SYNTHETIC DATA GENERATION FOR ANN MODELING OF THE HYDRODYNAMIC PROCESSES OF IN-SITU LEACHING

Authors

DOI:

https://doi.org/10.37943/17STXF5228

Keywords:

In-Situ Leaching modelling, neural networks, data preparation, data normalization, computational fluid dynamics, synthetic data generation

Abstract

The work presents an approach to enhance the forecasting capabilities of In-Situ Leaching processes during both the production stage and early prognosis. ISL, a crucial method for resource extraction, demands rapid on-site forecasting to guide the deployment of new technological blocks. Traditional modeling techniques, though effective, are hindered by their computational demands and network throughput requirements, particularly when dealing with substantial datasets or remote computing needs. The integration of AI technologies, specifically neural networks, offers a promising opportunity for expedited calculations by leveraging the power of forward propagation through pretrained neural models. However, a critical challenge lies in transforming conventional numerical datasets into a format suitable for neural modeling. Furthermore, the scarcity of training data during the production phase, where vital parameters are concealed underground, poses an additional challenge in training AI models for In-Situ Leaching processes. This research addresses these challenges by proposing a methodology for generating training data tailored to the most resource-intensive Computational Fluid Dynamics problems encountered during modeling. Traditional numerical modeling techniques are harnessed to construct training datasets comprising input and corresponding expected output data, with a particular focus on varying well network patterns. Subsequent efforts are directed at the conversion of the acquired data into a format compatible with neural networks. The data is normalized to align with the data ranges stipulated by the activation functions employed within the neural network architecture. This preprocessing step ensures that the neural model can effectively learn from the generated data, facilitating accurate forecasting of In-Situ Leaching processes. An advantage of proposed technique lies in provision of large, reliable datasets to train neural network to predict hydrodynamic properties based on technological regimes currently active or expected on ISL site. A major implication of this approach lies in applicability of pre-trained AI technologies to forecast future or determine current hydrodynamic regime in the stratum circumventing cost deterministic simulations currently deployed at mining sites. Hence, innovative approach outlined in this paper holds promise for optimizing forecasting, allowing for quicker and more efficient decision-making in resource extraction operations while getting around the computational barriers associated with traditional methods.

References

World Uranium Mining Production. (2024). https://world-nuclear.org/information-library/nuclear-fuel-cycle/mining-of-uranium/world-uranium-mining-production.aspx

Collet, A., Regnault, O., Ozhogin, A., Imantayeva, A., & Garnier, L. (2022). Three-dimensional reactive transport simulation of Uranium in situ recovery: Large-scale well field applications in Shu Saryssu Bassin, Tortkuduk deposit (Kazakhstan). Hydrometallurgy, 211(105873), 105873. https://doi.org/10.1016/j.hydromet.2022.105873

van der Lee, J., De Windt, L., Lagneau, V., & Goblet, P. (2003). Module-oriented modeling of reactive transport with HYTEC. Computers & Geosciences, 29(3), 265–275. https://doi.org/10.1016/s0098-3004(03)00004-9

Laurent, G., Izart, C., Lechenard, B., Golfier, F., Marion, P., Collon, P., Laurent, T., Jean-Jacques, R., & Lev, F. (2019). Numerical modelling of column experiments to investigate in-situ bioleaching as an alternative mining technology. Hydrometallurgy, 188, 272–290. https://doi.org/10.1016/j.hydromet.2019.07.002

Li, G., & Yao, J. (2024). A review of in situ leaching (ISL) for uranium mining. Mining, 4(1), 120–148. https://doi.org/10.3390/mining4010009

Poezhaev, I.P., Polinovskiy, K.D., & Gorbatenko, O.A. (2017). Uranium Geotechnology: A Training Manual. Almaty: Insitute of High Technology Kazatomprom.

Shayakhmetov, N.M., Aizhulov, D.Y., Alibayeva, K.A., Serovajsky, S., & Panfilov, I. (2020). Application of hydrochemical simulation model to determination of optimal well pattern for mineral production with In-Situ Leaching. Procedia Computer Science, 178, 84-93. https://doi.org/10.1016/j.procs.2020.11.010

IAEA. (2001). Manual of acid in situ uranium mining technology. Vienna: IAEA.

Aizhulov, D., Tungatarova, M., & Kaltayev, A. (2022). Streamlines Based Stochastic Methods and Reactive Transport Simulation Applied to Resource Estimation of Roll-Front Uranium Deposits Exploited by In-Situ Leaching. Minerals, 12(10), 1209. https://doi.org/10.3390/min12101209

Aizhulov, D.Y., Shayakhmetov, N.M., & Kaltayev, A. (2018). Quantitative model of the formation mechanism of the rollfront uranium deposits. Eurasian Chemico-Technological Journal, 20(3), 213-221. https://doi.org/10.18321/ectj724

Shayakhmetov, N.M., Alibayeva, K.A., Kaltayev, A., & Panfilov, I. (2023) Enhancing uranium in-situ leaching efficiency through the well reverse technique: A study of the effects of reversal time on production efficiency and cost. Hydrometallurgy, 219, 106086. https://doi.org/10.1016/j.hydromet.2023.106086

Li, H., Tang, Z., & Xiang, D. (2024). Study on numerical simulation of reactive-transport of groundwater pollutants caused by acid leaching of uranium: A case study in Bayan-Uul area, northern China. Water, 16(3), 500. https://doi.org/10.3390/w16030500

Regnault, O., Lagneau, V., & Fiet, N. (2014). 3D Reactive Transport simulations of Uranium In Situ Leaching: Forecast and Process Optimization. Uranium - Past and Future Challenges, 725-730. https://doi.org/10.1007/978-3-319-11059-2_83

Tungatarova, M.S., Kurmanseiit, M.B., & Shayakhmetov, N.M. (2020). GPU Accelerated Modeling of In-Situ Leaching Process and Streamline Based Reactive Transport Simulation. Procedia Computer Science, 178, 145-152. https://doi.org/10.1016/j.procs.2020.11.016

Wang, S., Fan, K., Luo, N., Cao, Y., Wu, F., Zhang, C., Heller, K.A., & You, L. (2019). Massive computational acceleration by using neural networks to emulate mechanism-based biological models. Nature Communications, 10(1), 4354. https://doi.org/10.1038/s41467-019-12342-y

Fu, Y., & Aldrich, C. (2020). Deep Learning in Mining and Mineral Processing Operations: A Review. IFAC PapersOnLine, 53(2), 11920–11925. https://doi.org/10.1016/j.ifacol.2020.12.712

Azhari, F., Sennersten, C.C., Lindley, C.A., & Sellers, E. (2023). Deep learning implementations in mining applications: a compact critical review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-023-10500-9

Jooshaki, M., Nad, A., & Michaux, S. (2021). A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry. Minerals 11, Article 816. https://doi.org/10.3390/min11080816

Aggarwal, C.C. (2023). Neural Networks and Deep Learning. Springer, (2).

Karniadakis, G.E., Kevrekidis, I.G., Lu L., Perdikaris, P., Wang, S. & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 6 (3), 422-440. https://doi.org/10.1038/s42254-021-00314-5

Lu, L., Meng, X., Mao, Z., & Karniadakis, G. (2021). DeepXDE: A deep learning library for solving differential equations. SIAM Review, 63(1), 208–228. https://doi.org/10.1137/19M1274067

Yu, L., Wang, S. & Lai, K.K. (2007). Foreign-exchange-rate forecasting with artificial neural networks. Springer.

Bhanja, S., & Das, A. (2021). Deep Neural Network for Multivariate Time-Series Forecasting. In Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, 1255. https://doi.org/10.1007/978-981-15-7834-2_25

Downloads

Published

2024-03-31

How to Cite

Aizhulov, D. ., Kurmanseiit, M. ., Shayakhmetov, N., Tungatarova, M., & Suleimenova, A. (2024). SYNTHETIC DATA GENERATION FOR ANN MODELING OF THE HYDRODYNAMIC PROCESSES OF IN-SITU LEACHING. Scientific Journal of Astana IT University, 17(17), 5–15. https://doi.org/10.37943/17STXF5228

Issue

Section

Information Technologies
betpas
pendik escort anadolu yakasi escort bostanci escort kadikoy escort kartal escort kurtkoy escort umraniye escort
maltepe escort ataşehir escort ataşehir escort ümraniye escort pendik escort kurtköy escort anadolu yakası escort üsküdar escort şerifali escort kartal escort gebze escort kadıköy escort bostancı escort göztepe escort kadıköy escort bostancı escort üsküdar escort ataşehir escort maltepe escort kurtköy escort anadolu yakası escort ataşehir escort beylikdüzü escort