Iron coagulation optimization during water treatment using artificial intelligence tools
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Main Article Content
Authors
Abstract
A process of water purification using electrical coagulation and its optimisation using artificial intelligence tools was presented. Experimental data was analysed and correlated. The experimental studies used were developed to optimise iron coagulation during water treatment. A neural network was developed to optimise iron coagulation and machine learning was performed. Neural network tests were conducted and methods for its practical application were proposed.
Keywords:
Sustainable Development Goals (SDG)
- 6 - Clean water and sanitation
- 9 - Industry, Innovation, Technology and Infrastructure
References
[1] Edzwald, J. K. (1993). Coagulation in Drinking Water Treatment: Particles, Organics and Coagulants. Water Science and Technology, 27(11), 21–35. https://doi.org/10.2166/wst.1993.0261
[2] Jiang, J.-Q. (2015). The role of coagulation in water treatment. Current Opinion in Chemical Engineering, 8, 36–44. https://doi.org/10.1016/j.coche.2015.01.008
[3] Konieczny, K., Sąkol, D., Płonka, J., Rajca, M., & Bodzek, M. (2009). Coagulation—Ultrafiltration system for river water treatment. Desalination, 240(1–3), 151–159. https://doi.org/10.1016/j.desal.2007.11.072
[4] Matilainen, A., Vepsäläinen, M., & Sillanpää, M. (2010). Natural organic matter removal by coagulation during drinking water treatment: A review. Advances in Colloid and Interface Science, 159(2), 189–197. https://doi.org/10.1016/j.cis.2010.06.007
[5] Moussa, D. T., El-Naas, M. H., Nasser, M., & Al-Marri, M. J. (2017). A comprehensive review of electrocoagulation for water treatment: Potentials and challenges. Journal of Environmental Management, 186, 24–41. https://doi.org/10.1016/j.jenvman.2016.10.032
[6] Safonyk, A., Hrytsiuk, I., Mishchanchuk, M., & Ilkiv, I. (2021). Information system of electrochemical obtaining of coagulant on the basis of photocolorimetric analysis. Measuring and Computing Devices in Technological Processes, (1), 97–104. https://doi.org/10.31891/2219-9365-2021-67-1-14
Article Details
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