Artificial intelligence system for the analysis of theoretical and experimental current-voltage characteristics

Abstract

Digitalization is one of the development priorities in the global scientific community. Digitalization at this point in time means the introduction of artificial intelligence systems in production, science, economics, management, etc. Artificial intelligence systems have shown their effectiveness in various fields of science and technology, including electrochemistry for such tasks as modeling membrane separation, controlling the variable operation of a simple seawater reverse osmosis plant, etc., however, the study and prediction of theoretical and experimental current-voltage characteristics of electromembrane systems by machine learning and artificial intelligence methods is still practically not carried out. The problem of finding connections between the regularities of changes in the current-voltage characteristics (CVC) and the regularities of the process of salt ion transfer in the electrodialysis apparatus (EDA) desalination channel, the classification of theoretical and experimental CVC depending on the values of the input parameters has not yet been set and investigated, and therefore is a new fundamental problem and an actual practical problem.

Influence of Space Charge Breakdown on the Performance of Electromembrane Systems

“…To assess the effect of the breakdown of the spatial charge on the transport of salt ions using formula (17) and Leveque's formula, the voltage characteristic [8][9][10][11][12][13][14][15] for the boundary value problem (1-16) is constructed and analyzed. It is shown that in the extreme current regime, the appearance of spatial charge breakdowns in the desalination channel worsens the mass transfer of salt ions: in region II (1.4-2.7 V) by 31%, in region III (2.7-3 V) by 42% (Fig.…”