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Diagnostics of harmful impurities in aqueous media using spectroscopic methods and machine learning algorithms
Russian version
DOI: 10.61011/EOS.2023.06.56664.106-23
Laptinskiy K. A. 1, Burikov S.A. 1,2, Sarmanova O.E. 2, Vervald A.M. 2, Utegenova L.S.2, Plastinin I.V. 1, Dolenko T. A. 1,2
1Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, Russia
2Department of Physics, Lomonosov Moscow State University, Moscow, Russia
Email: laptinskiy@physics.msu.ru, sergey.burikov@gmail.com, oe.sarmanova@physics.msu.ru, alexey.vervald@physics.msu.ru, plastinin_ivan@mail.ru, tdolenko@mail.ru
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The results of the development of a method for diagnosing 8-component aqueous solutions containing lithium, ammonium, iron (III), nickel, copper and zinc cations, as well as sulfate and nitrate anions, by IR absorption spectra and optical density spectra using artificial neural networks are presented. The application of artificial neural networks to the obtained arrays of spectroscopic data made it possible to ensure the simultaneous determination of the studied ions in a multicomponent mixture with an accuracy that satisfies the needs of environmental monitoring of natural and waste waters, as well as diagnostics of technological environments. Keywords: diagnostics of aqueous environments, spectroscopy, IR spectroscopy, absorption spectroscopy, machine learning methods, neural networks. DOI: 10.61011/EOS.2023.06.56664.106-23
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