Evaluation of a Permittivity Sensor for Continuous Monitoring of Suspended Sediment Concentration
Location
Eccles Conference Center
Event Website
http://water.usu.edu
Start Date
4-20-2010 11:20 AM
End Date
4-20-2010 11:40 AM
Description
According to the US Environmental Protection Agency (USEPA), sediment is a leading cause of water quality impairment (US EPA, 2002). A survey of streams throughout the United States determined 46% of streams analyzed suffer from excessive siltation (Berkman and Rabeni, 1987). 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 can be difficult. The majority of sediment movement occurs infrequently during large rainfall events, requiring the rapid mobilization of field personnel into potentially hazardous flood conditions. Traditional methods for measuring suspended sediment are also expensive. As of 2006 only a quarter of the USGS stations collecting daily sediment data in 1981 were still in service. The decrease in sediment monitoring stations is primarily due to cost (Gray and Gartner, 2009). 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). Permittivity is a physical description of the effects of an electric field on a dielectric medium and how the medium then affects the electric field (Robinson et al., 1999). For this study, gain (dB) was the ratio of the input voltage to the output voltage and phase (deg) was the difference between the phase of the input signal to the output signal (Tang, 2009). To test this concept, prediction models for SSC were built with input variables of temperature, specific conductivity, and gain and/or phase at multiple frequencies. The permittivity sensor was comprised of an electrode, power source, and a control box or frequency generator. To complete the study, a bench-scale suspension system was built to maintain a homogeneous suspension throughout the testing period (Utley, 2009). The suspended sediment sensor was calibrated via a static calibration procedure using known concentrations over the required sensor range. The prediction model was verified by randomly generating nine concentrations, measuring the permittivity of each suspension, and predicting the SSC with the transfer function. Partial Least Squares (PLS) regression techniques were 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%. The prediction accuracy for the independent validation data set increased for the top three models at levels of near 1000 mg/L. 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.
Evaluation of a Permittivity Sensor for Continuous Monitoring of Suspended Sediment Concentration
Eccles Conference Center
According to the US Environmental Protection Agency (USEPA), sediment is a leading cause of water quality impairment (US EPA, 2002). A survey of streams throughout the United States determined 46% of streams analyzed suffer from excessive siltation (Berkman and Rabeni, 1987). 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 can be difficult. The majority of sediment movement occurs infrequently during large rainfall events, requiring the rapid mobilization of field personnel into potentially hazardous flood conditions. Traditional methods for measuring suspended sediment are also expensive. As of 2006 only a quarter of the USGS stations collecting daily sediment data in 1981 were still in service. The decrease in sediment monitoring stations is primarily due to cost (Gray and Gartner, 2009). 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). Permittivity is a physical description of the effects of an electric field on a dielectric medium and how the medium then affects the electric field (Robinson et al., 1999). For this study, gain (dB) was the ratio of the input voltage to the output voltage and phase (deg) was the difference between the phase of the input signal to the output signal (Tang, 2009). To test this concept, prediction models for SSC were built with input variables of temperature, specific conductivity, and gain and/or phase at multiple frequencies. The permittivity sensor was comprised of an electrode, power source, and a control box or frequency generator. To complete the study, a bench-scale suspension system was built to maintain a homogeneous suspension throughout the testing period (Utley, 2009). The suspended sediment sensor was calibrated via a static calibration procedure using known concentrations over the required sensor range. The prediction model was verified by randomly generating nine concentrations, measuring the permittivity of each suspension, and predicting the SSC with the transfer function. Partial Least Squares (PLS) regression techniques were 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%. The prediction accuracy for the independent validation data set increased for the top three models at levels of near 1000 mg/L. 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.
https://digitalcommons.usu.edu/runoff/2010/AllAbstracts/19