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

Masoume Kazemi; Davood Moradkhani; Alireza Abbas Alipour

doi:doi:10.11648/j.ijmpem.20230802.11

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

Masoume Kazemi, Davood Moradkhani^*, Alireza Abbas Alipour

Published in International Journal of Mineral Processing and Extractive Metallurgy (Volume 8, Issue 2)

Received: 20 November 2023 Accepted: 18 December 2023 Published: 26 December 2023

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Abstract

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.

Published in	International Journal of Mineral Processing and Extractive Metallurgy (Volume 8, Issue 2)
DOI	10.11648/j.ijmpem.20230802.11
Page(s)	15-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

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

References

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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

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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

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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

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@article{10.11648/j.ijmpem.20230802.11,
  author = {Masoume Kazemi and Davood Moradkhani and Alireza Abbas Alipour},
  title = {Application of Random Forest and Support Vector Machine for Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake Modelling},
  journal = {International Journal of Mineral Processing and Extractive Metallurgy},
  volume = {8},
  number = {2},
  pages = {15-23},
  doi = {10.11648/j.ijmpem.20230802.11},
  url = {https://doi.org/10.11648/j.ijmpem.20230802.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmpem.20230802.11},
  abstract = {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.
},
 year = {2023}
}

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TY  - JOUR
T1  - Application of Random Forest and Support Vector Machine for Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake Modelling
AU  - Masoume Kazemi
AU  - Davood Moradkhani
AU  - Alireza Abbas Alipour
Y1  - 2023/12/26
PY  - 2023
N1  - https://doi.org/10.11648/j.ijmpem.20230802.11
DO  - 10.11648/j.ijmpem.20230802.11
T2  - International Journal of Mineral Processing and Extractive Metallurgy
JF  - International Journal of Mineral Processing and Extractive Metallurgy
JO  - International Journal of Mineral Processing and Extractive Metallurgy
SP  - 15
EP  - 23
PB  - Science Publishing Group
SN  - 2575-1859
UR  - https://doi.org/10.11648/j.ijmpem.20230802.11
AB  - 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.

VL  - 8
IS  - 2
ER  -

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Author Information

Masoume Kazemi

Department of Materials Science and Engineering, University of Zanjan, Zanjan, Iran

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Davood Moradkhani

Department of Materials Science and Engineering, University of Zanjan, Zanjan, Iran

Contact Email
Alireza Abbas Alipour

Department of Computer Science, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran

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Plain Text BibTeX RIS

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

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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

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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

Copy | Download

@article{10.11648/j.ijmpem.20230802.11,
  author = {Masoume Kazemi and Davood Moradkhani and Alireza Abbas Alipour},
  title = {Application of Random Forest and Support Vector Machine for Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake Modelling},
  journal = {International Journal of Mineral Processing and Extractive Metallurgy},
  volume = {8},
  number = {2},
  pages = {15-23},
  doi = {10.11648/j.ijmpem.20230802.11},
  url = {https://doi.org/10.11648/j.ijmpem.20230802.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmpem.20230802.11},
  abstract = {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.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Application of Random Forest and Support Vector Machine for Investigation of Pressure Filtration Performance, a Zinc Plant Filter Cake Modelling
AU  - Masoume Kazemi
AU  - Davood Moradkhani
AU  - Alireza Abbas Alipour
Y1  - 2023/12/26
PY  - 2023
N1  - https://doi.org/10.11648/j.ijmpem.20230802.11
DO  - 10.11648/j.ijmpem.20230802.11
T2  - International Journal of Mineral Processing and Extractive Metallurgy
JF  - International Journal of Mineral Processing and Extractive Metallurgy
JO  - International Journal of Mineral Processing and Extractive Metallurgy
SP  - 15
EP  - 23
PB  - Science Publishing Group
SN  - 2575-1859
UR  - https://doi.org/10.11648/j.ijmpem.20230802.11
AB  - 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.

VL  - 8
IS  - 2
ER  -

Copy | Download