Title page for ETD etd-11132009-165802


Type of Document Dissertation
Author Utley, Barbra Crompton
Author's Email Address barb@vt.edu
URN etd-11132009-165802
Title Evaluation of a Permittivity Sensor for Continuous Monitoring of Suspended Sediment Concentration
Degree PhD
Department Biological Systems Engineering
Advisory Committee
Advisor Name Title
Theresa Wynn Committee Chair
Naiqian Zhang Committee Member
Naraine Persaud Committee Member
Panos Diplas Committee Member
W. Cully Hession Committee Member
Zipper, Carl E. Committee Member
Keywords
  • streams
  • sediment transport
  • permittivity
  • suspended sediment concentration
  • sediment
Date of Defense 2009-10-30
Availability unrestricted
Abstract
According to the US Environmental Protection Agency (USEPA) sediment is a leading cause of water quality impairment (US EPA, 2002). The annual costs of sediment pollution in North America alone are estimated to range between $20 and $50 billion (Pimentel et al., 1995; Osterkamp et al, 1998, 2004). Due to the large spatial and temporal variations inherent in sediment transport, suspended sediment measurement is challenging.

The overall goal of this research was to develop and test an inexpensive sensor for continuous suspended sediment monitoring in streams. This study was designed to determine if the gain and phase components of permittivity could be used to predict suspended sediment concentrations (SSC). A bench-scale suspension system was designed and tested to guarantee that there were no significant differences in the sediment suspension vertically or horizontally within the system. This study developed prediction models for SSC with input variables of temperature, specific conductivity, and gain and/or phase at multiple frequencies. The permittivity sensor is comprised of an electrode, power source, and a control box or frequency generator.

Fixed and mixed effect, multiple, linear regression models were created and compared for target frequencies. However, it was not possible to meet the normality requirements for prediction accuracy. Partial Least Squares (PLS) regression techniques were also applied to gain and phase data for 127 of the 635 frequencies. The three models with the lowest error between predicted and actual values of SSC for validation were further tested with nine levels of independent validation data. The largest model error (error>50%) occurred for the top three models at 0 and 500 mg/L. At the higher concentrations error varied from 1-40%.

Once the treatment levels, of the independent validation data set, were near 1000 mg/L the prediction accuracy increased for the top three models. Model 3A, a phase based model, preformed the best. Model 3A was able to predict six of the nine independent validation treatment levels within 300 mg/L. Future research will provide additional laboratory and field testing of the prototype sensor.

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