Abstract
Spectrophotometric analysis based on artificial neural network (ANN), partial least squares regression (PLS) and basic component regression (PCR) models has been proposed to simultaneously determine coagulant (Fe) during the electrocoagulation process. An experimental laboratory installation was created to study the processes of photometric research with a device that analyzes the color and intensity of light in real time. The color sensor determines the color parameters: RGB, which, based on ANN, are translated into the HSL color space. Software for determining the concentration of iron in the coagulant using artificial intelligence, which is a web application for displaying the color parameters of the coagulant, the determined concentration of iron in the coagulant, as well as saving the history of all measurements in the database. ANN, trained using different teaching methods. An optimizer was selected for the appropriate process, the standard deviation (RMSE) is 6.91%.
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Safonyk, A., Mishchanchuk, M., Lytvynenko, V.: Intelligent information system for the determination of iron in coagulants based on a neural network. Intell. Inf. Technol. Syst. Inf. Secur. 2021 2853, 142–150 (2021)
Alberti, G., Emma, G., Colleoni, R., Nurchi, V. M., Pesavento, M., Biesuz, R.: Simple solid-phase spectrophotometric method for free iron (iii) determination. Arab. J. Chem. 12(4), 573–579 (2019)
Heidari-Bafroui, H., Ribeiro, B., Charbaji, A., Anagnostopoulos, C., Faghri, M.: Portable infrared lightbox for improving the detection limits of paper-based phosphate devices. Measurement 173 (2021). https://doi.org/10.1016/j.measurement.2020.108607
Barros, J.A., Oliveira, F.M.D., Santos, G.D.O., Wisniewski, C., Luccas, P.O.: Digital image analysis for the colorimetric determination of aluminum, total iron, nitrite and soluble phosphorus in waters. Anal. Lett. 50(2), 414–430 (2016)
Zarei, K., Atabati, M., Malekshabani, Z.: Simultaneous spectrophotometric determination of iron, nickel and cobalt in micellar media by using direct orthogonal signal correction-partial least squares method. Analytica Chimica Acta 556(1), 247–254 (2006)
Firdaus, M.L., Alwi, W., Trinoveldi, F., Rahayu, I., Rahmidar, L., Warsito, K.: Determination of chromium and iron using digital image-based colorimetry. Procedia Environ. Sci. 20, 298–304 (2014)
Suliman, M.S., Yasin, S., Ali, M.S.: Development of colorimetric analysis for determination the concentration of oil in produce water. Int. J. Eng. Inf. Syst. 1(5), 9–13 (2017)
Ni, Y., Huang, C., Kokot, S.: Simultaneous determination of iron and aluminium by differential kinetic spectrophotometric method and chemometrics. Analytica Chimica Acta 599(2), 209–218 (2007)
Masawat, P., Harfield, A., Srihirun, N., Namwong, A.: Green determination of total iron in water by digital image colorimetry. Anal. Lett. 50(1), 173–185 (2016)
Place, B.: Activity analysis of iron in water using a simple led spectrophotometer. J. Chem. Educ. 29(6), 677–680 (2013)
e Silva, A.F.D.O., de Castro, W.V., de Andrade, F.P.: Development of spectrophotometric method for iron determination in fortified wheat and maize flours. Food Chem. 242, 205–210 (2018)
Sreenivasareddy, A.: Determination of iron content in water. Governors State University OPUS Open Portal to University Scholarship (2017)
Ribas, T.C., Mesquita, R.B., Moniz, T., Rangel, M., Rangel, A.O.: Greener and wide applicability range flow-based spectrophotometric method for iron determination in fresh and marine water. Talanta 216 (2020)
Iqbal, Z., Bjorklund, R.B.: Colorimetric analysis of water and sand samples performed on a mobile phone. Talanta 84(4), 24–39 (2011). https://doi.org/10.1016/j.talanta.2011.03.016
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Safonyk, A., Mishchanchuk, M., Hrytsiuk, I. (2022). Spectrophotometric Method for Coagulant Determining in a Stream Based on an Artificial Neural Network. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_40
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DOI: https://doi.org/10.1007/978-3-030-82014-5_40
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