This review article discusses the latest advances on molten phase monitoring in metallurgical processes by using Laser-Induced Breakdown Spectroscopy (LIBS). LIBS is an analytical laser-based technique, where a pulsed laser is focused on a sample to create a plasma. The optical emission from the plasma can be transferred through open-path optical configuration or via an optical fiber to a spectrometer to receive analytical information in the form of elemental composition. Thus, a relatively long-distance analysis can be performed using LIBS. Several modern experimental arrangements, patents and industrial notes are assessed, and the literature is reviewed. The review includes applications of LIBS to analyze steel, iron, aluminum, copper, slags, metal melts, and other materials. Temperature, pressure, and atmospheric composition are crucial parameters of any melting process. Hence, past studies on molten phases describing these parameters have been discussed. Finally, the review addresses the last technological advances for these types of applications. It also points out the need of development in some fields and some limitations to overcome. In addition, the review highlights the use of modern machine learning and data processing techniques to increase the effectiveness of calibration and quantification approaches. These developments are expected to improve the performance of LIBS systems already implemented at an industrial scale and ease the development of new applications in pyrometallurgical processes to address the stringent market and environmental regulations.

The direct identification of mineral species in raw rocks was performed using laser induced breakdown spectroscopy (LIBS). A total of 162 sulfide rocks with mineralogical relevance in the copper industry were analyzed. These contained bornite (Cu₅FeS₄), chalcocite (Cu₂S), chalcopyrite (CuFeS₂), covellite (CuS), enargite (Cu₃AsS₄), molybdenite (MoS₂), and pyrite (FeS₂). The samples were collected from different mining locations to account for sample variability. Unsupervised multivariate methods like principal component analysis (PCA) and dendrogram analysis were explored, while supervised pattern recognition techniques, such as soft independent modelling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), K-nearest neighbor (KNN), decision tree analysis and artificial neural networks (ANNs) were compared. The sensitivity test performed on the LIBS data shows that the models KNN, SIMCA, PLS-DA and ANN achieve an average classification accuracy of 96.2, 98.1, 90.6 and 100%, respectively. In contrast, the robustness test of the models SIMCA and PLS-DA yields accuracies of 97.7 and 98.8%, respectively. The correct identification of very similar species in terms of their elemental composition such as bornite/chalcopyrite and chalcocite/covellite is also achieved.

We report the ‘standoff detection’ of explosives at 1 m in laboratory conditions, for the first time in India, using Laser Induced Breakdown Spectroscopy combined with multivariate analysis. The spectra of a set of five secondary explosives were recorded at a distance of 1 m from the focusing as well as collection optics. The plasma characteristics viz., plasma temperature and electron density were estimated from Boltzmann statistics and Stark broadening respectively. Plasma temperature was estimated to be of the order of (10.9 ± 2.1) .103 K and electron density of (3.9 ± 0.5) .1016 cm-3. Using a ratiometric approach, C/H and H/O ratios showed a good correlation with the actual stoichiometric ratios and a partial identification success could be achieved. Finally employing principle component analysis, an excellent classification could be attained.