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The use of artificial neural networks for the assessment of the operation of settling tanks

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the research was to develop and evaluate the usefulness of artificial neural network models for predicting the key operating parameters of centrifugal settlers. Various settler structures were analyzed, taking into account such elements as internal partitions and also inlet and outlet nozzles. Neural network modeling was continued until the highest possible quality was achieved in terms of training, testing and validation, with the occurrence of errors also being minimized. This process involved multiple iterations and adjustments of the network’s parameters to achieve optimal results. It was shown that artificial neural networks are characterized by having high accuracy in predicting the efficiency and damming values of centrifungal sedimentation tanks with regards to their design and hydraulic load. The designed network is able to determine both efficiency and liquid level with satisfactory accuracy.
Czasopismo
Rocznik
Strony
1--10
Opis fizyczny
Bibliogr. 23 poz., rys., tab., wykr., wz.
Twórcy
  • Poznan University of Technology, Department of Chemical Engineering and Equipment, 60-965 Poznan, Poland
  • Poznan University of Technology, Department of Chemical Engineering and Equipment, 60-965 Poznan, Poland
  • Poznan University of Technology, Department of Chemical Engineering and Equipment, 60-965 Poznan, Poland
  • Poznan University of Technology, Department of Chemical Engineering and Equipment, 60-965 Poznan, Poland
  • The Pontifical University of John Paul II in Krakow, Faculty of Social Sciences, Cracow, 31-002, Poland
  • HAAS sp. z o.o., Poznań 60-124, Poland
  • HAAS sp. z o.o., Poznań 60-124, Poland
  • Poznan University of Technology, Department of Chemical Engineering and Equipment, 60-965 Poznan, Poland
Bibliografia
  • 1. 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).
  • 2. Southard, M.Z., Green, D.W. (2018). Perry’s Chemical Engineers’ Handbook, 9th Edition, McGraw-Hill Education, New York.
  • 3. Królikowska, J. (2011). Influence of wastewater treatment technology on particle size distribution in the effluent. Inż. Ekol. 26, 156–170.
  • 4. Czernek, K., Ochowiak, M., Janecki, D., Zawilski, T., Dudek, L., Witczak, S., Krupińska, A., Matuszak, 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.
  • 5. Ochowiak, M., Matuszak, M., Włodarczak, S., Ancukiewicz, M., Gościniak, A. (2016). Study on efficiency of rainwater stream purification in a swirl sedimentation tank. Inż. Ap. Chem. 5, 199–200.
  • 6. 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.
  • 7. Ochowiak, M., Markowska, M., Matuszak, M., Włodarczak, S. (2018). Analysis of work of a modified swirl separation tank. Inż. Ap. Chem. 1, 12–13.
  • 8. 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.
  • 9. Din, M., Smith, D., Gamal, A. (2004). Application of artificial neural networks in wastewater treatment. J. Environ. Eng. Sci. 3, S1. DOI: 10.1139/s03-067.
  • 10. Yanbo, H. (2009). Advances in Artificial Neural Networks – methodological development and application. Algorithms. 2(3), 973–1007. DOI: 10.3390/algor2030973.
  • 11. 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.
  • 12. 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.
  • 13. 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.
  • 14. Gamal El-Din, A., Smith, D. (2002). Modeling a full-scale primary sedimentation tank. Environ. Tech. 23(5), 479–496. DOI: 10.1080/09593332308618384.
  • 15. Szaleniec, M. (2008). Neural networks and multiple regression: How to tackle complexity in scientific research?, Kraków, www.statsoft.pl, (in Polish).
  • 16. 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.
  • 17. Haykin, S. (1999). Neural networks: A comprehensive foundation, Second Edition, Prentice-Hall, Englewood Cliffs, Corpus ID: 60577818.
  • 18. Rummelhart, D., Hinton, G., Williams, R. (1986). Learning representations by back-propagating errors, Nature. DOI: 10.1038/323533a0.
  • 19. 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.
  • 20. Han, J., Kamber, M., Pei, J. (2011). Data mining: concepts and techniques, Morgan Kaufmann Pulisers In. DOI: 10.1016/C2009-0-61819-5.
  • 21. Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep learning, MIT Press, 19: 305–307. DOI: 10.1007/s10710-017-9314-z.
  • 22. 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.
  • 23. Domurat, K. (2024). The application of artificial neural networks for evaluating the performance of settling tanks, MSc Thesis, Poznan University of Technology, Poznan.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ac7082e9-6c82-49b9-962a-a5850436eb44
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