This paper presents the results of statistical modeling of the bauxite
leaching process, as part of Bayer technology for an alumina production.
Based on the data, collected during the period between 2008 - 2009 (659 days)
from the industrial production in the alumina factory Birac, Zvornik (Bosnia
and Herzegovina), the statistical modeling of the above mentioned process was
performed. The dependant variable, which was the main target of the modeling
procedure, was the degree of Al2O3 recovery from boehmite bauxite during the
leaching process. The statistical model was developed as an attempt to define
the dependence of the Al2O3 degree of recovery as a function of input
variables of the leaching process: composition of bauxite, composition of the
sodium aluminate solution and the caustic module of the solution before and
after the leaching process. As the statistical modeling tools, Multiple
Linear Regression Analysis (MLRA) and Artificial Neural Networks (ANNs) were
used. The fitting level, obtained by using the MLRA, was R2 = 0.463, while
ANN resulted with the value of R2 = 0.723. This way, the model, defined by
using the ANN methodology, can be used for the efficient prediction of the
Al2O3 degree of recovery as a function of the process inputs, under the
industrial conditions of the alumina factory Birac, Zvornik. The proposed
model also has got a universal character and, as such, is applicable in other
factories practicing the Bayer technology for alumina production.
Development of statistical modeling tools for the analysis of conformationally flexible chiral catalysts