EP3904544A1 - Procédé de réglage d'une perméabilité d'un produit fritté - Google Patents

Procédé de réglage d'une perméabilité d'un produit fritté Download PDF

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Publication number
EP3904544A1
EP3904544A1 EP20172392.1A EP20172392A EP3904544A1 EP 3904544 A1 EP3904544 A1 EP 3904544A1 EP 20172392 A EP20172392 A EP 20172392A EP 3904544 A1 EP3904544 A1 EP 3904544A1
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EP
European Patent Office
Prior art keywords
permeability
data
material mixture
sintered material
moisture content
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP20172392.1A
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German (de)
English (en)
Inventor
Petra Krahwinkler
Sonja Strasser
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Primetals Technologies Austria GmbH
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Primetals Technologies Austria GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Primetals Technologies Austria GmbH filed Critical Primetals Technologies Austria GmbH
Priority to EP20172392.1A priority Critical patent/EP3904544A1/fr
Priority to CN202180031391.1A priority patent/CN115461478B/zh
Priority to EP21721131.7A priority patent/EP4143352A1/fr
Priority to PCT/EP2021/061272 priority patent/WO2021219790A1/fr
Publication of EP3904544A1 publication Critical patent/EP3904544A1/fr
Withdrawn legal-status Critical Current

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    • 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
    • C22B1/14—Agglomerating; Briquetting; Binding; Granulating
    • C22B1/16—Sintering; Agglomerating
    • C22B1/20—Sintering; Agglomerating in sintering machines with movable grates
    • 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
    • C22B1/14—Agglomerating; Briquetting; Binding; Granulating
    • C22B1/16—Sintering; Agglomerating
    • C22B1/20—Sintering; Agglomerating in sintering machines with movable grates
    • C22B1/205—Sintering; Agglomerating in sintering machines with movable grates regulation of the sintering process

Definitions

  • the present invention relates to the field of sintering in ferrous metallurgy.
  • the invention relates to a method for setting a permeability of a sintered material mixture for a sintering plant.
  • the invention relates to a signal processing device and a machine-readable code for carrying out the method for setting a permeability.
  • the invention also relates to a storage medium on which the machine-readable code is stored.
  • fine-grained raw materials are used to produce lumpy iron ore sinter for use in a blast furnace.
  • Fine ores, limestone, recycling dusts are mixed with coke breeze - as fuel - in a mixing drum to form a sintered material mixture.
  • There are also known methods for producing mixtures of sintered goods which, in addition to a mixer, also have a granulator and a rolling drum - as in FIG EP 2848299 B1 shown.
  • the sintered material mixture is piled up as a sinter bed on a sinter belt consisting of so-called grate carriages.
  • the coke breeze on the surface is ignited by means of a gas burner via an ignition hood.
  • a vacuum is applied via wind boxes or suction boxes, whereby air and resulting exhaust gases are sucked from top to bottom through the sintered layer and the combustion zone migrates from the surface in the direction of the suction boxes.
  • the iron ores begin to melt, stick together and form a sinter cake.
  • a speed of the sintering belt is chosen so that the sintered layer is completely burned through shortly before the end of the belt.
  • the sintered material is thrown off, broken and cooled on coolers, and then fed to the blast furnace via long conveyor belts.
  • the productivity of the sintering plant is closely related to the permeability of the sintering bed.
  • High permeability leads to good burning behavior and thus to a sinter cake that is evenly burned through.
  • moisture content is one of the most important parameters influencing permeability.
  • fine particles block the passage of air and lead to low permeability.
  • water blocks the passage of air between the grains of the sinter mix. In between there is an optimum where the fine particles are bound to the larger grains by the moisture and the permeability and thus the air passage is maximized.
  • the object of the present invention is to enable optimal operation of a sintering plant by allowing maximum air passage through the sintered material mixture.
  • the object is achieved by a method for setting the permeability which, using a data-based online model for the permeability, determines a target value for the moisture content of the sintered material mixture at which a maximum permeability of the sintered material mixture occurs.
  • the data-based online model has - for ongoing updating - data on at least one moisture content and the permeability of the sintered material mixture as input variables.
  • the determined moisture content is transferred as a setpoint to a moisture addition controller.
  • the data can be, for example, direct measurement data, data calculated from other measurement data, or estimated data.
  • a data-based model is created in an offline phase.
  • a validation of different models with different combinations of influencing variables is carried out in the offline phase on the basis of independent test data not used in the model creation.
  • the model performance is assessed with the help of a root mean square error, which provides a measure of the deviation of the model prediction for the permeability and the measured permeability.
  • Models are preferably used which provide a comprehensible formula for the relationship, for example a multiple linear regression.
  • This data-based model has at least the moisture content as an input variable.
  • the data-based model is continuously updated with data, preferably measurement data, the permeability and the moisture content when the sintering plant is in operation - i.e.
  • the permeability can also be determined indirectly by productivity. Productivity results from the weight of the sintered material produced over time. By recording the productivity, the permeability can also be determined to a good approximation.
  • This data-based online model makes it possible to determine the moisture content of the sintered material mixture at which a maximum permeability occurs. This moisture content is then transmitted as a setpoint to a moisture addition controller. This transmission should take place in reasonable time segments, which depend on the measurement intervals for the moisture content and / or the permeability. An update of the setpoint should always take place after a time interval which is at least greater than or equal to the measurement interval.
  • the measurement interval for the moisture content which is advantageously determined with a mix tester, is, for example, in a range from 5 to 30 minutes.
  • other regression models from machine learning - such as lasso regression, ridge regression, symbolic regression, decision tree, random forest, gradient boosting or neural networks.
  • the moisture content is set during the preparation of the sintered mix in a unit provided for this purpose - for example in a mixer, intensive mixer, granulator and / or a roller-burnishing drum.
  • the maximum permeability cannot be clearly detected, it could also be regulated with a slope at the point of the last specified permeability value. Depending on the gradient, the new predetermined permeability value would be increased, decreased or left unchanged. In this case, a linear model could also be used from which the slope is calculated. The use of the slope can be used, for example, if the operating point is on the edge of the curve and is well away from the optimum. Alternatively, the maximum or optimum can be determined by the slope. A combination of the two methods - slope and determination of the maximum - is possible. If a maximum can be determined, its position is used for regulation. If this cannot be determined, the control can be switched to by means of a gradient.
  • the online model of the permeability has a functional relationship between the moisture content, a square of the moisture content and preferably the bulk material density. It has been shown that the model can be further improved by taking into account the square of the moisture content and the determination of the maximum permeability is further improved by such a relationship.
  • the bulk density as a further input parameter enables a further improvement of the model and the determination of the permeability.
  • the coefficients c0, c1, c2 used here are adapted on the basis of existing data records from measurement data.
  • An advantageous embodiment provides that the moisture content, as a setpoint value, is transferred to the moisture addition controller only after at least thirty minutes, preferably after one hour. With this measure, the moisture addition controller has enough time to adjust to the new default value, and fluctuations are avoided. The goal is the most stable operation possible with few changes.
  • the desired value of the moisture content is in the range between 3.5% and 10%, preferably 3.8% -8%, particularly preferably 3.8% -4.3%.
  • this is the area in which the moisture content should move.
  • the maximum permeability normally occurs in this area.
  • Another preferred embodiment provides that the setpoint of the moisture content, which is transferred to the moisture addition controller, has a maximum deviation of 0.2% from the previous setpoint. This measure prevents the controller from overshooting and ensures stable operation.
  • the data-based online model takes into account at least two, preferably at least five, particularly preferably at least ten data sets with data - preferably measurement data.
  • the data used are, among other things, the moisture content and the permeability of the sintered material mixture.
  • the period of data acquisition has a different length of time for each of these data records.
  • Using multiple data sets makes the data-based model more stable. For example, data records are used which cover a short period of time but are very current. These data sets allow a quick adjustment to changed material properties.
  • Other data records that can be used are those with a long period of time, which are usually more stable. The data records can therefore overlap or be completely contained in another data record.
  • records with a short period of time can be completely contained in one of the records - with a longer period of time. It is conceivable that all data records have the same end time and differ only in a different start time.
  • the combination of several data sets, each with different time periods, ensures that the model can react stably on the one hand and react quickly to changed conditions on the other.
  • the periods of time range from a few hours to a few weeks and months.
  • At least one data record is weighted differently.
  • the data sets with the more recent periods are preferred a higher weighting factor than those data sets with a longer period in the past.
  • the data-based online model can be additionally improved by the weighting factors, since more recent data sets - i.e. data sets with a current and short period of time - have a stronger influence than those that are further back in time. This leads to a stable model, but also to a model that reacts very quickly to changed material conditions. It is therefore conceivable to combine data sets without weighting factors and data sets with weighting factors with one another or to weight all data sets. It is conceivable that individual or a number of data points in the respective data records have different weighting factors.
  • An embodiment that has proven to be advantageous provides that only data records are used for the data-based online model which have a clearly determinable maximum. If this is not the case, this data set is not used for the data-based online model. This measure is intended to ensure that only data records are used which each have a clear maximum. This ensures an unambiguous determination of the maximum permeability.
  • a further expedient embodiment provides that the online model has the functional relationship of the third degree.
  • the third-degree moisture content can also flow into the data-based online model.
  • the online model is determined by means of symbolic regression.
  • symbolic regression a number of operations and functions can be permitted, such as powers, logarithmic functions, exponential functions, trigonometric functions and / or types of calculation - such as plus, minus, division and / or multiplication.
  • a symbolic regression algorithm searches for the best formula using heuristic optimization methods matches the given training data. The relationship in the data records can possibly be described better because one also has more degrees of freedom.
  • the object is further achieved by a signal processing device for a sintering plant, with a machine-readable program code which has control commands which cause the signal processing device to carry out a method according to the method described above.
  • the object is also achieved by a machine-readable program code for a signal processing device for a sintering plant.
  • the program code has control commands which cause the signal processing device to carry out the method described above.
  • the object is also achieved by a storage medium with a machine-readable program code stored on it, as described above.
  • FIG. 1 is a schematic representation of a sintering plant 1 and an exemplary preparation of a Sinter mix 5 shown.
  • an intensive mixer 10 iron ore 11, which is in a grain size range from 200 ⁇ m to 10 mm, and raw materials 12 such as coke breeze, limestone, sintered debris, binders, dust and steel mill residues are introduced.
  • a moisture content is set by adding 13 water.
  • the addition of water 13 is controlled by a valve 13a which is connected to a water supply line 13b.
  • the moisture content of the sintered mix 5 is set by a moisture control 15.
  • the humidity control 15 receives a setpoint value for the humidity content 16a from a data-based online model 16. This nominal value of the moisture content 16a is compared with the measured moisture content 17a.
  • the humidity control 15 adjusts the valve 13a with a control variable 14 in order to ensure the optimal moisture content of the sintered material mixture 5 - as specified by the data-based online model 16.
  • the data-based online model 16 has as input variables at least the moisture content 17a and the permeability 17b of the sintered material mixture 5. It is also conceivable that the productivity is used instead of the permeability 17b. Further data 17 can also be transmitted to the data-based online model 16 as input parameters - for example grain size distribution, bulk material density and others. The moisture, permeability and bulk material density are preferably determined by a mixed material tester.
  • the sintered material mixture is applied to the sintering plant 1 at a material addition point 2.
  • a gas burner ignites coke breeze on the surface of the sintered material and a vacuum is applied through wind boxes 4, whereby air and resulting exhaust gases are sucked from top to bottom through the sintered layer.
  • the iron ores begin to melt, stick together and form a sinter cake.
  • At the end of the sintering plant there can also be a pin crusher (not shown) in order to crush the sinter cake.
  • the sintered material 6 is dropped at a material delivery point 3. Subsequently, the sintered material 6 is cooled on coolers and one Blast furnace fed (not shown).
  • the adjustment of the moisture content of the sintered material mixture 5 is essential in order to adjust the permeability 17b to an optimum.
  • the optimum of the permeability 17b makes it possible to ensure a maximum passage of air so that at the end of the sintering plant 1 a sintered material 6 that is uniformly well burned through is produced.
  • Fig. 2 is analogous to Fig. 1 an intensive mixer 10 is available.
  • the intensive mixer 10 is connected to a granulator 18 into which the mixed material 10a is transferred.
  • the mixed material 10a is fed to a further process step in the granulator 18 and then the granulated material 18a can be transferred to a roller-burnishing drum 19.
  • another unit such as a disk pelletizer is also possible.
  • the moisture content 17a is set not only in the intensive mixer 10 but also in the granulator 18 and the roller-burnishing drum 19.
  • the moisture addition controller 15 transmits the control signals 14 to the valves 13a for setting the water addition 13.
  • the moisture content can be specifically adjusted in each unit - intensive mixer 10, granulator 18 and roller drum 19.
  • the moisture content 17a is set in only two or one unit.
  • Fig. 3 a diagram of the permeability over time is shown.
  • the permeability is recorded as the Japanese Permeability Unit (JPU).
  • Measurement data from the permeability measurement 17b and a permeability prediction 20 of the data-based online model are entered in the diagram.
  • This diagram shows the good agreement between the measurement data and the prediction of the permeability 20.
  • the coefficients c0, c1, c2 were determined on the basis of existing data sets.
  • the humidity value 23 determined by the data-based online model is plotted in a diagram and the set humidity value 22.
  • the set humidity value 22 was only changed in this example if the determined humidity value changes by an amount of 0.05%. This amount can also be higher or lower. It would also be conceivable to make the settings almost continuously.
  • a number of individual model results 21 used are also shown in the diagram.
  • the number of individual model results used 21 are those data sets that are taken into account for the data-based online model. These data sets are individual model results from which a maximum can be clearly determined.
  • the widely varying number of data sets used shows that it can be important to use several data sets, as only a few data sets have a well-determinable maximum at certain points in time.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Manufacturing & Machinery (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • Materials Engineering (AREA)
  • Mechanical Engineering (AREA)
  • Metallurgy (AREA)
  • Organic Chemistry (AREA)
  • Manufacture And Refinement Of Metals (AREA)
EP20172392.1A 2020-04-30 2020-04-30 Procédé de réglage d'une perméabilité d'un produit fritté Withdrawn EP3904544A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
EP20172392.1A EP3904544A1 (fr) 2020-04-30 2020-04-30 Procédé de réglage d'une perméabilité d'un produit fritté
CN202180031391.1A CN115461478B (zh) 2020-04-30 2021-04-29 用于调整烧结材料的渗透率的方法
EP21721131.7A EP4143352A1 (fr) 2020-04-30 2021-04-29 Procédé de réglage de la perméabilité d'un produit à fritter
PCT/EP2021/061272 WO2021219790A1 (fr) 2020-04-30 2021-04-29 Procédé de réglage de la perméabilité d'un produit à fritter

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Application Number Priority Date Filing Date Title
EP20172392.1A EP3904544A1 (fr) 2020-04-30 2020-04-30 Procédé de réglage d'une perméabilité d'un produit fritté

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EP3904544A1 true EP3904544A1 (fr) 2021-11-03

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EP20172392.1A Withdrawn EP3904544A1 (fr) 2020-04-30 2020-04-30 Procédé de réglage d'une perméabilité d'un produit fritté
EP21721131.7A Pending EP4143352A1 (fr) 2020-04-30 2021-04-29 Procédé de réglage de la perméabilité d'un produit à fritter

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EP (2) EP3904544A1 (fr)
CN (1) CN115461478B (fr)
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114668164A (zh) * 2022-04-01 2022-06-28 河南中烟工业有限责任公司 基于来料差异性的松散回潮加水量自适应控制系统

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JP7529186B1 (ja) 2023-03-27 2024-08-06 Ｊｆｅスチール株式会社 ペレットの製造方法
CN117524386B (zh) * 2024-01-04 2024-06-04 之江实验室 基于微磁学和机器学习的磁性合金磁导率计算方法和装置

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EP1541700A1 (fr) * 2002-08-21 2005-06-15 Nippon Steel Corporation Procede de granulation de materiau de frittage pour la fabrication de fer
EP2330468A1 (fr) * 2009-12-04 2011-06-08 Tata Consultancy Services Limited Optimisation en ligne du durcissement de boulettes de minerai de fer humide sur une grille mobile
EP2848299B1 (fr) 2013-09-11 2019-08-14 Primetals Technologies Austria GmbH Procédé et dispositif destinés à la fabrication de granulés

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US4410355A (en) * 1979-11-06 1983-10-18 Voest-Alpine Aktiengesellschaft Process for controlling a pelletizing plant for fine-grained ores
JPH0797639A (ja) * 1993-08-06 1995-04-11 Sumitomo Metal Ind Ltd 焼結原料の造粒方法
JPH08134554A (ja) * 1994-09-14 1996-05-28 Sumitomo Metal Ind Ltd 焼結原料の造粒方法
EP1541700A1 (fr) * 2002-08-21 2005-06-15 Nippon Steel Corporation Procede de granulation de materiau de frittage pour la fabrication de fer
EP2330468A1 (fr) * 2009-12-04 2011-06-08 Tata Consultancy Services Limited Optimisation en ligne du durcissement de boulettes de minerai de fer humide sur une grille mobile
EP2848299B1 (fr) 2013-09-11 2019-08-14 Primetals Technologies Austria GmbH Procédé et dispositif destinés à la fabrication de granulés

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114668164A (zh) * 2022-04-01 2022-06-28 河南中烟工业有限责任公司 基于来料差异性的松散回潮加水量自适应控制系统

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Publication number Publication date
EP4143352A1 (fr) 2023-03-08
WO2021219790A1 (fr) 2021-11-04
CN115461478B (zh) 2024-08-06
CN115461478A (zh) 2022-12-09

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