Ph.D. Candidate: Özgür Korkmaz
Program: Information Systems
Date: 11.01.2024 / 09:30
Place: B-116

Abstract: Steel production serves as the backbone of countless infrastructure projects and industrial applications worldwide. In order to maintain and improve its productivity, quality and environmental sustainability, the steel industry is constantly looking for innovative technologies and methods. Hyperspectral imaging is a promising technology in this context. A novel, non-destructive approach is presented to quantify the free lime content in steel slag by utilizing an integrated algorithm applied to hyperspectral images. This method includes spectral unmixing for mixture component quantification and endmember extraction of mixture. The methodology involved various experiments with both fresh and six-month-aged steel slag, demonstrating its accuracy compared to the Rietveld Analysis of X-ray Diffraction patterns, with an RMSE of 5.57% for aged slag and 6.51% for fresh slag. Furthermore, the thesis explores the application of hyperspectral imaging in identifying foreign substances in iron ore and detecting copper accumulations in continuous pickling lines. As a result of the experiments, the impurities in the raw iron ore were clearly identified and copper accumulation areas were detected at various sensitivities on the steel sheet produced in the continuous pickling line process. An in-depth analysis of the challenges and limitations associated with the use of hyperspectral imaging in steelmaking are provided. The findings of this research advance the technological capabilities in steel production.