Comparison of ANN and RSM in modeling the VP-WHIMS process parameters

Vertical ring and Pulsating Wet High Intensity Magnetic Separator (VP-WHIMS) is widely used for week magnetic mineral beneficiation or ferrous impurity removal from non-metallic minerals. Because of the complexity of this machine, mathematical modelling of its process is complicated and time consuming. In this paper, artificial neural network (ANN) and response surface methodology (RSM) were applied to evaluate the VP-WHIMS performance and optimize its main operating parameters in beneficiating of the Chadormalu hematite sample. The operational parameters of this magnetic separator are considered as: magnetic field intensity (2500-8500 gauss), pulsating frequency (20-100 %max), ring rotation speed (0.6-4.6 r/min) and slurry concentration (15-35 w/w %). Concentrate grade and recoveries (weight and Fe) are monitored as responses. Centre composite design (CCD) was used for experimental study. Four process variables were set using small CCD. Eight star points (a=±1), eight axial points (a=±2) and five replicates at the centre point (a=0) were chosen as experimental points. In ANN system, the best network topology was obtained as (4-5-3). The weights connection and biases were obtained using batch back propagation (BBP) method for 21 experimental data points. The results indicate that ANN