Using Complex Permittivity and Artificial Neural Networks for Contaminant Prediction

Abstract

The use of the measured complex permittivity of electrolyte solutions for predicting ionic species and concentration is investigated. Four artificial neural networks (ANNs) are created using a database containing permittivities (at 1.0, 1.5, and 2.0 GHz) and loss factors (at 0.3, 1.5, and 3.0 GHz) of 12 aqueous salts at various concentrations. The first ANN correctly identifies cationic species in 83% of the samples and distinguishes between pure water and electrolyte solutions with 100% accuracy. The second ANN predicts cationic concentrations with a RMS error of 190 mg/L for the range of concentrations examined (0–3,910 mg/L) and explains 90% of the variability in these data. The third ANN correctly identifies 98% of the anionic species in samples and accurately distinguishes between pure water and anion-containing solutions. The last ANN predicts anionic concentrations with a RMS error of 164 mg/L for the range of concentrations examined (0–5,654 mg/L) with an