References
- Markowska, M. (2021). The analysis of separation process of solid-liquid and liquid-liquid in modified swirl settling tanks. PhD Thesis, Poznan University of Technology, Poznan (in Polish).
- Southard, M.Z., Green, D.W. (2018). Perry’s Chemical Engineers’ Handbook, 9th Edition, McGraw-Hill Education, New York.
- Królikowska, J. (2011). Influence of wastewater treatment technology on particle size distribution in the effluent. Inż. Ekol. 26, 156–170.
- Czernek, K., Ochowiak, M., Janecki, D., Zawilski, T., Dudek, L., Witczak, S., Krupińska, A., Matu-szak, M., Włodarczak, S., Hyrycz, M., Pavlenko, I. (2021). Sedimentation tanks for treating rainwater: CFD simulations and PIV experiments. Energies, 14(23), 7852. DOI: 10.3390/en14237852.
- Ochowiak, M., Matuszak, M., Włodarczak, S., Ancukiewicz, M., Gościniak, A. (2016). Study on ef-ficiency of rainwater stream purification in a swirl sedimentation tank. Inż. Ap. Chem. 5, 199–200.
- Ochowiak, M., Matuszak, M., Włodarczak, S., Ancukiewicz, M., Krupińska, A. (2017). Evaluation of the work of modified swirl sedimentation tank for purification of rainwater stream contaminated by light fraction. Inż. Ap. Chem. 4, 132–133.
- Ochowiak, M., Markowska, M., Matuszak, M., Włodarczak, S. (2018). Analysis of work of a modified swirl separation tank. Inż. Ap. Chem. 1, 12–13.
- Aglodiya, A. (2017). Application of Artificial Neural Network (ANN) in chemical engineering: A review. Korean J. Chem. Eng. 17(4), 373–392. DOI: 16.0415/IJARIIE-5013.
- Din, M., Smith, D., Gamal, A. (2004). Application of artificial neural networks in wastewater treat-ment. J. Environ. Eng. Sci. 3, S1. DOI: 10.1139/s03-067.
- Yanbo, H. (2009). Advances in Artificial Neural Networks – methodological development and ap-plication. Algorithms. 2(3), 973–1007. DOI: 10.3390/algor2030973.
- Guiné, R., Dets, C. (2019). The use of Artificial Neural Networks (ANN) in food process engineering. ETP Int. J. Food Eng. 5(1), 15–21. DOI: 10.18178/ijfe.5.1.15-21.
- Saharawat, D., Kashyap, P., Kisi, O. (2018). Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques. Environ. Earth Sci. 77, 1–14. DOI: 10.1007/s12665-018-7892-6.
- Taloba, A.I. (2022). An Artificial Neural Network mechanism for optimizing the water treatment process and desalination process. Alexandria Eng. J. 61, 9287–9295. DOI: 10.1016/j.aej.2022.03.029.
- Gamal El-Din, A., Smith, D. (2002). Modeling a full-scale primary sedimentation tank. Environ. Tech. 23(5), 479–496. DOI: 10.1080/09593332308618384.
- Szaleniec, M. (2008). Neural networks and multiple regression: How to tackle complexity in scientific research?, Kraków, www.statsoft.pl, (in Polish).
- Yang, Y., Rosenbaum, M. (2001). Artificial Neural Networks linked to GIS for determining sedi-mentology in harbours. J. Petroleum Sci. Eng. 29(3), 213–220. DOI: 10.1016/s0920-4105(01)00091-2.
- Haykin, S. (1999). Neural networks: A comprehensive foundation, Second Edition, Prentice-Hall, Englewood Cliffs, Corpus ID: 60577818.
- Rummelhart, D., Hinton, G., Williams, R. (1986). Learning representations by back-propagating errors, Nature. DOI: 10.1038/323533a0.
- Meireles, M., Almeida, P., Simoes, M. (2003). A comprehensive review for industrial applicability of Artificial Neural Networks. IEEE Trans. Ind. Elect. 50(3), 585–601. DOI: 10.1109/TIE.2003.812470.
- Han, J., Kamber, M., Pei, J. (2011). Data mining: concepts and techniques, Morgan Kaufmann Pulisers In. DOI: 10.1016/C2009-0-61819-5.
- Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep learning, MIT Press, 19: 305–307. DOI: 10.1007/s10710-017-9314-z.
- Bichri, H., Chergui, A., Mustapha, H. (2024). Investigating the impact of train/test split ratio on the performance of pre-trained models with custom datasets. Int. J. Adv. Comp. Sci. Appl. 15. DOI: 10.14569/IJACSA.2024.0150235.
- Domurat, K. (2024). The application of artificial neural networks for evaluating the performance of settling tanks, MSc Thesis, Poznan University of Technology, Poznan.