Preview

Bulletin of Shakarim University. Technical Sciences

Advanced search

DEVELOPMENT OF AN INFORMATION SYSTEM FOR MODELING AND ANALYZING HYDROMETALLURGICAL PROCESSING OF MOLYBDENITE CONCENTRATES

https://doi.org/10.53360/2788-7995-2025-4(20)-21

Abstract

The depletion of high-grade molybdenum ores and the need to process complex sulfide concentrates pose significant challenges for the hydrometallurgical industry. Traditional approaches to processing experimental data based on manual calculations are characterized by high labor intensity, risk of computational errors, and information fragmentation. This paper describes the development of a specialized information system for modeling and analyzing the hydrometallurgical processing of molybdenite concentrates. The software is implemented using the Django web framework and includes modules for experimental data input, automated calculation of material balances for leaching and sorption processes, and visualization of results through interactive analytical dashboards. The system has been validated using data from 6 leaching experiments and 9 molybdenum sorption experiments. The modular three-tier architecture ensures solution scalability and enables its adaptation for modeling other hydrometallurgical processes. The developed calculators allow predicting molybdenum recovery rates at various stages of the technological cycle without conducting additional physical experiments. The practical significance of this work lies in creating a digital twin of the technological process that establishes a unified information space for researchers, ensures transparency and reproducibility of experiments, and lays the foundation for industrial scaling of molybdenite concentrate processing technology.

About the Authors

B. K. Kenzhaliev
Institute of Metallurgy and Ore Beneficiation, Satbayev University
Kazakhstan

Bagdaulet Kenzhaliyev – Doctor of Technical Sciences, General Director-Chairman of the Management Board 

050010, Kazakhstan, Almaty, Shevchenko Street, 29



S. Zh. Aibagarov
LLP DigitAlem
Kazakhstan

Serik Aibagarov – Researcher

050042, Kazakhstan, Almaty, 150/1 Zhandosov Street



N. Azatbekuly
LLP DigitAlem
Kazakhstan

Nurtugan Azatbekuly – Researcher

050042, Kazakhstan, Almaty, 150/1 Zhandosov Street



A. A. Ultarakova
Institute of Metallurgy and Ore Beneficiation, Satbayev University
Kazakhstan

Almagul Ultarakova – Candidate of Technical Sciences, Associate Professor, Senior researcher at the titanium and rare refractory metals laboratory

050010, Kazakhstan, Almaty, Shevchenko Street, 29



References

1. The FAIR Guiding Principles for scientific data management and stewardship / M.D. Wilkinson et al // Scientific Data. – 2016. – № 3. – Р. 160018.

2. Bazan V. Extraction of molybdenite concentrates by leaching / V. Bazan, M. Medina, I. Orozco // DYNA. – 2024. – № 91(234). – Р. 54-61.

3. Laputka M. A review of recent advances in pyrometallurgical process measurement and modeling, and their applications to process improvement / M. Laputka, W. Xie // Mining, Metallurgy & Exploration. – 2021. – № 38(2). – Р. 1135-1165.

4. Grieves M. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems / M. Grieves, J. Vickers // In Transdisciplinary Perspectives on Complex Systems. – 2017. – Р. 85-113.

5. Digital Twins and Enabling Technology Applications in Mining: Research Trends, Opportunities, and Challenges / M. Don et al // IEEE Access. – 2025. – Р. 1-1.

6. Hydrometallurgical processing of molybdenum middlings from Shatyrkul-Zhaysan cluster ore / L. Karimova et al // Journal of Mining and Metallurgy, Section B: Metallurgy. – 2024. – № 60(1). – Р. 71-83.

7. Sun T. A Python-based platform for thermodynamic calculation and process simulation in metallurgy / T. Sun, S. Du // JOM. – 2019. – № 71(10). – Р. 3504-3512.

8. McDowell, D.L. The materials innovation ecosystem: a key enabler for the materials genome initiative / D.L. McDowell, S.R. Kalidindi // MRS Bulletin. – 2016. – № 41(4). – Р. 326-337.

9. Zhang J. Metallurgical Process Simulation and Optimization / J. Zhang, Y. Liu, Q. Liu // Materials. – 2022. – № 15(23). – Р. 8421.

10. Kenzhaliyev B. INFORMATION SYSTEM FOR METALLURGICAL PROCESS ANALYSIS AND OPTIMIZATION / B. Kenzhaliyev, S. Aibagarov // JPCSIT [Internet]. – 2025. – № 3(3). – Р. 101-12. Available from: https://jpcsit.kaznu.kz/index.php/kaznu/article/view/248.

11. Hydrometallurgical processes for the recovery of metals from steel industry by-products: a critical review / K. Binnemans et al // Journal of Sustainable Metallurgy. – 2020. – № 6(4). – Р. 505-540.

12. Predicting Copper Production Cycles in Hydrometallurgy with Interpretable Machine Learning / B. Kenzhaliyev et al // Kompleksnoe Ispolzovanie Mineralnogo Syra = Complex Use of Mineral Resources. – 2025. – № 341(2). – Р. 5-15. https://doi.org/10.31643/2027/6445.13.


Review

For citations:


Kenzhaliev B.K., Aibagarov S.Zh., Azatbekuly N., Ultarakova A.A. DEVELOPMENT OF AN INFORMATION SYSTEM FOR MODELING AND ANALYZING HYDROMETALLURGICAL PROCESSING OF MOLYBDENITE CONCENTRATES. Bulletin of Shakarim University. Technical Sciences. 2025;1(4(20)):179-186. (In Russ.) https://doi.org/10.53360/2788-7995-2025-4(20)-21

Views: 11

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2788-7995 (Print)
ISSN 3006-0524 (Online)
X