Mohanty, Sunati and Das, Suchandan K and Majumder, A K (2020) Artificial neural network modeling and experimental investigation to characterize the dewatering performance of a hydrocyclone. Mineral Processing and Extractive Metallurgy (Trans. IMM C), Online 29th Oct, 2019 .
Full text not available from this repository.
Dewatering in mineral processing industries is of paramount importance as most wet beneficiation of minerals needs removal of water. For this purpose, we have evaluated a 50.8 mm diameter hydrocyclone in order to assess whether it can be used as a partial replacement for a thickener. A multi-layer perceptron based artificial neural network (ANN) model was developed to characterise the dewatering performance of a hydrocyclone using experimentally generated data for silica and magnetite. Parametric sensitivity analysis was undertaken by studying the influence of vortex finder diameter, spigot diameter and inlet pressure on dewatering performance. The ANN model predictions showed that solid recovery to underflow increases and water recovery to overflow decreases with increasing spigot diameter whereas solid recovery to underflow decreases and water recovery to overflow increases with increased vortex finder diameter. Both increase monotonically with increase in inlet pressure. The neural model prediction was successfully validated with the experimental data.
|Uncontrolled Keywords:||Neural network model; network learning algorithm; dewatering hydrocyclone; design of experiment; solid recovery; water recovery; multi-layer perceptron|
|Deposited By:||Dr. A K Sahu|
|Deposited On:||22 Mar 2021 09:49|
|Last Modified:||22 Mar 2021 09:49|
Repository Staff Only: item control page