Control Systems and Computers, N4, 2024, Article 7
Control Systems and Computers, 2024, Issue 4 (308), pp.
UDK 004.942
I.V. Surovtsev, Dr (Eng.), Senior Researcher, Department of ecological digital systems, International Research and Training Centre of Information Technologies and Systems of the NAS and MES of Ukraine, Acad. Hlushkov ave., 40, Kiev, 03187, Ukraine, ORCID: https://orcid.org/0000-0003-1133-6207, igorsur52@gmail.com
V.S. Stepashko, Doctor (Eng.), Professor, Department for Information Technologies of Inductive Modelling, International Research and Training Center for Information Technologies and Systems of the NAS and MES of Ukraine, Akadimik Hlushkov ave, 40, Kyiv, 03187, Ukraine, ORCID: https://orcid.org/0000-0001-7882-3208, stepashko@irtc.org.ua,
Ye.A. Savchenko-Syniakova, Senior Researcher, International Research and Training Centre of Information Technologies and Systems of the NAS and MES of Ukraine, Acad. Hlushkov ave., 40, Kiev, 03187, Ukraine, ORCID: https://orcid.org/0000-0003-4851-9664, Savchenko_E@meta.ua
O.H. Moroz, PhD (Eng.), Senior Researcher, International Research and Training Centre of Information Technologies and Systems of the NAS and MES of Ukraine, Acad. Hlushkov ave., 40, Kiev, 03187, Ukraine, ORCID: https://orcid.org/0000-0002-0356-8780, olhahryhmoroz@gmail.com,
V.M. Galimova, PhD in Chemistry, Associate Professor, Senior Lecturer, the Department of Analytical and Bioinorganic Chemistry and Water Quality, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str.17, building 2, of. 16, 18, Kyiv, 03041, Ukraine, ORCID: https://orcid.org/0000-0001-9602-1006, galimova2201@gmail.com
CONSTRUCTION OF A BASELINE MODEL OF DIFFERENTIAL
MEASUREMENT SIGNALS
Introduction. Environmental pollution has a significant impact on people’s lives. Drinking water pollution with heavy metals is especially noticeable for humans. To solve this problem, it is necessary to ensure continuous monitoring of water quality, which will allow determining the concentration of toxic elements in it. It is necessary to isolate the useful component of the signal containing information on unknown concentrations of the measured elements, against the background of a mixture of various signals of elements present in the background solution. For this purpose, a method for constructing a basic model is proposed, according to which it is possible to separate the differential signal of the inversion of chemical elements in water from the background signal of impurities present in water. Due to this, a spectrum of a multicomponent intensity signal is formed in pure form, the analysis of which allows one to accurately estimate the unknown concentrations of a mixture of these dissolved elements.
Purpose. To develop a method for constructing an approximation function for the lower envelope of the background intensity signal in different classes of basic functions using GMDH in the problem of determining the concentrations of chemical elements in multicomponent signals when measuring the ecological state of environmental objects using electrochemical methods of inversion chronopotentiometry.
Methods. The methods that are used in this article are of inversion chronopotentiometry method and GMDH neural network.
Results. The problem of constructing a baseline for the multicomponent signal of the intensity of inverse chronopotentiometry, the determination of which allows to estimate the concentration of various chemical elements dissolved in water quite accurately, is investigated. To solve this problem, an approach to construction of the approximation function of the lower envelope line of the differential signal in different classes of basic functions with the use of GMDH is proposed. The approach was used for constructing the best model of the differential signal baseline on the real example of measuring the Zn concentration under the presence of ions Cd, Pb, Cu. The built model of optimal complexity is the sum of arguments with the direct and inverse degrees which is necessary for clearing the intensity signal from background to obtain the intensity spectrum of the measured chemical elements.
Conclusion. Produced in the C2 class, it can be recommended for use in the task of providing the baseline of a differential signal, since it can practically be the same R2, but also the richly shortening MAPE.
Keywords – identification, modeling, base curve, lower curve, differential signal, intensity signal, concentration of toxic elements, least squares method, GMDH.
