Analysis of Ilmenite Slag Using Laser-Induced Breakdown Spectroscopy

• Published in: Minerals (Basel) 2020-10, Vol.10 (10), p.855
• Main Authors: Gupta, Avishek Kumar, Aula, Matti, Negre, Erwan, Viljanen, Jan, Pauna, Henri, Mäkelä, Pasi, Toivonen, Juha, Huttula, Marko, Fabritius, Timo
• Format: Article
• Language: English
• Subjects: Ablation; Accuracy; Analytical methods; Calibration; Cameras; Composition; Feasibility studies; ICP-OES; Ilmenite; Inductively coupled plasma; Investigations; Iron; Laboratories; Laser induced breakdown spectroscopy; Lasers; LIBS; Magnesium; Magnesium oxide; Measurement techniques; Metallurgy; Neodymium lasers; Oxides; Plasma; plasma characterization; Qualitative analysis; Quantitative analysis; Root-mean-square errors; Semiconductor lasers; Slag; slag composition; Smelting furnaces; Spectroscopy; Statistical methods; Steel products; Surface analysis (chemical); Temperature; Titanium; Titanium dioxide; YAG lasers
• ISSN: 2075-163X; 2075-163X
• DOI: 10.3390/min10100855

The feasibility of using laser-induced breakdown spectroscopy (LIBS) for the compositional analysis of ilmenite slag was explored. The slag was obtained from a pilot-scale ilmenite smelting furnace. The composition of major oxides TiO2, FeO, and MgO are determined by the calibrated LIBS method. LIBS measurements are done under normal atmosphere and temperature. A Q-switched Nd:YAG laser operating at 355 nm was used to create a plasma on an ilmenite slag sample. The characteristic lines based on the NIST database of Fe, Mg, and Ti can be identified on the normalized LIBS spectra for the slag samples. The spectral range chosen for the study is 370 to 390 nm. Calibration curves were plotted using the data collected from various industrial ilmenite samples of varying compositions of TiO2, FeO, and MgO. The univariate simple linear regression technique was used to do the analysis and the prediction accuracy was checked by the root mean square error (RMSE). To validate the application of LIBS, both qualitative and quantitative analysis is done and compared to the analytical ICP-OES results. The model predicts the magnesium content with the highest accuracy and gives good prediction for iron and titanium content. This study demonstrates the capability of using LIBS for the surface analysis of the ilmenite slag sample.