Calibration of permittivity sensors to measure contaminants in water and in biodiesel fuel

Abstract

Four permittivity probes have been developed and tested to measure contaminants in water and in biodiesel fuel. An impedance meter was also used to measure the same contaminants. The pollutants measured in water were nitrate salts (potassium nitrate, calcium nitrate, and ammonium nitrate) and atrazine. The contaminants measured in biodiesel were water, glycerol, and glyceride. Each sensor measured the gain and phase of a sample with a known concentration of one of these pollutants. The resulting signals were analyzed using stepwise regression, partial least squares regression, artificial neural network, and wavelet transformation followed by stepwise regression to predict the concentration of the contaminant using changes in the gain and phase data measured by the sensor. The same methods were used to predict the molecular weight of the nitrate salts. The reliability of the probes and the regression methods were compared using the coefficient of determination and the root mean square error. The frequencies selected using stepwise regression were studied to determine if any frequencies were more useful than others in detecting the contaminants. The results showed that the probes were able to predict the concentration and the molecular weight of nitrates in water very accurately, with R2-values as high as 1.00 for the training data and 0.999 for the validation data for both concentration predictions and molecular weight predictions. The atrazine measurements were somewhat promising, the training R2-values were as high as 1.00 in some cases, but there were many low validation values, often below 0.400. The results for the biodiesel tests were also good; the highest training R2-value was 1.00 and the highest validation R2-value was 0.966.