International Journal of Mineral Processing and Extractive Metallurgy

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Research Article |

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

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°C), 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 (called R2) parameter that obtained 0.991, 0.987 by RFR and 0.48 via SVR for S1 and S2, in turn. The results revealed that the RFR model is superior to the SVR model for cake moisture prediction.

Zinc Plant Residue, Moisture, Machine Learning, Random Forest (RF), Support Vector Machine (SVM)

APA Style

Kazemi, M., Moradkhani, D., Abbas Alipour, A. (2023). Application of Random Forest and Support Vector Machine for Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake Modelling. International Journal of Mineral Processing and Extractive Metallurgy, 8(2), 15-23. https://doi.org/10.11648/j.ijmpem.20230802.11

ACS Style

Kazemi, M.; Moradkhani, D.; Abbas Alipour, A. Application of Random Forest and Support Vector Machine for Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake Modelling. Int. J. Miner. Process. Extr. Metall. 2023, 8(2), 15-23. doi: 10.11648/j.ijmpem.20230802.11

AMA Style

Kazemi M, Moradkhani D, Abbas Alipour A. Application of Random Forest and Support Vector Machine for Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake Modelling. Int J Miner Process Extr Metall. 2023;8(2):15-23. doi: 10.11648/j.ijmpem.20230802.11

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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