Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms

Basit Öğe Kaydı
dc.authoridKilinc, Huseyin Cagan/0000-0003-1848-2856
dc.authoridKrolczyk, Jolanta/0000-0002-7404-0377
dc.authoridGhose, Joyjeet/0000-0001-5611-5139
dc.authoridWojciechowski, Szymon/0000-0002-3380-4588
dc.authoridWalczak, Dominik/0000-0003-2679-7575
dc.authoridAnsari, Dr. Md Obaidullah/0000-0001-7542-7410
dc.authoridKozak, Drazan/0000-0001-6542-0688
dc.contributor.authorAnsari, Md Obaidullah
dc.contributor.authorChattopadhyaya, Somnath
dc.contributor.authorGhose, Joyjeet
dc.contributor.authorSharma, Shubham
dc.contributor.authorKozak, Drazan
dc.contributor.authorLi, Changhe
dc.contributor.authorWojciechowski, Szymon
dc.date.accessioned2025-03-26T17:34:46Z
dc.date.available2025-03-26T17:34:46Z
dc.date.issued2022
dc.departmentİstanbul Esenyurt Üniversitesi, Fakülteler, Mühendislik ve Mimarlık Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractBreakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants.
dc.identifier.doi10.3390/ma15020670
dc.identifier.issn1996-1944
dc.identifier.issue2
dc.identifier.pmid35057387
dc.identifier.scopus2-s2.0-85122855624
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/ma15020670
dc.identifier.urihttps://hdl.handle.net/20.500.14704/886
dc.identifier.volume15
dc.identifier.wosWOS:000747506500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofMaterials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250326
dc.subjectcontinuous casting; steel slab; mold breakout; artificial neural network; breakout prediction system
dc.titleProductivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Mühendislik ve Mimarlık Fakültesi Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu