Statistical Modeling of the Industrial Sodium Aluminate Solutions Decomposition Process

• Published in: Metallurgical and materials transactions. B, Process metallurgy and materials processing science Vol. 41; no. 5; pp. 1116 - 1122
• Main Authors: IVKOVIC, Zivan Z, MIHAJLOVIC, Ivan, DJURIC, Isidora, STRBAC, Nada
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.10.2010; Springer; Springer Nature B.V
• Subjects: Alumina; Applied sciences; Characterization and Evaluation of Materials; Chemistry and Materials Science; Decomposition; Exact sciences and technology; Materials Science; Mathematical models; Metallic Materials; Metallurgy; Metals. Metallurgy; Nanotechnology; Production of metals; Structural Materials; Surfaces and Interfaces; Thin Films; Multiple Linear Regression Analysis; Boehmite; Alumina Production; Gibbsite; Hide Layer; Modeling
• ISSN: 1073-5615; 1543-1916
• DOI: 10.1007/s11663-010-9407-z

This article presents the results of the statistical modeling of industrial sodium aluminate solution decomposition as part of the Bayer alumina production process. The aim of this study was to define the correlation dependence of degree of the aluminate solution decomposition on the following parameters of technological processes: concentration of the Na 2 O (caustic), caustic ratio and crystallization ratio, starting temperature, final temperature, average diameter of crystallization seed, and duration of decomposition process. Multiple linear regression analysis (MLRA) and artificial neural networks (ANNs) were used as the tools for the mathematical analysis of the indicated problem. On the one hand, the attempt of process modeling, using MLRA, resulted in a linear model whose correlation coefficient was equal to R 2  = 0.731. On the other hand, ANNs enabled, to some extent, better process modeling, with a correlation coefficient equal to R 2  = 0.895. Both models obtained using MLRA and ANNs can be used for the efficient prediction of the degree of sodium aluminate solution decomposition, as the function of the input parameters, under industrial conditions of the Bayer alumina production process.