Determining silica solubility in bayer process liquor

Abstract

The efficient precipitation of dissolved silica from Bayer process liquor is essential for the production of high-quality alumina and the reduction of excessive scaling in the heat exchangers in the evaporation building of Bayer processes. The accurate prediction of silica solubility in Bayer liquor is one of the key parameters in improving the design and operation of the desilication process. Previous findings, particularly with respect to the influence of temperature and concentrations of caustic soda and alumina on the solubility of silica, are inconclusive. In this article, experimental results are presented over a wide range of temperature and alumina and caustic soda concentrations. Attempts are made to utilize artificial neural networks for identifying the process variables and modeling. The radial basis function neural network architecture was used successfully to generate a nonlinear correlation for the prediction of the solubility of silica in Bayer process liquor. The resulting correlation can predict the present data and the control data of other investigators with good accuracy.