An artificial neural network-based non-destructive microwave technique for monitoring fluoride contamination in water

Abstract

ABSTRACTThis article presents a novel non-destructive microwave technique for predicting fluoride contamination in pure water. The proposed microwave-based sensing technique uses an open-ended coaxial probe (OECP) microwave sensor for monitoring fluoride concentration in water. The sensor output is the input of Artificial Neural Network (ANN) for predicting the complex dielectric constant of contaminated water, which has direct correlation with fluoride contamination in water. The ANN is trained through analytically generated sensor output for various lossy liquid materials and tested for experimental data obtained through laboratory prepared samples. Hence, the proposed technique has the capability to compute the amount of fluoride contamination faster, when compared to analysis only method. The results shows that a well-trained ANN is computationally efficient and capable of predicting the amount of fluoride level in the pure water. The results also has good agreement with the data published in the literature at room temperature.