, GZ is G-G0, when the sample to be detected is removed, the upper layer or/and the lower layer of the sample to be detected is removed when the sample to be detected is determined to be removed according to the irregular coefficient G of the particulate steel after the height is selected to be removed, so as to determine the final sample to be detected, and when the determination:
when GZ is less than or equal to G01, judging that the upper layer sample of the sample to be detected needs to be removed;
g01 is more than or equal to G02 and the sample to be detected does not need to be removed;
and when the GZ is larger than or equal to G02, judging that the lower layer sample needs to be removed from the sample to be detected.
Specifically, before the third step, an estimated parameter matrix K (K1, K2, K3, K4) is preset, where K1 represents a first estimated parameter value of 70%, K2 represents a second estimated parameter value of 77%, K3 represents a third estimated parameter value of 84%, and K4 represents a fourth estimated parameter value of 94%, the proportion of each group is measured after grouping is completed, an initial parameter value is determined, first, the proportion Y1 of the coarse material group is calculated,
if Y1 is not more than YO1, selecting a first parameter value K1 as an initial parameter value, and further selecting a compensation parameter;
if Y01 is more than Y1 and less than or equal to YO2, selecting a first parameter value K2 as an initial parameter value, and further selecting a compensation parameter;
if Y02 is more than Y1 and less than or equal to YO3, selecting a first parameter value K3 as an initial parameter value, and further selecting a compensation parameter;
if Y1 is not less than Y03, selecting a first parameter value K3 as an initial parameter value, and further selecting a compensation parameter;
specifically, before the third step, an i-th compensation parameter matrix Bi (Bi1, Bi2, Bi3) is preset, i is 1,2,3, where Bi1 represents an i-th grade 1-th parameter, Bi2 represents an i-th grade 2-th parameter, and Bi3 represents an i-th grade 3-th parameter, and the weight ratio of the oversize material group to the undersize material group is first calculated
And the weight ratio is
Comparing the parameters with the parameters in the compensation parameter matrix B to determine compensation parameters, and when determining the compensation parameters:
when the initial parameter value is the first parameter value K1, a first compensation parameter matrix B1 is selected as a comparison matrix, wherein:
when in use
When the compensation parameter is selected, the 1 st grade 1 parameter B11 is used as the compensation parameter;
when in use
When the compensation parameter is selected, the 1 st level 2 parameter B12 is used as the compensation parameter;
when in use
When the compensation parameter is selected, the 1 st level 3 parameter B13 is used as the compensation parameter;
by analogy, when the initial parameter value is the ith parameter value Ki, i is 1,2,3, 4, the ith compensation parameter matrix Bi is selected as the contrast matrix, wherein:
when in use
When the current is measured, the 1 st parameter Bi1 of the ith grade is selected as a compensation parameter;
when in use
When the current is measured, the ith grade 2 parameter Bi1 is selected as a compensation parameter;
when in use
When the compensation parameter is selected as the ith grade 3 parameter Bi 2.
Calculating the final estimated coefficient D, D equal to the compensation parameter plus the initial parameter value, and calculating the estimated water yield Y0 according to the following formula,
wherein: h1 represents the stacking height of the coarse material group, H2 represents the stacking height of the oversize material group, H3 represents the stacking height of the undersize material group, M1 represents the weighed weight of the coarse material group, M2 represents the weighed weight of the oversize material group, M3 represents the weighed weight of the undersize material group, G0 represents a standard parameter which is a preset value, S represents the bottom area of the detection box, and D represents a final estimation coefficient.
Specifically, when judging whether the estimated water yield Y0 can be the actual water yield in the third step, presetting a comparison parameter Z00 and presetting a water yield comparison parameter matrix F (F1, F2... Fn), wherein F1 represents a 1 st comparison parameter matrix, F2 represents a 2 nd comparison parameter matrix, Fn represents an nth comparison parameter matrix, and for an ith comparison parameter matrix Fi (Fi1, Fi2), i-1, 2.. n, wherein Fi1 represents an irregularity coefficient G fluctuation range, Fi2 represents a particle steel mass m fluctuation range, and when judging whether the estimated water yield Y0 can be the actual water yield in the third step, comparing the particle steel mass m with a particle steel mass m fluctuation range 2 in the ith comparison parameter matrix Fi (Fi1, Fi2), if the particle steel mass m belongs to any particle steel mass m range Fi2, and fluctuating further, and if the particle steel irregularity coefficient G belongs to the irregularity coefficient G fluctuation range Fi1 and the mass difference coefficient Z is less than the preset contrast parameter Z00, the estimated water yield Y0 can be judged as the formal water yield, and if the estimated water yield is not less than the formal water yield, the following steps are carried out.
Specifically, in the fourth step, when the heating period is divided, a preheating period and a melting period are divided, the preheating period is used for preheating the granular steel in the crucible, preheating current matrixes Ii (Ii1, Ii2, Ii3, Ii4) and Ei (Ei1, Ei2, Ei3, Ei4) are required to be arranged before the fourth step is carried out,
for the ith preheating current intensity matrix Ii, Ii (I1, I2, I3, I4), wherein Ii1 is a preset preheating first current intensity, Ii2 is a preset preheating second current intensity, Ii3 is a preset preheating third current intensity, and Ii4 is a preset preheating fourth current intensity, each of the preset preheating current intensities is increased in a descending order;
for an ith preheating current starting time matrix Ei, Ei (Ei1, Ei2, Ei3, Ei4), wherein Ei1 presets a first preheating current starting time, Ei2 presets a second preheating current starting time, Ei3 presets a third preheating current starting time, Ei4 presets a fourth preheating current starting time, and all the preset preheating current starting times are sequentially and progressively decreased;
when the particle steel is preheated, for a coarse material group, determining a preheating parameter of a high-frequency reaction furnace according to data in a 1 st preheating current intensity matrix I1 and a 1 st preheating current starting time matrix E1, for an oversize material group, determining a preheating parameter of the high-frequency reaction furnace according to data in a 2 nd preheating current intensity matrix I2 and a 2 nd preheating current starting time matrix E2, for an undersize material group, determining a preheating parameter of the high-frequency reaction furnace according to data in a 3 rd preheating current intensity matrix I3 and a 3 rd preheating current starting time matrix E3, comparing the quality of the particle steel of different groups with preset parameters M01, MO2 and M03 to obtain preheating current intensity and preheating current starting time:
when M is less than or equal to M1, selecting Ii1 from an Ii matrix as the intensity of the preheating current and selecting Ei1 from an Ei matrix as the starting duration of the preheating current;
when M1 is larger than M and is not larger than M2, selecting Ii2 from the Ii matrix as the intensity of the preheating current and selecting Ei2 from the Ei matrix as the starting duration of the preheating current;
when M2 is larger than M and is not larger than M3, selecting Ii3 from the Ii matrix as the intensity of the preheating current and selecting Ei3 from the Ei matrix as the starting duration of the preheating current;
when M3 is larger than M and is not larger than M4, selecting Ii4 from the Ii matrix as the intensity of the preheating current and selecting Ei4 from the Ei matrix as the starting duration of the preheating current;
and when the selection of the preheating current intensity and the preheating current starting time length is finished respectively, corresponding operation is executed on the high-audio induction furnace.
Specifically, in the melting period, when melting the granular steel, establishing a minimum surface slag steel area ratio s0 and an ith melting time matrix Ti (Ti1, Ti2, Ti3, Ti4), i ═ 1,2,3, where Ti1 is a preset first melting time, Ti2 is a preset second melting time, Ti3 is a preset third melting time, Ti4 is a preset fourth melting time, Ti4> Ti3> Ti2> Ti1, after completion of the establishment, comparing and selecting data in the ith melting time matrix Ti (Ti1, Ti2, Ti3, Ti4) according to a weight M of the granular steel and preset parameters M01, MO2, M03 to determine a melting time period of the granular steel, for a coarse material group, determining melting of the granular steel according to data in the melting time matrix T1 for a coarse material group, determining melting time of the granular steel according to data in the melting time matrix T632 for an oversize material group, determining data in the melting time matrix T639 according to a melting time matrix T3 for the steel, when the determination is carried out:
when M is less than or equal to M1, setting the melting time of the high-audio-frequency induction furnace to the particle steel as Ti 1;
when M1 is more than M and less than or equal to M2, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 2;
when M2 is more than M and less than or equal to M3, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 3;
when M3 is more than M and less than or equal to M4, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 4;
meanwhile, a camera device is arranged to detect the melting condition of the particle steel in the crucible and the surface slag steel area ratio s of the particle steel in real time:
when the area ratio s of the slag steel on the surface of the particle steel is more than or equal to s0, judging that the particle steel is completely melted until the slag steel is separated, and closing the high-frequency induction furnace;
when the surface slag steel area ratio s of the particle steel is smaller than s0, judging that the particle steel is not completely melted until the slag steel is separated, and calculating the supplement melting time t according to the surface slag steel area ratio s of the particle steel:
wherein gamma is a compensation parameter of the remelting time T, M represents the weight of the particle steel during melting, S represents the area ratio of slag steel, and T represents the melting time of the particle steel;
when the melting time passes t, the surface slag steel area ratio s 'of the particle steel in the crucible is detected again, and when s' < s0, the steps are repeated to supplement the melting of the particle steel until the particle steel is completely melted until the slag steel is separated.
Specifically, the detection box body applied in this embodiment is a common box body, and includes box body 1, bottom discharge ring 3 and pushing hands 2, 1 bottom of box body is provided with gravity sensor for detecting the weight of particulate steel, and 1 inner wall of box body is provided with the scale mark for observing the pile height of particulate steel, discharge ring 3 sets up 1 bottom of box body, its with handle 2 is connected, 1 size of box body with discharge ring size phase-match, and the bottom of box body leaves the breach that supplies discharge ring business turn over, so that discharge ring can slide in 1 bottom of box body, in order to release the material of 1 bottom of box body.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (8)
1. A high-efficiency detection method for particle steel is characterized by comprising the following steps:
sampling every 50cm in a granular steel material pile by using the same sampling tool for three times, respectively measuring the weight of the sampled samples of three times, marking as m1, m2 and m3, simultaneously, uniformly mixing the granular steel sampled for three times, pouring the mixture into a detection box, flattening the upper surface of the granular steel stacked in the detection box, measuring the stacking height H of the granular steel, calculating the irregular coefficient G of the granular steel,
wherein:
representing preset parameters, S representing the bottom area of the detection box, M representing the weight of the particle steel, removing a certain amount of particle steel according to the irregularity coefficient G to form a final particle steel sample group to be detected, judging whether a particle steel sample is removed according to the irregularity coefficient G, and removing an upper layer sample or/and a lower layer sample in the detection box according to a preset mode to form a final particle steel sample group to be detected;
step two, sieving the final particle steel sample group to be detected formed in the step one, and screening and grouping the sieved particle steel into a coarse material group, an oversize material group and an undersize material group;
step three, respectively putting the particle steel grouped in the step one into a detection box for weighing, flattening the upper surface of the particle steel accumulated in the detection box, and recordingRecording the weighed weight M1, M2 and M3 of each group of pellet steel, wherein M1 represents the weighed weight of a coarse material group, M2 represents the weighed weight of an oversize material group, M3 represents the weighed weight of an undersize material group, and recording the stacking height H1, H2 and H3 of each group of pellet steel in the detection box, wherein H1 represents the stacking height of the coarse material group, H2 represents the stacking height of the oversize material group, and H3 represents the stacking height of the undersize material group; estimating and calculating the water yield of the particle steel to be detected according to the weighed weight and the stacking height to obtain an estimated water yield Y0, and judging whether the estimated water yield can be used as the formal total water yield YGeneral assembly；
Step four, heating the particle steel in the crucible in a segmented manner by using a high-audio frequency induction furnace in the detection box body, dividing a heating time interval, and adjusting the current of the high-audio frequency induction furnace in the heating time interval in real time to prevent splashing in the heating process;
step five, after the segmented heating is finished, closing the high-audio reaction furnace, and calculating the water yield of the coarse material group, the oversize material group and the undersize material group according to the following formulas;
wherein MT represents the weight of the iron block left after heating, and M represents the weighing weight; and the total water yield is calculated according to the following formula, the melted particle steel residue is sent to a laboratory for testing,
wherein: m1 represents the grain steel mass of the coarse material group, M2 represents the grain steel mass of the oversize material group, M3 represents the grain steel mass of the undersize material group, Y1 represents the water yield of the coarse material group, Y2 represents the water yield of the oversize material group, and Y3 represents the water yield of the undersize material group.
2. The method according to claim 1, wherein in the first step, when a predetermined amount of grain steel is removed according to the irregularity coefficient G, a mass difference coefficient Z is calculated in advance according to the sample mass of the three samplings,
before the first step is carried out, a particle steel adjusting matrix T (T1, T2, T3 and T4) is required to be preset, wherein T1 represents the adjusting height of the first-grade particle steel, T2 represents the adjusting height of the second-grade particle steel, T3 represents the adjusting height of the third-grade particle steel, T4 represents the adjusting height of the fourth-grade particle steel, T4> T3> T2> T1, and meanwhile, preset detection parameters Z01, Z02 and Z03 are set, whether the particle steel sample to be detected needs to be removed and the removed height are judged according to the mass difference Z and preset detection parameters Z01, ZO2 and Z03, and during adjustment:
when Z is less than or equal to Z01, removing the height of the sample to be detected, and selecting the first-grade particle steel to adjust the height T1;
when Z01 is larger than or equal to Z02, removing the height of the sample to be detected, and selecting the second-grade grain steel to adjust the height T2;
when Z02 is more than or equal to Z03, the height of the sample to be detected is removed, and the third grade particle steel is selected to adjust the height T3
And when the Z is more than or equal to Z03, removing the height of the sample to be detected, and selecting fourth grade particle steel to adjust the height T4.
3. The method for efficiently detecting the particle steel as claimed in claim 1, wherein an irregularity coefficient standard parameter G0 is required to be preset before the third step, the standard parameter G0 is an average value obtained by sampling and calculating the irregularity coefficient G of the particle steel for a plurality of times in advance, irregularity coefficient comparison parameters G01 and G02 are preset, G01 is less than 0, G02>0, an irregularity coefficient difference GZ is calculated, and the GZ is equal to G-G0, when the sample to be detected is removed, the upper layer or/and the lower layer of the sample to be detected is removed to determine the final sample to be detected according to the irregularity coefficient G of the particle steel after the height is selected and removed, and when the determination:
when GZ is less than or equal to G01, judging that the upper layer sample of the sample to be detected needs to be removed;
g01 is more than or equal to G02 and the sample to be detected does not need to be removed;
and when the GZ is larger than or equal to G02, judging that the lower layer sample needs to be removed from the sample to be detected.
4. A high-efficiency detection method for particle steel as claimed in claim 3, wherein, before the third step, a pre-estimated parameter matrix K (K1, K2, K3, K4) is preset, wherein K1 represents a first pre-estimated parameter value of 70%, K2 represents a second pre-estimated parameter value of 77%, K3 represents a third pre-estimated parameter value of 84%, K4 represents a fourth pre-estimated parameter value of 94%, the proportion of each group is measured after the grouping is completed, the initial parameter value is determined, first, the proportion Y1 of the coarse material group is calculated,
if Y1 is not more than YO1, selecting a first parameter value K1 as an initial parameter value, and further selecting a compensation parameter;
if Y01 is more than Y1 and less than or equal to YO2, selecting a first parameter value K2 as an initial parameter value, and further selecting a compensation parameter;
if Y02 is more than Y1 and less than or equal to YO3, selecting a first parameter value K3 as an initial parameter value, and further selecting a compensation parameter;
if Y1 is larger than or equal to Y03, the first parameter value K3 is selected as the initial parameter value, and further a compensation parameter is selected.
5. The method for efficiently detecting the particle steel as claimed in claim 4, wherein, when determining whether the estimated water yield Y0 can be used as the formal water yield in the third step, a preset comparison parameter Z00 is provided, and a water yield comparison parameter matrix F (F1, F2... Fn) is provided, wherein F1 represents a 1 st comparison parameter matrix, F2 represents a 2 nd comparison parameter matrix.. Fn represents an nth comparison parameter matrix, and for an i-th comparison parameter matrix Fi (Fi1, Fi2), i is 1, 2.. n, wherein Fi1 represents an irregular coefficient G fluctuation range, Fi2 represents a fluctuation range of particle steel quality m, when determining whether the estimated water yield Y0 can be used as the formal water yield in the third step, the particle steel quality m is compared with a fluctuation range Fi2 in the i-th comparison parameter matrix (Fi 3625, Fi 733), and if the particle steel quality m belongs to any fluctuation range 2, and the irregular coefficient G of the particle steel belongs to the fluctuation range Fi1 of the irregular coefficient G, and the quality difference coefficient Z is less than the preset contrast parameter Z00, so that the estimated water yield Y0 can be used as the formal water yield.
6. The method for detecting the particle steel of claim 1, wherein in the fourth step, the heating period is divided into a preheating period and a melting period, the preheating period is used for preheating the particle steel in the crucible, and the fourth step is preceded by setting preheating current matrixes Ii (Ii1, Ii2, Ii3, Ii4) and Ei (Ei1, Ei2, Ei3, Ei4),
for the ith preheating current intensity matrix Ii, Ii (I1, I2, I3, I4), wherein Ii1 is a preset preheating first current intensity, Ii2 is a preset preheating second current intensity, Ii3 is a preset preheating third current intensity, and Ii4 is a preset preheating fourth current intensity, each of the preset preheating current intensities is increased in a descending order;
for an ith preheating current starting time matrix Ei, Ei (Ei1, Ei2, Ei3, Ei4), wherein Ei1 presets a first preheating current starting time, Ei2 presets a second preheating current starting time, Ei3 presets a third preheating current starting time, Ei4 presets a fourth preheating current starting time, and all the preset preheating current starting times are sequentially and progressively decreased;
when the particle steel is preheated, for a coarse material group, determining a preheating parameter of a high-frequency reaction furnace according to data in a 1 st preheating current intensity matrix I1 and a 1 st preheating current starting time matrix E1, for an oversize material group, determining a preheating parameter of the high-frequency reaction furnace according to data in a 2 nd preheating current intensity matrix I2 and a 2 nd preheating current starting time matrix E2, for an undersize material group, determining a preheating parameter of the high-frequency reaction furnace according to data in a 3 rd preheating current intensity matrix I3 and a 3 rd preheating current starting time matrix E3, comparing the quality of the particle steel of different groups with preset parameters M01, MO2 and M03 to obtain preheating current intensity and preheating current starting time:
when M is less than or equal to M1, selecting Ii1 from an Ii matrix as the intensity of the preheating current and selecting Ei1 from an Ei matrix as the starting duration of the preheating current;
when M1 is larger than M and is not larger than M2, selecting Ii2 from the Ii matrix as the intensity of the preheating current and selecting Ei2 from the Ei matrix as the starting duration of the preheating current;
when M2 is larger than M and is not larger than M3, selecting Ii3 from the Ii matrix as the intensity of the preheating current and selecting Ei3 from the Ei matrix as the starting duration of the preheating current;
when M3 is larger than M and is not larger than M4, selecting Ii4 from the Ii matrix as the intensity of the preheating current and selecting Ei4 from the Ei matrix as the starting duration of the preheating current;
and when the selection of the preheating current intensity and the preheating current starting time length is finished respectively, corresponding operation is executed on the high-audio induction furnace.
7. A method for efficient testing of particle steel as claimed in claim 6, wherein during the melting period in said fourth step, when melting said particle steel, the minimum surface slag area ratio s0 and the ith melting time matrix Ti (Ti1, Ti2, Ti3, Ti4) i-1, 2,3 are established, wherein Ti1 is the preset first melting time, Ti2 is the preset second melting time, Ti3 is the preset third melting time, Ti4 is the preset fourth melting time, Ti4> Ti3> Ti2> Ti1, after completion of the establishment, the melting time of the particle steel is determined by comparing the weight M of the particle steel with preset parameters M01, MO2, M03 and selecting the data in said ith melting time matrix Ti (Ti1, Ti2, Ti3, Ti 5) to determine the melting time of the particle steel, and for the group of coarse material, the melting time matrix T1 is determined by selecting the melting time matrix T2 for the group of coarse material, for the sieve group, the duration of the melting of the particle steel is determined from the data in the 3 rd melting time matrix T3, when:
when M is less than or equal to M1, setting the melting time of the high-audio-frequency induction furnace to the particle steel as Ti 1;
when M1 is more than M and less than or equal to M2, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 2;
when M2 is more than M and less than or equal to M3, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 3;
when M3 is more than M and less than or equal to M4, the melting time of the high-audio-frequency induction furnace on the particle steel is set to be Ti 4.
8. The method for efficiently detecting the granular steel according to claim 7, wherein when the fourth step is performed and whether the molten state of the granular steel meets the preset requirement is determined, an image pickup device is arranged to detect the melting condition of the granular steel in the crucible and the area ratio s of the surface slag steel of the granular steel in real time:
when the area ratio s of the slag steel on the surface of the particle steel is more than or equal to s0, judging that the particle steel is completely melted until the slag steel is separated, and closing the high-frequency induction furnace;
when the surface slag steel area ratio s of the particle steel is smaller than s0, judging that the particle steel is not completely melted until the slag steel is separated, and calculating the supplement melting time t according to the surface slag steel area ratio s of the particle steel:
wherein gamma is a compensation parameter of the remelting time T, M represents the weight of the particle steel during melting, S represents the area ratio of slag steel, and T represents the melting time of the particle steel;
when the melting time passes t, the surface slag steel area ratio s 'of the particle steel in the crucible is detected again, and when s' < s0, the steps are repeated to supplement the melting of the particle steel until the particle steel is completely melted until the slag steel is separated.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2128619A1 (en) * | 1971-03-05 | 1972-10-20 | British Steel Corp | |
| CN102854080A (en) * | 2012-09-19 | 2013-01-02 | 武钢集团昆明钢铁股份有限公司 | Method for measuring iron content in steel slag |
| CN103528536A (en) * | 2013-10-16 | 2014-01-22 | 哈尔滨工程大学 | Ship deformation measurement method based on optical fiber gyro inertia navigation systems |
| CN103558108A (en) * | 2013-07-25 | 2014-02-05 | 南京钢铁股份有限公司 | Method for determining metal iron content in converter steel slag |
| CN103614616A (en) * | 2013-11-12 | 2014-03-05 | 唐山曹妃甸区通鑫再生资源回收利用有限公司 | Steel-making cooling cold material-remelted steel and preparation method thereof |
| CN108285948A (en) * | 2018-02-11 | 2018-07-17 | 新兴铸管阜康能源有限公司 | The method for improving carburant absorptivity in synthetic cast iron technique |
| CN108760558A (en) * | 2018-04-10 | 2018-11-06 | 唐山新宝泰钢铁有限公司 | For detecting the method for ingredient and water rate measurement method in steel hot wafering |
-
2020
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2128619A1 (en) * | 1971-03-05 | 1972-10-20 | British Steel Corp | |
| CN102854080A (en) * | 2012-09-19 | 2013-01-02 | 武钢集团昆明钢铁股份有限公司 | Method for measuring iron content in steel slag |
| CN103558108A (en) * | 2013-07-25 | 2014-02-05 | 南京钢铁股份有限公司 | Method for determining metal iron content in converter steel slag |
| CN103528536A (en) * | 2013-10-16 | 2014-01-22 | 哈尔滨工程大学 | Ship deformation measurement method based on optical fiber gyro inertia navigation systems |
| CN103614616A (en) * | 2013-11-12 | 2014-03-05 | 唐山曹妃甸区通鑫再生资源回收利用有限公司 | Steel-making cooling cold material-remelted steel and preparation method thereof |
| CN108285948A (en) * | 2018-02-11 | 2018-07-17 | 新兴铸管阜康能源有限公司 | The method for improving carburant absorptivity in synthetic cast iron technique |
| CN108760558A (en) * | 2018-04-10 | 2018-11-06 | 唐山新宝泰钢铁有限公司 | For detecting the method for ingredient and water rate measurement method in steel hot wafering |
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Denomination of invention: An Efficient Detection Method for Particle Steel Granted publication date: 20220318 Pledgee: Agricultural Bank of China Tangshan High tech Development Zone Branch Pledgor: TANGSHAN CAOFEIDIAN AREA TONGXIN RENEWABLE RESOURCE RECYCLING UTILIZATION Co.,Ltd. Registration number: Y2024980017252 |