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The hydrometallurgical method of zinc production involves leaching zinc fromore and then separating the solid residue from the liquid solution by pressurefiltration. This separation process is very important since the solid residuecontains some moisture that can reduce the amount of zinc recovered. This studymodeled the pressure filtration process through Random Forest (RF) and SupportVector Machine (SVM). The models take continuous variables (extracted features)from the lab samples as inputs. Thus, regression models namely Random ForestRegression (RFR) and Support Vector Regression (SVR) were chosen. A totaldataset was obtained during the pressure filtration process in two conditions:1) Polypropylene (S1) and 2) Polyester fabrics (S2). To predict the cakemoisture, solids concentration (0.2 and 0.38), temperature (35 and 65centigrade), pH (2, 3.5, and 5), pressure, cake thickness (14, 20, 26, and 34mm), air-blow time (2, 10 and 15 min) and filtration time were applied as inputvariables. The models' predictive accuracy was evaluated by the coefficient ofdetermination (R2) parameter. The results revealed that the RFR model issuperior to the SVR model for cake moisture prediction.

Application of Random Forest and Support Vector Machine for Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake Modeling