Nombre: ANA PAULA MIRANDA DINIZ
Fecha de publicación: 25/04/2024
Junta de examinadores:
Nombre |
Papel |
|---|---|
| ALESSANDRO DO NASCIMENTO VARGAS | Examinador Externo |
| DANIEL CRUZ CAVALIERI | Examinador Externo |
| EVANDRO OTTONI TEATINI SALLES | Coorientador |
| JORGE LEONID ACHING SAMATELO | Examinador Interno |
| JOSE LEANDRO FELIX SALLES | Examinador Interno |
Páginas
Sumario: The continuous casting process, used in the manufacture of steel plates, is currently the most economical and efficient way of production within the industry. Although continuous casting is a widely used process, some problems associated with the process have not yet been resolved, one of them being the obstruction of the Submerged Entry Nozzle (SEN), which controls the flow of steel between the tundish and the mold. This obstruction, also called clogging, not only impairs the quality of the product but also results in lower process yield, resulting in losses. Thus, clogging detection is of fundamental importance, because control actions can allow the system to operate for a longer time. In this work, methodologies based on Machine Learning and Deep Learning will be presented and compared in order to detect the occurrences of clogging from historical data of process
variables. In general, the performance of the classifiers achieved very promising results in real data with unbalanced classes. In particular, the method employing spatiotemporal analysis, using four process variables, obtained a remarkably superior performance when compared to the others, reaching averages of Precision and Recall, respectively, of 95.53% and 97.33%. In order to reduce the false positive and negative rates, a post-processing heuristic was implemented and applied to the model output, achieving a Recall and an Accuracy, respectively, of approximately 99% and 98%. To the best of our knowledge, these results have never been found in the literature. Although a detailed comparison is unfeasible due to the differences between the datasets and their inaccessibility, the modeling proposed here reached higher performance levels when compared to the results
found in the literature to solve this industry’s problem. The high and unprecedented results obtained in this work, therefore, will contribute both to the improvement of the quality of the final product and to the reduction of costs associated with steel production, since the more effective classification of clogging occurrences can help operators in the corrective action planning.
