Academic Journal
Optimizing Pig Iron Desulfurization Using Physics-Informed Neural Networks (PINNs)
| العنوان: | Optimizing Pig Iron Desulfurization Using Physics-Informed Neural Networks (PINNs) |
|---|---|
| المؤلفون: | Andrii Pylypenko, Peter Demeter, Branislav Buľko, Slavomír Hubatka, Lukáš Fogaraš, Jaroslav Legemza, Jaroslav Demeter |
| المصدر: | Engineering Proceedings, Vol 64, Iss 1, p 3 (2024) |
| بيانات النشر: | MDPI AG |
| سنة النشر: | 2024 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | pig iron, steelmaking, desulfurization, neural networks, Physics-Informed Neural Networks (PINNs), data analytics, Engineering machinery, tools, and implements, TA213-215 |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2673-4591 |
| Relation: | https://www.mdpi.com/2673-4591/64/1/3; https://doaj.org/toc/2673-4591; https://doaj.org/article/4ca449552a8b4e88bcff69080e121f22 |
| DOI: | 10.3390/engproc2024064003 |
| الاتاحة: | https://doi.org/10.3390/engproc2024064003 https://doaj.org/article/4ca449552a8b4e88bcff69080e121f22 |
| رقم الانضمام: | edsbas.A1796F5A |
| قاعدة البيانات: | BASE |
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