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.