Modelling the process of Al(OH)3 crystallization from industrial sodium aluminate solutions using artificial neural networks

Abstract

This paper presents an attempt to define the non-linear correlation dependence between the degree of decomposition of the aluminate solution, the average diameter of the crystallized gibbsite, the total Na2O content in the ob­tained alumina and the specific utilization level of the process on the one hand and important input parameters of the process on the other. As input pa­rameters having an influence on the process, the concentration of Na2O (caus­tic), the caustic ratio and the crystallization ratio, the starting and final tem­pe­rature of the process, the average diameter of the crystallization seed and the duration of the decomposition process were considered. As the result of mea­surements of these process parameters and the acquisition of the resulting out­put parameters of the process, a database with 500 data lines was obtained. To define the correlation dependence, with the aim of predicting the process para­meters of the decomposition process of the sodium aluminate solution, the arti­ficial neural network (ANN) methodology was applied.

Keywords

• aluminate solution
• crystallization
• modelling
• artificial neural net­works