Szczegóły publikacji
Opis bibliograficzny
Use of machine learning for modelling the wear of MgO-C refractories in Basic Oxygen Furnace / Sebastian SADO, Wiesław Zelik, Ryszard LECH // Journal of Ceramic Processing Research ; ISSN 1229-9162. — 2022 — vol. 23 no. 4, s. 421–429. — Bibliogr. s. 429, Abstr.
Autorzy (3)
- AGHSado Sebastian
- Zelik Wiesław
- AGHLech Ryszard
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 141818 |
|---|---|
| Data dodania do BaDAP | 2022-09-17 |
| Tekst źródłowy | URL |
| DOI | 10.36410/jcpr.2022.23.4.421 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Ceramic Processing Research |
Abstract
Basic Oxygen Furnace (BOF), TBM type (Thyssen – Blas – Metallurgie)is one of the heat units occurring in a steel production process. The refractory lining of BOF consists of several zones and is lined with MgO-C bricks. For the above mentioned zones refractories with different properties are selected due to the different factors influencing the corrosion process. Intense wear of refractories is observed mainly at the slag spout zone in accordance to the influence of thermochemical, thermomechanical factors (including the oxidizing atmosphere). The aim of this paper is to find the regression formula with satisfactory forecast measure of fit, which will make it possible to predict the refractory material wear in the slag spout zone of BOF depending on the real wear measurement made during the BOF operation. Calculations were conducted with the use of regression trees with CART algorithm (Classification and Regression Trees), Multivariate Adaptive Regression Splines (MARS), Boosted Trees algorithm and Multilayer Neural Networks MLP type (Multilayer Perceptron).Selected metallurgical parameters registered during the BOF campaign are the independent variables discussed in refractory material wear models, whereas the wear rate of refractory materials calculated per one heat is set as a dependent variable.