Modeling and optimization of compositions of three-component modifier mixtures by simplex planning method to analyze their effect on the flotation of copper-zinc pyrite ores

In this paper, we explore the potential of mathematical modeling for optimizing the compositions of three-component mixtures of reagent modifiers employed in the flotation of copper-zinc pyrite ores. To attain this objective, the simplex experiment planning method is employed. Significantly, our focus is directed towards investigating the prospects for enhancing the flotation technology of copper-zinc pyrite ores by employing mixtures of metal-containing modifiers with sodium sulfide. The experimental data obtained from the flotation process reveals the impact of the modifier reagents considered in this study on the technological parameters of flotation. The results of our research furnish valuable insights into the potential for optimizing the flotation processes of these ores through a systematic examination of combinations of these reagents. Consequently, optimal reagent costs were determined, and mathematical models were developed to calculate the extraction of copper, zinc, and iron into concentrate. Utilizing these models, we identified the influence of three key factors: the consumption of iron sulfate, zinc sulfate, and sodium sulfide on the extraction of valuable metals from copper-zinc ore into flotation concentrate. Based on the established models, we identify four primary compositions of mixtures of modifiers that contribute to the optimization of modifier compositions, aiming to achieve the maximum extraction of copper and zinc minerals while minimizing the extraction of pyrite into the concentrate.

Htet Zaw Oo, Kyaw Zay Ya, Goryachev B. E. Modeling and optimization of compositions of three-component modifier mixtures by simplex planning method to analyze their effect on the flotation of copper-zinc pyrite ores. MIAB. Mining Inf. Anal. Bull. 2024;(8):141-152. [In Russ]. DOI: 10.25018/0236_1493_2024_8_0_141.

Htet Zaw Oo¹, Graduate Student, e-mail: htetzawoo68099@gmail.com, ORCID ID: 0000-0003-2040-2552,
Kyaw Zay Ya¹, Cand. Sci. (Eng.), Intern-Doctoral Student, e-mail: kokyawgyi49@gmail.com, ORCID ID: 0000-0003-4364-9574,
B.E. Goryachev¹, Dr. Sci. (Eng.), Professor, e-mail: beg@misis.ru,
¹ NUST MISIS, 119049, Moscow, Russia.

Htet Zaw Oo, e-mail: htetzawoo68099@gmail.com.

1. Chanturia V. A. Innovation-based processes of integrated and high-level processing of natural and technogenic minerals. Gornyi Zhurnal. 2015, no. 7, pp. 29—37. [In Russ]. DOI: 10.17580/gzh. 2015.07.05.

2. Mamonov S. V., Dresvyankina T. P., Ziyatdinov S. V., Ershov A. A. Technological solutions for processing copper and copper-zinc ores of the pyrite deposit of the Urals. Globus: geologiya i biznes. 2020, no. 3 (62), pp. 140—144. [In Russ].

3. Bocharov V. A., Ryskin M. Ya., Pospelov N. D. Development of technology for processing copper-zinc ores of the Urals. Tsvetnye Metally. 1979, no. 10, pp. 105—107. [In Russ].

4. Romanenko Sergey A., Ushakov Egоr K. The study of technological variability of copper-zinc pyrite pyrrhotite-containing ores on the example of the Priorskoye deposit. Herald of the Kola Science Centre of RAS. 2019, no. 4 (11), pp. 56—61. [In Russ].

5. Zimin A. V., Nemchinova L. A. Intensification of selective flotation of copper-zinc ores by thermomechanical modification of zinc minerals' surface. Gornyi Zhurnal. 2014, no. 11, pp. 56—60. [In Russ].

6. Karasov Yu. К., Yagudina Yu. P. Enhancement of flotation technology for copper-zinc ores processed at concentrating plant of Aktyubinsk copper company. Russian Mining Industry Journal. 2021, no. 5-2, pp. 29—32. [In Russ].

7. Chandra A. P., Gerson A. R. A review of the fundamental studies of the copper activation mechanisms for selective flotation of the sulfide minerals, sphalerite and pyrite. Advances in Colloid and Interface Science. 2009, vol. 145, pp. 97—110.

8. Zimin A. V., Arustamyan M. A., Kalinin E. P., Solovyova L. M., Nemchinova L. A. Development of technological schemes for flotation enrichment of pyrite copper and copper-zinc ores. Gornyi Zhurnal. 2012, no. 11, pp. 28—33. [In Russ].

9. Gorbatova E. A., Emelianenko E. A., Ozhogina E. G. The expediency of predictive assessment of the enrichment of pyrite ores. Prospect and protection of mineral resources. Prospect and protection of mineral resources. 2019, no. 3, pp. 63—66. [In Russ].

10. Chanturia V. A., Kozlov A. P., Vaisberg L. A. Priority areas of research in the field of mineral processing. Obogashchenie Rud. 2014, no. 2, pp. 3—9. [In Russ].

11. Afonin Yu. S., Dubrovin V. I. Modeling of mixtures by the simplex lattice method. Radioelectronics. Computer science. Management. 2004, no. 2, pp. 60—63. [In Russ].

12. Zedginidze I. G. Planirovanie eksperimenta dlya issledovaniya mnogokomponentnykh sistem [Experiment planning for the study of multicomponent systems], Moscow, Nauka, 1976, 390 p.

13. Novik F. S. Planirovanie eksperimenta na simplekse pri izuchenii metallicheskikh sistem [Planning an experiment on a simplex in the study of metal systems], Moscow, Metallurgiya, 1985, 256 p.

14. Phyoe Kyaw Kyaw, Kyaw Zaya Ya, Goryachev B. E. Effect of composition of metal-bearing surface modifiers for sulfide minerals of base heavy metals in copper–zinc ore flotation. MIAB. Mining Inf. Anal. Bull. 2023, no. 11, pp. 128—142. [In Russ]. DOI: 10.25018/0236_1493_2023_11_0_128.

15. Htet Zaw Oo, Kyaw Zay Ya, Goryachev B. E. Effect of iron/zinc vitriol–sodium sulphide blends on copper–zinc sulphide ore flotation. MIAB. Mining Inf. Anal. Bull. 2023, no. 12, pp. 139—151. [In Russ]. DOI: 10.25018/0236_1493_2023_12_0_139.

16. Obam S. O. The accuracy of Scheffe’s third degree over second-degree, optimization regression polynomials. Nigerian Journal of Technology. 2006, vol. 25, no. 2, pp. 1—10.

17. Okafor F. O., Oguaghamba O. A. Procedure for optimization using Scheffe's models. Journal of Engineering Science and Application (JESA). 2009, vol. 7, no. 1, pp. 36—47.

18. Oguaghamba O. A., Mama B. O. Generalized Scheffe’s second degree mathematical methods approach in engineering mixture design. 16th International Conference and Annual General Meeting, Nigerian Institute of Civil Engineers. 2018, no. 6, pp. 32—44.

19. Duangjit S., Mehr L. M., Kumpugdee-Vollrath M., Ngawhirunpat T. Role of simplex lattice statistical design in the formulationand optimization of microemulsions for transdermal delivery. Biological & Pharmaceutical Bulletin. 2014, vol. 37, no. 12, pp. 1948—1957.

20. Nwachukwu K. C., Okodugha D. A., Akosubo I. S., Atulomah F. K. Optimization of compressive strength of steel fibre reinforced concrete (SFRC) using scheffe’s third-degree regression model. Iconic Research and Engineering Journals. 2022, vol. 5, no. 11, pp. 168—183.