Imputation of reactive silica and available alumina in bauxites by self-organizing maps

Abstract

Geochemical analyses can provide multiple analytical variables. Accordingly, the generation of large geochemical databases enables imputation studies or analytical estimates of missing values or complex measuring. The processing of bauxite is a key step in the production of aluminum, in which the determination of Reactive Silica (RxSiO2) and Available Alumina (AvAl2O3) are very relevant. The traditional analytical method for achieving RxSiO2 has limitations associated with poor repeatability and reproducibility of results. Based on the values from the unsupervised Self-Organizing Maps technique, this study aims to develop, systematically, the imputation of missing grades of the geochemical composition of bauxite samples of a database from three trial projects, for the variables: total Al2O3; total SiO2; total Fe2O3; and total TiO2. Each project was submitted to partial exclusion of AvAl2O3 and RxSiO2 values, in proportion of 20%, 30%, 40% and 50%, to investigate the SOM technique as imputation method for RxSiO2 and AvAl2O3. By comparing the imputed values from the SOM analysis with the original values, SOM technique demonstrated to be an imputation tool capable of obtaining analytical results with up to 50% of missing data. Specifically, the best results demonstrate that AvAl2O3 can be obtained by imputation with a higher correlation than RxSiO2, based on the parameters and variables involved in the study. Similarity in the nature of samples and an increase in the number of embedded analytical variables are factors that provided better imputation results.