Digital Transformation of Pyrometallurgical Technologies. State, Scientific Problems, and Prospects of Development

The article provides an overview and critical analysis of the digitalization of the leading Russian ferrous metallurgy enterprises in accordance with the Industry 4.0 development concept. This concept provides for the creation of digital twins of pyrometallurgical technologies and the widespread use...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Steel in translation Ročník 51; číslo 8; s. 522 - 530
Hlavní autoři: Spirin, N. A., Lavrov, V. V., Rybolovlev, V. Yu, Schnaider, D. A., Krasnobaev, A. V., Gurin, I. A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Moscow Pleiades Publishing 01.08.2021
Springer Nature B.V
Témata:
Algorithms
Artificial intelligence
Blast furnace practice
Blast furnaces
Chemistry and Materials Science
Control systems
Decision support systems
Digitization
Industrial applications
Industrial development
Materials Science
Metallurgical analysis
Metallurgy
Modules
Parameters
Smelting
Software
Subsystems
Systems (metallurgical)
Tuyeres
Vision systems
software
optimization
pyrometallurgical technologies
digital twins
digital transformation
intelligent control systems
technological tasks
machine vision
algorithm
ISSN:0967-0912, 1935-0988
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract The article provides an overview and critical analysis of the digitalization of the leading Russian ferrous metallurgy enterprises in accordance with the Industry 4.0 development concept. This concept provides for the creation of digital twins of pyrometallurgical technologies and the widespread use of machine (technical) vision and artificial intelligence. Examples of domestic industrial systems using machine (technical) vision, digital assistants (twins) of metallurgical facilities and their complexes in the production cycle are presented. Technical vision systems used to control the processes in the upper and lower zones of blast furnaces are considered with regard to blast-furnace production. A promising area is the integration of technical vision and decision support systems, including algorithms and software modules for implementing deterministic mathematical models of individual blast furnace smelting phenomena. These algorithms and modules are based on the basic physical concepts of blast-furnace smelting processes. One of the main directions of the digital transformation of pyrometallurgical technologies is the creation of intelligent control systems for the real-time management of industrial processes in individual units and their systems in metallurgy. The formulation and solution of tasks require not only studying the characteristics that describe the effect of changes in smelting conditions on the performance indicators of individual furnaces, but also a detailed analysis for mathematically describing external and internal constraints. The authors present examples of subsystems for controlling heat losses in a blast furnace, predicting the parameters of tuyere zones and controlling the distribution of air-blast parameters around the blast furnace perimeter, an automated system of analyzing and predicting production situations in the blast-furnace plant. These systems are based on modern principles and technologies of developing appropriate mathematical, algorithmic and software support.
AbstractList The article provides an overview and critical analysis of the digitalization of the leading Russian ferrous metallurgy enterprises in accordance with the Industry 4.0 development concept. This concept provides for the creation of digital twins of pyrometallurgical technologies and the widespread use of machine (technical) vision and artificial intelligence. Examples of domestic industrial systems using machine (technical) vision, digital assistants (twins) of metallurgical facilities and their complexes in the production cycle are presented. Technical vision systems used to control the processes in the upper and lower zones of blast furnaces are considered with regard to blast-furnace production. A promising area is the integration of technical vision and decision support systems, including algorithms and software modules for implementing deterministic mathematical models of individual blast furnace smelting phenomena. These algorithms and modules are based on the basic physical concepts of blast-furnace smelting processes. One of the main directions of the digital transformation of pyrometallurgical technologies is the creation of intelligent control systems for the real-time management of industrial processes in individual units and their systems in metallurgy. The formulation and solution of tasks require not only studying the characteristics that describe the effect of changes in smelting conditions on the performance indicators of individual furnaces, but also a detailed analysis for mathematically describing external and internal constraints. The authors present examples of subsystems for controlling heat losses in a blast furnace, predicting the parameters of tuyere zones and controlling the distribution of air-blast parameters around the blast furnace perimeter, an automated system of analyzing and predicting production situations in the blast-furnace plant. These systems are based on modern principles and technologies of developing appropriate mathematical, algorithmic and software support.
Author Rybolovlev, V. Yu
Gurin, I. A.
Schnaider, D. A.
Spirin, N. A.
Lavrov, V. V.
Krasnobaev, A. V.
Author_xml – sequence: 1
  givenname: N. A.
  surname: Spirin
  fullname: Spirin, N. A.
  email: n.a.spirin@urfu.ru
  organization: Ural Federal University named after the first President of Russia B.N. Yeltsin
– sequence: 2
  givenname: V. V.
  surname: Lavrov
  fullname: Lavrov, V. V.
  email: v.v.lavrov@urfu.ru
  organization: PJSC Magnitogorsk Iron and Steel Works
– sequence: 3
  givenname: V. Yu
  surname: Rybolovlev
  fullname: Rybolovlev, V. Yu
  email: rybolovlev@mmk.ru
  organization: PJSC Magnitogorsk Iron and Steel Works
– sequence: 4
  givenname: D. A.
  surname: Schnaider
  fullname: Schnaider, D. A.
  email: Shnayder.DA@mmk.ru
  organization: PJSC Magnitogorsk Iron and Steel Works
– sequence: 5
  givenname: A. V.
  surname: Krasnobaev
  fullname: Krasnobaev, A. V.
  email: krasnobaev.av@mmk.ru
  organization: PJSC Magnitogorsk Iron and Steel Works
– sequence: 6
  givenname: I. A.
  surname: Gurin
  fullname: Gurin, I. A.
  email: Ivan.gurin@urfu.ru
  organization: Ural Federal University named after the first President of Russia B.N. Yeltsin
BookMark eNp9kFtLw0AQhRepYFv9Ab4FfG3qXrJJ9lFab1Cw0PocNptJ3JLs1t1U6L93QwVB0adh5pxvZjgTNDLWAELXBM8Zwex2g0WaYUEoJTjHJGFnaEwE4zEWeT5C40GOB_0CTbzfYcxTyskYuaVudC_baOuk8bV1ney1NZGto_XR2Q6C1h5co9XgAfVmbGsbDX4ebXrZwyzaKA2m17VW0drZsoXOzyJpqqHze1C9H5Yt4QNau--C9RKd17L1cPVVp-j14X67eIpXL4_Pi7tVrCgjLKYpB8AVJKpmNWNCgsCJCCOeVaVSQqg8BaJKIcpKprzCCed5LoVIc0VrDmyKbk57986-H8D3xc4enAknC5rijNE8YSK4yMmlwrveQV3sne6kOxYEF0O0xa9oA5P9YFTIcMitd1K3_5L0RPpwxTTgvn_6G_oE8IaPTg
CitedBy_id crossref_primary_10_1016_j_dche_2023_100085
crossref_primary_10_3390_met12101624
Cites_doi 10.1088/1757-899X/411/1/012072
10.32339/0135-5910-2019-11-1231-1236
10.33313/380/213
10.1109/TII.2012.2226897
10.1016/j.powtec.2018.04.062
10.1109/TIM.2011.2178675
10.1587/transinf.2017EDP7127
10.1007/s11015-018-0676-0
10.2355/isijinternational.ISIJINT-2017-344
10.1109/MITP.2016.15
10.3103/S0967091218100108
10.3390/s18113792
10.1007/s11015-017-0516-7
10.2355/isijinternational.42.44
10.33313/377/061
10.1007/s11015-018-0648-4
10.33313/377/050
10.1016/S0952-1976(00)00062-2
10.1109/IntelliSys.2017.8324364
10.3103/S0967091210010079
10.1016/S1006-706X(13)60108-9
10.1016/S1006-706X(16)30035-8
10.1016/C2017-0-00007-1
10.1080/25726641.2020.1733357
10.2355/isijinternational.50.914
10.33313/377/062
10.2355/isijinternational.46.1297
10.1016/j.conengprac.2018.10.009
10.33313/380/035
10.32339/0135-5910-2020-4-339-343
10.1109/ICIEAM.2016.7910936
10.1002/srin.201700071
10.1016/j.orp.2015.05.001
10.1007/s11015-020-00907-y
10.2355/isijinternational.44.573
ContentType Journal Article
Copyright Allerton Press, Inc. 2021. ISSN 0967-0912, Steel in Translation, 2021, Vol. 51, No. 8, pp. 522–530. © Allerton Press, Inc., 2021. Russian Text © The Author(s), 2021, published in Izvestiya Vysshikh Uchebnykh Zavedenii, Chernaya Metallurgiya, 2021, No. 8, pp. 588–598.
Copyright_xml – notice: Allerton Press, Inc. 2021. ISSN 0967-0912, Steel in Translation, 2021, Vol. 51, No. 8, pp. 522–530. © Allerton Press, Inc., 2021. Russian Text © The Author(s), 2021, published in Izvestiya Vysshikh Uchebnykh Zavedenii, Chernaya Metallurgiya, 2021, No. 8, pp. 588–598.
DBID AAYXX
CITATION
8BQ
8FD
JG9
DOI 10.3103/S0967091221080143
DatabaseName CrossRef
METADEX
Technology Research Database
Materials Research Database
DatabaseTitle CrossRef
Materials Research Database
Technology Research Database
METADEX
DatabaseTitleList Materials Research Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1935-0988
EndPage 530
ExternalDocumentID 10_3103_S0967091221080143
GroupedDBID -58
-5G
-BR
-EM
-Y2
-~C
.86
.VR
06C
06D
0R~
0VY
123
1N0
29Q
2J2
2JN
2JY
2KG
2KM
2LR
2VQ
2~H
30V
4.4
408
40D
40E
5VS
6NX
8FE
8FG
8TC
95-
95.
95~
96X
AAAVM
AABHQ
AACDK
AAHNG
AAIAL
AAJBT
AAJKR
AANZL
AARHV
AARTL
AASML
AATNV
AATVU
AAUYE
AAWCG
AAYIU
AAYQN
AAYTO
AAYZH
ABAKF
ABDZT
ABECU
ABFTD
ABFTV
ABHQN
ABJCF
ABJNI
ABJOX
ABKCH
ABMNI
ABMQK
ABNWP
ABQBU
ABSXP
ABTEG
ABTHY
ABTKH
ABTMW
ABULA
ABWNU
ABXPI
ACAOD
ACBXY
ACCUX
ACDTI
ACGFS
ACHSB
ACHXU
ACIWK
ACKNC
ACMDZ
ACMLO
ACOKC
ACOMO
ACPIV
ACSNA
ACZOJ
ADHHG
ADHIR
ADINQ
ADKNI
ADRFC
ADURQ
ADYFF
ADZKW
AEBTG
AEFQL
AEGAL
AEGNC
AEJHL
AEJRE
AEMSY
AEOHA
AEPYU
AETLH
AEVLU
AEXYK
AFBBN
AFGCZ
AFKRA
AFLOW
AFQWF
AFWTZ
AFZKB
AGAYW
AGDGC
AGJBK
AGMZJ
AGQMX
AGRTI
AGWIL
AGWZB
AGYKE
AHAVH
AHBYD
AHSBF
AIAKS
AIGIU
AIIXL
AILAN
AITGF
AJBLW
AJRNO
ALMA_UNASSIGNED_HOLDINGS
ALWAN
AMKLP
AMXSW
AMYLF
AMYQR
AOCGG
ARMRJ
AXYYD
B-.
BA0
BDATZ
BENPR
BGLVJ
BGNMA
CAG
CCPQU
COF
CS3
D1I
DDRTE
DNIVK
DPUIP
EBLON
EBS
EIOEI
EJD
ESBYG
FERAY
FFXSO
FIGPU
FINBP
FNLPD
FRRFC
FSGXE
FWDCC
G-Y
G-Z
GGCAI
GGRSB
GJIRD
GNWQR
GQ6
GQ7
H13
HCIFZ
HF~
HG6
HLICF
HMJXF
HRMNR
HZ~
IJ-
IKXTQ
IWAJR
I~X
I~Z
J-C
JBSCW
JZLTJ
KB.
KOV
LLZTM
M4Y
MA-
NPVJJ
NQJWS
NU0
O9-
O93
O9J
P9N
PDBOC
PF0
PT4
Q2X
QOR
QOS
R89
R9I
ROL
RPX
RSV
S16
S1Z
S27
S3B
SAP
SCM
SDH
SHX
SJYHP
SNE
SNPRN
SNX
SOHCF
SOJ
SPISZ
SRMVM
SSLCW
STPWE
SZN
T13
TSG
TUC
U2A
UG4
UOJIU
UTJUX
UZXMN
VC2
VFIZW
W48
WK8
XU3
YLTOR
Z7R
Z7S
Z7Y
Z7Z
Z85
ZMTXR
~A9
AAPKM
AAYXX
ABDBE
ABFSG
ABRTQ
ACSTC
AEZWR
AFDZB
AFFHD
AFHIU
AFOHR
AHPBZ
AHWEU
AIXLP
ATHPR
CITATION
PHGZM
PHGZT
PQGLB
8BQ
8FD
JG9
ID FETCH-LOGICAL-c2313-265ee0de4cf3f339ae9049ee057dbcc99c86e1cb99bda65d045588a9968c2f5e3
IEDL.DBID RSV
ISSN 0967-0912
IngestDate Thu Sep 25 00:39:31 EDT 2025
Tue Nov 18 21:01:41 EST 2025
Sat Nov 29 05:29:07 EST 2025
Fri Feb 21 02:47:51 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 8
Keywords software
optimization
pyrometallurgical technologies
digital twins
digital transformation
intelligent control systems
technological tasks
machine vision
algorithm
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c2313-265ee0de4cf3f339ae9049ee057dbcc99c86e1cb99bda65d045588a9968c2f5e3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
PQID 2607328439
PQPubID 326300
PageCount 9
ParticipantIDs proquest_journals_2607328439
crossref_primary_10_3103_S0967091221080143
crossref_citationtrail_10_3103_S0967091221080143
springer_journals_10_3103_S0967091221080143
PublicationCentury 2000
PublicationDate 20210800
PublicationDateYYYYMMDD 2021-08-01
PublicationDate_xml – month: 8
  year: 2021
  text: 20210800
PublicationDecade 2020
PublicationPlace Moscow
PublicationPlace_xml – name: Moscow
– name: London
PublicationTitle Steel in translation
PublicationTitleAbbrev Steel Transl
PublicationYear 2021
Publisher Pleiades Publishing
Springer Nature B.V
Publisher_xml – name: Pleiades Publishing
– name: Springer Nature B.V
References Zagainov, S.A., Onorin, O.P., Gileva, L.Yu., Volkov, D.N., and Tleugobulov, B.S., Development and implementation of mathematical and software support for flexible technological modes of blast furnace operation, Stal’, 2000, no. 9, pp. 12–15.
Frenkel’, M.M., Fedulov, Yu.V., Belova, O.A., et al., Expert control system for blast furnace smelting, Stal’, 1992, no. 7, pp. 15–18.
Spirin, N.A., Onorin, O.P., Istomin, A.S., Lavrov, V.V., and Gurin, I.A., Information modelling system for diagnostics of different types of blast-furnace smelting deviations from normal conditions, IOP Conf. Ser.: Mater. Sci. Eng., 2018, vol. 411, no. 1, art. ID 012072. https://doi.org/10.1088/1757-899X/411/1/012072
HashimotoY.KitamuraY.OhashiT.SawaY.KanoM.Transient model-based operation guidance on blast furnaceControl Eng. Pract20198213014110.1016/j.conengprac.2018.10.009
BambauerF.WirtzS.SchererV.BartuschH.Transient DEM-CFD simulation of solid and fluid flow in a three dimensional blast furnace modelPowder Technol201833453641:CAS:528:DC%2BC1cXovVOltLc%3D10.1016/j.powtec.2018.04.062
Digital twin, Digital twin of organization. http:// www.tadviser.ru/index.php/Статья:Цифровой_двойник_ (Digital_Twin). Cited August 5, 2021.
Spirin, N.A., Lavrov, V.V., Rybolovlev, V.Yu., Gileva, L.Yu., Krasnobaev, A.V., Shvydkii, V.S., Onorin, O.P., Shchipanov, K.A., and Burykin, A.A., Matematicheskoe modelirovanie metallurgicheskikh protsessov v ASU TP (Mathematical Modeling of Metallurgical Processes in APCS), Yekaterinburg: Ural. Fed. Univ., 2014.
SpirinN.A.PolinovA.A.GurinI.A.BeginyukV.A.PishnograevS.N.IstominA.S.Information system for real-time prediction of the silicon content of iron in a blast furnaceMetallurgist2020638989051:CAS:528:DC%2BB3cXislelsLc%3D10.1007/s11015-020-00907-y
SibagatullinS.K.KharchenkoA.S.DevyatchenkoL.D.Assessment of nonuniform batch distribution in blast furnaceSteel Transl20184862463010.3103/S0967091218100108
Spirin, N.A., Lavrov, V.V., Rybolovlev, V.Yu., Krasnobaev, A.V., Onorin, O.P., and Kosachenko, I.E., Model’nye sistemy podderzhki prinyatiya reshenii v ASU TP domennoi plavki (Model Decision Support Systems in APCS of Blast Furnace Smelting), Yekaterinburg: Ural. Fed. Univ., 2011.
Bol’shakov, V.I., Tekhnologiya vysokoeffektivnoi energosberegayushchei domennoi plavki (Technology of High-Efficiency Energy-Saving Blast Furnace Smelting), Kiev: Naukova Dumka, 2007.
Dobroskok, V.A., Kuznetsov, N.A., and Tumanov, A.I., Mathematical models of gas dynamics and reduction processes in a blast furnace, Izv. Vyssh. Uchebn. Zaved., Chern. Metall., 1985, no. 3, pp. 145, 146.
AbhaleP.B.ViswanathanN.N.SaxenH.Numerical modeling of blast furnace—Evolution and recent trendsMiner. Process. Extr. Metall20201291661831:CAS:528:DC%2BB3cXls1GhsLg%3D10.1080/25726641.2020.1733357
KuangS.LiZ.YuA.Review on modeling and simulation of blast furnaceSteel Res. Int20188917000711:CAS:528:DC%2BC2sXhtFamt7bF10.1002/srin.201700071
Dmitriev, A.N., Matematicheskoe modelirovanie domennogo protsessa (Mathematical Modeling of Blast Furnace Process), Yekaterinburg: Ural. Otd., Ross. Akad. Nauk, 2011.
ZhuQ.LüC.-L.YinY.-X.ChenX.-Z.Burden distribution calculation of bell-less top of blast furnace based on multi-radar dataJ. Iron Steel Res. Int201320333710.1016/S1006-706X(13)60108-9
Alter, M.A., Optimization of parameters of blast furnace smelting under conditions of plant operation with limited supply of coke, natural gas or iron-bearing materials, Proc. AISTech 2020 of the Iron and Steel Technology Conf., Cleveland, OH, Warrendale, PA: Assoc. Iron Steel Technol., 2020, vol. 1, pp. 302–309. https://doi.org/10.33313/380/035
Solov’ev, V.I., Pavlova, E.A., and Krasnobaev, V.A., Intelligent automated control system for metallurgical units, Chern. Met., 2004, nos. 7–8, pp. 26–29.
ChenJ.Predictive system for blast furnaces by integrating a neural network with qualitative analysisEng. Appl. Artif. Intell200114778510.1016/S0952-1976(00)00062-2
Spirin, N.A., Ovchinnikov, Yu.N., Shvydkii, V.S., and Yaroshenko, Yu.G., Teploobmen i povyshenie effektivnosti domennoi plavki (Heat Transfer and Improving the Efficiency of Blast-Furnace Smelting), Yekaterinburg: Ural. Gos. Tekh. Univ., 1995.
Bettinger, D., Fritschek, H., Schaler, M., Kronberger, T., and Wollhofen, R., A holistic approach to ironmaking digitalization, Proc. AISTech 2019 of the Iron and Steel Technology Conf., Pittsburgh, PA, Warrendale, PA: Assoc. Iron Steel Technol., 2019, pp. 577–585. https://doi.org/10.33313/377/061
PavlovA.V.PolinovA.A.SpirinN.A.OnorinO.P.LavrovV.V.Use of model systems for solving new technological problems in blast-furnace productionMetallurgist2017614484541:CAS:528:DC%2BC2sXhslKgurzN10.1007/s11015-017-0516-7
Kitaev, B.I., Yaroshenko, Yu.G., Sukhanov, E.L., Ovchinnikov, Yu.N., and Shvydkii, V.S., Teplotekhnika domennogo protsessa (Heat Engineering of Blast Furnace), Moscow: Metallurgiya, 1978.
PolinovA.A.PavlovA.V.OnorinO.P.SpirinN.A.GurinI.A.Blast distribution over the air tuyeres of a blast furnaceMetallurgist2018624184241:CAS:528:DC%2BC1cXhslKqtb7M10.1007/s11015-018-0676-0
The ChelPipe Group has introduced a new technology for testing pipes based on machine vision, Press Center of the ChelPipe Group. https://chelpipe.ru/press-center/gruppa-chtpz-vnedrila-novuyu-tekhnologiyu-ispytaniya-trub-na-osnove-mashinnogo-zreniya-/. Cited August 5, 2021.
OnorinO.P.PolinovA.A.PavlovA.V.SpirinN.A.GurinI.A.About a possibility of using blast furnace heat balance to control heat lossesMetallurgist2018622182241:CAS:528:DC%2BC1cXhsFOlur3F10.1007/s11015-018-0648-4
WaissiG.R.DemirM.HumbleJ.E.LevB.Automation of strategy using IDEF0—A proof of conceptOper. Res. Persp2015210611310.1016/j.orp.2015.05.001
Kulakov, S.M. and Trofimov, V.B., Intellektual’nye sistemy upravleniya tekhnologicheskimi ob”ektami: teoriya i praktika (Intelligent Control Systems for Technological Objects: Theory and Practice), Novokuznetsk: Sib. Gos. Ind. Univ., 2009.
Machine (technical) vision, Metallurgy, Mallenom Systems. https://www.mallenom.ru/resheniya/mashinnoe-zrenie/po-otroslyam/metallurgiia. Cited August 5, 2021.
Murav’evaI.G.TogobitskayaD.N.NesterovA.S.IvanchaN.G.A new level of blast furnace smelting control in FMI developments, Chern. Metall., Byull. Nauchno-Tekh.Ekon. Inf2019751231123610.32339/0135-5910-2019-11-1231-1236
Chentsov, A.V., Chesnokov, Yu.A., and Shavrin, S.V., Balansovaya logiko-statisticheskaya model’ domennogo protsessa (Balance Logical- Statistical Model of Blast-Furnace Process), Yekaterinburg: Ural. Otd., Ross. Akad. Nauk, 2003.
Tsymbal, V.P., Matematicheskoe modelirovanie slozhnykh sistem v metallurgii (Mathematical Modeling of Complex Systems in Metallurgy), Kemerovo: Kuzbassvuzizdat, 2006.
Ramm, A.N., Sovremennyi domennyi protsess (Modern Blast Furnace Process), Moscow: Metallurgiya, 1980.
Spirin, N.A., Ipatov, Yu.V., Lobanov, V.I., Krasnobaev, V.A., Lavrov, V.V., Rybolovlev, V.Yu., Shvydkii, V.S., Zagainov, S.A., and Onorin, O.P., Informatsionnye sistemy v metallurgii (Information Systems in Metallurgy), Yekaterinburg: Ural. Gos. Tekh. Univ.–Ural. Politekh. Inst., 2001.
Fu, D., Chen, Y., Rahman, M.T., Zhou, C.Q., D’Alessio, J., and Ferron, K.J., Validation of the numerical model for blast furnace shaft process, Proc. AISTech 2012 of the Iron and Steel Technology Conf., Atlanta, GA, Warrendale, PA: Assoc. Iron Steel Technol., 2012, no. 92531, pp. 417–427.
Kazarinov, L.S. and Barbasova, T.A., Elliptic component analysis, Proc. 2016 Int. Conf. on Industrial Engineering, Applications and Manufacturing (ICIEAM), Chelyabinsk, 2016, no. 7910936. https://doi.org/10.1109/ICIEAM.2016.7910936
SaxenH.GaoC.GaoZ.Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—A reviewIEEE Trans. Ind. Inf201392213222510.1109/TII.2012.2226897
Control of position of hot-rolled coils on a conveyor, Mallenom Systems. https://www.mallenom.ru/vnedrenia/pmz/metallurgy2. Cited August 5, 2021.
De CastroJ.A.NogamiH.YagiJ.-I.Three-dimensional multiphase mathematical modeling of the blast furnace based on the multifluid modelISIJ Int200242445210.2355/isijinternational.42.44
Zhang, Y., Sukhram, M., Cameron, I., Bolen, J., and Rozo, A., Industrial perspective of digital twin development and applications for iron and steel processes, Proc. AISTech 2020 of the Iron and Steel Technology Conf., Cleveland, OH, Warrendale, PA: Assoc. Iron Steel Technol., 2020, vol. 3, pp. 1975–1984. https://doi.org/10.33313/380/213
JimenezJ.MochonJ.De AyalaJ.S.ObesoF.Blast furnace hot metal temperature prediction through neural networks-based modelsISIJ Int2004445735801:CAS:528:DC%2BD2cXis1KntL0%3D10.2355/isijinternational.44.573
KamoK.HamamotoK.MaedaT.NarazakiH.YakeyaM.TanakaY.Method for predicting gas channeling in blast furnaceRes. Dev. Kobe Steel Eng. Rep201868711
Severstal is mastering manufacture of video inspection systems for rolled surfaces, Metal supply and sales. https://www.metalinfo.ru/ru/news/118725. Cited August 5, 2021.
Andronov, V.N., Ekstraktsiya chernykh metallov iz prirodnogo i tekhnogennogo syr’ya. Domennyi protsess (Extraction of Ferrous Metals from Natural and Technogenic Raw Materials. Blast Furnace Process), Donetsk: Nord-Press, 2009.
PeaceyJ.G.DavenportW.G.The Iron Blast Furnace: Theory and Practice2013AmsterdamElsevier
Tovarovskii, I.G., Domennaya plavka (Blast Furnace Smelting), Dnepropetrovsk: Porogi, 2009.
PanD.JiangZ.ChenZ.GuiW.XieY.YangC.Temperature measurement method for blast furnace molten iron based on infrared thermography and temperature reduction modelSensors20181837921:CAS:528:DC%2BC1MXhtFWhurzP10.3390/s18113792
UedaS.NatsuiS.NogamiH.YagiJ.-I.AriyamaT.Recent progress and future perspective on mathematical modeling of blast furnaceISIJ Int2010509149231:CAS:528:DC%2BC3cXhtVWlsb7L10.2355/isijinternational.50.914
DmitrievA.N.ZolotykhM.O.Vit’kinaG.Yu.Improvement of sintering and blast-furnace production using digital technologies within the framework of Industry 4.0, Chern. Metall., Byull. Nauchno-Tekh.Ekon. Inf2020633934510.32339/
K. Kamo (1381_CR58) 2018; 68
B. Chen (1381_CR69) 2016; 18
Y. Hashimoto (1381_CR42) 2019; 82
A.V. Pavlov (1381_CR40) 2017; 61
H. Saxen (1381_CR43) 2013; 9
S. Ueda (1381_CR35) 2010; 50
J. Jimenez (1381_CR50) 2004; 44
J. Chen (1381_CR49) 2001; 14
1381_CR2
A.N. Dmitriev (1381_CR4) 2020; 6
1381_CR1
P.B. Abhale (1381_CR19) 2020; 129
1381_CR47
1381_CR48
R. Usamentiaga (1381_CR13) 2012; 61
1381_CR45
1381_CR46
G. Stockman (1381_CR5) 2001
E.N. Ishmet’ev (1381_CR17) 2010; 40
1381_CR16
N.A. Spirin (1381_CR18) 1998; 28
1381_CR10
1381_CR54
1381_CR11
1381_CR55
1381_CR52
1381_CR53
1381_CR56
J.A. De Castro (1381_CR22) 2002; 42
1381_CR57
A.A. Polinov (1381_CR60) 2018; 62
F. Pettersson (1381_CR62) 2006; 46
L. Shi (1381_CR14) 2016; 23
I.G. Murav’eva (1381_CR41) 2019; 75
D. Pan (1381_CR12) 2018; 18
1381_CR29
1381_CR27
1381_CR28
O.P. Onorin (1381_CR59) 2018; 62
S. Kuang (1381_CR61) 2018; 89
1381_CR21
1381_CR65
1381_CR66
1381_CR63
Q. Zhu (1381_CR15) 2013; 20
1381_CR64
1381_CR25
1381_CR26
1381_CR68
1381_CR8
1381_CR7
1381_CR9
J.G. Peacey (1381_CR23) 2013
1381_CR3
S.K. Sibagatullin (1381_CR51) 2018; 48
1381_CR6
F. Bambauer (1381_CR20) 2018; 334
J. Phan (1381_CR70) 2010
N.A. Spirin (1381_CR44) 2020; 63
1381_CR38
1381_CR39
M. Baniasadi (1381_CR24) 2018; 58
1381_CR32
1381_CR33
1381_CR30
1381_CR31
1381_CR36
G.R. Waissi (1381_CR67) 2015; 2
1381_CR37
1381_CR34
References_xml – reference: ShiL.WenY.-B.ZhaoG.-S.YuT.Recognition of blast furnace gas flow center distribution based on infrared image processingJ. Iron Steel Res. Int20162320320910.1016/S1006-706X(16)30035-8
– reference: Kurunov, I.F. and Yashchenko, S.B., Methodology for calculating the technical and economic indicators of blast-furnace smelting, Nauchn. Tr. Mosk. Inst. Stali Splavov, 1983, no. 152, pp. 57–64.
– reference: Spirin, N.A., Ovchinnikov, Yu.N., Shvydkii, V.S., and Yaroshenko, Yu.G., Teploobmen i povyshenie effektivnosti domennoi plavki (Heat Transfer and Improving the Efficiency of Blast-Furnace Smelting), Yekaterinburg: Ural. Gos. Tekh. Univ., 1995.
– reference: Cameron, I., Sukhram, M., Lefebvre, K., and Davenport, W., Blast Furnace Ironmaking: Analysis, Control and Optimization, Amsterdam: Elsevier, 2019, 1st ed. https://doi.org/10.1016/C2017-0-00007-1
– reference: SpirinN.A.PolinovA.A.GurinI.A.BeginyukV.A.PishnograevS.N.IstominA.S.Information system for real-time prediction of the silicon content of iron in a blast furnaceMetallurgist2020638989051:CAS:528:DC%2BB3cXislelsLc%3D10.1007/s11015-020-00907-y
– reference: BaniasadiM.PetersB.Preliminary investigation on the capability of extended discrete element method for treating the dripping zone of a blast furnaceISIJ Int20185825341:CAS:528:DC%2BC1cXptFaqsbY%3D10.2355/isijinternational.ISIJINT-2017-344
– reference: Zhang, Y., Sukhram, M., Cameron, I., Bolen, J., and Rozo, A., Industrial perspective of digital twin development and applications for iron and steel processes, Proc. AISTech 2020 of the Iron and Steel Technology Conf., Cleveland, OH, Warrendale, PA: Assoc. Iron Steel Technol., 2020, vol. 3, pp. 1975–1984. https://doi.org/10.33313/380/213
– reference: PanD.JiangZ.ChenZ.GuiW.XieY.YangC.Temperature measurement method for blast furnace molten iron based on infrared thermography and temperature reduction modelSensors20181837921:CAS:528:DC%2BC1MXhtFWhurzP10.3390/s18113792
– reference: Alter, M.A., Optimization of parameters of blast furnace smelting under conditions of plant operation with limited supply of coke, natural gas or iron-bearing materials, Proc. AISTech 2020 of the Iron and Steel Technology Conf., Cleveland, OH, Warrendale, PA: Assoc. Iron Steel Technol., 2020, vol. 1, pp. 302–309. https://doi.org/10.33313/380/035
– reference: PetterssonF.SaxenH.Model for economic optimization of iron production in the blast furnaceISIJ Int200646129713051:CAS:528:DC%2BD28XhtV2hsbnL10.2355/isijinternational.46.1297
– reference: Spirin, N.A., Ipatov, Yu.V., Lobanov, V.I., Krasnobaev, V.A., Lavrov, V.V., Rybolovlev, V.Yu., Shvydkii, V.S., Zagainov, S.A., and Onorin, O.P., Informatsionnye sistemy v metallurgii (Information Systems in Metallurgy), Yekaterinburg: Ural. Gos. Tekh. Univ.–Ural. Politekh. Inst., 2001.
– reference: OnorinO.P.PolinovA.A.PavlovA.V.SpirinN.A.GurinI.A.About a possibility of using blast furnace heat balance to control heat lossesMetallurgist2018622182241:CAS:528:DC%2BC1cXhsFOlur3F10.1007/s11015-018-0648-4
– reference: Chentsov, A.V., Chesnokov, Yu.A., and Shavrin, S.V., Balansovaya logiko-statisticheskaya model’ domennogo protsessa (Balance Logical- Statistical Model of Blast-Furnace Process), Yekaterinburg: Ural. Otd., Ross. Akad. Nauk, 2003.
– reference: Forum “Information Technologies in Metallurgy and Metalworking,” ITMETALL Forum. https:// итметалл.рф/. Cited January 10, 2021.
– reference: UedaS.NatsuiS.NogamiH.YagiJ.-I.AriyamaT.Recent progress and future perspective on mathematical modeling of blast furnaceISIJ Int2010509149231:CAS:528:DC%2BC3cXhtVWlsb7L10.2355/isijinternational.50.914
– reference: JimenezJ.MochonJ.De AyalaJ.S.ObesoF.Blast furnace hot metal temperature prediction through neural networks-based modelsISIJ Int2004445735801:CAS:528:DC%2BD2cXis1KntL0%3D10.2355/isijinternational.44.573
– reference: Gordon, Y., Izumskiy, N., Matveienko, G., Chaika, O., Lebid, V., and Vyshinskya, O., Diagnostics, optimization and mathematical models of coke-sinter-hot metal production process, Proc. AISTech 2019 of the Iron and Steel Technology Conf., Pittsburgh, PA, 2019, pp. 479–484. https://doi.org/10.33313/377/050
– reference: Digital twin. Gartner glossary. https://www.gartner.com/en/information-technology/glossary/digital-twin. Cited August 5, 2021.
– reference: Ramm, A.N., Sovremennyi domennyi protsess (Modern Blast Furnace Process), Moscow: Metallurgiya, 1980.
– reference: Andronov, V.N., Ekstraktsiya chernykh metallov iz prirodnogo i tekhnogennogo syr’ya. Domennyi protsess (Extraction of Ferrous Metals from Natural and Technogenic Raw Materials. Blast Furnace Process), Donetsk: Nord-Press, 2009.
– reference: Emel’yanov, S.V., Korovin, S.K., Myshlyaev, L.P., Rykov, A.S., and Evtushenko, V.F., Teoriya i praktika prognozirovaniya v sistemakh upravleniya (Theory and Practice of Prediction in Control Systems), Moscow: Rossiiskie Universitety, 2008.
– reference: SpirinN.A.ShvydkiiV.S.OvchinnikovYu.N.LavrovV.V.GusevA.A.Mathematical modeling of heat transfer in blast furnace racewaySteel Transl19982858
– reference: PolinovA.A.PavlovA.V.OnorinO.P.SpirinN.A.GurinI.A.Blast distribution over the air tuyeres of a blast furnaceMetallurgist2018624184241:CAS:528:DC%2BC1cXhslKqtb7M10.1007/s11015-018-0676-0
– reference: Ishmet’evE.N.SalikhovZ.G.ShchetininA.P.BudadinZ.G.Automatic diagnostics of the state of hazardous zones in pyrometallurgical systemsSteel Transl201040273010.3103/S0967091210010079
– reference: Digital twin, Digital twin of organization. http:// www.tadviser.ru/index.php/Статья:Цифровой_двойник_ (Digital_Twin). Cited August 5, 2021.
– reference: Bol’shakov, V.I., Tekhnologiya vysokoeffektivnoi energosberegayushchei domennoi plavki (Technology of High-Efficiency Energy-Saving Blast Furnace Smelting), Kiev: Naukova Dumka, 2007.
– reference: StockmanG.ShapiroL.G.Computer Vision2001Upper Saddle River, NJPrentice Hall
– reference: AbhaleP.B.ViswanathanN.N.SaxenH.Numerical modeling of blast furnace—Evolution and recent trendsMiner. Process. Extr. Metall20201291661831:CAS:528:DC%2BB3cXls1GhsLg%3D10.1080/25726641.2020.1733357
– reference: WaissiG.R.DemirM.HumbleJ.E.LevB.Automation of strategy using IDEF0—A proof of conceptOper. Res. Persp2015210611310.1016/j.orp.2015.05.001
– reference: HashimotoY.KitamuraY.OhashiT.SawaY.KanoM.Transient model-based operation guidance on blast furnaceControl Eng. Pract20198213014110.1016/j.conengprac.2018.10.009
– reference: Spirin, N.A., Lavrov, V.V., Rybolovlev, V.Yu., Gileva, L.Yu., Krasnobaev, A.V., Shvydkii, V.S., Onorin, O.P., Shchipanov, K.A., and Burykin, A.A., Matematicheskoe modelirovanie metallurgicheskikh protsessov v ASU TP (Mathematical Modeling of Metallurgical Processes in APCS), Yekaterinburg: Ural. Fed. Univ., 2014.
– reference: Frenkel’, M.M., Fedulov, Yu.V., Belova, O.A., et al., Expert control system for blast furnace smelting, Stal’, 1992, no. 7, pp. 15–18.
– reference: Dobroskok, V.A., Kuznetsov, N.A., and Tumanov, A.I., Mathematical models of gas dynamics and reduction processes in a blast furnace, Izv. Vyssh. Uchebn. Zaved., Chern. Metall., 1985, no. 3, pp. 145, 146.
– reference: ZhuQ.LüC.-L.YinY.-X.ChenX.-Z.Burden distribution calculation of bell-less top of blast furnace based on multi-radar dataJ. Iron Steel Res. Int201320333710.1016/S1006-706X(13)60108-9
– reference: Control of position of hot-rolled coils on a conveyor, Mallenom Systems. https://www.mallenom.ru/vnedrenia/pmz/metallurgy2. Cited August 5, 2021.
– reference: Murav’evaI.G.TogobitskayaD.N.NesterovA.S.IvanchaN.G.A new level of blast furnace smelting control in FMI developments, Chern. Metall., Byull. Nauchno-Tekh.Ekon. Inf2019751231123610.32339/0135-5910-2019-11-1231-1236
– reference: Ge, A.X., A neural network approach to the modeling of blast furnace, MSc Thesis, Cambridge, MA: Mass. Inst. Technol., 1999.
– reference: Kitaev, B.I., Yaroshenko, Yu.G., Sukhanov, E.L., Ovchinnikov, Yu.N., and Shvydkii, V.S., Teplotekhnika domennogo protsessa (Heat Engineering of Blast Furnace), Moscow: Metallurgiya, 1978.
– reference: ChenJ.Predictive system for blast furnaces by integrating a neural network with qualitative analysisEng. Appl. Artif. Intell200114778510.1016/S0952-1976(00)00062-2
– reference: The ChelPipe Group has introduced a new technology for testing pipes based on machine vision, Press Center of the ChelPipe Group. https://chelpipe.ru/press-center/gruppa-chtpz-vnedrila-novuyu-tekhnologiyu-ispytaniya-trub-na-osnove-mashinnogo-zreniya-/. Cited August 5, 2021.
– reference: Tsymbal, V.P., Matematicheskoe modelirovanie slozhnykh sistem v metallurgii (Mathematical Modeling of Complex Systems in Metallurgy), Kemerovo: Kuzbassvuzizdat, 2006.
– reference: Spirin, N.A., Onorin, O.P., Istomin, A.S., Lavrov, V.V., and Gurin, I.A., Information modelling system for diagnostics of different types of blast-furnace smelting deviations from normal conditions, IOP Conf. Ser.: Mater. Sci. Eng., 2018, vol. 411, no. 1, art. ID 012072. https://doi.org/10.1088/1757-899X/411/1/012072
– reference: Xie, H., Wang, J., Wang, G., and Sun, X.-D., Application of big data in optimization of blast furnace operation, Proc. AISTech 2019 of the Iron and Steel Technology Conf., Pittsburgh, PA, Warrendale, PA: Assoc. Iron Steel Technol., 2019, pp. 587–591. https://doi.org/10.33313/377/062
– reference: Bettinger, D., Fritschek, H., Schaler, M., Kronberger, T., and Wollhofen, R., A holistic approach to ironmaking digitalization, Proc. AISTech 2019 of the Iron and Steel Technology Conf., Pittsburgh, PA, Warrendale, PA: Assoc. Iron Steel Technol., 2019, pp. 577–585. https://doi.org/10.33313/377/061
– reference: KamoK.HamamotoK.MaedaT.NarazakiH.YakeyaM.TanakaY.Method for predicting gas channeling in blast furnaceRes. Dev. Kobe Steel Eng. Rep201868711
– reference: Kulakov, S.M. and Trofimov, V.B., Intellektual’nye sistemy upravleniya tekhnologicheskimi ob”ektami: teoriya i praktika (Intelligent Control Systems for Technological Objects: Theory and Practice), Novokuznetsk: Sib. Gos. Ind. Univ., 2009.
– reference: Dmitriev, A.N., Matematicheskoe modelirovanie domennogo protsessa (Mathematical Modeling of Blast Furnace Process), Yekaterinburg: Ural. Otd., Ross. Akad. Nauk, 2011.
– reference: Hou, C., Wang, J., and Chen, C., Using hierarchical scenarios to predict the reliability of component-based software, IEICE Trans. Inf. Syst., 2018, vol. E101D, no. 2, pp. 405–414. https://doi.org/10.1587/transinf.2017EDP7127
– reference: UsamentiagaR.MolledaJ.GarciaD.GrandaJ.C.RenduelesJ.L.Temperature measurement of molten pig iron with slag characterization and detection using infrared computer visionIEEE Trans. Instrum. Meas2012611149115910.1109/TIM.2011.2178675
– reference: PavlovA.V.PolinovA.A.SpirinN.A.OnorinO.P.LavrovV.V.Use of model systems for solving new technological problems in blast-furnace productionMetallurgist2017614484541:CAS:528:DC%2BC2sXhslKgurzN10.1007/s11015-017-0516-7
– reference: Kazarinov, L.S. and Barbasova, T.A., Elliptic component analysis, Proc. 2016 Int. Conf. on Industrial Engineering, Applications and Manufacturing (ICIEAM), Chelyabinsk, 2016, no. 7910936. https://doi.org/10.1109/ICIEAM.2016.7910936
– reference: PeaceyJ.G.DavenportW.G.The Iron Blast Furnace: Theory and Practice2013AmsterdamElsevier
– reference: Babarykin, N.N., Teoriya i tekhnologiya domennogo protsessa (Theory and Technology of Blast Furnace Process), Magnitogorsk: Magnitogorsk. Gos. Tekh. Univ., 2009.
– reference: Shnayder, D.A., Kazarinov, L.S., Barbasova, T.A., and Lipatnikov, A.V., Data mining and model-predictive approach for blast furnace thermal control, Proc. 2017 Intelligent Systems Conf. (IntelliSys), Red Hook, NY: Curran Assoc., 2017, pp. 653–660. https://doi.org/10.1109/IntelliSys.2017.8324364
– reference: TV-MMK, Leader of digitalization, air: 17-12-2020, YouTube. https://www.youtube.com/watch?v=7opclYs93dA. Cited August 5, 2021.
– reference: SibagatullinS.K.KharchenkoA.S.DevyatchenkoL.D.Assessment of nonuniform batch distribution in blast furnaceSteel Transl20184862463010.3103/S0967091218100108
– reference: SaxenH.GaoC.GaoZ.Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—A reviewIEEE Trans. Ind. Inf201392213222510.1109/TII.2012.2226897
– reference: KuangS.LiZ.YuA.Review on modeling and simulation of blast furnaceSteel Res. Int20188917000711:CAS:528:DC%2BC2sXhtFamt7bF10.1002/srin.201700071
– reference: Tovarovskii, I.G., Domennaya plavka (Blast Furnace Smelting), Dnepropetrovsk: Porogi, 2009.
– reference: De CastroJ.A.NogamiH.YagiJ.-I.Three-dimensional multiphase mathematical modeling of the blast furnace based on the multifluid modelISIJ Int200242445210.2355/isijinternational.42.44
– reference: Spirin, N.A., Lavrov, V.V., Rybolovlev, V.Yu., Krasnobaev, A.V., Onorin, O.P., and Kosachenko, I.E., Model’nye sistemy podderzhki prinyatiya reshenii v ASU TP domennoi plavki (Model Decision Support Systems in APCS of Blast Furnace Smelting), Yekaterinburg: Ural. Fed. Univ., 2011.
– reference: Machine (technical) vision, Metallurgy, Mallenom Systems. https://www.mallenom.ru/resheniya/mashinnoe-zrenie/po-otroslyam/metallurgiia. Cited August 5, 2021.
– reference: Severstal is mastering manufacture of video inspection systems for rolled surfaces, Metal supply and sales. https://www.metalinfo.ru/ru/news/118725. Cited August 5, 2021.
– reference: PhanJ.MATLAB—C# for Engineers2010
– reference: BambauerF.WirtzS.SchererV.BartuschH.Transient DEM-CFD simulation of solid and fluid flow in a three dimensional blast furnace modelPowder Technol201833453641:CAS:528:DC%2BC1cXovVOltLc%3D10.1016/j.powtec.2018.04.062
– reference: DmitrievA.N.ZolotykhM.O.Vit’kinaG.Yu.Improvement of sintering and blast-furnace production using digital technologies within the framework of Industry 4.0, Chern. Metall., Byull. Nauchno-Tekh.Ekon. Inf2020633934510.32339/0135-5910-2020-4-339-343
– reference: Zagainov, S.A., Onorin, O.P., Gileva, L.Yu., Volkov, D.N., and Tleugobulov, B.S., Development and implementation of mathematical and software support for flexible technological modes of blast furnace operation, Stal’, 2000, no. 9, pp. 12–15.
– reference: Fu, D., Chen, Y., Rahman, M.T., Zhou, C.Q., D’Alessio, J., and Ferron, K.J., Validation of the numerical model for blast furnace shaft process, Proc. AISTech 2012 of the Iron and Steel Technology Conf., Atlanta, GA, Warrendale, PA: Assoc. Iron Steel Technol., 2012, no. 92531, pp. 417–427.
– reference: Solov’ev, V.I., Pavlova, E.A., and Krasnobaev, V.A., Intelligent automated control system for metallurgical units, Chern. Met., 2004, nos. 7–8, pp. 26–29.
– reference: ChenB.HsuH.-P.HuangY.-L.Bringing desktop applications to the webIT Prof201618344010.1109/MITP.2016.15
– ident: 1381_CR47
  doi: 10.1088/1757-899X/411/1/012072
– volume: 68
  start-page: 7
  year: 2018
  ident: 1381_CR58
  publication-title: Res. Dev. Kobe Steel Eng. Rep
– volume: 75
  start-page: 1231
  year: 2019
  ident: 1381_CR41
  publication-title: Ekon. Inf
  doi: 10.32339/0135-5910-2019-11-1231-1236
– ident: 1381_CR10
– ident: 1381_CR27
– ident: 1381_CR52
– ident: 1381_CR33
– ident: 1381_CR54
  doi: 10.33313/380/213
– ident: 1381_CR3
– volume: 9
  start-page: 2213
  year: 2013
  ident: 1381_CR43
  publication-title: IEEE Trans. Ind. Inf
  doi: 10.1109/TII.2012.2226897
– ident: 1381_CR7
– ident: 1381_CR66
– volume: 334
  start-page: 53
  year: 2018
  ident: 1381_CR20
  publication-title: Powder Technol
  doi: 10.1016/j.powtec.2018.04.062
– volume: 61
  start-page: 1149
  year: 2012
  ident: 1381_CR13
  publication-title: IEEE Trans. Instrum. Meas
  doi: 10.1109/TIM.2011.2178675
– ident: 1381_CR36
– ident: 1381_CR68
  doi: 10.1587/transinf.2017EDP7127
– volume: 62
  start-page: 418
  year: 2018
  ident: 1381_CR60
  publication-title: Metallurgist
  doi: 10.1007/s11015-018-0676-0
– volume: 58
  start-page: 25
  year: 2018
  ident: 1381_CR24
  publication-title: ISIJ Int
  doi: 10.2355/isijinternational.ISIJINT-2017-344
– volume-title: Computer Vision
  year: 2001
  ident: 1381_CR5
– volume: 18
  start-page: 34
  year: 2016
  ident: 1381_CR69
  publication-title: IT Prof
  doi: 10.1109/MITP.2016.15
– volume: 48
  start-page: 624
  year: 2018
  ident: 1381_CR51
  publication-title: Steel Transl
  doi: 10.3103/S0967091218100108
– ident: 1381_CR34
– ident: 1381_CR30
– ident: 1381_CR26
– volume: 18
  start-page: 3792
  year: 2018
  ident: 1381_CR12
  publication-title: Sensors
  doi: 10.3390/s18113792
– volume: 61
  start-page: 448
  year: 2017
  ident: 1381_CR40
  publication-title: Metallurgist
  doi: 10.1007/s11015-017-0516-7
– ident: 1381_CR8
– volume: 42
  start-page: 44
  year: 2002
  ident: 1381_CR22
  publication-title: ISIJ Int
  doi: 10.2355/isijinternational.42.44
– ident: 1381_CR16
– ident: 1381_CR64
  doi: 10.33313/377/061
– volume: 62
  start-page: 218
  year: 2018
  ident: 1381_CR59
  publication-title: Metallurgist
  doi: 10.1007/s11015-018-0648-4
– ident: 1381_CR63
  doi: 10.33313/377/050
– ident: 1381_CR37
– volume: 14
  start-page: 77
  year: 2001
  ident: 1381_CR49
  publication-title: Eng. Appl. Artif. Intell
  doi: 10.1016/S0952-1976(00)00062-2
– ident: 1381_CR57
  doi: 10.1109/IntelliSys.2017.8324364
– ident: 1381_CR9
– volume: 40
  start-page: 27
  year: 2010
  ident: 1381_CR17
  publication-title: Steel Transl
  doi: 10.3103/S0967091210010079
– ident: 1381_CR29
– volume: 20
  start-page: 33
  year: 2013
  ident: 1381_CR15
  publication-title: J. Iron Steel Res. Int
  doi: 10.1016/S1006-706X(13)60108-9
– volume-title: MATLAB—C# for Engineers
  year: 2010
  ident: 1381_CR70
– volume: 23
  start-page: 203
  year: 2016
  ident: 1381_CR14
  publication-title: J. Iron Steel Res. Int
  doi: 10.1016/S1006-706X(16)30035-8
– ident: 1381_CR31
– ident: 1381_CR55
  doi: 10.1016/C2017-0-00007-1
– ident: 1381_CR25
– volume: 129
  start-page: 166
  year: 2020
  ident: 1381_CR19
  publication-title: Miner. Process. Extr. Metall
  doi: 10.1080/25726641.2020.1733357
– ident: 1381_CR48
– volume: 50
  start-page: 914
  year: 2010
  ident: 1381_CR35
  publication-title: ISIJ Int
  doi: 10.2355/isijinternational.50.914
– ident: 1381_CR1
– ident: 1381_CR53
  doi: 10.33313/377/062
– ident: 1381_CR45
– volume: 46
  start-page: 1297
  year: 2006
  ident: 1381_CR62
  publication-title: ISIJ Int
  doi: 10.2355/isijinternational.46.1297
– volume: 82
  start-page: 130
  year: 2019
  ident: 1381_CR42
  publication-title: Control Eng. Pract
  doi: 10.1016/j.conengprac.2018.10.009
– ident: 1381_CR38
– volume: 28
  start-page: 5
  year: 1998
  ident: 1381_CR18
  publication-title: Steel Transl
– ident: 1381_CR65
  doi: 10.33313/380/035
– volume: 6
  start-page: 339
  year: 2020
  ident: 1381_CR4
  publication-title: Ekon. Inf
  doi: 10.32339/0135-5910-2020-4-339-343
– ident: 1381_CR56
  doi: 10.1109/ICIEAM.2016.7910936
– ident: 1381_CR32
– ident: 1381_CR28
– ident: 1381_CR11
– volume-title: The Iron Blast Furnace: Theory and Practice
  year: 2013
  ident: 1381_CR23
– volume: 89
  start-page: 1700071
  year: 2018
  ident: 1381_CR61
  publication-title: Steel Res. Int
  doi: 10.1002/srin.201700071
– ident: 1381_CR2
– ident: 1381_CR6
– volume: 2
  start-page: 106
  year: 2015
  ident: 1381_CR67
  publication-title: Oper. Res. Persp
  doi: 10.1016/j.orp.2015.05.001
– ident: 1381_CR46
– ident: 1381_CR21
– ident: 1381_CR39
– volume: 63
  start-page: 898
  year: 2020
  ident: 1381_CR44
  publication-title: Metallurgist
  doi: 10.1007/s11015-020-00907-y
– volume: 44
  start-page: 573
  year: 2004
  ident: 1381_CR50
  publication-title: ISIJ Int
  doi: 10.2355/isijinternational.44.573
SSID ssj0056251
Score 2.181894
Snippet The article provides an overview and critical analysis of the digitalization of the leading Russian ferrous metallurgy enterprises in accordance with the...
SourceID proquest
crossref
springer
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 522
SubjectTerms Algorithms
Artificial intelligence
Blast furnace practice
Blast furnaces
Chemistry and Materials Science
Control systems
Decision support systems
Digitization
Industrial applications
Industrial development
Materials Science
Metallurgical analysis
Metallurgy
Modules
Parameters
Smelting
Software
Subsystems
Systems (metallurgical)
Tuyeres
Vision systems
Title Digital Transformation of Pyrometallurgical Technologies. State, Scientific Problems, and Prospects of Development
URI https://link.springer.com/article/10.3103/S0967091221080143
https://www.proquest.com/docview/2607328439
Volume 51
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAVX
  databaseName: SpringerLINK Contemporary 1997-Present
  customDbUrl:
  eissn: 1935-0988
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0056251
  issn: 0967-0912
  databaseCode: RSV
  dateStart: 20070101
  isFulltext: true
  titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22
  providerName: Springer Nature
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDLZgcIADb8RgoBw4wSq6Zm3TIwImDmia2Jh2q9o0QZOmDrUbEv8eO2u38ZTg2MaNqiT29zlxbIDzGH0SRzckqrjtWs3IQ53TXFIxM23zpvA8k6y6_-C322IwCDrFPe68jHYvjySNpTZ-pc2vuki2fUQ3x6GwOIT5VVhDtBOkjY_dfml-idCbMnkobZH47Cjz-y4-gtGCYX46FDVY09r-11_uwFZBLdn1bC3swopK92BzKeHgPmS3w2eqEsJ6S3x1nLKxZp03SlyAbaNpZqwhm--6kzPNDCutM2MLTHwR68xq0eR1FqUJPZlbmzl1thSKdABPrbvezb1VVF2wJHI9bjmeq5SdqKbUXHNOybvRi8BXrp_EUgaBFJ5qyDgI4iTy3AQ5oStEhH6TkI52FT-ESjpO1REwoT3ONRllH21D7EYRgiFSJsRE2oy2q2CXwx_KIiU5VcYYheia0HCGX4azChfzT15m-Th-E66VcxoWqpmH6MBRgiIkYlW4LOdw0fxjZ8d_kj6BDYeiX0yoYA0qk2yqTmFdvk6GeXZmVuw7uMLhMQ
linkProvider Springer Nature
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED90CuqD3-J0ah58Uotd089HUcfEOYabY2-lTRsZjE7WTfC_9y5tt_kJ-tjmGsolufv9kssdwGmInMSQVYFLXLc0M7BxzUkuqJiZ1Lnp2rZKVt1tOM2m2-t5rfwed1pEuxdHkspSK16p88s2gm0HvZthUFgcuvlFWDLRYVEc32O7W5hfAvSqTB5KaySeHWV-38VHZzRDmJ8ORZWvqW386y83YT2HluwqmwtbsBAn27A2l3BwB0Y3_WeqEsI6c3h1mLChZK03SlyAbYPJSFlDNt11JzLNFCq9YMoWqPgi1spq0aQXLEgielK3NlPqbC4UaReeared67qWV13QBGI9rhm2Fcd6FJtCcsk5Je9GFoGvLCcKhfA84dpxVYSeF0aBbUWICS3XDZA3ucKQVsz3oJQMk3gfmCttziUZZQdtQ2gFATpDhEzoE2kzWi-DXqjfF3lKcqqMMfCRmpA6_S_qLMPZ9JOXLB_Hb8KVYkz9fGmmPhI4SlCEQKwM58UYzpp_7OzgT9InsFLvPDT8xl3z_hBWDYqEUWGDFSiNR5P4CJbF67ifjo7V7H0HT1TkFQ
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB60iujBt1itugdPamiabV5HsRbFUgKtpbeQbLJSKGlpWsF_78wmaesTxGOymyXsY-b7dme_AbgMkZMYsiZwieumVg8sXHOSC0pmJnVedyxLiVX3Wna77fT7rpfnOU2LaPfiSDK700AqTcm0Oo5kVXFMnVc7CLxt9HSGQSFy6PJXYa1OOYOIrnd6hSkmcK9S5mFtjapnx5rfN_HRMS3Q5qcDUuV3mjv__uNd2M4hJ7vN5sgerMTJPmwtCREewKQxeKHsIay7hGNHCRtJ5r2RoAGWDWcTZSXZfDeeSDZTaPWGKRuh4o6Yl-WoSW9YkET0pG5zptTYUojSITw377t3D1qejUETiAG5ZlhmHOtRXBeSS85J1BvZBb4y7SgUwnWFY8U1EbpuGAWWGSFWNB0nQD7lCEOaMT-CUjJK4mNgjrQ4l2SsbbQZoRkE6CQRSqGvpE1qvQx6MRS-yKXKKWPG0EfKQt3pf-nOMlzNPxlnOh2_Va4U4-vnSzb1kdiRcBECtDJcF-O5KP6xsZM_1b6ADa_R9FuP7adT2DQoQEZFE1agNJ3M4jNYF6_TQTo5VxP5HW037Pk
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Digital+Transformation+of+Pyrometallurgical+Technologies.+State%2C+Scientific+Problems%2C+and+Prospects+of+Development&rft.jtitle=Steel+in+translation&rft.au=Spirin%2C+N+A&rft.au=Lavrov%2C+V+V&rft.au=Yu%2C+Rybolovlev+V&rft.au=Schnaider%2C+D+A&rft.date=2021-08-01&rft.pub=Springer+Nature+B.V&rft.issn=0967-0912&rft.eissn=1935-0988&rft.volume=51&rft.issue=8&rft.spage=522&rft.epage=530&rft_id=info:doi/10.3103%2FS0967091221080143&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0967-0912&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0967-0912&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0967-0912&client=summon