Master of Science / Department of Biological and Agricultural Engineering / Naiqian Zhang / Permittivity, displayed when a dielectric material is exposed to an electric field, is a useful property for measuring impurities in a dielectric medium. These impurities often have a dipole moment different from the pure material, and the dipoles align through polarization and impede electric current. By measuring the resulting impedance in a known geometry, the permittivity can be determined. Four permittivity sensors were utilized to measure contaminants that are associated with biofuels, specifically glycerol, ethanol, and ammonia. These sensors were based around either stainless steel or aluminum plates to ensure durability and reliability. By connecting each of these sensors to a signal generating control box, the gain and phase can be measured at 609 frequencies, from 10 kHz up to 120 MHz. Data from each of the three contaminants were run through a method for detection. Measurements for ambient air and air with the contaminants were compared with a statistical analysis. Glycerol, ethanol, and ammonia each had significantly different measurements in the gain and phase data at a unique set of frequencies. Using a neural network analysis for detection resulted in a 95.8%, 93.9%, and 97.1% success rate for detecting glycerol, ethanol, and ammonia, respectively. For ethanol and ammonia, where multiple concentrations were measured, regression methods were used to relate the frequency response data to the contaminant concentration. Stepwise regression, wavelet transformation followed by stepwise regression, partial least squares regression, and neural network regression were the four methods used to establish these relationships. Several regressions over-fit the data, showing coefficient of determination (R[superscript]2) values of 1.000 for training data, yet very low R[superscript]2 values for validation data. However, the best R[superscript]2 values of all the regressions were 1.000 and 0.996 for the training and validation data, respectively, from measuring ammonia.

Master of Science / Department of Biological & Agricultural Engineering / Naiqian Zhang / 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.