Application of Random Forest and Support Vector Machine for
  Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake
  Modeling
Masoume Kazemi Davood Moradkhani Alireza Abbas Alipour
The hydrometallurgical method of zinc production involves leaching zinc from
ore and then separating the solid residue from the liquid solution by pressure
filtration. This separation process is very important since the solid residue
contains some moisture that can reduce the amount of zinc recovered. This study
modeled the pressure filtration process through Random Forest (RF) and Support
Vector Machine (SVM). The models take continuous variables (extracted features)
from the lab samples as inputs. Thus, regression models namely Random Forest
Regression (RFR) and Support Vector Regression (SVR) were chosen. A total
dataset was obtained during the pressure filtration process in two conditions:
1) Polypropylene (S1) and 2) Polyester fabrics (S2). To predict the cake
moisture, solids concentration (0.2 and 0.38), temperature (35 and 65
centigrade), pH (2, 3.5, and 5), pressure, cake thickness (14, 20, 26, and 34
mm), air-blow time (2, 10 and 15 min) and filtration time were applied as input
variables. The models' predictive accuracy was evaluated by the coefficient of
determination (R2) parameter. The results revealed that the RFR model is
superior to the SVR model for cake moisture prediction.