Pub Date : 2024-04-02DOI: 10.3390/engproc2024064016
K. Koza, K. Gryc, L. Socha, Martin Pinta, Roman Kubeš, V. Sochacký, Adnan Mohamed, J. Trobl
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Pub Date : 2024-02-21DOI: 10.3390/engproc2024064003
Andrii Pylypenko, P. Demeter, B. Buľko, Slavomír Hubatka, Lukáš Fogaraš, Jaroslav Legemza, J. Demeter
: The aim of the presented research was to optimize a pig iron desulfurization process through data-driven machine learning methods. Utilizing historical data, chemical analysis of pig iron and slag, and the thermodynamics of the process including simulations of the chemical reactions between individual phases, a neural network was trained for the predictive modeling of desulfurization efficiency. The accuracy of the model was enhanced by integrating Physics-Informed Neural Networks (PINNs), which incorporate chemical reaction principles. The results show better performance of PINNs in comparison to the Feedforward Neural Network (FNN) in the generalization of the desulfurization process, bringing better reliability to the model.
{"title":"Optimizing Pig Iron Desulfurization Using Physics-Informed Neural Networks (PINNs)","authors":"Andrii Pylypenko, P. Demeter, B. Buľko, Slavomír Hubatka, Lukáš Fogaraš, Jaroslav Legemza, J. Demeter","doi":"10.3390/engproc2024064003","DOIUrl":"https://doi.org/10.3390/engproc2024064003","url":null,"abstract":": The aim of the presented research was to optimize a pig iron desulfurization process through data-driven machine learning methods. Utilizing historical data, chemical analysis of pig iron and slag, and the thermodynamics of the process including simulations of the chemical reactions between individual phases, a neural network was trained for the predictive modeling of desulfurization efficiency. The accuracy of the model was enhanced by integrating Physics-Informed Neural Networks (PINNs), which incorporate chemical reaction principles. The results show better performance of PINNs in comparison to the Feedforward Neural Network (FNN) in the generalization of the desulfurization process, bringing better reliability to the model.","PeriodicalId":518766,"journal":{"name":"MM 2023","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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