Simplicial analysis of the water resource distribution system of an enrichment plant

  1. Airapetian, T.S. (2010). Water management of industrial enterprises. Kharkiv: KhNAMG.
  2. Baskar, G., Omer, S.N., Saravanan, P., Rajeshkannan, R., Saravanan, V., Rajasimman, M., & Shanmugam, V. (2024). Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques. Chemosphere, 362, article number 142477. doi: 10.1016/j.chemosphere.2024.142477.
  3. Bras, M., Carrico, N., & Covas, D. (2025). Cost efficiency in water supply systems: An applied review on optimization models for the pump scheduling problem. European Journal of Operational Research, 323(1), 1-19. doi: 10.1016/j.ejor.2024.07.039.
  4. Camargo, L.F.R., Rodrigues, L.H., Lacerda, D.P., & Piran, F.S. (2018). A method for integrated process simulation in the mining industry. European Journal of Operational Research, 264(3), 1116-1129. doi: 10.1016/j.ejor.2017.07.013.
  5. Elshazly, D., Gawai, R., Ali, T., Mortula, M.M., Atabay, S., & Khalil, L. (2024). An automated geographical information system-based spatial machine learning method for leak detection in water distribution networks (WDNs) using monitoring sensors. Applied Sciences, 14(13), article number 5853. doi: 10.3390/app14135853.
  6. Fan, X., Zhang, X., & Yu, X. (2022). A graph convolution network-deep reinforcement learning model for resilient water distribution network repair decisions. Computer-Aided Civil and Infrastructure Engineering, 37(12), 1547-1565. doi: 10.1111/mice.12836.
  7. Gaurav, & Rathi, S. (2025). Machine learning-based leakage identification in water distribution system. Water and Environment Journal, 39(2), 203-219. doi: 10.1111/wej.12970.
  8. Li, Z., Tian, Y., & Peng, S. (2025). Detection and localization of pipeline leakage of water distribution networks based on graph convolutional networks. Computer-Aided Civil and Infrastructure Engineering, 40(31), 6656-6677. doi: 10.1111/mice.70173.
  9. Matsui, A.M., Kondratets, V.O., & Serbul, O.M. (2013). Automation of pulp dilution control processes during ore grinding by drum mills. Kropyvnytskiy: KOD.
  10. Ponti, A., Candelieri, A., Giordani, I., & Archetti, F. (2021). A novel graph-based vulnerability metric in urban network infrastructures: The case of water distribution networks. Water, 13(11), article number 1502. doi: 10.3390/w13111502.
  11. Salieva, O.V., & Yaremchuk, Yu.E. (2020). Simplicial analysis of the structure of the cognitive model for studying the security level of a critical infrastructure object. Data Recording, Storage & Processing, 22(3), 68-75. doi: 10.35681/1560-9189.2020.22.3.218974.
  12. Smyrnov, V.O., & Biletskyi, V.S. (2002). Design of enrichment plants. Donetsk: Skhidnyi Vydavnychyi Dim.
  13. Suzaimi, N.D., Abuhabib, A., Darwish, M., AlHadi, A.C., Mohammad, A.W., & Hamzah, S. (2025). Machine learning in industrial wastewater treatment. Journal of Industrial and Engineering Chemistry, 159, 48-64. doi: 10.1016/j.jiec.2025.12.035.
  14. Tian, X., Negenborn, R., van Overloop, P.-J., Maestre Torreblanca, J.M., Sadowska, A., & van de Giesen, N. (2017). Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts. Advances in Water Resources, 109, 58-68. doi: 10.1016/j.advwatres.2017.08.015.
  15. Vittori, G., Falkouskaya, Y., Jimenez-Gutierrez, D.M., Cattai, T., & Chatzigiannakis, I. (2025). Graph neural networks to model and optimize the operation of water distribution networks: A review. Journal of Industrial Information Integration, 47, article number 100880. doi: 10.1016/j.jii.2025.100880.
  16. Wang, J., Fu, G., & Savic, D. (2025). Transfer learning with graph neural networks for pressure estimation in monitoring-limited water distribution networks. Water Research, 287, article number 124475. doi: 10.1016/j.watres.2025.124475.
  17. Yang, S., Behzadian, K., Coleman, C., Holloway, T.G., & Campos, L.C. (2025). Application of AI-based techniques for anomaly management in wastewater treatment plants: A review. Journal of Environmental Management, 392, article number 126886. doi: 10.1016/j.jenvman.2025.126886.
  18. Yang, Y., & Lo, E.Y. (2025). Synthetic modeling of water distribution systems for interdependent infrastructure systems resilience analysis with interdependencies via building-mediated clustering. Resilient Cities and Structures, 4, 21-36. doi: 10.1016/j.rcns.2025.10.002.
  19. Zapolskyi, A.K. (2005). Water supply, drainage and water quality. Kyiv: Vyshcha shkola.
  20. Zhao, X., Liu, D., Wu, Z., & Huang, C. (2024). Chance constrained optimal power-water flow: An iterative algorithm assisted by deep neural networks. Electric Power Systems Research, 234, article number 110599. doi: 10.1016/j.epsr.2024.110599.
Tymokhin, Ye., & Kharlamenko, V. (2026). Simplicial analysis of the water resource distribution system of an enrichment plant. Journal of Kryvyi Rih National University, 24(1), 54-62. https://doi.org/10.31721/2306-5451-2026-1-24-54-62
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