CN117821744B - Preparation method of weathering steel for iron tower - Google Patents
Preparation method of weathering steel for iron tower Download PDFInfo
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- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 title claims abstract description 68
- 229910052742 iron Inorganic materials 0.000 title claims abstract description 34
- 229910000870 Weathering steel Inorganic materials 0.000 title claims abstract description 21
- 238000002360 preparation method Methods 0.000 title claims abstract description 19
- 238000003723 Smelting Methods 0.000 claims abstract description 110
- PXHVJJICTQNCMI-UHFFFAOYSA-N nickel Substances [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 claims abstract description 59
- 230000009467 reduction Effects 0.000 claims abstract description 51
- 229910052759 nickel Inorganic materials 0.000 claims abstract description 47
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 30
- 239000010959 steel Substances 0.000 claims abstract description 30
- 238000001035 drying Methods 0.000 claims abstract description 18
- 238000010891 electric arc Methods 0.000 claims abstract description 16
- 238000004070 electrodeposition Methods 0.000 claims abstract description 16
- 239000007788 liquid Substances 0.000 claims abstract description 15
- 229910000863 Ferronickel Inorganic materials 0.000 claims abstract description 14
- 239000000956 alloy Substances 0.000 claims abstract description 13
- 229910045601 alloy Inorganic materials 0.000 claims abstract description 11
- 239000003638 chemical reducing agent Substances 0.000 claims abstract description 11
- 239000002994 raw material Substances 0.000 claims abstract description 11
- 238000007670 refining Methods 0.000 claims abstract description 10
- 229910001030 Iron–nickel alloy Inorganic materials 0.000 claims abstract description 7
- 238000012216 screening Methods 0.000 claims abstract description 7
- 238000005266 casting Methods 0.000 claims abstract description 6
- 239000006184 cosolvent Substances 0.000 claims abstract description 6
- 239000000571 coke Substances 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 43
- 230000008569 process Effects 0.000 claims description 20
- 239000013598 vector Substances 0.000 claims description 16
- CPLXHLVBOLITMK-UHFFFAOYSA-N Magnesium oxide Chemical compound [Mg]=O CPLXHLVBOLITMK-UHFFFAOYSA-N 0.000 claims description 15
- 238000005299 abrasion Methods 0.000 claims description 14
- UQSXHKLRYXJYBZ-UHFFFAOYSA-N Iron oxide Chemical compound [Fe]=O UQSXHKLRYXJYBZ-UHFFFAOYSA-N 0.000 claims description 12
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 12
- 239000000395 magnesium oxide Substances 0.000 claims description 12
- 239000000292 calcium oxide Substances 0.000 claims description 11
- ODINCKMPIJJUCX-UHFFFAOYSA-N calcium oxide Inorganic materials [Ca]=O ODINCKMPIJJUCX-UHFFFAOYSA-N 0.000 claims description 11
- 230000001174 ascending effect Effects 0.000 claims description 10
- 238000002844 melting Methods 0.000 claims description 7
- 230000008018 melting Effects 0.000 claims description 7
- 229910004298 SiO 2 Inorganic materials 0.000 claims description 6
- BRPQOXSCLDDYGP-UHFFFAOYSA-N calcium oxide Chemical compound [O-2].[Ca+2] BRPQOXSCLDDYGP-UHFFFAOYSA-N 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000013506 data mapping Methods 0.000 claims description 6
- 239000000377 silicon dioxide Substances 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 6
- AXZKOIWUVFPNLO-UHFFFAOYSA-N magnesium;oxygen(2-) Chemical compound [O-2].[Mg+2] AXZKOIWUVFPNLO-UHFFFAOYSA-N 0.000 claims description 5
- TWNQGVIAIRXVLR-UHFFFAOYSA-N oxo(oxoalumanyloxy)alumane Chemical compound O=[Al]O[Al]=O TWNQGVIAIRXVLR-UHFFFAOYSA-N 0.000 claims description 5
- 235000012239 silicon dioxide Nutrition 0.000 claims description 5
- 238000000354 decomposition reaction Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 239000000203 mixture Substances 0.000 claims description 4
- 230000035772 mutation Effects 0.000 claims description 4
- 230000005856 abnormality Effects 0.000 claims description 3
- 239000003245 coal Substances 0.000 claims description 3
- 238000002156 mixing Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 2
- 229910052782 aluminium Inorganic materials 0.000 claims 1
- PNEYBMLMFCGWSK-UHFFFAOYSA-N aluminium oxide Inorganic materials [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 claims 1
- 239000000463 material Substances 0.000 abstract description 9
- 235000008733 Citrus aurantifolia Nutrition 0.000 abstract description 4
- 235000011941 Tilia x europaea Nutrition 0.000 abstract description 4
- 239000004571 lime Substances 0.000 abstract description 4
- 238000004458 analytical method Methods 0.000 abstract description 2
- 238000006722 reduction reaction Methods 0.000 description 42
- 238000005516 engineering process Methods 0.000 description 8
- 230000008859 change Effects 0.000 description 7
- 238000009749 continuous casting Methods 0.000 description 7
- 239000003795 chemical substances by application Substances 0.000 description 6
- 238000011049 filling Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 5
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 4
- 229910052717 sulfur Inorganic materials 0.000 description 4
- 239000011593 sulfur Substances 0.000 description 4
- 230000007797 corrosion Effects 0.000 description 3
- 238000005260 corrosion Methods 0.000 description 3
- 230000003009 desulfurizing effect Effects 0.000 description 3
- 239000011504 laterite Substances 0.000 description 3
- 229910001710 laterite Inorganic materials 0.000 description 3
- 229910052751 metal Inorganic materials 0.000 description 3
- 239000002184 metal Substances 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 238000007664 blowing Methods 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 238000001816 cooling Methods 0.000 description 2
- 239000012535 impurity Substances 0.000 description 2
- 229910052500 inorganic mineral Inorganic materials 0.000 description 2
- UGKDIUIOSMUOAW-UHFFFAOYSA-N iron nickel Chemical compound [Fe].[Ni] UGKDIUIOSMUOAW-UHFFFAOYSA-N 0.000 description 2
- 238000003064 k means clustering Methods 0.000 description 2
- 239000011707 mineral Substances 0.000 description 2
- 230000003647 oxidation Effects 0.000 description 2
- 238000007254 oxidation reaction Methods 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000000087 stabilizing effect Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 229910000975 Carbon steel Inorganic materials 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- 241001062472 Stokellia anisodon Species 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000010962 carbon steel Substances 0.000 description 1
- 238000005536 corrosion prevention Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 229910021645 metal ion Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 229910052755 nonmetal Inorganic materials 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 150000003568 thioethers Chemical class 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22B—PRODUCTION AND REFINING OF METALS; PRETREATMENT OF RAW MATERIALS
- C22B1/00—Preliminary treatment of ores or scrap
- C22B1/02—Roasting processes
-
- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22C—ALLOYS
- C22C1/00—Making non-ferrous alloys
- C22C1/02—Making non-ferrous alloys by melting
- C22C1/023—Alloys based on nickel
-
- 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
- Y02P10/00—Technologies related to metal processing
- Y02P10/20—Recycling
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- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Materials Engineering (AREA)
- Mechanical Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Manufacturing & Machinery (AREA)
- Manufacture And Refinement Of Metals (AREA)
Abstract
The invention relates to the technical field of alloy material preparation, and provides a preparation method of weather-resistant steel for an iron tower, which comprises the following steps: taking laterite-nickel ore as a raw material for preparing nickel-iron alloy, crushing and screening the laterite-nickel ore, and conveying the crushed and screened laterite-nickel ore to a rotary drying kiln for drying treatment; taking coke as a reducing agent, lime as a cosolvent, and conveying the laterite-nickel ore after the drying treatment, the reducing agent and the cosolvent to a rotary kiln for baking-pre-reduction treatment; transferring the baked and pre-reduced material into an electric arc furnace for smelting to obtain ferronickel alloy liquid, and adjusting the electrode position in real time according to the characteristic analysis results of current density, temperature and electrode resistance data of the electric arc furnace in the smelting stage to obtain the ferronickel alloy liquid with full smelting; refining and casting the ferronickel alloy liquid to obtain the weather-resistant steel for preparing the iron tower. According to the invention, the electrode position in the ferronickel smelting stage is adaptively adjusted, so that the preparation quality of the weathering steel is improved.
Description
Technical Field
The invention relates to the technical field of alloy material preparation, in particular to a preparation method of weather-resistant steel for an iron tower.
Background
The electric iron tower is an important infrastructure for supporting and transmitting electric power lines, at present, the iron tower in the electric power industry adopts common carbon steel with the marks of Q235, Q355, Q420 and the like, and is subjected to corrosion prevention treatment in a galvanization mode after manufacturing, a large amount of electric energy and fuel can be consumed in the galvanization process, extra carbon emission pollution is brought, and the double carbon target is not met. Therefore, the weather-resistant steel is used as the preparation material of the electric iron tower, so that the bearing capacity of the electric iron tower can be increased, and the electric iron tower has better and easier corrosion resistance.
The preparation of weathering steel generally requires adding nickel element into the formulation of common steel, improving the corrosion resistance of the steel, ensuring the stress of the steel and comprehensively improving the physical properties and chemical properties of the steel in all aspects. The nickel-iron alloy is often used as a main preparation raw material of weathering steel, RKEF (rotating KLIN ELECTRIC Furnace) Rotary kiln drying prereduction-electric Furnace reduction smelting is the most mature process for obtaining nickel-iron alloy when smelting laterite nickel ore, the electric Furnace smelting stage in the RKEF technology is responsible for reducing metallic nickel and partial iron and separating nickel iron from residues to generate crude nickel-iron liquid, the height of an electrode needs to be strictly controlled in the stage, the current density on the surface of Furnace burden is overlarge due to the fact that the electrode is too close to the Furnace burden, the energy consumption is increased and the electrode is worn, and the environment is greatly negatively affected; too far from the furnace burden, the electrode can lead to smaller current density on the surface of the furnace burden, which results in low smelting reduction reaction efficiency and waste of resources.
Disclosure of Invention
The invention provides a preparation method of weathering steel for an iron tower, which aims to solve the problem of loss increase caused by improper electrode position in an arc furnace smelting process in the preparation process of the weathering steel, and adopts the following specific technical scheme:
An embodiment of the invention provides a preparation method of weather-resistant steel for an iron tower, which comprises the following steps:
Taking laterite-nickel ore as a raw material for preparing nickel-iron alloy, and crushing and screening the laterite-nickel ore; conveying the crushed and sieved laterite-nickel ore to a rotary drying kiln for drying treatment; uniformly mixing the dried laterite-nickel ore with a reducing agent and a cosolvent, and then conveying the mixture to a rotary kiln for baking-pre-reduction treatment; conveying the laterite-nickel ore subjected to baking-pre-reduction treatment and the reducing coal to an electric arc furnace at a blanking speed of 25-50 t/h;
And adjusting the electrode position in real time according to the data characteristics of arc area data, electrode current data, furnace temperature data and electrode resistance data in the arc furnace smelting process, obtaining crude ferronickel alloy liquid through arc furnace smelting, and then obtaining weather-resistant steel for iron tower preparation through refining and casting.
Preferably, the laterite-nickel ore comprises the following main chemical components in percentage by mass: 2.6% of Ni nickel, 16.72% of TFe total iron, 0.2% of FeO ferrous oxide, 35.5% of SiO 2 silicon dioxide, 0.39% of CaO calcium oxide, 5.74% of Al 2O3 aluminum oxide and 13.39% of MgO magnesium oxide.
Preferably, the size range of the screen is: 50-150 nm.
Preferably, the temperature and stop conditions of the drying process are: the initial temperature of the crushed and sieved laterite-nickel ore is set to 800 ℃ when the laterite-nickel ore is dried, and the drying is stopped until the volatilization amount of the water in the laterite-nickel ore is 20% of the total mass of the ore.
Preferably, the reducing agent is coke accounting for 8% of the mass of the laterite-nickel ore, and the cosolvent is lime consisting of 80.40% of CaO calcium oxide, 11.56% of SiO 2 silicon dioxide, 4.59% of MgO magnesium oxide and 5.74% of Al 2O3 aluminum oxide.
Preferably, the preheating temperature of the baking-prereduction is 750 ℃, and the reduction temperature is 850-1000 ℃.
Preferably, the method for adjusting the electrode position in real time according to the data characteristics of arc area data, electrode current data, furnace temperature data and electrode resistance data in the arc furnace smelting process comprises the following steps:
Uniformly dividing the time of the whole arc furnace smelting process into a plurality of time sequence intervals, and acquiring a furnace burden reduction sufficient coefficient of each time sequence interval according to arc area crossing data and electrode current data corresponding to each time sequence interval;
Acquiring electrode abrasion factors of each time sequence interval according to the electrode resistance data of each time sequence interval, and acquiring smelting sufficient duration coefficients of each time sequence interval based on furnace burden reduction sufficient coefficients and the electrode abrasion factors of each time sequence interval;
taking initial preset time of an arc furnace smelting process as an initial adjustment interval, taking a sequence formed by smelting full continuous coefficients of all time sequence intervals corresponding to the initial adjustment interval according to time ascending sequence as an initial smelting full continuous coefficient sequence, acquiring a smelting full continuous coefficient predicted value of the initial smelting full continuous coefficient sequence by adopting an exponential moving average algorithm, acquiring a smelting full continuous coefficient sequence according to the initial smelting full continuous coefficient sequence and the smelting full continuous coefficient predicted value, and acquiring a clustering result of the smelting full continuous coefficient sequence by adopting a clustering algorithm;
Taking the difference value between the predicted value of the smelting sufficient sustaining coefficient and the average value of all elements in the sequence of the smelting sufficient sustaining coefficients as a first difference value; taking the number of data in a cluster where a predicted value of a smelting sufficient continuous coefficient is located in a cluster result of the smelting sufficient continuous coefficient sequence as a numerator, taking the number of data in another cluster in the cluster result of the smelting sufficient continuous coefficient sequence as a denominator, taking the ratio of the numerator to the denominator as a second difference value, and taking the product of a data mapping result of the first difference value and the second difference value as an electrode adjustment index; and adjusting the electrode position in real time according to the electrode adjustment index.
Preferably, the method for obtaining the furnace burden reduction full coefficient of each time sequence interval according to the arc area crossing data and the electrode current data corresponding to each time sequence interval comprises the following steps:
for each acquisition time of the arc furnace smelting stage, taking the ratio of the arc crossing area data corresponding to each acquisition time to the electrode current data as the current density value of each acquisition time; taking the difference value of the arc crossing area data at each acquisition time and the average value of all the arc crossing area data corresponding to the time sequence interval at each acquisition time as the arc area difference value at each acquisition time; taking the difference value of the average value of the electrode current data at each acquisition time and all the electrode current data corresponding to the time sequence interval at each acquisition time as the current difference value at each acquisition time;
taking a sequence formed by all corresponding current density values in the arc furnace smelting stage according to the ascending order of time as a current density data sequence, acquiring trend items and period items corresponding to each data in the current density data sequence by adopting an STL sequence decomposition algorithm, and taking a vector formed by the trend items and the period items as a characteristic binary vector of each data;
For each time sequence interval of an arc melting furnace stage, taking the sum of an arc area difference value and a current difference value corresponding to each acquisition time in each time sequence interval as a first characteristic value of each acquisition time, taking the sum of the first difference values corresponding to all the acquisition times in each time sequence interval as a second characteristic value of each time sequence interval, and taking a data mapping result of the second characteristic value as a molecule; taking the sum of the information entropy of the arc crossing area data corresponding to all the acquisition moments in each time sequence interval and the information entropy of the electrode current data corresponding to all the acquisition moments as a first credible coefficient, taking the sum of the first credible coefficient and a preset parameter as a denominator, and taking the ratio of a numerator to the denominator as the current density credible weight of each time sequence interval;
and acquiring a current density steady-state factor of each time sequence interval according to the characteristic binary vector and the current density value corresponding to each time sequence interval, and acquiring a furnace burden reduction sufficient coefficient based on the current density credible weight and the current density steady-state factor.
Preferably, the method for obtaining the current density steady-state factor of each time sequence interval according to the characteristic binary vector and the current density value corresponding to each time sequence interval and obtaining the furnace burden reduction sufficient coefficient based on the current density credible weight and the current density steady-state factor comprises the following steps:
for each time sequence interval of the arc melting furnace stage, taking the dot product ratio of the characteristic binary vectors corresponding to any two acquisition moments in each time sequence interval as a first steady-state value, and taking the sum of all the first steady-state values corresponding to each time sequence interval as a second steady-state value; taking a sequence formed by current density values corresponding to all acquisition moments in each time sequence interval according to a time ascending sequence as a current density data sequence of each time sequence interval, acquiring mutation points in the current density data sequence by adopting a sequence segmentation algorithm, taking the product of the data mapping results of the number of all the mutation points and a second steady-state value as a molecule, taking the sum of the variance of the current density data sequence and a preset parameter as a denominator, and taking the ratio of the molecule to the denominator as a current density steady-state factor of each time sequence interval;
Taking the calculation result of the current density credible weight of each time sequence interval in an exponential function taking the current density steady-state factor of each time sequence interval as a base as a first reduction coefficient of each time sequence interval, and taking the product of the first reduction coefficient and the maximum value of the furnace temperature data corresponding to all moments in each time sequence interval as a molecule; and acquiring local outlier factors of furnace temperature data corresponding to each acquisition time in each time sequence interval by adopting an LOF abnormality detection algorithm, taking the sum of the average value of all the local outlier factors corresponding to each time sequence interval and preset parameters as a denominator, and taking the ratio of the numerator to the denominator as a furnace burden reduction full coefficient of each time sequence interval.
Preferably, the method for obtaining the electrode wear factor of each time interval according to the electrode resistance data of each time interval and obtaining the smelting sufficient duration coefficient of each time interval based on the furnace burden reduction sufficient coefficient and the electrode wear factor of each time interval comprises the following steps:
Wherein Fs i represents a smelting adequacy continuous coefficient in the ith time interval; ew i and Fr i represent the electrode wear factor and the charge reduction sufficiency coefficient of the ith time interval, respectively, exp () represents an exponential function based on a natural constant; represents the maximum value of all electrode resistance data corresponding to the ith time sequence interval,/> Representing the average value of all electrode resistance data corresponding to the ith time sequence interval; /(I)Representing the average value of electrode resistance data corresponding to all acquisition time points before the jth acquisition time point in the ith time sequence interval,/>Representing the average value of electrode resistance data corresponding to all acquisition moments after the jth acquisition moment in the ith time sequence interval; j i denotes the number of all electrode resistance data corresponding to the i-th timing section.
The beneficial effects of the invention are as follows: according to various data characteristics of an electric furnace smelting stage in the preparation process of the ferronickel, a smelting sufficient continuous coefficient is obtained, an initial adjustment interval is constructed, an electrode adjustment index is obtained through a predicted value of the smelting sufficient continuous coefficient and a clustering result of a smelting sufficient continuous coefficient sequence in a next time interval, real-time adjustment of the electrode position in the electric furnace smelting stage is realized, and on the basis of ensuring that electrode abrasion is slight, sufficient smelting reduction of furnace burden is ensured, and a crude ferronickel liquid with higher quality is obtained. The weathering steel prepared by refining the obtained crude ferronickel alloy liquid has the beneficial effects of not only having better tensile toughness, but also having higher corrosion resistance, and the weathering steel is applied to the electric iron tower industry, so that the defects of wasting a large amount of resources and causing a large amount of environmental pollution in a galvanization stage when the traditional steel is used as a raw material of the electric iron tower are avoided.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for manufacturing a weathering steel for an iron tower according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an initial adjustment interval and a movement direction according to an embodiment of the present invention;
Fig. 3 is a schematic flow chart of preparing weathering steel by adopting an electric furnace-external refining-continuous casting mode according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for preparing weathering steel for iron towers according to an embodiment of the present invention is shown, the method includes the following steps:
and S001, acquiring smelting monitoring data of the arc furnace smelting stage.
The process flow for preparing the nickel-iron alloy by treating the laterite-nickel ore by the RKEF technology by adopting the laterite-nickel ore as a raw material for preparing the nickel-iron alloy comprises the following steps: crushing and screening, drying, roasting-pre-reducing, smelting in an electric furnace and refining.
Crushing and screening: coarse crushing is carried out on the laterite-nickel ore raw material by a jaw crusher, so that the raw material is crushed into proper particle size, and the laterite-nickel ore comprises the following main chemical components in percentage by mass: 2.6% of Ni nickel, 16.72% of TFe total iron, 0.2% of FeO ferrous oxide, 35.5% of SiO 2 silicon dioxide, 0.39% of CaO calcium oxide, 5.74% of Al 2O3 aluminum oxide and 13.39% of MgO magnesium oxide; and screening the crushed laterite-nickel ore raw material by a vibrating screen to ensure that the granularity of 80% of the laterite-nickel ore after crushing and screening is 50-150 nm.
And (3) drying: and conveying the crushed and screened laterite-nickel ore into a rotary drying kiln through a belt conveyor, setting the drying temperature of the rotary drying kiln to 800 ℃, and stopping drying after testing that the moisture volatilization amount in the crushed and screened laterite-nickel ore accounts for 20% of the total mass of the ore.
Roasting-pre-reduction: naturally cooling the dried laterite-nickel ore to room temperature, adding a reducing agent and a fluxing agent, uniformly mixing, and conveying the mixture into a rotary kiln through a belt conveyor to perform roasting-pre-reduction treatment, wherein the cooling is required because the temperature of the dried laterite-nickel ore is higher, and the direct roasting-pre-reduction treatment can influence the control of the reduction reaction and the product quality. The laterite-nickel ore after adding the reducing agent and the fluxing agent is preheated, and then roasting-pre-reduction treatment is carried out, so that reduction of part of nickel and iron is realized. The preheating temperature in the invention is 750 ℃, the roasting pre-reduction temperature is 850-1000 ℃, the reducing agent is coke accounting for 8% of the mass of the laterite-nickel ore raw material, the fluxing agent is lime, and the chemical components of the lime consist of 80.40% of CaO calcium oxide, 11.56% of SiO 2 silicon dioxide, 4.59% of MgO magnesium oxide and 5.74% of Al 2O3 aluminum oxide by mass. The reducing agent is used for reducing oxides in the laterite nickel ore to reduce the oxides into metal, and then reducing metal ions from a high oxidation state to a low oxidation state to promote the separation and precipitation of the metal; the fluxing agent has the functions of promoting the melting and the separation of laterite-nickel ore, influencing the viscosity of a melting system and being beneficial to the separation and precipitation of metal and non-metal substances.
Smelting in an electric furnace: transferring the ore material after roasting-pre-reduction treatment into an electric arc furnace, wherein the feeding speed of the electric arc furnace is 25-50 t/h, reducing coal or semi-coke with the granularity of 10-30 nm accounting for 1-16% of the weight of the hot furnace material is added, direct current is conducted to the ore material through an electrode, and high-temperature electric arc is generated to smelt the ore material.
The cross-sectional area of the real-time arc passing through mineral aggregate is obtained through a monitoring module in an electric arc furnace control system, real-time electrode current, electric arc furnace temperature and electrode resistance data are obtained through current, temperature and resistance sensors, the acquisition time interval of the control system is 0.5s, in order to prevent missing phenomena in the data transmission process, a data filling algorithm is used for filling missing values in the acquired data, the data filling algorithm used in the invention is a Lagrangian interpolation method, the beneficial effect of the data filling algorithm is that the filling effect of the Lagrangian interpolation method is relatively smooth, the trend and the change rule of original data can be kept, the follow-up data analysis and processing are convenient, an implementer can select other algorithms to carry out filling processing on the missing values according to actual conditions, and the specific implementation process of the Lagrangian difference algorithm is a known technology and is not repeated.
Refining: and impurities such as phosphorus, sulfur and the like in the crude ferronickel alloy liquid obtained in the steps are removed by adopting a ladle refining treatment mode, so that the product quality is improved.
So far, a smelting monitoring data sequence in the arc furnace smelting process is obtained, wherein the smelting monitoring data sequence comprises an arc crossing area sequence, an electrode current data sequence, a furnace temperature data sequence and an electrode resistance data sequence.
And step S002, calculating current density credible weight and current density stabilizing factor according to arc crossing area data and electrode current data in the smelting monitoring data, and obtaining furnace burden reduction sufficient coefficient according to the current density credible weight and the current density stabilizing factor.
In the electric furnace smelting stage, the current density is taken as an important parameter, the smelting condition of the laterite-nickel ore material can be directly reflected, when the current density data is stable, the reduction reaction of the laterite-nickel ore material is in a stable state, and the reduction reaction is more sufficient at the moment, and the smelting efficiency is higher; when current density data continuously fluctuate, the distribution of laterite nickel ore materials in the electric arc furnace is uneven, the height of an electrode is required to be adjusted through a control system at the moment, the current density, namely the reduction reaction degree of furnace burden, is enhanced, the waste of raw materials caused by incomplete reduction of the furnace burden is avoided, meanwhile, when the current density becomes large, the energy density of arc discharge is increased, the temperature in the electric arc furnace is also increased, and the furnace burden is more easily smelted at the moment.
The total duration of the arc furnace smelting stage is uniformly divided, specifically, each interval divided in the arc furnace smelting stage is taken as one time sequence interval of the arc furnace smelting stage, and the duration of the one time sequence interval is 1 minute.
Further, for each acquisition time of the arc furnace smelting stage, taking the ratio of the arc crossing area data corresponding to each acquisition time to the electrode current data as the current density value of each acquisition time; taking the difference value of the arc crossing area data at each acquisition time and the average value of all the arc crossing area data corresponding to the time sequence interval at each acquisition time as the arc area difference value at each acquisition time; taking the difference value of the average value of the electrode current data at each acquisition time and all the electrode current data corresponding to the time sequence interval at each acquisition time as the current difference value at each acquisition time; and taking a sequence formed by all corresponding current density values of the arc furnace smelting stage according to the sequence of time ascending order as a current density data sequence, acquiring trend items and period items corresponding to each data in the current density data sequence by adopting an STL (serial-Trend decomposition procedure based on Loess) sequence decomposition algorithm, and taking a vector formed by the trend items and the period items as a characteristic binary vector of each data.
Further, according to the arc area difference value and the current difference value, calculating the current density credible weight of each time sequence interval in the arc furnace smelting stage, wherein a specific calculation formula is as follows:
Wherein Cf i represents the current density confidence weight of the ith time interval; a i,x represents the arc area difference value corresponding to the x-th acquisition time in the i-th time sequence interval, B i,x represents the current difference value corresponding to the x-th acquisition time in the i-th time sequence interval, exp [ ] represents an exponential function based on a natural constant, and L i represents the number of arc area difference values and current difference values corresponding to all the acquisition times in the i-th time sequence interval; information entropy of arc crossing area data corresponding to all acquisition moments of the ith time sequence interval is represented, and theta i represents information entropy of electrode current data corresponding to all acquisition moments of the ith time sequence interval.
If the degree of change of the arc crossing area data and the electrode current data is small in the ith time sequence interval, the arc crossing area data and the electrode current data are calculatedThe smaller the value of (2) is, and the smaller the relative change between the arc crossing area data and the electrode current data corresponding to all the acquisition time points in the ith time sequence interval is, the calculated/>The smaller the value of (c) is,The larger the value of the current density credibility weight Cf i in the ith time sequence interval is, the smaller the relative difference change of the arc crossing area data and the electrode current in the ith time sequence interval is, and the higher the credibility of the current density analysis is.
Further, a sequence formed by current density values corresponding to all acquisition moments in each time sequence interval according to a time ascending sequence is used as a current density data sequence of each time sequence interval, a BG (Bernaola Galvan) sequence segmentation algorithm is adopted to obtain abrupt points in the current density data sequence, and a current density steady-state factor is calculated according to the current density data sequence corresponding to each time sequence interval and characteristic binary vectors corresponding to each data in the current density data sequence, wherein a specific calculation formula is as follows:
Wherein As i represents the current density steady-state factor of the ith timing interval; f i denotes the number of abrupt points in the current density data sequence of the i-th time interval, exp () denotes an exponential function based on a natural constant; mu i represents the variance of all elements in the current density data sequence of the ith timing interval; and/> Respectively representing characteristic binary vectors corresponding to the c-th and d-th acquisition moments in the ith time sequence interval,/>Representation/>And/>Is a dot product ratio of (2); k i represents the number of feature binary vectors corresponding to all acquisition moments in the ith time sequence interval.
If the stable characteristic of the current density data sequence in the ith time sequence interval is better, the smaller the calculated value of f i is, the larger the value of exp (-f i) is, the smaller the value of mu i is, and the smaller the characteristic binary vectors corresponding to different acquisition moments in the ith time sequence interval are, the calculated value isThe larger the value of the calculated current density steady-state factor As i for the ith timing interval, the better the relative stability characteristics for the ith timing interval.
Further, when the current density change based on the time series section is more stable and the furnace temperature of the arc furnace is higher on a stable basis, the degree of the reduction reaction of the mineral aggregate in the arc furnace is more sufficient. Further, the input is furnace temperature data corresponding to all acquisition moments of each time sequence interval in the arc furnace smelting stage, a LOF (Local Outlier Factor) anomaly detection algorithm is adopted to obtain local outlier factors of the furnace temperature data corresponding to all acquisition moments of each time sequence interval, the LOF anomaly detection algorithm is a known technology, the specific process is not repeated, a furnace burden reduction sufficient coefficient is calculated according to the local outlier factors of prime numbers, the current density credible weight and the current density steady-state factor of each time sequence interval, and a specific calculation formula is as follows:
Wherein Fr i represents a charge reduction completion coefficient of the ith time interval; as i and Cf i respectively represent the current density credible weight and the current density steady-state factor of the ith time sequence interval; representing the maximum value in the furnace temperature data corresponding to all the acquisition moments in the ith time sequence interval; gamma i represents the mean value of the local outlier factor of the furnace temperature data corresponding to all the acquisition moments in the ith time interval.
If the maximum value of the furnace temperature data corresponding to all the acquisition time points in the ith time sequence interval is larger and the influence degree of the furnace temperature abnormality is smaller, calculatingThe larger the value of gamma i is, the smaller the value of gamma i is, and meanwhile, the better the relative stability characteristic of the ith time sequence interval is, the larger the calculated value of As i is, the larger the value of Cf i is, and the larger the influence of the current density credible weight relative to the current density steady-state factor is, namely/>The larger the value of the calculated charge reduction full coefficient Fr i in the ith time interval is, the higher the charge reduction efficiency in the ith time interval is.
So far, the furnace burden reduction full coefficient of each time sequence interval in the arc furnace smelting stage is obtained.
Step S003, electrode abrasion factors are obtained according to electrode resistance data in the smelting data monitoring data, smelting sufficient sustaining coefficients are calculated according to the electrode abrasion factors and furnace burden reduction sufficient coefficients, electrode adjustment indexes are obtained according to smelting component sustaining coefficients, and electrode positions of an arc furnace in a smelting stage are adjusted according to the electrode adjustment indexes.
In the ferronickel liquid preparation process, the shorter the arc length is, the larger the current density on the surface of the furnace burden is, the heat of the arc is concentrated in a smaller area, the smelting process of the furnace burden can be accelerated to a certain extent, the electrode resistance can be increased, the abrasion of the electrode is accelerated, the smelting reduction of the furnace burden can be influenced finally, and the electrode position is properly far away from the furnace burden.
Further, electrode abrasion factors are calculated according to electrode resistance data corresponding to all acquisition moments of each time sequence interval in the arc furnace smelting stage, and smelting sufficient continuous coefficients are calculated based on the electrode abrasion factors and furnace burden reduction sufficient coefficients, wherein a specific calculation formula is as follows:
Wherein Fs i represents a smelting adequacy continuous coefficient in the ith time interval; ew i and Fr i represent the electrode wear factor and the charge reduction sufficiency coefficient of the ith time interval, respectively, exp () represents an exponential function based on a natural constant; represents the maximum value of all electrode resistance data corresponding to the ith time sequence interval,/> Representing the average value of all electrode resistance data corresponding to the ith time sequence interval; /(I)Representing the average value of electrode resistance data corresponding to all acquisition time points before the jth acquisition time point in the ith time sequence interval,/>Representing the average value of electrode resistance data corresponding to all acquisition moments after the jth acquisition moment in the ith time sequence interval; j i denotes the number of all electrode resistance data corresponding to the i-th timing section.
If the variation difference of the electrode resistance data corresponding to all the acquisition moments in the ith time sequence interval is large, calculatingThe larger the value of (2) is, and meanwhile, the electrode resistance data corresponding to all the acquisition moments in the ith time sequence interval show a gain change trend, so that the calculated/>The smaller the value of/>The larger the value of the electrode abrasion factor Ew i in the ith time sequence interval is, the larger the electrode abrasion factor Ew i in the ith time sequence interval is, which means that the gain change trend of the electrode resistance in the ith time sequence interval is obvious; meanwhile, the lower the furnace burden reduction efficiency in the ith time sequence interval is, the smaller the calculated value of Fr i, namely the smaller the value of exp (Fr i), the smaller the calculated value of the smelting sufficient duration coefficient Fs i in the ith time sequence interval is, which means that the smelting reduction duration degree in the ith time sequence interval is higher.
Further, taking the initial 20 minutes of the smelting process of the electric arc furnace as an initial adjustment interval, taking a sequence formed by smelting full duration coefficients of all time sequence intervals corresponding to the initial adjustment interval according to the time ascending sequence as an initial smelting full duration coefficient sequence, and acquiring a smelting full duration coefficient predicted value of the initial smelting full duration coefficient sequence by adopting an exponential moving average algorithm, wherein the smelting full duration coefficient predicted value is a smelting full duration coefficient of the next time sequence interval of the initial adjustment interval, and the specific implementation process of the exponential moving average algorithm is a known technology and is not repeated.
Further, a sequence formed by all elements in the initial smelting sufficient continuous coefficient sequence and the smelting sufficient continuous coefficient predicted value according to the time ascending sequence is used as the smelting sufficient continuous coefficient sequence, a K-means clustering algorithm is adopted to obtain a clustering result of the smelting sufficient continuous coefficient sequence, and the specific implementation process of the clustering algorithm with the number of 2,K-means clustering clusters in the clustering result is a known technology and is not repeated.
Further, electrode adjustment indexes are calculated according to clustering results of the smelting sufficient duration coefficient sequences and smelting sufficient duration coefficient predicted values. Specifically, taking the difference value between the predicted value of the smelting sufficient sustaining coefficient and the average value of all elements in the sequence of the smelting sufficient sustaining coefficients as a first difference value; taking the number of data in a cluster where a predicted value of a smelting sufficient continuous coefficient is located in a clustering result of the smelting sufficient continuous coefficient sequence as a numerator, taking the number of data in another cluster in the clustering result of the smelting sufficient continuous coefficient sequence as a denominator, taking the ratio of the numerator to the denominator as a second difference value, and taking the product of the calculated result of the opposite number of the first difference value in an exponential function taking a natural constant as a base number and the second difference value as an electrode adjustment index; and adjusting the electrode position in real time according to the electrode adjustment index.
Further, as the arc furnace melting proceeds, the initial adjustment section is continuously adjusted according to the time lapse, the step length of the movement of the initial adjustment section is 1, and the electrode adjustment index of the next time interval is calculated according to the adjusted initial adjustment section, and the specific initial adjustment section and the movement direction are shown in fig. 2. Setting the electrode position adjustment amplitude to be 0.5m, adjusting the threshold value to be 0.5, and adjusting the electrode position to be high when the electrode adjustment index is larger than the adjustment threshold value, wherein the electrode position is unchanged when the electrode adjustment index is smaller than the adjustment threshold value.
Thus, the adjustment of the electrode position according to the electrode adjustment index is completed.
And S004, smelting by an electric arc furnace to obtain ferronickel alloy liquid, and preparing the weather-resistant steel for preparing the iron tower by adopting an electric furnace-external refining-continuous casting mode.
The crude ferronickel alloy liquid obtained in the steps is refined through a refining procedure, a continuous casting machine is connected with an electric arc furnace by using a ladle refining mode, and weather-resistant steel is prepared by adopting an electric furnace-external refining-continuous casting mode, wherein the specific preparation process is shown in figure 3:
step S1, preheating the steel ladle, avoiding the temperature reduction of the steel liquid by the cold steel ladle, and being beneficial to removing residual impurities in the steel ladle.
And S2, ladle transfer, namely transferring the alloy liquid to be refined from the electric arc furnace to a preheated ladle, and then adding ordinary molten steel for full fusion.
And S3, deoxidizing, namely blowing a reducing agent into molten steel in the steel ladle to remove gaseous oxygen in the molten steel and reduce the content of gaseous inclusions.
S4, desulfurizing, namely blowing oxygen, nitrogen or other desulfurizing agents into the molten steel, so that sulfur in the molten steel is promoted to react with the desulfurizing agents to generate volatile sulfides or sulfur gas, and the sulfur content in the molten steel is reduced.
And S5, controlling the temperature, and adjusting the temperature of the molten steel to reach the casting temperature range required by continuous casting.
And S6, casting, namely casting the weathering steel in a continuous casting mode.
The concrete implementation processes of ladle refining and steel continuous casting technology are known technologies, and are not repeated, an operator can refine the obtained crude ferronickel alloy liquid according to actual conditions, then cast weather-resistant steel according to requirements, and the weather-resistant steel is used for preparing an iron tower.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. A preparation method of weather-resistant steel for an iron tower is characterized by comprising the following steps:
Taking laterite-nickel ore as a raw material for preparing nickel-iron alloy, and crushing and screening the laterite-nickel ore; conveying the crushed and sieved laterite-nickel ore to a rotary drying kiln for drying treatment; uniformly mixing the dried laterite-nickel ore with a reducing agent and a cosolvent, and then conveying the mixture to a rotary kiln for baking-pre-reduction treatment; conveying the laterite-nickel ore subjected to baking-pre-reduction treatment and the reducing coal to an electric arc furnace at a blanking speed of 25-50 t/h;
The method comprises the steps of adjusting the position of an electrode in real time according to the data characteristics of arc area data, electrode current data, furnace temperature data and electrode resistance data in the arc furnace smelting process, obtaining crude ferronickel alloy liquid through arc furnace smelting, and then obtaining weather-resistant steel for iron tower preparation through refining and casting;
The method for adjusting the electrode position in real time according to the data characteristics of arc area data, electrode current data, furnace temperature data and electrode resistance data in the arc furnace smelting process comprises the following steps:
Uniformly dividing the time of the whole arc furnace smelting process into a plurality of time sequence intervals, and acquiring a furnace burden reduction sufficient coefficient of each time sequence interval according to arc area crossing data and electrode current data corresponding to each time sequence interval;
Acquiring electrode abrasion factors of each time sequence interval according to the electrode resistance data of each time sequence interval, and acquiring smelting sufficient duration coefficients of each time sequence interval based on furnace burden reduction sufficient coefficients and the electrode abrasion factors of each time sequence interval;
taking initial preset time of an arc furnace smelting process as an initial adjustment interval, taking a sequence formed by smelting full continuous coefficients of all time sequence intervals corresponding to the initial adjustment interval according to time ascending sequence as an initial smelting full continuous coefficient sequence, acquiring a smelting full continuous coefficient predicted value of the initial smelting full continuous coefficient sequence by adopting an exponential moving average algorithm, acquiring a smelting full continuous coefficient sequence according to the initial smelting full continuous coefficient sequence and the smelting full continuous coefficient predicted value, and acquiring a clustering result of the smelting full continuous coefficient sequence by adopting a clustering algorithm;
Taking the difference value between the predicted value of the smelting sufficient sustaining coefficient and the average value of all elements in the sequence of the smelting sufficient sustaining coefficients as a first difference value; taking the number of data in a cluster where a predicted value of a smelting sufficient continuous coefficient is located in a cluster result of the smelting sufficient continuous coefficient sequence as a numerator, taking the number of data in another cluster in the cluster result of the smelting sufficient continuous coefficient sequence as a denominator, taking the ratio of the numerator to the denominator as a second difference value, and taking the product of a data mapping result of the first difference value and the second difference value as an electrode adjustment index; and adjusting the electrode position in real time according to the electrode adjustment index.
2. The method for preparing the weathering steel for the iron tower according to claim 1, wherein the laterite-nickel ore comprises the following main chemical components in percentage by mass: 2.6% of Ni nickel, 16.72% of TFe total iron, 0.2% of FeO ferrous oxide, 35.5% of SiO 2 silicon dioxide, 0.39% of CaO calcium oxide, 5.74% of Al 2O3 aluminum oxide and MgO
13.39% Of magnesium oxide.
3. The method for preparing weathering steel for iron towers according to claim 1, wherein the size range of the screen is: 50-150 nm.
4. The method for manufacturing a weathering steel for iron towers according to claim 1, wherein the drying process temperature and stop conditions are: the initial temperature of the crushed and sieved laterite-nickel ore is set to 800 ℃ when the laterite-nickel ore is dried, and the drying is stopped until the volatilization amount of the water in the laterite-nickel ore is 20% of the total mass of the ore.
5. The method for preparing the weathering steel for the iron tower according to claim 1, wherein the reducing agent is coke accounting for 8% of the mass of the laterite-nickel ore, and the cosolvent is calcium oxide of 80.40% and SiO 2 of 11.56%
Silica, 4.59% MgO magnesia and 5.74% Al 2O3 alumina.
6. A method for producing a weathering steel for iron towers according to claim 1, characterized in that the baking
The pre-reduction preheating temperature is 750 ℃ and the reduction temperature is 850-1000 ℃.
7. The method for preparing the weathering steel for the iron tower according to claim 1, wherein the method for obtaining the furnace burden reduction sufficient coefficient of each time sequence interval according to the arc area crossing data and the electrode current data corresponding to each time sequence interval is as follows:
for each acquisition time of the arc furnace smelting stage, taking the ratio of the arc crossing area data corresponding to each acquisition time to the electrode current data as the current density value of each acquisition time; taking the difference value of the arc crossing area data at each acquisition time and the average value of all the arc crossing area data corresponding to the time sequence interval at each acquisition time as the arc area difference value at each acquisition time; taking the difference value of the average value of the electrode current data at each acquisition time and all the electrode current data corresponding to the time sequence interval at each acquisition time as the current difference value at each acquisition time;
taking a sequence formed by all corresponding current density values in the arc furnace smelting stage according to the ascending order of time as a current density data sequence, acquiring trend items and period items corresponding to each data in the current density data sequence by adopting an STL sequence decomposition algorithm, and taking a vector formed by the trend items and the period items as a characteristic binary vector of each data;
For each time sequence interval of an arc melting furnace stage, taking the sum of an arc area difference value and a current difference value corresponding to each acquisition time in each time sequence interval as a first characteristic value of each acquisition time, taking the sum of the first difference values corresponding to all the acquisition times in each time sequence interval as a second characteristic value of each time sequence interval, and taking a data mapping result of the second characteristic value as a molecule; taking the sum of the information entropy of the arc crossing area data corresponding to all the acquisition moments in each time sequence interval and the information entropy of the electrode current data corresponding to all the acquisition moments as a first credible coefficient, taking the sum of the first credible coefficient and a preset parameter as a denominator, and taking the ratio of a numerator to the denominator as the current density credible weight of each time sequence interval;
and acquiring a current density steady-state factor of each time sequence interval according to the characteristic binary vector and the current density value corresponding to each time sequence interval, and acquiring a furnace burden reduction sufficient coefficient based on the current density credible weight and the current density steady-state factor.
8. The method for preparing the weathering steel for the iron tower according to claim 7, wherein the method for obtaining the current density steady-state factor of each time sequence interval according to the characteristic binary vector and the current density value corresponding to each time sequence interval and obtaining the furnace burden reduction sufficient coefficient based on the current density credible weight and the current density steady-state factor is as follows:
for each time sequence interval of the arc melting furnace stage, taking the dot product ratio of the characteristic binary vectors corresponding to any two acquisition moments in each time sequence interval as a first steady-state value, and taking the sum of all the first steady-state values corresponding to each time sequence interval as a second steady-state value; taking a sequence formed by current density values corresponding to all acquisition moments in each time sequence interval according to a time ascending sequence as a current density data sequence of each time sequence interval, acquiring mutation points in the current density data sequence by adopting a sequence segmentation algorithm, taking the product of the data mapping results of the number of all the mutation points and a second steady-state value as a molecule, taking the sum of the variance of the current density data sequence and a preset parameter as a denominator, and taking the ratio of the molecule to the denominator as a current density steady-state factor of each time sequence interval;
Taking the calculation result of the current density credible weight of each time sequence interval in an exponential function taking the current density steady-state factor of each time sequence interval as a base as a first reduction coefficient of each time sequence interval, and taking the product of the first reduction coefficient and the maximum value of the furnace temperature data corresponding to all moments in each time sequence interval as a molecule; and acquiring local outlier factors of furnace temperature data corresponding to each acquisition time in each time sequence interval by adopting an LOF abnormality detection algorithm, taking the sum of the average value of all the local outlier factors corresponding to each time sequence interval and preset parameters as a denominator, and taking the ratio of the numerator to the denominator as a furnace burden reduction full coefficient of each time sequence interval.
9. The method for preparing the weathering steel for the iron tower according to claim 1, wherein the method for obtaining the electrode abrasion factor of each time sequence interval according to the electrode resistance data of each time sequence interval and obtaining the smelting sufficient duration coefficient of each time sequence interval based on the furnace burden reduction sufficient coefficient and the electrode abrasion factor of each time sequence interval comprises the following steps:
Wherein Fs i represents a smelting adequacy continuous coefficient in the ith time interval; ew i and Fr i represent the electrode wear factor and the charge reduction sufficiency coefficient of the ith time interval, respectively, exp () represents an exponential function based on a natural constant; represents the maximum value of all electrode resistance data corresponding to the ith time sequence interval,/> Representing the average value of all electrode resistance data corresponding to the ith time sequence interval; /(I)Representing the average value of electrode resistance data corresponding to all acquisition time points before the jth acquisition time point in the ith time sequence interval,/>Representing the average value of electrode resistance data corresponding to all acquisition moments after the jth acquisition moment in the ith time sequence interval; j i denotes the number of all electrode resistance data corresponding to the i-th timing section.
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