Near-real-time analysis of binary mixtures of organic compounds in water using SH-SAW sensors and estimation theory

Abstract

Sensor systems for on-site monitoring of contaminated water for trace organic compounds are currently under development. To permit near-real-time analysis of samples containing multiple analytes, we investigate a sensor signal processing approach based on estimation theory, specifically, the Kalman Filter. The approach permits estimation of analyte concentration(s) in binary mixtures on-line, before the sensor response reaches equilibrium. Sensor signals from binary mixtures of BTEX compounds (benzene, toluene, ethylbenzene, and xylenes) were analyzed because these compounds are good indicators of accidental releases of fuel and oil into groundwater. Based on previous and recent experimental results, models for the sensor response to binary mixtures were developed. The sensor response model was transformed into a state-space representation so that estimation theory could be used to estimate the sensor parameters. The state-space form was tested using the available measured data; the results indicate that relatively accurate estimates of analyte concentration(s) can be obtained within a short period of time (four - six minutes or less for the tested sensor system) well before the sensor response reaches equilibrium (10 - 16 minutes).