Abstract
Blast furnace slag is a key large-tonnage waste product of metallurgical production, which is considered to be a promising alternative material in construction. In order to determine the scope of potential use of slag as a marketable product, it is necessary to study its structure and composition, which is determined by means of modern analytical instrumental methods. This paper analyzes the application of Fourier transform infrared spectroscopy (FTIR) and chemometrics methods to develop calibration models for identifying pelletized slag by elemental composition. In a comparative analysis of FTIR-spectra of slag the characteristic frequencies of absorption bands responsible for the content of calcite, silicates and aluminosilicates in the composition of samples were determined. Multivariate regression methods (principal components regression, partial least squares regression) and data of elemental composition results by EDX method were used to develop calibration models for determining elemental composition of granulated blast furnace slag. Using the developed PLS models with high performance (R2 from 0.91 to 0.96 for different components), the prediction of the elemental composition (Ca, Si, O, Mg) of the test sample was carried out and a low deviation of the prediction in contrast to the EDX reference data was obtained. The use of PLS calibration models for rapid and nondestructive determination of the quantitative content of components of the composition of granulated blast furnace slag has been proposed.