Neural Networks Based Modeling of Viscosity for Facilitating Transportation of Magnetite Orewater Slurry

Transportation of ores from mines to distant processing plants can be done in different ways. One method, which is found to be suitable in certain situations, is to carry magnetite through a pipeline in the form of solid-liquid slurry. For smooth flow of slurry, maintaining appropriate slurry viscosity along with flow pressure and flow velocity is essential. The Neural Networks (NN) algorithm can be used to find out the condition to guarantee smooth transportation process of magnetite-water slurry. In the present work, experiments are conducted to determine the effect of solids concentration, particle size and additive on the apparent viscosity of magnetite ore water slurry. The back propagation type Neural Networks (NN), constructed based on experimental results, is utilized to estimate apparent, or dynamic, viscosity of magnetite-water slurry under different parametric conditions. The NN model outputs show good prediction of apparent viscosity of the slurry, and hence, the applicability of the system adopted.