MLR Model Based Sensor Reduction For Remote Cr6 Detector

Abstract

Clean drinking water is essential for healthy living. The main objective of this research is to design an Internet of Things (IoT) based water quality monitoring device for detection of the Hexavalent Chromium (Cr6) in drinking water. pH, TDS and conductivity are the major Water Quality Parameters (WQPs) measured for the determination of the quality of water. In this research, the impact of contaminant concentration on WQPs was analyzed using Linear Regression Model for determination of the concentration of Cr6 in the real time sample. The above WQPs were measured for synthesized Cr6 contaminated samples. The measurement was made for varied concentrations of the samples. A customized database was created from the above measurement. In order to reduce the number of WQPs involved in detection, dependency analysis between the parameters and chromium concentration was made. Further, the relative factors among the parameters were computed. The inference drawn from the analysis is that the estimation of Cr6 contaminant concentration using TDS and conductivity results in 90% accuracy when compared to pH for all the four synthesized Cr6 samples.