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.

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 ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Wang, X., Gu, X., Liu, Z., Wang, Q., Xu, X., Zheng, M. 2018. Production process optimization of metal mines considering economic benefit and resource efficiency using an NSGA-II model. Processes, 6(11), 228.
2. Long, Y. Y., Feng, Y. J., Cai, S. S., Hu, L. F., Shen, D. S. 2014. Reduction of heavy metals in residues from the dismantling of waste electrical and electronic equipment before incineration. Journal of Hazardous Materials, 272, 59-65.
3. Loan, M., Newman, O. M. G., Cooper, R. M. G., Farrow, J. B., Parkinson, G. M. 2006. Defining the Paragoethite process for iron removal in zinc hydrometallurgy. Hydrometallurgy, 81(2), 104-129.
4. Safarzadeh, M. S., Moradkhani, D., & Ojaghi-Ilkhchi, M. (2009). Kinetics of sulfuric acid leaching of cadmium from Cd–Ni zinc plant residues. Journal of Hazardous Materials, 163(2-3), 880-890.
5. Vapnik, V. 1999. The nature of statistical learning theory. Springer Science & Business Media.
6. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., Walsh, A. 2018. Machine learning for molecular and materials science. Nature, 559(7715), 547-555.
7. Vahidi, E., Rashchi, F., & Moradkhani, D. (2009). Recovery of zinc from an industrial zinc leach residue by solvent extraction using D2EHPA. Minerals Engineering, 22(2), 204-206.
8. Du, Y., Tong, X., Xie, X., Zhang, W., Yang, H., & Song, Q. (2021). Recovery of Zinc and Silver from Zinc Acid-Leaching Residues with Reduction of Their Environmental Impact Using a Novel Water Leaching-Flotation Process. Minerals, 11(6), 586.
9. GÜLER, E., SEYRANKAYA, A., & Cocen, I. (2011). Hydrometallurgical evaluation of zinc leach plant residue. Asian Journal of Chemistry, 23(7).
10. Turan, M. D., Altundoğan, H. S., & Tümen, F. (2004). Recovery of zinc and lead from zinc plant residue. Hydrometallurgy, 75(1-4), 169-176.
11. Yan, H., Chai, L. Y., Peng, B., Li, M., Peng, N., & Hou, D. K. (2014). A novel method to recover zinc and iron from zinc leaching residue. Minerals Engineering, 55, 103-110.
12. Nantasenamat, C., Isarankura-Na-Ayudhya, C., Prachayasittikul, V. 2010. Advances in computational methods to predict the biological activity of compounds. Expert opinion on drug discovery, 5(7), 633-654.
13. Nash, W., Drummond, T., Birbilis, N. 201. A review of deep learning in the study of materials degradation. npj Materials Degradation, 2(1), 1-12.
14. Wang, M., Wang, T., Cai, P., Chen, X. 2019. Nanomaterials discovery and design through machine learning. Small Methods, 3(5), 1900025.
15. Dai, J., Chen, N., Luo, B., Gui, W., Yang, C. 2020. Multi-scale local LSSVM-based spatiotemporal modeling and optimal control for the goethite process. Neurocomputing, 385, 88-99.
16. Shi, X., Li, Y., Sun, B., Xu, H., Yang, C., Zhu, H. 2020. Optimizing zinc electrowinning processes with current switching via Deep Deterministic Policy Gradient learning. Neurocomputing, 380, 190-200.
17. Xiong, J., Zhang, T., Shi, S. 2020. Machine learning of mechanical properties of steels. Science China Technological Sciences, 63, 1247-1255.
18. Recovery of secondary lead smelting industry based on two-stage BPNLP network model. Journal of Cleaner Production, 284, 124717.
19. Abdollahi, H., Noaparast, M., Shafaei, S. Z., Akcil, A., Panda, S., Kashi, M. H., & Karimi, P. 2019. Prediction and optimization studies for bioleaching of molybdenite concentrate using artificial neural networks and genetic algorithm. Minerals Engineering, 130, 24-35.
20. Ebrahimzade, H., Khayati, G. R., Schaffie, M. 2018. Leaching kinetics of valuable metals from waste Li-ion batteries using a neural network approach. Journal of Material Cycles and Waste Management, 20(4), 2117-2129.
21. Li, H., Wang, F., Li, H. 2017. A safe control scheme under the abnormity for the thickening process of gold hydrometallurgy based on a Bayesian network. Knowledge-Based Systems, 119, 10-19.
22. Samuel AL. Some studies in machine learning using the game of checkers ii – recent progress. IBM J. Res. Dev. 1967; 11(6): 601–17.
23. Alpaydin E. Introduction to Machine Learning. MIT Press; 2009.
24. Tandon, N., Tandon, R. 2019. Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype. Schizophrenia Research, 214, 70-75.
25. Tang, Q. Y., Zhang, C. X., 2013. Data Processing System (DPS) software with experimental design, statistical analysis, and data mining developed for use in entomological research. Insect Sci. 20(2), 254–260.
26. Avalos, S., Kracht, W., & Ortiz, J. M. (2020). Machine learning and deep learning methods in mining operations: A data-driven SAG mill energy consumption prediction application. Mining, Metallurgy & Exploration, 37, 1197-1212.
27. Wang, J., Wang, Z., Zhang, Z., & Zhang, G. (2019). Comparison of butyric acid leaching behaviors of zinc from three basic oxygen steelmaking filter cakes. Metals, 9(4), 417.
28. Hu, R. Y., Wang, X., Meng, Q., & Wang, Z. (2019, December). Prediction Model of Filter Cake Moisture in Ceramic Production by Least Squares Support Vector Machine based on Particle Swarm Optimization. In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (Vol. 1, pp. 1365-1369). IEEE.
29. Yu, F., Wei, C., Deng, P., Peng, T., & Hu, X. (2021). Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles. Science advances, 7(22), eabf4130.
30. Guo, K., Yang, Z., Yu, C. H., & Buehler, M. J. (2021). Artificial intelligence and machine learning in design of mechanical materials. Materials Horizons, 8(4), 1153-1172.