Sensor systems for measuring an interface level in a multi-phase fluid composition

Information

  • Patent Grant
  • 9658178
  • Patent Number
    9,658,178
  • Date Filed
    Friday, September 28, 2012
    11 years ago
  • Date Issued
    Tuesday, May 23, 2017
    7 years ago
Abstract
A sensor includes a resonant transducer, the resonant transducer being configured to determine the composition of an emulsion. The composition of the emulsion is determined by measuring the complex impedance spectrum values of the mixture of the emulsion and applying multivariate data analysis to the values.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the concurrently filed application Ser. No. U.S. Ser. No. 13/630,739, titled SYSTEMS AND METHODS FOR MEASURING AN INTERFACE LEVEL IN A MULT-PHASE FLUID COMPOSITION, filed jointly in the names of Cheryl Surman, William Platt, William Morris, Steven Go, Jon Dieringer and Radislav A. Potyrailo, assigned to General Electric Company, the assignee of the present invention.


TECHNICAL FIELD

The subject matter disclosed herein generally relates to sensors, and more particularly to level sensors to determine the interface level of a multi-phase fluid composition.


BACKGROUND

Measurement of the composition of emulsions and the interface level of immiscible fluids is important in many applications. For example, it is important to characterize emulsions in oil field management. The measurement of the water and oil content of emulsions from individual oil wells may vary over the life of an oil field and may indicate the overall health of a field. In the case of injection wells, it is critical to control water quality to reduce hydrate formation and corrosion. Characterization of the composition of the oil and water mixture (e.g., measurement of the relative proportions of oil and water in the mixture) helps the operator improve well productivity and capacity. The information obtained is also useful to reduce back-pressure of wells, flowline size and complexity, and thermal insulation requirements.


Characterization of emulsions is also important in the operation of systems that contain fluids in a vessel (vessel systems) such as fluid processing systems. Vessel systems may include storage tanks, reactors, separators and desalters. Vessel systems are used in many industries and processes, such as the oil and gas, chemical, pharmaceutical, food processing industries, among others. For example, separation of water from raw oil is important to establishing production streams of oil and gas. Crude oil leaving the wellhead is both sour (contains hydrogen sulfide gas) and wet (contains water). The crude leaving the wellhead must be processed and treated to make it economically viable for storage, processing and export. One way of treating the raw oil is through the use of a separator. Most separators are driven by gravity and use the density differences between individual fluid phases of oil, water, gas, and solids to accomplish the separation. Identification of the interface levels of these layers is critical to the control of the separation process. Another fluid processing system where characterization of emulsions and measurement of the interface level is important is a desalter. Desalters are used in a refinery to control overhead corrosion downstream. In a desalter water and crude oil are mixed, inorganic salts are extracted into the water, and water is then separated and removed.


Finally, it is important to accurately characterize the water and salinity in the crude oil itself at various stages of the life of the product from a cost standpoint. Oil is a valuable commodity and underestimation of the water content in a typical tanker load can have significant cost consequences.


Wastewater management is another application where measurement and characterization of emulsion is important. Large quantities of oily wastewater are generated in the petroleum industry from both recovery and refining. A key factor in controlling the oil discharge concentrations in wastewater is improved instrumentation for monitoring the oil content of emulsions.


Many types of level and interface instruments have been contemplated over the years and a subset of those have been commercialized. Among those are gamma-ray sensors, guided wave sensors, magnetostrictive sensors, microwave sensors, ultrasonic sensors, single plate capacitance/admittance sensors, segmented capacitance sensors, inductive sensors, and computed tomography sensors. Each of the sensors has advantages and disadvantages. Some of the sensors are prohibitively expensive for many users. Some of the sensors may require a cooling jacket to perform at operating temperatures (above 125° C.). Some interface instruments require a clear interface to work, which can be problematic when working with diffuse emulsions. Some are susceptible to fouling. Other sensors do not have the ability to provide a profile of the tank, but rather monitor discreet points in the desalting process. Systems using electrodes are susceptible to the shorting of electrodes in high salinity applications and are susceptible to fouling. Finally, many of these systems are complex and difficult to implement.


Some existing sensor systems have used individual capacitive elements to measure fluid levels. A key limitation of those sensor systems is their inability to simultaneously quantify several components in the liquid. Capacitance methods have been used to measure dielectric constant of a liquid using specially designed electrodes for capacitance measurements. These designs are limited by the need for separate types of electrodes for capacitance measurements and for conductivity measurements. Inductor capacitor circuits also have been used to monitor the fluid level in a container using an electromagnetic resonator where change in capacitance was related to fluid level and fluid type. However, it has been the consensus of those of ordinary skill in the art that the filling of the resonator by a conducting liquid increased the uncertainties and noise in measurements by about one order of magnitude as compared to the values in a non-conducting fluid such as in air. However, these methods do not provide accurate measurements of concentrations of individual analytes at the limits of their minimum and maximum concentrations in the mixture.


With existing sensor systems, no one system is capable of delivering a combination of low cost, high sensitivity, favorable signal-to-noise ratio, high selectivity, high accuracy, and high data acquisition speeds. Additionally no existing system has been described as capable of accurately characterizing or quantifying fluid mixtures where one of the fluids is at a low concentration (i.e. at their minimum and maximum limits).


BRIEF DESCRIPTION OF THE INVENTION

The disclosure provides a technical solution to the expense, reliability and accuracy problems of existing level sensor systems. An electrically resonant transducer (resonant transducer) provides a combination of low cost, high sensitivity, favorable signal-to-noise ratio, high selectivity, high accuracy, and high data acquisition speeds. The resonant transducer is incorporated in a robust sensor without the need for a clear interface. The solution also provides a sensor that is less susceptible to fouling, particularly in applications involving emulsions.


In accordance with one exemplary non-limiting embodiment, the disclosure relates to a sensor having a resonant transducer configured to determine a composition of an emulsion and includes a sampling assembly and an impedance analyzer.


In another embodiment, the disclosure relates to a system including a fluid processing system; a fluid sampling assembly; and a resonant sensor system coupled to the fluid sampling assembly.


In another embodiment, the disclosure relates to a method for measuring a level of a mixture of fluids in a vessel. The method includes the steps of detecting a signal from a resonant sensor system at a plurality of locations in the vessel; converting each signal to values of the complex impedance spectrum for the plurality of locations; storing the values of the complex impedance spectrum and frequency values; and determining a fluid phase inversion point from the values of the complex impedance spectrum.


In another embodiment, the disclosure relates to a method for determining a composition of a mixture of oil and water in a vessel. The method includes the step of determining values of the complex impedance spectrum of the mixture of oil and water as a function of a height in the vessel with a resonant transducer. The method also includes the step of determining a fluid phase inversion point from the values of the complex impedance spectrum; applying an oil phase model to the values of the complex impedance spectrum and conductivity values above the fluid phase inversion point, and applying a water phase model to the values of the complex impedance spectrum below the fluid phase inversion point.


In another embodiment, the disclosure relates to a sensor comprising a resonant transducer configured to simultaneously determine concentration of a first and a second component of an emulsion.


In another embodiment, the disclosure relates to a sensor having a resonant transducer configured to determine a composition of an emulsion.


In another embodiment, the disclosure relates to a sensor system having a resonant transducer configured to determine a composition of an emulsion. The sensor system includes a sampling assembly and an impedance analyzer.


In another embodiment, the disclosure relates to a method for determining a composition of a mixture of a first fluid and a second fluid in a vessel. The determination of the composition is accomplished by determining, with a sensor system, a set of complex impedance spectrum values of the mixture of the first fluid and the second fluid as a function of a height in the vessel. The method includes the step of determining a fluid phase inversion point from the set of complex impedance spectrum values. The method also includes the steps of applying a phase model of the first fluid to the set of complex impedance spectrum values above the fluid phase inversion point, and applying a phase model of the second fluid to the set of complex impedance spectrum values below the fluid phase inversion point.





BRIEF DESCRIPTION OF THE DRAWINGS

Other features and advantages of the present disclosure will be apparent from the following more detailed description of the preferred embodiment, taken in conjunction with the accompanying drawings which illustrate, by way of example, the principles of certain aspects of the disclosure.



FIG. 1 is a schematic of a non-limiting embodiment of a resonant sensor system.



FIG. 2 is a non-limiting illustration of the operation of a resonant transducer.



FIG. 3 is an example of a measured complex impedance spectrum used for multivariate analysis.



FIG. 4 illustrates an embodiment of a two-dimensional resonant transducer.



FIG. 5 illustrates an embodiment of a three-dimensional resonant transducer.



FIG. 6 is a schematic electrical diagram of the equivalent circuit of a three-dimensional resonant transducer.



FIG. 7 is a chart illustrating the Rp response of a resonant transducer to varying mixtures of oil and water.



FIG. 8 is a chart illustrating the Cp response of a resonant transducer to varying mixtures of oil and water.



FIG. 9 is a partial cutaway side view of an embodiment of a resonant transducer assembly.



FIG. 10 is a schematic diagram of an embodiment of a fluid processing system.



FIG. 11 is a schematic diagram of an embodiment of a desalter.



FIG. 12 is a schematic diagram of an embodiment of a separator.



FIG. 13 is a chart illustrating the frequency (Fp) response of a three-dimensional resonant transducer to increasing concentrations of oil-in-water and water-in-oil emulsions.



FIG. 14 is a chart illustrating the frequency (Fp) response of a two-dimensional resonant transducer to increasing concentrations of oil-in-water and water-in-oil emulsions.



FIG. 15 is a flow chart of an embodiment of a method for determining the composition of an oil and water mixture as a function of height.



FIG. 16 is a chart illustrating data used to determine a fluid phase inversion point and conductivity.



FIG. 17 is a chart illustrating the results of an analysis of the experimental data of an embodiment of a resonant sensor system.



FIG. 18 is a chart illustrating test results of a resonant sensor system in a simulated desalter.



FIG. 19 is an embodiment of a display of a data report from a resonant sensor system.



FIG. 20 is a flowchart of an embodiment of a method for determining the level of a fluid in a vessel.



FIG. 21 is a block diagram of a non-limiting representative embodiment of a processor system for use in a resonant sensor system.





DETAILED DESCRIPTION OF THE INVENTION

As discussed in detail below, embodiments of the present invention provide low cost systems for reliably and accurately measuring the fluid level in a fluid processing vessel. A resonant sensor system provides effective and accurate measurement of the level of the transition or emulsion layer through the use of a resonant transducer such as an inductor-capacitor-resistor structure (LCR) multivariable resonant transducer and the application of multivariate data analysis applied to the signals from the transducer. The resonant sensor system also provides the ability to determine the composition of water and oil mixtures, oil and water mixtures and, where applicable, the emulsion layer.


The resonant transducer includes a resonant circuit and a pick up coil. The electrical response of the resonant transducer immersed in a fluid is translated into simultaneous changes to a number of parameters. These parameters may include the complex impedance response, resonance peak position, peak width, peak height and peak symmetry of the impedance response of the sensor antenna, magnitude of the real part of the impedance, resonant frequency of the imaginary part of the impedance, antiresonant frequency of the imaginary part of the impedance, zero-reactance frequency, phase angle, and magnitude of impedance, and others as described in the definition of the term sensor “spectral parameters.” These spectral parameters may change depending upon the dielectric properties of the surrounding fluids. The typical configuration of a resonant transducer may include an LCR resonant circuit and an antenna. The resonant transducer may operate with a pickup coil connected to the detector reader (impedance analyzer) where the pickup coil provides excitation of the transducer and detection of the transducer response. The resonant transducer may also operate when the excitation of the transducer and detection transducer response is performed when the transducer is directly connected to the detector reader (impedance analyzer).


A resonant transducer offers a combination of high sensitivity, favorable signal-to-noise ratio, high selectivity, high accuracy, and high data acquisition speeds in a robust sensor without the need for optical transparency of the analyzed fluid and the measurement flow path. Instead of conventional impedance spectroscopy that scans across a wide frequency range (from a fraction of Hz to tens of MHz or GHz) a resonant transducer is used to acquire a spectrum rapidly and with high signal-to-noise across only a narrow frequency range. The sensing capability is enhanced by putting the sensing region between the electrodes that constitute a resonant circuit. As implemented in a fluid processing system such as a desalter or a separator, the resonant sensor system may include a sampling assembly and a resonant transducer coupled to the fluid sampling assembly. The resonant sensor system implements a method for measuring the level of a mixture of fluids in a vessel, and may also implement a method for determining the composition of a mixture of oil and water in a vessel. The resonant transducers are capable of accurately quantifying individual analytes at their minimum and maximum limits. The resonant sensor system is able to determine the composition of fluid mixtures even when one of the fluids is at a low concentration.


A nonlimiting examples of fluid processing systems include reactors, chemical reactors, biological reactors, storage vessels, containers, and others known in the art.


Illustrated in FIG. 1 is a schematic of an embodiment of a resonant sensor system 11. The resonant sensor system 11 includes a resonant transducer 12, a sampling assembly 13, and an impedance analyzer (analyzer 15). The analyzer 15 is coupled to a processor 16 such as a microcomputer. Data received from the analyzer 15 is processed using multivariate analysis, and the output may be provided through a user interface 17. Analyzer 15 may be an impedance analyzer that measures both amplitude and phase properties and correlates the changes in impedance to the physical parameters of interest. The analyzer 15 scans the frequencies over the range of interest (i.e., the resonant frequency range of the LCR circuit) and collects the impedance response from the resonant transducer 12.


As shown in FIG. 2, resonant transducer 12 includes an antenna 20 disposed on a substrate 22. The resonant transducer may be separated from the ambient environment with a dielectric layer 21. In some embodiments, the thickness of the dielectric layer 21 may range from 2 nm to 50 cm, more specifically from 5 nm to 20 cm; and even more specifically from 10 nm to 10 cm. In some applications the resonant transducer 12 may include a sensing film deposited onto the transducer. In response to environmental parameters an electromagnetic field 23 may be generated in the antenna 20 that extends out from the plane of the resonant transducer 12. The electromagnetic field 23 may be affected by the dielectric property of an ambient environment providing the opportunity for measurements of physical parameters. The resonant transducer 12 responds to changes in the complex permittivity of the environment. The real part of the complex permittivity of the fluid is referred to as a “dielectric constant”. The imaginary part of the complex permittivity of the fluid is referred to as a “dielectric loss factor”. The imaginary part of the complex permittivity of the fluid is directly proportional to conductivity of the fluid.


Measurements of fluids can be performed using a protecting layer that separates the conducting medium from the antenna 20. Response of the resonant transducer 12 to the composition of the fluids may involve changes in the dielectric and dimensional properties of the resonant transducer 12. These changes are related to the analyzed environment that interacts with the resonant transducer 12. The fluid-induced changes in the resonant transducer 12 affect the complex impedance of the antenna circuit through the changes in material resistance and capacitance between the antenna turns.


For selective fluid characterization using a resonant transducer 12, the complex impedance spectra of the sensor antenna 20 are measured as shown in FIG. 3. At least three data points of impedance spectra of the emulsion are measured. Better results may be achieved when at least five data points of the impedance spectra of the emulsion are measured. Non limiting examples of number of measured data points are 8, 16, 32, 64, 101, 128, 201, 256, 501, 512, 901, 1024, 2048 data points. Spectra may be measured as a real part of impedance spectra or an imaginary part of impedance spectra or both parts of impedance spectra. Non-limiting examples of LCR resonant circuit parameters include impedance spectrum, real part of the impedance spectrum, imaginary part of the impedance spectrum, both real and imaginary parts of the impedance spectrum, frequency of the maximum of the real part of the complex impedance (Fp), magnitude of the real part of the complex impedance (Zp), resonant frequency (F1) and its magnitude (Z1) of the imaginary part of the complex impedance, and anti-resonant frequency (F2) and its magnitude (Z2) of the imaginary part of the complex impedance.


Additional parameters may be extracted from the response of the equivalent circuit of the resonant transducer 12. Non-limiting examples of the resonant circuit parameters may include quality factor of resonance, zero-reactance frequency, phase angle, and magnitude of impedance of the resonance circuit response of the resonant transducer 12. Applied multivariate analysis reduces the dimensionality of the multi-variable response of the resonant transducer 12 to a single data point in multidimensional space for selective quantitation of different environmental parameters of interest. Non-limiting examples of multivariate analysis tools are canonical correlation analysis, regression analysis, nonlinear regression analysis, principal components analysis, discriminate function analysis, multidimensional scaling, linear discriminate analysis, logistic regression, and/or neural network analysis. By applying multivariate analysis of the full complex impedance spectra or the calculated spectral parameters, quantitation of analytes and their mixtures with interferences may be performed with a resonant transducer 12. Besides measurements of the complex impedance spectra parameters, it is possible to measure other spectral parameters related to the complex impedance spectra. Examples include, but are not limited to, S-parameters (scattering parameters) and Y-parameters (admittance parameters). Using multivariate analysis of data from the sensor, it is possible to achieve simultaneous quantitation of multiple parameters of interest with a single resonant transducer 12.


A resonant transducer 12 may be characterized as one-dimensional, two-dimensional, or three-dimensional. A one-dimensional resonant transducer 12 may include two wires where one wire is disposed adjacent to the other wire and may include additional components.


Shown in FIG. 4 is a two-dimensional resonant transducer 25 having a transducer antenna 27. The two-dimensional resonant transducer 25 is a resonant circuit that includes an LCR circuit. In some embodiments, the two-dimensional resonant transducer 25 may be coated with a sensing film 21 applied onto the sensing region between the electrodes. The transducer antenna 27 may be in the form of coiled wire disposed in a plane. The two-dimensional resonant transducer 25 may be wired or wireless. In some embodiments, the two-dimensional resonant transducer 25 may also include an IC chip 29 coupled to transducer antenna 27. The IC chip 29 may store manufacturing, user, calibration and/or other data. The IC chip 29 is an integrated circuit device and it includes RF signal modulation circuitry that may be fabricated using a complementary metal-oxide semiconductor (CMOS) process and a nonvolatile memory. The RF signal modulation circuitry components may include a diode rectifier, a power supply voltage control, a modulator, a demodulator, a clock generator, and other components.


Sensing is performed via monitoring of the changes in the complex impedance spectrum of the two-dimensional resonant transducer 25 as probed by the electromagnetic field 23 generated in the transducer antenna 27. The electromagnetic field 23 generated in the transducer antenna 27 extends out from the plane of the two-dimensional resonant transducer 25 and is affected by the dielectric property of the ambient environment, providing the opportunity for measurements of physical, chemical, and biological parameters.


Shown in FIG. 5 is a three-dimensional resonant transducer 31. The three-dimensional resonant transducer 31 includes a top winding 33 and a bottom winding 35 coupled to a capacitor 37. The top winding 33 is wrapped around an upper portion of a sampling cell 39 and the bottom winding 35 is wrapped around a lower portion of the sampling cell 39. The sampling cell 39 may, for example, be made of a material resistant to fouling such as Polytetrafluoroethylene (PTFE), a synthetic fluoropolymer of tetrafluoroethylene.


The three-dimensional resonant transducer 31 utilizes mutual inductance of the top winding 33 to sense the bottom winding 35. Illustrated in FIG. 6 is an equivalent circuit 41, including a current source 43, R0 resistor 45, C0 capacitor 47, and L0 inductor 49. The equivalent circuit 41 also includes L1 inductor 51, R1 resistor 53 and C1 capacitor 55. The circuit also includes Cp capacitor 57 and Rp resistor 59. The circled portion of the equivalent circuit 41 shows a sensitive portion 61 that is sensitive to the properties of the surrounding test fluid. A typical Rp response and Cp response of resonant a transducer 12 to varying mixtures of oil and water are shown in FIGS. 7 and 8 respectively.


The three-dimensional resonant transducer 31 may be shielded as shown in FIG. 9. A resonant transducer assembly 63 includes a radio frequency absorber (RF absorber layer 67) surrounding the sampling cell 39, top winding 33, and bottom winding 35. A spacer 69 may be provided surrounded by a metal shield 71. The metal shield 71 is optional, and is not part of the transducer 31. The metal shield 71 allows operation inside or near metal objects and piping, reduces noise, and creates a stable environment such that any changes in the sensor response is directly due to changes in the test fluid. In order to successfully encapsulate the sensor in a metal shield 71 the RF absorber layer 67 may be placed between the sensor and the metal shield 71. This prevents the RF field from interacting with the metal and quenching the response of the sensor. The metal shield 71 may be wrapped with a cover 73 of suitable material. The RF absorber layer 67 can absorb electromagnetic radiation in different frequency ranges with non-limiting examples in the kilohertz, megahertz, gigahertz, terahertz frequency ranges depending on the operation frequency of the transducer 31 and the potential sources of interference. The absorber layer 67 can be a combination of individual layers for particular frequency ranges so the combinations of these individual layers provide a broader spectral range of shielding.


Fouling of the resonant sensor system 11 may be reduced by providing the resonant transducer 12 with a geometry that enables resonant transducer 12 to probe the environment over the sample depth perpendicular to the transducer ranging from 0.1 mm to 1000 mm. Signal processing of the complex impedance spectrum reduces the effects of fouling over the sample depth.


As shown in FIG. 10, the resonant sensor system 11 may be used to determine the level and composition of fluids in a fluid processing system 111. Fluid processing system 111 includes a vessel 113 with a sampling assembly 115 and a resonant sensor system 11. The resonant sensor system 11 includes at least one resonant transducer 12 coupled to the sampling assembly 115. Resonant sensor system 11 also includes an analyzer 15 and a processor 16.


In operation, a normally immiscible combination of fluids enters the vessel through a raw fluid input 123. The combination of fluids may include a first fluid and a second fluid normally immiscible with the first fluid. As the combination of fluids is processed, the combination of fluids is separated into a first fluid layer 117, and a second fluid layer 119. In between the first fluid layer 117 and second fluid layer 119, there may be a rag layer 121. After processing, a first fluid may be extracted through first fluid output 125, and a second fluid may be extracted through second fluid output 127. The resonant sensor system 11 is used to measure the level of the first fluid layer 117, the second fluid layer 119 and the rag layer 121. The resonant sensor system 11 may also be used to characterize the content of the first fluid layer 117, the second fluid layer 119 and the rag layer 121.


An embodiment of a fluid processing system 111 is a desalter 141 illustrated in FIG. 11. The desalter 141 includes a desalter vessel 143. Raw oil enters the desalter 141 through crude oil input 145 and is mixed with water from water input 147. The combination of crude oil and water flows through mixing valve 149 and into the desalter vessel 143. The desalter 141 includes a treated oil output 151 and a wastewater output 153. Disposed within the desalter vessel 143 are an oil collection header 155 and a water collection header 157. Transformer 159 and transformer 161 provide electricity to top electrical grid 163 and bottom electrical grid 165. Disposed between top electrical grid 163 and bottom electrical grid 165 are emulsion distributors 167.


In operation, crude oil mixed with water enters the desalter vessel 143 and the two fluids are mixed and distributed by emulsion distributors 167 thereby forming an emulsion. The emulsion is maintained between the top electrical grid 163 and the bottom electrical grid 165. Salt containing water is separated from the oil/water mixture by the passage through the top electrical grid 163 and bottom electrical grid 165 and drops towards the bottom of the desalter vessel 143 where it is collected as waste water.


Control of the level of the emulsion layer and characterization of the contents of the oil-in-water and water-in-oil emulsions is important in the operation of the desalter 141. Determination of the level of the emulsion layer may be accomplished using a sampling assembly such as a try-line assembly 169 coupled to the desalter vessel 143 and having at least one resonant transducer 12 disposed on try-line output conduit 172. The resonant transducer 12 may be coupled to a data collection component 173. In operation, the resonant transducer 12 is used to measure the level of water and the oil and to enable operators to control the process. The try-line assembly 169 may be a plurality of pipes open at one end inside the desalter vessel 143 with an open end permanently positioned at the desired vertical position or level in the desalter vessel 143 for withdrawing liquid samples at that level. There are generally a plurality of sample pipes in a processing vessel, each with its own sample valve, with the open end of each pipe at a different vertical position inside the unit, so that liquid samples can be withdrawn from a plurality of fixed vertical positions in the unit. Another approach to measuring the level of the emulsion layer is to use a swing arm sampler. A swing arm sampler is a pipe with an open end inside the desalter vessel 143 typically connected to a sampling valve outside the unit. It includes an assembly used to change the vertical position of the open end of the angled pipe in the desalter 141, by rotating it, so that liquid samples can be withdrawn (or sampled) from any desired vertical position.


Another method to measure the level of the oil and water is to dispose at least one resonant transducer 12 on a dipstick 175. A dipstick 175 may be a rod with a resonant transducer 12 that is inserted into the desalter vessel 143. Measurements are made at a number of levels. Alternately, the dipstick 175 may be a stationary rod having a plurality of multiplexed resonant transducers 12. The resonant transducer 12 may be coupled to a data collection component 179 that collects data from the various readings for further processing.


Another embodiment of a fluid processing system 111 is a separator 191 illustrated in FIG. 12. The separator 191 includes a separator vessel 193 having an input conduit 195 for crude oil. Crude oil flowing from input conduit 195 impacts an inlet diverter 197. The impact of the crude oil on the inlet diverter 197 causes water particles to begin to separate from the crude oil. The crude oil flows into the processing chamber 199 where it is separated into a water layer 201 and an oil layer 203. The crude oil is conveyed into the processing chamber 199 below the oil/water interface 204. This forces the inlet mixture of oil and water to mix with the water continuous phase in the bottom of the vessel and rise through the oil/water interface 204 thereby promoting the precipitation of water droplets which are entrained in the oil. Water settles to the bottom while the oil rises to the top. The oil is skimmed over a weir 205 where it is collected in oil chamber 207. Water may be withdrawn from the system through a water output conduit 209 that is controlled by a water level control valve 211. Similarly oil may be withdrawn from the system through an oil output conduit 213 controlled by an oil level control valve 215. The height of the oil/water interface may be detected using a try-line assembly 217 having at least one resonant transducer 12 disposed in a try-line output conduit 218 and coupled to a data processor 221. Alternately a dip stick 223 having at least one resonant transducer 12 coupled to a processor 227 may be used to determine the level of the oil/water interface 204. The determined level is used to control the water level control valve 211 to allow water to be withdrawn so that the oil/water interface is maintained at the desired height.


The following examples are given by way of illustration only and are not intended as a limitation of the scope of this disclosure. A model system of heavy mineral oil, tap water and detergent was used to carry out static tests for various designs of resonant transducer 12. The level of detergent was kept constant for all of the mixtures.


EXAMPLE 1

In the case of the three-dimensional resonant transducer 31 disposed on a try-line or swing arm sampling assembly 13, different compositions of oil and water were poured into a sample cell with the three-dimensional resonant transducer 31 wound around the outside of the sample cell. FIG. 13 shows the try-line/swing arm response in terms of Fp (frequency shift of the real impedance) as oil concentration increases. The calculated detection limit of the composition of oil in oil-in-water emulsions (FIG. 13 part A) is 0.28% and of oil in water-in-oil emulsions (FIG. 13 part B) is 0.58%.


EXAMPLE 2

In the case of the two-dimensional resonant transducer 25, the two-dimensional resonant transducer 25 was immersed in different compositions of oil and water. FIG. 14 shows the response of a two-dimensional resonant transducer 25 (2 cm circular) in terms of Fp (frequency shift of the real impedance) as oil concentration increases. The calculated detection limit of the composition of oil in oil-in-water emulsions (FIG. 14 part A) is 0.089% and of oil in water-in-oil emulsions (FIG. 14 part B) is 0.044%. This example illustrates that small concentrations of one fluid mixed large concentrations of another fluid can be measured with a high degree of accuracy.


EXAMPLE 3

The model system was loaded with 250 mL of mineral oil and treated with detergent at a concentration of 1 drop per 50 mL (5 drops). The mineral oil was stirred and injected through the sensor and the impedance spectra are recorded. Small additions of water were added with constant salinity and same detergent treatment. After the water volume exceeded 66% or 500 mL of water, the system was cleaned and the experiment is repeated with different salinity waters. The multivariate response of the two-dimensional resonant transducer 25 was sensitive to changes in composition and conductivity at all levels in the test vessel of the model system. Although the effect of conductivity and composition are somewhat convoluted, the fact that the sensor monitors a composition gradient allows the data analysis procedure to deconvolute these effects.



FIG. 15 is a generalized process diagram illustrating a method 261 for determining the composition of an oil and water mixture as a function of height.


In step 263 data (a set of LCR resonant circuit parameters) is collected as a function of height from top to bottom (in the lab, this is simulated by starting with 100% oil and gradually adding water).


In step 265 the conductivity of water using calibration is determined. At 100% water, the multivariate response is compared to a calibration for water conductivity.


In step 267 the fluid phase inversion point is determined using Z parameters.


In step 269 the Z parameters are combined with conductivity and fluid phase data.


In step 271 an oil phase model is applied. The oil phase model is a set of values correlating measured frequency values, impedance values and conductivity values to oil content in an oil and water mixture.


In step 273 a water phase model is applied. The water phase model is a set of values correlating measured frequency values, impedance values and conductivity values to water content in a water and oil mixture.


In step 275 the composition as a function of height is determined using the conductivity and the fluid phase inversion point as input parameters in the multivariate analysis and a report is generated.



FIG. 16 shows the raw impedance (Zp) vs. frequency (Fp) data for a profile containing 0-66% water from right to left. At approximately 8.12 MHz, the water content is high enough (˜25%) to induce fluid phase inversion from oil to water continuous phase. This is apparent from the drastic change in Zp due to the increased conductivity of the test fluid in water continuous phase. An oil continuous phase model is applied to any data points to the right of the fluid phase inversion and a water model to the left. Additionally, a calibration is applied to the endpoint to determine the conductivity of the water, which in this case was 2.78 mS/cm.



FIG. 17 shows the results of an analysis of the experiment data from an embodiment of a three-dimensional resonant sensor system illustrated the correlation between the actual and predicted values of oil in water and water in oil and the residual errors of prediction based on developed model. Part A of the chart plots the actual and predicted values of oil in water. Part B of the chart plots the actual and predicted values of water in oil. In part A, the data points were modeled separately from the data points in part B (water continuous phase). Parts C and D of the chart plot the residual error between the actual and predicted values of oil in water and water in oil respectively. Generally, the residual error was less than 0.5% when the actual percentage of oil is between 0% to 60%. The residual error was less than 0.04% when the actual percentage of oil is between 70% to 100%. At the fluid phase inversion the residual error increases up to 10% where prediction capability is difficult due to fluctuations in the composition of the test fluid in the dynamic test rig. The prediction capability of the sensor will improve at compositions >66% water with more training data.



FIG. 18 illustrates the results obtained in a simulated desalter. The chart shows a profile developed by plotting the composition as a function of time. To simulate the sampling using a swing arm that is slowly rotated through the rag layer, a test rig was operated such that the composition of the test fluid was slowly modulated with time by adding small additions of water.



FIG. 19 is an illustration of the expected level of reporting from the sensor data analysis system. The end user will be shown a plot that displays a representation of the composition as a function of height in the desalter, the level of fluid phase inversion, and the width of the rag layer. On the left are fluid phase indicators (black—oil, gray—oil continuous, cross hatched—water continuous, white—water) that indicate the percent water/height curve. The height of the rag layer is the sum of the water continuous and oil continuous regions. The level of detail indicated will allow the operator of the desalter to optimize the feed rate of chemicals into the process, provide more detailed feedback on the performance of a fluid processing system, and highlight process upsets that may cause damage to downstream process infrastructure.


Illustrated in FIG. 20 is a method 281 for measuring the level of a mixture of fluids in a vessel 113.


In step 283, the method 281 may detect signals (a set of signals) from a resonant sensor system 11 at a plurality of locations in a vessel. The signals are generated by a resonant transducer 12 immersed in the mixture of fluids. The resonant transducer 12 generates a set of transducer signals corresponding to changes in dielectric properties of the resonant transducer 12, and the signals are detected by an analyzer 15.


In step 285, the method 281 may convert the signals to a set of values of the complex impedance spectrum for the plurality of locations. The conversion is accomplished using multivariate data analysis.


In step 287, the method 281 may store the values of the complex impedance spectrum.


In step 289, the method 281 may determine if a sufficient number of locations have been measured.


In step 291, the method 281 may change the resonant transducer 12 being read (or the location of the resonant transducer 12) if an insufficient number of locations have been measured.


In step 293, the method 281 may determine the fluid phase inversion point if a sufficient number of locations has been measured. The fluid phase inversion point is determined from the values of the complex impedance spectrum by identifying a drastic change in the impedance values.


In step 295, the method 281 may assign a value for the interface level based on the fluid phase inversion point.



FIG. 21 is a block diagram of non-limiting example of a processor system 810 that may be used to implement the apparatus and methods described herein. As shown in FIG. 21, the processor system 810 includes a processor 812 that is coupled to an interconnection bus 814. The processor 812 may be any suitable processor, processing unit or microprocessor. Although not shown in FIG. 21, the processor system 810 may be a multi-processor system and, thus, may include one or more additional processors that are identical or similar to the processor 812 and that are communicatively coupled to the interconnection bus 814.


The processor 812 of FIG. 21 is coupled to a chipset 818, which includes a memory controller 820 and an input/output (I/O) controller 822. As is well known, a chipset typically provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset 818. The memory controller 820 performs functions that enable the processor 812 (or processors if there are multiple processors) to access a system memory 824 and a mass storage memory 825.


The system memory 824 may include any desired type of volatile and/or non-volatile memory such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, read-only memory (ROM), etc. The mass storage memory 825 may include any desired type of mass storage device including hard disk drives, optical drives, tape storage devices, etc.


The I/O controller 822 performs functions that enable the processor 812 to communicate with peripheral input/output (I/O) devices 826 and 828 and a network interface 830 via an I/O bus 832. The I/O devices 826 and 828 may be any desired type of I/O device such as, for example, a keyboard, a video display or monitor, a mouse, etc. The I/O devices 826 and 828 also may be The network interface 830 may be, for example, an Ethernet device, an asynchronous transfer mode (ATM) device, an 802.11 device, a DSL modem, a cable modem, a cellular modem, etc. that enables the processor system 810 to communicate with another processor system. Data from analyzer 15 may be communicated to the processor 812 through the I/O bus 832 using the appropriate bus connectors.


While the memory controller 820 and the I/O controller 822 are depicted in FIG. 21 as separate blocks within the chipset 818, the functions performed by these blocks may be integrated within a single semiconductor circuit or may be implemented using two or more separate integrated circuits.


Certain embodiments contemplate methods, systems and computer program products on any machine-readable media to implement functionality described above. Certain embodiments may be implemented using an existing computer processor, or by a special purpose computer processor incorporated for this or another purpose or by a hardwired and/or firmware system, for example. Certain embodiments include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media may be any available media that may be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such computer-readable media may comprise RAM, ROM, PROM, EPROM, EEPROM, Flash, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of computer-readable media. Computer-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.


Generally, computer-executable instructions include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of certain methods and systems disclosed herein. The particular sequence of such executable instructions or associated data structures represent examples of corresponding acts for implementing the functions described in such steps.


Embodiments of the present disclosure may be practiced in a networked environment using logical connections to one or more remote computers having processors. Logical connections may include a local area network (LAN) and a wide area network (WAN) that are presented here by way of example and not limitation. Such networking environments are commonplace in office-wide or enterprise-wide computer networks, intranets and the Internet, and may use a wide variety of different communication protocols. Those skilled in the art will appreciate that such network-computing environments will typically encompass many types of computer system configurations, including personal computers, handheld devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination of hardwired or wireless links) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.


Monitoring changes of the complex impedance of the circuit and applying chemometric analysis of the impedance spectra allows for the composition and continuous phase of oil-in-water and water-in-oil mixtures to be predicted with a standard error of 0.04% in 0-30% water and 0.26% in 30-100% water.


Multivariate analysis tools in combination with data-rich impedance spectra allow for elimination of interferences, and transducers designed for maximum penetration depth decreases the impact of fouling. As the penetration depth of the resonator is extended further into the bulk of the fluid, surface fouling becomes less significant.


The term “analyte” includes any desired measured environmental parameter.


The term “environmental parameters” is used to refer to measurable environmental variables within or surrounding a manufacturing or monitoring system. The measurable environmental variables comprise at least one of physical, chemical and biological properties and include, but are not limited to, measurement of temperature, pressure, material concentration, conductivity, dielectric property, number of dielectric, metallic, chemical, or biological particles in the proximity or in contact with the sensor, dose of ionizing radiation, and light intensity.


The term “fluids” includes gases, vapors, liquids, and solids.


The term “interference” includes any undesired environmental parameter that undesirably affects the accuracy and precision of measurements with the sensor. The term “interferent” refers to a fluid or an environmental parameter (that includes, but is not limited to temperature, pressure, light, etc.) that potentially may produce an interference response by the sensor.


The term “transducer” means a device that converts one form of energy to another.


The term “sensor” means a device that measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument.


The term “multivariate data analysis” means a mathematical procedure that is used to analyze more than one variable from a sensor response and to provide the information about the type of at least one environmental parameter from the measured sensor spectral parameters and/or to quantitative information about the level of at least one environmental parameter from the measured sensor spectral parameters.


The term “resonance impedance” or “impedance” refers to measured sensor frequency response around the resonance of the sensor from which the sensor “spectral parameters” are extracted.


The term “spectral parameters” is used to refer to measurable variables of the sensor response. The sensor response is the impedance spectrum of the resonance sensor circuit of the resonant transducer 12. In addition to measuring the impedance spectrum in the form of Z-parameters, S-parameters, and other parameters, the impedance spectrum (both real and imaginary parts) may be analyzed simultaneously using various parameters for analysis, such as, the frequency of the maximum of the real part of the impedance (Fp), the magnitude of the real part of the impedance (Zp), the resonant frequency of the imaginary part of the impedance (F1), and the anti-resonant frequency of the imaginary part of the impedance (F2), signal magnitude (Z1) at the resonant frequency of the imaginary part of the impedance (F1), signal magnitude (Z2) at the anti-resonant frequency of the imaginary part of the impedance (F2), and zero-reactance frequency (Fz), frequency at which the imaginary portion of impedance is zero). Other spectral parameters may be simultaneously measured using the entire impedance spectra, for example, quality factor of resonance, phase angle, and magnitude of impedance. Collectively, “spectral parameters” calculated from the impedance spectra, are called here “features” or “descriptors”. The appropriate selection of features is performed from all potential features that can be calculated from spectra. Multivariable spectral parameters are described in U.S. patent application, Ser. No. 12/118,950 entitled “Methods and systems for calibration of RFID sensors”, which is incorporated herein by reference.


The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Where the definition of terms departs from the commonly used meaning of the term, applicant intends to utilize the definitions provided herein, unless specifically indicated. The singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be understood that, although the terms first, second, etc. may be used to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. The term “and/or” includes any, and all, combinations of one or more of the associated listed items. The phrases “coupled to” and “coupled with” contemplates direct or indirect coupling.


This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements.

Claims
  • 1. A sensor comprising: an electrically resonant transducer comprised of a top winding and a bottom winding, said bottom winding coupled at opposite ends across a capacitor, wherein said top winding provides excitation and detection of the bottom winding, wherein mutual inductance of the top winding is used to sense the bottom winding, said mutual inductance used to measure values of real and imaginary parts of an impedance spectra of the bottom winding across said capacitor and said top winding, said impedance spectra determined from an electrical response of the bottom winding to the top winding of the electrically resonant transducer while said bottom winding is proximate to an emulsion and said measured values of real and imaginary parts of the impedance spectra of the bottom winding are each independently used to determine a composition of the emulsion.
  • 2. The sensor of claim 1 further comprising a sampling cell, wherein the top winding of the electrically resonant transducer is disposed around an upper portion of the sampling cell; and the bottom winding of the electrically resonant transducer is disposed around a lower portion of the sampling cell.
  • 3. The sensor of claim 2, further comprising a radio frequency absorber disposed around the top winding and the bottom winding.
  • 4. The sensor of claim 3 further comprising a metal shield disposed around the radio frequency absorber.
  • 5. The sensor of claim 4 further comprising a cover disposed around the metal shield.
  • 6. The sensor of claim 2 further comprising an absorber of electromagnetic radiation disposed around the top winding and the bottom winding where the absorber absorbs electromagnetic radiation in the kilohertz, megahertz, gigahertz, and terahertz frequency ranges depending on the operation frequency of the electrically resonant transducer and a source of interferences.
  • 7. The sensor of claim 2 further comprising an absorber of electromagnetic radiation disposed around the top winding and the bottom winding where the absorber is a combination of individual layers for particular frequency ranges.
  • 8. The sensor of claim 1, wherein the measured values for the real and the imaginary parts of the impedance spectra are each independently used to simultaneously determine a concentration of a first and a second component of the emulsion.
  • 9. The sensor of claim 8 wherein the electrically resonant transducer measures at least three data points of the real and the imaginary parts of the impedance spectra of the electrically resonant transducer.
  • 10. The sensor of claim 8 wherein the electrically resonant transducer comprises a sensitive portion that detects changes in capacitance and resistance of the electrically resonant transducer.
  • 11. The sensor of claim 8 wherein the electrically resonant transducer comprises a sensitive portion that detects changes in capacitance, resistance, inductance of the electrically resonant transducer.
  • 12. The sensor of claim 8 where the electrically resonant transducer probes an environment over a sample depth perpendicular to the electrically resonant transducer of between 0.1 mm to 1,000 mm.
  • 13. The sensor of claim 1, wherein the electrically resonant transducer measures a set of LCR resonant circuit parameters, said set of LCR resonant circuit parameters comprise at least one of an impedance spectrum, a real part of the impedance spectrum, an imaginary part of the impedance spectrum, both a real part and an imaginary part of the impedance spectrum, a frequency associated with a maximum of the real part of a complex impedance, a magnitude of the real part of the complex impedance, a resonant frequency, a magnitude of the imaginary part of the complex impedance, and an anti-resonant frequency and a magnitude of the imaginary part of the complex impedance.
US Referenced Citations (278)
Number Name Date Kind
3675121 Thompson Jul 1972 A
3778706 Thompson Dec 1973 A
3927369 Billeter et al. Dec 1975 A
4096385 Marett et al. Jun 1978 A
4273636 Shimada et al. Jun 1981 A
4275364 Skatvold, Jr. Jun 1981 A
4372164 Brown et al. Feb 1983 A
4553434 Spaargaren Nov 1985 A
4728892 Vinegar et al. Mar 1988 A
4820989 Vail et al. Apr 1989 A
4844097 Bellhouse et al. Jul 1989 A
4876512 Kroeger et al. Oct 1989 A
4882542 Vail et al. Nov 1989 A
4887455 Payne et al. Dec 1989 A
4887798 Julius Dec 1989 A
4922745 Rudkin et al. May 1990 A
4941958 Byers Jul 1990 A
4965522 Hazen et al. Oct 1990 A
4996490 Scott et al. Feb 1991 A
5010301 Leung et al. Apr 1991 A
5025346 Tang et al. Jun 1991 A
5059790 Klainer et al. Oct 1991 A
5089780 Megerle Feb 1992 A
5157338 Motherbaugh et al. Oct 1992 A
5208165 Law et al. May 1993 A
5241364 Kimura Aug 1993 A
5260569 Kimura Nov 1993 A
5306644 Myerholtz et al. Apr 1994 A
5344547 Vlasov et al. Sep 1994 A
5421983 Slack et al. Jun 1995 A
5443985 Lu et al. Aug 1995 A
5497140 Tuttle Mar 1996 A
5543722 Suzuki et al. Aug 1996 A
5591896 Lin Jan 1997 A
5592040 Yamamoto Jan 1997 A
5607566 Brown et al. Mar 1997 A
5646592 Tuttle Jul 1997 A
5672319 Eisum Sep 1997 A
5744902 Vig Apr 1998 A
5751475 Ishiwata et al. May 1998 A
5754055 McAdoo et al. May 1998 A
5785181 Quartararo, Jr. Jul 1998 A
5786595 Herron et al. Jul 1998 A
5817943 Welles, II et al. Oct 1998 A
5831439 Suenram et al. Nov 1998 A
5840168 Chaniotakis et al. Nov 1998 A
5874047 Schoning et al. Feb 1999 A
5961923 Nova et al. Oct 1999 A
6025725 Gershenfeld et al. Feb 2000 A
6025783 Steffens, Jr. Feb 2000 A
6111520 Allen et al. Aug 2000 A
6166546 Scheihing et al. Dec 2000 A
6189656 Morgenstern et al. Feb 2001 B1
6192753 Czarnek Feb 2001 B1
6204764 Maloney Mar 2001 B1
6278379 Allen et al. Aug 2001 B1
6287765 Cubicciotti Sep 2001 B1
6359444 Grimes Mar 2002 B1
6360585 Potyrailo et al. Mar 2002 B1
6398931 Burchette et al. Jun 2002 B1
6399375 Vajta Jun 2002 B2
6406668 Dordick et al. Jun 2002 B1
6461872 Sivavec et al. Oct 2002 B1
6471838 Igel et al. Oct 2002 B1
6506346 Monro Jan 2003 B1
6586946 Hefti et al. Jul 2003 B2
6614229 Clark et al. Sep 2003 B1
6657429 Goldfine et al. Dec 2003 B1
6672512 Bridgelall Jan 2004 B2
6676903 Potyrailo et al. Jan 2004 B2
6730201 Kuhlman et al. May 2004 B1
6751557 Shehab et al. Jun 2004 B1
6771074 Zou et al. Aug 2004 B2
6773926 Freund et al. Aug 2004 B1
6780307 Kidwell Aug 2004 B2
6782736 Hammer Aug 2004 B1
6804544 Van Antwerp et al. Oct 2004 B2
6818450 Eaton et al. Nov 2004 B2
6864801 Tabanou et al. Mar 2005 B2
6891383 Nicholson et al. May 2005 B2
6911818 Julius Jun 2005 B2
6953520 Yengoyan et al. Oct 2005 B2
7017404 Kain Mar 2006 B1
7031560 Lelong-Feneyrou et al. Apr 2006 B2
7034660 Watters et al. Apr 2006 B2
7038470 Johnson May 2006 B1
7076858 Eckstein et al. Jul 2006 B2
7113125 Le Sesne Sep 2006 B2
7126013 Heeney et al. Oct 2006 B2
7168310 Al-Ruwaili et al. Jan 2007 B2
7171312 Steinthal et al. Jan 2007 B2
7178416 Whelan et al. Feb 2007 B2
7204128 Liu et al. Apr 2007 B1
7252010 Ohta et al. Aug 2007 B2
7276916 Hammer Oct 2007 B2
7293450 Liu et al. Nov 2007 B2
7317989 DiFoggio et al. Jan 2008 B2
7335336 Kim Feb 2008 B1
7343800 Harman et al. Mar 2008 B2
7350367 Matsiev et al. Apr 2008 B2
7434457 Goodwin et al. Oct 2008 B2
7445143 Pang et al. Nov 2008 B2
7449893 Tsironis Nov 2008 B1
7455108 Jenkins et al. Nov 2008 B2
7456744 Kuhns Nov 2008 B2
7466041 Urman Dec 2008 B2
7481771 Fonseca et al. Jan 2009 B2
7483743 Mann et al. Jan 2009 B2
7486495 Diederichs et al. Feb 2009 B1
7495454 Rivera Feb 2009 B2
7523647 Scott Apr 2009 B2
7562557 Bennett et al. Jul 2009 B2
7569810 Troxler et al. Aug 2009 B1
7629167 Hodge et al. Dec 2009 B2
7677307 Vasques et al. Mar 2010 B2
7808235 Rollins et al. Oct 2010 B2
7812609 Martinez et al. Oct 2010 B2
7814786 Woodard Oct 2010 B2
7911345 Potyrailo Mar 2011 B2
7948385 Potyrailo et al. May 2011 B2
7958772 Permuy et al. Jun 2011 B2
7969307 Peeters Jun 2011 B2
8018342 Monk et al. Sep 2011 B2
8111143 Tong et al. Feb 2012 B2
8155891 Kong et al. Apr 2012 B2
8159347 Potyrailo et al. Apr 2012 B2
8184290 Hertens et al. May 2012 B2
8190394 Davis et al. May 2012 B2
8215166 Cunningham et al. Jul 2012 B2
8232091 Maltezos et al. Jul 2012 B2
8246910 Dhirani et al. Aug 2012 B2
8261618 Engle et al. Sep 2012 B2
8318099 Potyrailo et al. Nov 2012 B2
8342242 Roddy et al. Jan 2013 B2
8429985 Furlong Apr 2013 B2
8452716 Howley et al. May 2013 B2
8468871 Potyrailo et al. Jun 2013 B2
8492706 Troxler et al. Jul 2013 B2
8508368 Potyrailo et al. Aug 2013 B2
8547110 Kesil et al. Oct 2013 B2
8643388 Hedges Feb 2014 B2
8676436 Raimarckers et al. Mar 2014 B2
8710973 Schneider et al. Apr 2014 B2
8732938 Kolosov et al. May 2014 B2
8736425 Potyrailo May 2014 B2
8833145 Fischer et al. Sep 2014 B2
8933706 Karlquist Jan 2015 B1
8936191 Potyrailo et al. Jan 2015 B2
8952708 Nikolenko Feb 2015 B2
9074966 Sanderlin et al. Jul 2015 B2
20010045355 Gephart et al. Nov 2001 A1
20020050929 Parrotta et al. May 2002 A1
20020081231 Shapiro et al. Jun 2002 A1
20020089356 Perrott et al. Jul 2002 A1
20020149466 Sunshine et al. Oct 2002 A1
20020173040 Potyrailo et al. Nov 2002 A1
20020177232 Melker et al. Nov 2002 A1
20030053936 Potyrailo et al. Mar 2003 A1
20030154031 Potyrailo et al. Aug 2003 A1
20030179024 Montagnana Sep 2003 A1
20030232223 Leddy et al. Dec 2003 A1
20040015990 Suematsu et al. Jan 2004 A1
20040035211 Pinto et al. Feb 2004 A1
20040051154 Yamakawa et al. Mar 2004 A1
20040113790 Hamel et al. Jun 2004 A1
20040125442 Yun et al. Jul 2004 A1
20040155667 Kesil et al. Aug 2004 A1
20040189487 Hoefel et al. Sep 2004 A1
20040219523 Stanton et al. Nov 2004 A1
20040227682 Anderson Nov 2004 A1
20040248315 Klein et al. Dec 2004 A1
20050022581 Sunshine Feb 2005 A1
20050058460 Wang Mar 2005 A1
20050093760 Rochelle et al. May 2005 A1
20050161405 Holland Jul 2005 A1
20050193832 Tombs et al. Sep 2005 A1
20050199731 Empedocles et al. Sep 2005 A9
20050261562 Zhou et al. Nov 2005 A1
20060014172 Muller et al. Jan 2006 A1
20060020427 Kahn et al. Jan 2006 A1
20060055531 Cook et al. Mar 2006 A1
20060081471 Kidwell Apr 2006 A1
20060133720 Hochberg et al. Jun 2006 A1
20060141469 Rossier et al. Jun 2006 A1
20060198760 Potyrailo et al. Sep 2006 A1
20060205093 Prins Sep 2006 A1
20060210440 Potyrailo et al. Sep 2006 A1
20060238349 Hu et al. Oct 2006 A1
20060265150 Hu et al. Nov 2006 A1
20070029195 Li et al. Feb 2007 A1
20070064839 Luu Mar 2007 A1
20070084277 Steinsiek Apr 2007 A1
20070085686 Oz Apr 2007 A1
20070090926 Potyrailo et al. Apr 2007 A1
20070111222 Chasin et al. May 2007 A1
20070131418 Barrow et al. Jun 2007 A1
20070148670 O'Malley Jun 2007 A1
20070176773 Smolander et al. Aug 2007 A1
20070236338 Maruyama Oct 2007 A1
20070241890 Yoshioka Oct 2007 A1
20080093219 Goldberg et al. Apr 2008 A1
20080116908 Potyrailo et al. May 2008 A1
20080135614 Werner et al. Jun 2008 A1
20080142366 Tamirisa et al. Jun 2008 A1
20080157901 Matekovits et al. Jul 2008 A1
20080177150 Il et al. Jul 2008 A1
20080179197 Wu Jul 2008 A1
20080180249 Butler et al. Jul 2008 A1
20080184787 Coates Aug 2008 A1
20080191859 Tiek et al. Aug 2008 A1
20080236814 Roddy Oct 2008 A1
20080280374 Potyrailo et al. Nov 2008 A1
20090035856 Galliher et al. Feb 2009 A1
20090039864 Gordon Feb 2009 A1
20090087862 Carter et al. Apr 2009 A1
20090095073 Fukumura et al. Apr 2009 A1
20090104707 Wang et al. Apr 2009 A1
20090120169 Chandler et al. May 2009 A1
20090189741 Rowland et al. Jul 2009 A1
20090204250 Potyrailo et al. Aug 2009 A1
20090215646 Anslyn et al. Aug 2009 A1
20090256679 Potyrailo et al. Oct 2009 A1
20090265037 Bassa Oct 2009 A1
20090278685 Potyrailo et al. Nov 2009 A1
20090289776 Moore et al. Nov 2009 A1
20090308155 Zhang Dec 2009 A1
20100021993 Wang et al. Jan 2010 A1
20100042338 Giurgiutiu et al. Feb 2010 A1
20100059221 Vannuffelen et al. Mar 2010 A1
20100075405 Broadley et al. Mar 2010 A1
20100102004 Holland Apr 2010 A1
20100109766 Nilsson et al. May 2010 A1
20100134286 Potyrailo et al. Jun 2010 A1
20100138267 Vittal et al. Jun 2010 A1
20100153323 Hennessy et al. Jun 2010 A1
20100231407 Carr Sep 2010 A1
20100250170 Kalinin et al. Sep 2010 A1
20100261226 Niazi Oct 2010 A1
20100295558 Eberheim et al. Nov 2010 A1
20110006878 Nyffeler et al. Jan 2011 A1
20110006900 Nyffeler et al. Jan 2011 A1
20110012736 Potyrailo et al. Jan 2011 A1
20110018649 David et al. Jan 2011 A1
20110029156 Vernacchia et al. Feb 2011 A1
20110045601 Gryska et al. Feb 2011 A1
20110051775 Ivanov et al. Mar 2011 A1
20110117538 Niazi May 2011 A1
20110152706 Christopherson et al. Jun 2011 A1
20110156177 Merz Jun 2011 A1
20110221667 Lee Sep 2011 A1
20110248825 Hamel et al. Oct 2011 A1
20110263036 Blauw et al. Oct 2011 A1
20110282540 Armitage et al. Nov 2011 A1
20110320142 Surman Dec 2011 A1
20120001730 Potyrailo et al. Jan 2012 A1
20120025526 Luo et al. Feb 2012 A1
20120053881 Schulz et al. Mar 2012 A1
20120116683 Potyrailo et al. May 2012 A1
20120161787 Potyrailo et al. Jun 2012 A1
20120166095 Potyrailo et al. Jun 2012 A1
20120231504 Niazi Sep 2012 A1
20120235690 Potyrailo et al. Sep 2012 A1
20120258441 Gebauer et al. Oct 2012 A1
20120265036 Estes et al. Oct 2012 A1
20120289757 Boyden et al. Nov 2012 A1
20130060112 Pryor et al. Mar 2013 A1
20130154847 Potyrailo et al. Jun 2013 A1
20130182819 Dvorkin et al. Jul 2013 A1
20130285677 Hammer Oct 2013 A1
20140002111 Potyrailo et al. Jan 2014 A1
20140028330 Potyrailo et al. Jan 2014 A1
20140090451 Surman Apr 2014 A1
20140090454 Surman Apr 2014 A1
20140091811 Potyrailo et al. Apr 2014 A1
20140095102 Potyrailo et al. Apr 2014 A1
20140182362 Potyrailo et al. Jul 2014 A1
20140182363 Potyrailo et al. Jul 2014 A1
20140305194 Surman et al. Oct 2014 A1
Foreign Referenced Citations (58)
Number Date Country
1363844 Aug 2002 CN
1532372 Sep 2004 CN
2809215 Aug 2006 CN
1865966 Nov 2006 CN
101022760 Aug 2007 CN
101057124 Oct 2007 CN
201000455 Jan 2008 CN
101988574 Mar 2011 CN
102022264 Apr 2011 CN
102422330 Apr 2012 CN
203923208 Nov 2014 CN
1173613 Jan 2002 EP
2498076 Sep 2012 EP
793953 Apr 1958 GB
1331327 Sep 1973 GB
5774097 May 1982 JP
59116855 Aug 1984 JP
59160746 Sep 1984 JP
0381659 Apr 1991 JP
06160317 Jun 1994 JP
06194333 Jul 1994 JP
6086057 Dec 1994 JP
0773282 Mar 1995 JP
07120423 May 1995 JP
08509549 Oct 1996 JP
09292453 Nov 1997 JP
10504388 Apr 1998 JP
2000111547 Apr 2000 JP
2001502791 Feb 2001 JP
2002125206 Apr 2002 JP
2003503011 Jan 2003 JP
2003506706 Feb 2003 JP
2003161637 Jun 2003 JP
2005156569 Jun 2005 JP
2006516721 Jul 2006 JP
2007516509 Jun 2007 JP
2008129009 Jun 2008 JP
2008236617 Oct 2008 JP
2008298565 Dec 2008 JP
2009092633 Apr 2009 JP
2009540292 Nov 2009 JP
9845779 Oct 1998 WO
0055583 Sep 2000 WO
0107874 Feb 2001 WO
0173380 Oct 2001 WO
0212129 Feb 2002 WO
0223176 Mar 2002 WO
03050529 Jun 2003 WO
2004025288 Mar 2004 WO
2004032191 Apr 2004 WO
2007075619 Jul 2007 WO
2007101992 Sep 2007 WO
2008082654 Jul 2008 WO
2012050460 Apr 2012 WO
2014051985 Apr 2014 WO
2014051989 Apr 2014 WO
2015090358 Jun 2015 WO
2015128050 Sep 2015 WO
Non-Patent Literature Citations (233)
Entry
International Search Report and Written Opinion issued in connection with corresponding PCT Application No. PCT/US2013/058932 on Dec. 12, 2013.
Agar, G., P. Clewis, and C. Spencer, “Energy absorption probes control oily-water discharges”, Hydrocarbon Processing, Aug. 1, 1993. 72(8):Abstract.
Alary, V. and J. Falcimaigne, “Subsea water separation: a cost-effective solution for ultra deep water production”. 17th World Petroleum Congress, Sep. 1-5, 2002, Rio de Janeiro, Brazil, 2002.
Asskildt, K. and P. Hansson, “New measuring sensor for level detection in subsea separators”, ABB Review, Apr. 1999(4): p. 11-17.
U.S. Appl. No. 13/630,587, filed Sep. 28, 2012, Surman.
Bennett, M.A. and R.A. Williams, “Monitoring the operation of an oil/water separator using impedance tomography” Minerals Engineering, 2004. 17(5): p. 605-614.
Casanella, R., O. Casas, and R. Pallàs-Areny, “Continuous liquid level measurement using a linear electrode array” Measurement Science and Technology, May 9, 2007. 18(7): p. 1859-1866.
Christian Michelsen Research, A.S., Ultrasonic Interface Level Detector. 2008, PO Box 6031, N05892 Bergen, Norway, www.cmr.no.
Chuang, J., D.J. Thomson, and G.E. Bridges, “Embeddable wireless strain sensor based on resonant rf cavities”, Rev. Sci. Instrum., 2005. 76: p. 1-7, published online Sep. 20, 2005.
Fauveau, E. and K. Hambrice, “Guided-wave radar helps level-detection in harsh settings Control Engineering” Mar. 2003. 50(3),16.
Fransen, G. “New control system detects desalter problems before upsets occur”. Apr. 2004, Agar Corporation, Prepared for presentation at The Aiche 2004 Spring National Meeting.
Garcia-Golding, F., et al., “Sensor for determining the water content of oil-in-water emulsion by specific admittance measurement”, Sensors and Actuators: A. Physical, 1995, 47(1-3), 337-341.
Grimes, C.A., et al., “Wireless Magnetoelastic Resonance Sensors: A Critical Review. Sensors”, Jul. 2002. 2, 294-313.
Gutzeit, J., “Controlling Crude Unit Overhead Corrosion—Rules of Thumb for Better Crude Desalting”. NACE—International Corrosion Conference Series 2007, 075671-0756721.
Holstad, M.B., et al., “Scattered Gamma Radiation Utilized for Level Measurements in Gravitational Separators”. IEEE Sensors Journal, Apr. 2005, 5(2),175-182.
Hutzler, S., et al., “Measurement of Foam Density Profiles Using AC capacitance”. Europhysics Letters, Sep. 10, 1995. 31(8), 497-502.
Hwili, A. and W. Yang, “Multi-modality multi-interface level measurement”, Journal of Physics: Conference Series, 2007, 76(1), 1-6.
Hwili, A. and W. Yang. “A single rod multi-modality multi-interface level sensor using an AC current source”, IEEE International Workshop on Imaging Systems and Techniques, Sep. 10-12, 2008.
Isaksen, O., A.S. Dico, and E.A. Hammer, “A capacitance-based tomography system for interface measurement in separation vessels”, Measurement Science and Technology, Jun. 1994, 5(10): p. 1262-1271.
Jaworski, A.J. and G. Meng, “On-line measurement of separation dynamics in primary gas/oil/water separators: Challenges and technical solutions—A review”. Journal of Petroleum Science and Engineering, 2009. 68, 47-59.
Jaworski, A.J. and T. Dyakowski, “Measurements of oil-water separation dynamics in primary separation systems using distributed capacitance sensors”, Flow Measurement and Instrumentation, 2005. 16(2-3): p. 113-127.
Jaworski, A.J., T. Dyakowski, and G.A. Davies, “A capacitance probe for interface detection in oil and gas extraction plant”, Measurement Science and Technology, Jan. 1999, 10(3), L15-L20.
Lee, R.P., “Increase oil production and reduce chemical usage through separator level measurement by density profiling” ISA TECH/EXPO Technology Update Conference Proceedings 2001. 416: p. 321-328.
Meng, G., A.J. Jaworski, and J.C.S. Kimber, “A multi-electrode capacitance probe for phase detection in oil-water separation processes: Design, modelling and validation”, Measurement Science and Technology, Mar. 2006. 17(4): p. 881-894.
Pal, R., “Techniques for measuring the composition (oil and water content) of emulsions—a state of the art review. Colloids and Surfaces A: Physicochemical and Engineering Aspects”, 1994. 84: 141-193.
Pasquale, M., “Mechanical sensors and actuators”. Sensors and Actuators, A: Physical, 2003,106(1-3),142-148.
Potyrailo, R.A., et al., “Integration of passive multivariable RFID sensors into single-use biopharmaceutical manufacturing components”, RFID 2010: International IEEE Conference on RFID, 2010,1-7.
Sakharov, V.E., et al., “Liquid level sensor using ultrasonic Lamb waves”. Ultrasonics, 2003, 41, 319-322.
Schuller, R.B., M. Halleraker, and B. Engebretsen, “Advanced Profile Gauge for Multiphase Systems. 1st World Congress on Industrial Process Tomography”, Buxton, Greater Manchester, Apr. 14-17, 1999.
Shi, T.M., et al., “Capacitance-based instrumentation for multi-interface level measurement”, Measurement Science and Technology, May 17, 1991, 2, 923-933.
Yang, W.Q., M.R. Brant, and M.S. Beck, “A multi-interface level measurement system using a segmented capacitance sensor for for separators”, Measurement Science and Technology, Jul. 19, 1994. 5, 1177-1180.
Yang, W., “Sensors and instrumentation for monitoring and control of multi-phase separation”. Measurement and Control, Jul. 2006. 39(6),178-184.
International Preliminary Report on Patentability and Written Opinion, dated Mar. 31, 2015, received in connection with related International Application No. PCT/US2013/058932.
International Preliminary Report on Patentability, dated Mar. 31, 2015, received in connection with related International Application No. PCT/US2013/058898.
International Search Report, dated Dec. 18, 2013, received in connection with related International Application No. PCT/US2013/058898.
Notice of Allowance, dated Jun. 18, 2015, received in connection with U.S. Appl. No. 13/630,739.
Casanella et al., “Oil-Water Interface Level Sensor Based on an Electrode Array”, Instrumentation and Measurement Technology Conference, 2006. IMTC 2006. Proceedings of the IEEE, pp. 710-713, Sorrento, Italy, Apr. 24-27, 2006.
Hewitt, “Oil/Water Interface Control for Desalters”, Petroleum Technology Quarterly 2007, vol. No. 12, Issue No. 5, pp. 75-78, 2007.
International Search Report and Written Opinion issued for PCT/US2015/027482, dated Jul. 15, 2015.
Office Action issued in related Chinese Application No. 201380050788.0, dated Jan. 20, 2016.
Bobrov, et al., “The effect of clay and organic matter content on the dielectric permittivity of soils and grounds at the frequency range from 10 MHz To 1 GHz”, IEEE, 2010, 4433-4435.
Garrouch, et al., “The influence of clay content, salinity, stress, and wettability on the dielectric properties of brine-saturated rocks: 10 Hz To 10 MHz”, Geophysics 59:6, 1994, 909-917.
Ikenishi, et al., “The dielectric characteristics of agricultural land for on-site and real time measurement”, SICE Annual Conf in Sapporo, 2004.
Ishida, et al., “Effects of pH on dielectric relaxation of montmorillonite, allophane, and imogolite suspensions”, J of Colloid and Interface Sci 212, 1999, 152161.
Jimenez, et al., “Surface characterization of clay particles via dielectric spectroscopy”, Dept of Applied Physics, 2008, 10.2478/v10063-008-0030-7.
Kaya, et al., “Electrical spectroscopy of Kaolin and bentonite slurries”, Turk J Eng Envir Sci 25, 2001, 345-354.
Owenier, et al., “Dielectric permittivity of geologic Materials at different waterer contents—Measurements with an impedance analyzer”, IEEE, 2011, 5 pages.
Raythatha, et al., “Dielectric properties of clay suspensions in MHz to GHz range”, J of Colliod and Interface Sci 109:2, 1986, 301-309.
Saltas, et al., “Dielectric properties of non-swelling bentonite: The effect of temperature and water saturation”, J Non-Crystalline Solids 354, 2008, 5533-5541.
Santamarina, et al., “Dielectric permittivity of soils mixed with organic and inorganic fluids (0.02GHz to 1.30 GHz)”, JEEG 2:1, 1997, 37-51.
Sen, et al., “Frequency Dependent Dielectric and Conuctivity Response of Sedimentary Rocks”, J of Microwave Power 18:1, 1983, 11 pages.
Seyfried, et al., “Measurement of soil water content with a 50-MHz soil dielectric sensor”, Soil Sci Soc Am J 68, 2004, 394-403.
Swiech, et al., “Dielectric properties of synthetic oil sands”, SPE 165403, 2013, 11 pages.
Vasilyeva, et al., “Differences in behaviour of adsorbed water in kaolinites and montmorillonites in temperature range from −90°C to +140°C by dielectric spectroscopy”, J of Physics: Conf Series 394, 2012, 6 pages.
Wensink, et al., “Dielectric properties of wet soils in the frequency range 1-3000 MHz”, Geophysical Prospecting 41, 1993, 671-696.
Butler et al., “Wireless, Passive, Resonant-Circuit, Inductively Coupled, Inductive Strain Sensor”, Sensors and Actuators A: Physical, vol. No. 102, Issue No. 1, pp. 61-66, Dec. 1, 2002.
Janata et al., “Conducting Polymers in Electronic Chemical Sensors”, Nature Materials, vol. No. 2, pp. 19-24, Jan. 2003.
Johns et al., “Sensitive Indirect Photometric Detection of Inorganic and Small Organic Anions by Capillary Electrophoresis Using Orange G as a Probe Ion”, Electrophoresis, vol. No. 24, Issue No. 3, pp. 557-566, Jan. 2003.
Potyrailo et al., “Fluorescence Spectroscopy and Multivariate Spectral Descriptor Analysis for High-Throughput Multiparameter Optimization of Polymerization Conditions of Combinatorial 96-Microreactor Arrays”, Journal of Combinatorial Chemistry, vol. No. 5, Issue No. 1, pp. 8-17, Jan.-Feb. 2003.
Grate et al., “Sorptive Behavior of Monolayer-Protected Gold Nanoparticle Films: Implications for Chemical Vapor Sensing”, Analytical Chemistry, vol. No. 75, Issue No. 8, pp. 1868-1879, Apr. 15, 2003.
De Borba et al., “Determination of Sodium at Low NG/L Concentrations in Simulated Power Plant Waters by Ion Chromatography”, Journal of Chromatography A, vol. No. 995, Issue No. 1-2, pp. 143-152, May 2, 2003.
Kumar et al., “Investigation into the Interaction between Surface-Bound Alkylamines and Gold Nanoparticles”, Langmuir, vol. No. 19, Issue No. 15, pp. 6277-6282, Jun. 25, 2003.
Mabic et al., “Adjusting the Quality of Treated Water to Experimental Detection Limits”, GIT Labor-Journal, vol. No. 47, pp. 724-727, Jul. 2003.
Armani et al., “Label-Free Single-Molecule Detection with Optical Microcavities”, Science, vol. No. 317, Issue No. 5839, pp. 783-787, Aug. 10, 2007.
Chopra et al., “Selective Gas Detection Using a Carbon Nanotube Sensor”, Applied Physics Letters, vol. No. 83, Issue No. 11, pp. 2280-2282, Sep. 15, 2003.
Bauer et al., “Resonant Nanocluster Technology—From Optical Coding and High Quality Security Features to Biochips”, Nanotechnology, vol. No. 14, Issue No. 12, pp. 1289-1311, Nov. 4, 2003.
Wang et al., “A New Technique for Detecting Antifreeze in Engine Oil During Early Stage of Leakage”, Sensors and Actuators B: Chemical, vol. No. 96, Issue No. 1-2, pp. 157-164, Nov. 15, 2003.
Briglin et al., “Detection of Organic Mercaptan Vapors using Thin Films of Alkylamine-Passivated Gold Nanocrystals”, Langmuir, vol. No. 20, Issue No. 2, pp. 299-305, Jan. 20, 2004.
Shamsipur et al., “New Macrocyclic Diamides as Neutral Ionophores for Highly Selective and Sensitive PVC-Membrane Electrodes for Be2+ Ion”, Electroanalysis, vol. No. 16, Issue No. 4, pp. 282-288, Mar. 2004.
Joseph et al., “Chemiresistor Coatings from Pt- and Au-Nanoparticle/Nonanedithiol Films: Sensitivity to Gases and Solvent Vapors”, Sensors and Actuators B: Chemical, vol. No. 98, Issue No. 2-3, pp. 188-195, Mar. 15, 2004.
Want et al., “Enabling Ubiquitous Sensing with RFID”, Computer, vol. No. 37, Issue No. 4, pp. 84-86, Apr. 2004.
Varma et al., “High-Speed Label-Free Detection by Spinning-Disk Micro-Interferometry”, Biosensors and Bioelectronics, vol. No. 19, Issue No. 11, pp. 1371-1376, Jun. 15, 2004.
Pavlov et al., “Aptamer-Functionalized Au Nanoparticles for the Amplified Optical Detection of Thrombin”, Journal of the American Chemical Society, vol. No. 126, Issue No. 38, pp. 11768-11769, Sep. 3, 2004.
Thomas et al., “Conjugated Polymer Sensors: Design Principles Towards Enhanced Versatility”, pp. 1-6, Dec. 2004.
Barsoukov et al., “Impedance Spectroscopy: Theory, Experiment, and Applications”, Second Edition, pp. 205-264, 2005.
Buhrdorf et al., “Multiparameteric Oil Condition Sensor Based on the Tuning Fork Technology for Automotive Applications”, Advanced Microsystems for Automotive Applications, pp. 289-298, 2005.
Rose et al., “Sensitivity Gains in Chemosensing by Lasing Action in Organic Polymers”, Nature, vol. No. 434, pp. 376-879, Apr. 14, 2005.
Cheung et al., “Impedance Spectroscopy Flow Cytometry: On-Chip Label-Free Cell Differentiation”, Cytometry Part A, vol. No. 65, Issue No. 2, pp. 124-132, Jun. 2005.
Jang et al., “Chemical Sensors Based on Highly Conductive Poly(3,4-Ethylene- Dioxythiophene) Nanorods”, Advanced Materials, vol. No. 17, Issue No. 13, pp. 1616-1620, Jul. 1, 2005.
Rakow et al., “Molecular Recognition and Discrimination of Amines with a Colorimetric Array”, Angewandte Chemie, vol. No. 44, Issue No. 29, pp. 4528-4532, Jul. 18, 2005.
Zhang et al., “A Colorimetric Sensor Array for Organics in Water”, Journal of the American Chemical Society, vol. No. 127, Issue No. 33, pp. 11548-11549, Aug. 2, 2005.
Burnell et al., “Synthesis and Electrooptical Properties of Copolymers Derived from Phenol-Functionalized Telechelic Oligofluorenes”, Macromolecules, vol. No. 38, Issue No. 26, pp. 10667-10677, Nov. 19, 2005.
Bang et al., “A Novel Electrochemical Detection Method for Aptamer Biosensors”, Biosensors and Bioelectronics, vol. No. 21, Issue No. 6, pp. 863-870, Dec. 15, 2005.
Lange et al., “Measuring Biomolecular Binding Events with a Compact Disc Player Device”, Angewandte Chemie International Edition, vol. No. 45, Issue No. 2, pp. 270-273, Dec. 23, 2005.
Locklin et al., “Effect of Morphology on Organic Thin Film Transistor Sensors”, Analytical and Bioanalytical Chemistry, vol. No. 384, Issue No. 2, pp. 336-342, 2006.
Morris et al., “Wireless Sensor Array System for Combinatorial Screening of Sensor Materials”, Materials Research Society Symposium Proceedings, vol. No. 894, pp. 0894-LL07-02.1-0894-LL07-02.6, 2006.
Lvovich et al., “Impedance Characterization of Industrial Lubricants”, Electrochimica Acta, vol. No. 51, Issue No. 3-9, pp. 1487-1496, Jan. 20, 2006.
Yang et al., “Chemical Identification Using an Impedance Sensor Based on Dispersive Charge Transport”, Applied Physics Letters, vol. No. 88, Issue No. 7, pp. 074104-1-074104-3, Feb. 2006.
Pejcic et al., “Impedance Spectroscopy: Over 35 Years of Electrochemical Sensor Optimization”, Electrochimica Acta, vol. No. 51, Issue No. 28, pp. 6217-6229, Sep. 15, 2006.
Benini et al., “Wireless Sensor Networks: Enabling Technology for Ambient Intelligence”, Microelectronics Journal, vol. No. 37, Issue No. 12, pp. 1639-1649, Dec. 2006.
Potyrailo et al., “Multianalyte Chemical Identification and Quantitation Using a Single Radio Frequency Identification Sensor”, Analytical Chemistry, vol. No. 79, Issue No. 1, pp. 45-51, 2007.
Li et al., “Chemosensory Performance of Molecularly Imprinted Fluorescent Conjugated Polymer Materials”, Journal of the American Chemical Society, vol. No. 129, Issue No. 51, pp. 15911-15918, 2007.
Wang et al., “Array of Molecularly Mediated Thin Film Assemblies of Nanoparticles: Correlation of Vapor Sensing with Interparticle Spatial Properties”, Journal of the American Chemical Society, vol. No. 129, Issue No. 7, pp. 2161-2170, 2007.
Hempel et al., “Application of a Portable RF Impedance Spectrum Analyzer for the Investigation of Lateral Field Excited Acoustic Wave Sensors in a Liquid Environment”, Ultrasonics Symposium Proceeding, vol. No. 1, pp. 373-376, Jan. 1, 2007.
Hwang et al., “Photoelectron Spectroscopic Study of the Electronic Band Structure of Polyfluorene and Fluorene-Arylamine Copolymers at Interfaces”, The Journal of Physical Chemistry C, vol. No. 111, Issue No. 3, pp. 1378-1384, Jan. 2007.
Qing et al., “RFID Tag Antennas”, Antennas for Portable Devices, John Wiley & Sons, Ltd, pp. 59-61; 65-69, Mar. 2007.
Bai et al., “Gas Sensors Based on Conducting Polymers”, Sensors, vol. No. 7, Issue No. 3, pp. 267-307, Mar. 2007.
Metzger et al., “Weight-Sensitive Foam to Monitor Product Availability on Retail Shelves”, 5th International Conference on Pervasive Computing, Toronto, Canada, vol. No. 4480, pp. 268-279, May 13-16, 2007.
Li et al., “Inkjet Printed Chemical Sensor Array Based on Polythiophene Conductive Polymers”, Sensors and Actuators B: Chemical, vol. No. 123, Issue No. 2, pp. 651-660, May 21, 2007.
Liu et al., “Measurement of Density and Viscosity of Dodecane and Decane with a Piezoelectric Tuning Fork Over 298-448 K and 0.1-137.9 MPa”, Sensors and Actuators A Physical, vol. No. 167, Issue No. 2, pp. 347-353, Jun. 2007.
Lu et al., “MEMS-Based Inductively Coupled RFID Transponder for Implantable Wireless Sensor Applications”, IEEE Transactions on Magnetics, vol. No. 43, Issue No. 6, pp. 2412-2414, Jun. 2007.
Wei et al., “Simple and Sensitive Aptamer-Based Colorimetric Sensing of Protein using Unmodified Gold Nanoparticle Probes”, Chemical Communications, Issue 36, pp. 3735-3737, Jun. 29, 2007.
Potyrailo et al., “Wireless Resonant Sensor Array for High-Throughput Screening of Materials”, Review of Scientific Instruments, vol. No. 78, Issue No. 7, pp. 072214-1-072214-6, Jul. 2007.
Sugiyasu et al., “Conducting-Polymer-Based Chemical Sensors: Transduction Mechanisms”, Bulletin of the Chemical Society of Japan, vol. No. 80, Issue No. 11, pp. 2074-2083, Aug. 2007.
Potyrailo et al., “RFID Sensors as the Common Sensing Platform for Single-Use Biopharmaceutical Manufacturing”, Measurement Science and Technology, vol. No. 22, Issue No. 8, pp. 082001-1-082001-17, Jul. 15, 2011.
Wang et al., “Impedance Analysis for Lateral Field Excited Acoustic Wave Sensors”, Sensors and Actuators B: Chemical, vol. No. 156, Issue No. 2, pp. 969-975, Aug. 2011.
Latif et al., “Conductometric Sensors for Monitoring Degradation of Automotive Engine Oil”, Sensors, vol. No. 11, Issue No. 9, pp. 8611-8625, Sep. 5, 2011.
Potyrailo et al., “Materials and Transducers Toward Selective Wireless Gas Sensing”, Chemical Reviews, vol. No. 111, Issue No. 11, pp. 7315-7354, Sep. 7, 2011.
Perez et al., “Low-Cost Oil Quality Sensor Based on Changes in Complex Permittivity”, Sensors, vol. No. 11, Issue No. 11, pp. 10675-10690, Nov. 2011.
Potyrailo et al., “Multivariable Passive RFID Vapor Sensors: Pilot-Scale Manufacturing and Laboratory Evaluation”, Future of Instrumentation International Workshop, pp. 1-2, Nov. 7-8, 2011.
Aghayan, “On-Line Monitoring of Engine Health through the Analysis of Contaminants in Engine Lubricant”, The School of Graduate and Postdoctoral Studies, The University of Western Ontario, London, Ontario, Canada, pp. 1-273, 2012.
Potyrailo et al., “Multivariable MHz and GHz Wireless Chem/Bio Sensors for Environmental, Industrial, and Security Applications”, The 14th International Meeting on Chemical Sensors, Nuremberg, Germany, pp. 399-402, May 20-23, 2012.
Datla et al., “Wireless Distributed Computing: A Survey of Research Challenges”, IEEE Communications Magazine, vol. No. 50, Issue No. 1, pp. 144-152, Jan. 5, 2012.
Datla et al., “Wireless Distributed Computing in Cognitive Radio Networks”, Ad Hoc Networks, vol. No. 10, Issue No. 5, pp. 845-857, Jul. 2012.
Fochtmann et al., “Optimization of the Lateral Field Excited Platform for Liquid Sensing Applications”, Sensors and Actuators B: Chemical, vol. No. 170, pp. 95-103, Jul. 31, 2012.
De Souza et al., “A Close Dielectric Spectroscopic Analysis of Diesel/Biodiesel Blends and Potential Dielectric Approaches for Biodiesel Content Assessment”, Fuel, vol. No. 105, pp. 705-710, Mar. 2013.
Zhu et al., “Survey of Lubrication Oil Condition Monitoring, Diagnostics, and Prognostics Techniques and Systems”, Journal of Chemical Science and Technology, vol. No. 2, Issue No. 3, pp. 100-115, Jul. 2013.
Agilent Impedance Measurement Handbook, “A Guide to Measurement Technology and Techniques”, 4th Edition, Agilent Technologies, pp. 1-140, Sep. 10, 2013.
Toledo et al., “Application of Quartz Tuning Forks and Extensional Microresonators for Viscosity and Density Measurements in Oil/Fuel Mixtures”, Microsystem Technologies, vol. No. 20, Issue No. 4, pp. 945-953, Apr. 2014.
Elzagzoug et al., “Condition Monitoring of High Voltage Transformer Oils Using Optical Chromaticity”, Measurement Science and Technology, vol. No. 25, Issue No. 6, pp. 065205-1-065205-9, Jun. 2014.
Soleimani et al., “Base Oil Oxidation Detection Using Novel Chemical Sensors and Impedance Spectroscopy Measurements”, Sensors and Actuators B: Chemical, vol. No. 199, pp. 247-258, Aug. 2014.
Hoja et al., “Miniaturized Impedance Analyzer Using AD5933”, Lecture Notes on Impedance Spectroscopy, vol. No. 5, pp. 93-100, Feb. 17, 2015.
Chabowski et al., “Simple Wide Frequency Range Impedance Meter Based on AD5933 Integrated Circuit”, Metrology and Measurement Systems, vol. No. 22, Issue No. 1, pp. 13-24, Mar. 15, 2015.
Simic, “Complex Impedance Measurement System for the Frequency Range from 5 kHz to 100 kHz”, Key Engineering Materials, vol. No. 644, pp. 133-136, May 11, 2015.
Chen et al., “Novel Undercoupled Radio-Frequency (RF) Resonant Sensor for Gaseous Ethanol and Interferents Detection”, Sensors and Actuators A: Physical, vol. No. 230, pp. 63-73, Jul. 1, 2015.
Ghaffari et al., “A Wireless Multi-Sensor Dielectric Impedance Spectroscopy Platform”, Sensors, vol. No. 15, Issue No. 9, pp. 23572-23588, Sep. 17, 2015.
Wang et al., “Probe Improvement of Inductive Sensor for Online Health Monitoring of Mechanical Transmission Systems”, IEEE Transactions on Magnetics, vol. No. 51, Issue No. 11, pp. 1-4, Nov. 2015.
Poseidon Systems, “Oil Quality Products”, Trident QM1100; Trident QM2100; Trident WM800, pp. 1-3, Retrieved from http://www.poseidonsys.com/products/oil-quality on Dec. 24, 2015.
Tandelta Systems, “Oil Quality Sensor”, Tandelta Oil Condition Monitoring, pp. 1-5, Retrieved from http://www.tandeltasystems.com/products/oil-quality-sensor-2/ on Dec. 24, 2015.
Zhu et al., “An Integrated Lubricant Oil Conditioning Sensor Using Signal Multiplexing”, Journal of Micromechanics and Microengineering, vol. No. 25, Issue No. 1, pp. 015006-1-015006-12, Dec. 24, 2015.
Eurasian Search Report issued in connection with related EA Application No. 201592216 on Aug. 4, 2016.
Australian Office Action issued in connection with related AU Application No. 2015268746 on Oct. 21, 2016.
US Non-Final Office Action issued in connection with related U.S. Appl. No. 14/697,086 on Oct. 31, 2016.
Tan et al., “A Wireless, Passive Sensor for Quantifying Packaged Food Quality”, Sensors, vol. No. 7, Issue No. 9, pp. 1747-1756, Sep. 2007.
Wang et al., “A New Method for On-line Monitoring of Brake Fluid Condition using an Enclosed Reference Probe”, Measurement Science and Technology, vol. No. 18, Issue No. 11, pp. 3625-3635, Oct. 16, 2007.
Wang et al., “A Gold Nanoparticle-Based Aptamer Target Binding Readout for ATP Assay”, Advanced Materials, vol. No. 19, Issue No. 22, pp. 3943-3946, Nov. 2007.
Tanaka et al., “Quartz Crystal Capacitive Sensor with Inductance-Capacitance Resonance Circuit for Vapor Sensing”, Japanese Journal of Applied Physics, vol. No. 46, Issue No. 11, pp. 7509-7511, Nov. 2007.
Ulrich et al., “Simultaneous Estimation of Soot and Diesel Contamination in Engine Oil Using Electrochemical Impedance Spectroscopy”, Sensors and Actuators B: Chemical, vol. No. 127, Issue No. 2, pp. 613-618, Nov. 15, 2007.
Surman et al., “Quantitation of Toxic Vapors in Variable Humidity Atmosphere Using Individual Passive Radio Frequency Identification (RFID) Sensors”, 12th International Meeting on Chemical Sensors, Columbus, pp. 1-2, 2008.
Potyrailo et al., “Modeling of Selectivity of Multi-Analyte Response of Passive Radio Frequency Identification (RFID) Sensors”, 12th International Meeting on Chemical Sensors, Columbus, pp. 1-3, 2008.
Wang et al., “Sensors and Biosensors for the Determination of Small Molecule Biological Toxins”, Sensors, vol. No. 8, Issue No. 9, pp. 6045-6054, 2008.
Metzger et al., “Flexible-Foam-Based Capacitive Sensor Arrays for Object Detection at Low Cost”, Applied Physics Letters, vol. No. 92, Issue No. 1, pp. 013506-1-013506-3, Jan. 2008.
Hatchett et al., “Composites of Intrinsically Conducting Polymers as Sensing Nanomaterials”, Chemical Reviews, vol. No. 108, Issue No. 2, pp. 746-769, Jan. 3, 2008.
Joo et al., “Chemical Sensors with Integrated Electronics”, Chemical Reviews, vol. No. 108, Issue No. 2, pp. 638-351, Jan. 10, 2008.
Rock et al., “Electronic Nose: Current Status and Future Trends”, Chemical Reviews, vol. No. 108, Issue No. 2, pp. 705-725, Jan. 19, 2008.
Diamond et al., “Wireless Sensor Networks and Chemo-/Biosensing”, Chemical Reviews, vol. No. 108, Issue No. 2, pp. 652-679, Jan. 24, 2008.
Wang et al., “Aptamer Biosensor for Protein Detection Using Gold Nanoparticles”, Analytical Biochemistry, vol. No. 373, Issue No. 2, pp. 213-219, Feb. 15, 2008.
Agoston et al., “A Concept of an Infrared Sensor System for Oil Condition Monitoring”, Elektrotechnik & Informationstechnik, vol. No. 125, Issue No. 3, pp. 71-75, Mar. 2008.
Wang et al., “Electrochemical Sensors for Clinic Analysis”, Sensors, vol. No. 8, Issue No. 4, pp. 2043-2081, Apr. 2008.
Capone et al., “Metal Oxide Gas Sensor Array for the Detection of Diesel Fuel in Engine Oil”, Sensors and Actuators 3: Chemical, vol. No. 131, Issue No. 1, pp. 125-133, Apr. 14, 2008.
Potyrailo et al., “Position-Independent Chemical Quantitation with Passive 1156-MHz Radio Frequency Identification (RFID) Sensors”, Talanta, vol. No. 75, Issue No. 3, pp. 624-628, May 15, 2008.
Hempel et al., “Lateral Field Excited Quartz Crystal Resonator Sensors for Determination of Acoustic and Electrical Properties of Liquids”, IEEE International Frequency Control Symposium, Honolulu, Hawaii, pp. 705-710, May 19-21, 2008.
Palacios et al., “Rational Design of a Minimal Size Sensor Array for Metal Ion Detection”, Journal of the American Chemical Society, vol. No. 130, Issue No. 31, pp. 10307-10314, Jul. 11, 2008.
Kauffman et al., “Carbon Nanotube Gas and Vapor Sensors”, Angewandte Chemie International Edition, vol. No. 47, pp. 6550-6570, Jul. 18, 2008.
Zheng et al., “Resonance Impedance Sensing of Human Blood Cells”, Sensors and Actuators A: Physical, vol. No. 145-146, pp. 29-36, Jul.-Aug. 2008.
Guan et al., “Engine Lubricating Oil Classification by SAE Grade and Source Based on Dielectric Spectroscopy Data”, Analytica Chimica Acta, vol. No. 628, Issue No. 1, pp. 117-120, Oct. 17, 2008.
Li et al., “Chemical Sensing Using Nanostructured Polythiophene Transistors”, Nano Letters, vol. No. 8, Issue No. 11, pp. 3563-3567, Oct. 28, 2008.
Wudy et al., “Rapid Impedance Scanning QCM for Electrochemical Applications Based on Miniaturized Hardware and High-Performance Curve Fitting”, Electrochimica Acta, vol. No. 53, Issue No. 22, pp. 6568-6574, Sep. 20, 2008.
Potyrailo et al., “RFID Sensors Based on Ubiquitous Passive 1356-MHz RFID Tags and Complex Impedance Detection”, Wireless Communications and Mobile Computing, pp. 1-13, Nov. 2008.
Hendrick., “Cellulose Acetate Fibers with Fluorescing Nanoparticles for Anti-Counterfeiting Purposes”, Erin Sue Hendrick, pp. 1-36, 2008.
Sacristan-Riquelme et al., “Low Power Impedance Measurement Integrated Circuit for Sensor Applications”, Microelectronics Journal, vol. No. 40, Issue No. 1, pp. 177-184, Jan. 2009.
Ertl et al., “Interdigitated Impedance Sensors for Analysis of Biological Cells in Microfluidic Biochips”, Elektrotechnik & Informationstechnik, vol. No. 126, Issue No. 1, pp. 47-50, Jan.-Feb. 2009.
Potyrailo et al., “Passive Radio Frequency Identification (RFID) Chemical Sensors for Homeland Security Applications”, Wiley Handbook of Science and Technology for Homeland Security, pp. 1-12, Jun. 15, 2009.
Potyrailo et al., “Selective Detection of Chemical Species in Liquids and Gases Using Passive Radio-Frequency Identification (RFID) Sensors”, IEEE Conference on Transducers, Denver, CO, USA, pp. 1650-1653, Jun. 21-25, 2009.
Potyrailo et al., “Combinatorial Screening of Polymeric Sensing Materials Using RFID Sensors”, Journal of Combinatorial Chemistry, vol. No. 11, Issue No. 4, pp. 598-603, Jul.-Aug. 2009.
McCann et al., “Recent Advances in Lateral Field Excited and Monolithic Spiral Coil Acoustic Transduction Bulk Acoustic Wave Sensor Platforms”, Measurement Science and Technology, vol. No. 20, Issue No. 12, pp. 124001-1-124001-12, Oct. 2009.
Westafer et al., “Functionalization of High Frequency SAW RFID Devices for Ozone Dosimetry”, IEEE Sensors Conference, pp. 1747-1752, Oct. 25-28, 2009.
Niedermayer et al., “Yet Another Precision Impedance Analyzer (YAPIA)—Readout Electronics for Resonating Sensors”, Sensors and Actuators A: Physical, vol. No. 156, Issue No. 1, pp. 245-250, Nov. 2009.
Potyrailo et al., “Development of Radio-Frequency Identification Sensors Based on Organic Electronic Sensing Materials for Selective Detection of Toxic Vapors”, Journal of Applied Physics, vol. No. 106, Issue No. 12, pp. 124902-1-124902-6, Dec. 2009.
Mortier et al., “Chemistry and Technology of Lubricants”, Third Edition, Springer, pp. 1-560, 2010.
Cho et al., “Capacitive Sensor for Automotive Engine Oil Degradation using Wireless Network”, International Symposium on Advanced Packaging Materials: Microtech, Cambridge, pp. 88-91, Feb. 28-Mar. 2, 2010.
Potyrailo et al., “Selective Quantitation of Vapors and their Mixtures using Individual Passive Multivariable RFID Sensors”, IEEE International Conference on RFID, Orlando, Florida, pp. 22-28, Apr. 14-16, 2010.
Becher et al., “The Detection of Evaporating Hazardous Material Released from Moving Sources Using a Gas Sensor Network”, Sensors and Actuators B: Chemical, vol. No. 146, Issue No. 2, pp. 513-520, Apr. 29, 2010.
Alexander et al., “Optimization of Interdigitated Electrode (IDE) Arrays for Impedance Based Evaluation of Hs 578T Cancer Cells”, Journal of Physics: Conference Series, vol. No. 224, Issue No. 1, pp. 012134-1-012134-4, May 2010.
Chen et al., “Based on ZigBee Wireless Sensor Network the Monitoring System Design for Production Process Toxic and Harmful Gas”, International Conference on Computer, Mechatronics, Control and Electronic Engineering, vol. No. 4, pp. 425-428, Aug. 24-26, 2010.
Hong et al., “Development of a Micro Liquid-Level Sensor for Harsh Environments using a Periodic Heating Technique”, Measurement Science and Technology, vol. No. 21, Issue No. 10, Sep. 2010.
Bianchi et al., “Model of an Interdigitated Microsensor to Detect and Quantify Cells Flowing in a Test Chamber”, 6th Annual COMSOL Conference, Paris, pp. 1-5, Nov. 2010.
Wang et al., “Flexible Chemiresistor Sensors: Thin film Assemblies of Nanoparticles on a Polyethylene Terephthalate Substrate”, Journal of Materials Chemistry, vol. No. 20, pp. 907-915, Dec. 15, 2010.
Suresh et al., “Piezoelectric Based Resonant Mass Sensor using Phase Measurement”, Measurement, vol. No. 14, Issue No. 2, pp. 320-325, Feb. 2011.
De Vito et al., “Wireless Sensor Networks for Distributed Chemical Sensing: Addressing Power Consumption Limits with On-Board Intelligence”, IEEE Sensors Journal, vol. No. 11, Issue No. 14, pp. 947-955, Apr. 2011.
Potyrailo et al., “Passive Multivariable Temperature and Conductivity RFID Sensors for Single-Use Biopharmaceutical Manufacturing Components”, Biotechnology Progress, vol. No. 27, Issue No. 3, pp. 875-884, May 2, 2011.
Sen et al., “Evaluation of Sensor Arrays for Engine Oils Using Artificial Oil Alteration”, Proceedings of SPIE 8066, Smart Sensors Actuators and MEMS, vol. No. 8066, pp. 1-7, May 5, 2011.
Guan et al., “Application of Dielectric Spectroscopy for Engine Lubricating Oil Degradation Monitoring”, Sensors and Actuators A: Physical, vol. No. 168, Issue No. 1, pp. 22-29, Jul. 2011.
Bauerle, “Study of Solid Electrolyte Polarization by a Complex Admittance Method”, Journal of Physics and Chemistry of Solids, vol. No. 30, Issue No. 12, pp. 2657-2670, Dec. 1969.
Matsui, “Complex-Impedance Analysis for the Development of Zirconia Oxygen Sensors”, Solid State Ionics, vol. No. 3-4, pp. 525-529, Aug. 1981.
Persaud et al., “Analysis of Discrimination Mechanisms in the Mammalian Olfactory System Using a Model Nose”, Nature, vol. No. 299, pp. 352-355, Sep. 23, 1982.
Ward et al., “In Situ Interfacial Mass Detection with Piezoelectric Transducers”, Science, vol. No. 249, Issue No. 1972, pp. 1000-1007, Aug. 31, 1990.
Gutierrez et al., “Use of Complex Impedance Spectroscopy in Chemical Sensor Characterization”, Sensors and Actuators B: Chemical, vol. No. 4, Issue No. 3-4, pp. 359-363, Jun. 1991.
Wise et al., “Microfabrication Techniques for Integrated Sensors and Microsystems”, Science, vol. No. 254, pp. 1335-1342, Nov. 29, 1991.
Mullen et al., “Trace Detection of Ionic Species with Surface Enhanced Raman Spectroscopy”, Spectroscopy, vol. No. 7, Issue No. 5, pp. 24-32, Jun. 1992.
Ervin et al., “Development of a Fiber-Optic Sensor for Trace Metal Detection in Aqueous Environments”, Applied Optics, vol. No. 32, Issue No. 22, pp. 4287-4290, Aug. 1, 1993.
Ghiotti et al., “Moisture Effects on Pure and Pd-Doped SnO2 Thick Films Analysed by FTIR Spectroscopy and Conductance Measurements”, Sensors and Actuators B: Chemical, vol. No. 25, Issue No. 1-3, pp. 520-524, Apr. 1995.
Legin et al., “Development and Analytical Evaluation of a Multisensor System for Water Quality Monitoring”, Sensors and Actuators B: Chemical, vol. No. 27, Issue No. 1-3, pp. 377-379, Jun. 1995.
Malinowska et al., “Enhanced Electrochemical Performance of Solid-State Ion Sensors Based on Silicone Rubber Membranes”, The 8th International Conference on Solid-State Sensors and Actuators, and Eurosensors IX, Stockholm, Sweden, pp. 851-854, Jun. 25-29, 1995.
Amrani et al., “High-Frequency Measurements of Conducting Polymers: Development of a New Technique for Sensing Volatile Chemicals”, Measurement Science and Technology, vol. No. 6, Issue No. 10, pp. 1500-1507, Jul. 20, 1995.
Nicolaou et al., “Radiofrequency Encoded Combinatorial Chemistry”, Angewandte Chemie International Edition, vol. No. 34, Issue No. 20, pp. 2289-2291, Nov. 3, 1995.
Leff et al., “Synthesis and Characterization of Hydrophobic, Organically-Soluble Gold Nanocrystals Functionalized with Primary Amines”, Langmuir, vol. No. 12, Issue No. 20, pp. 4723-4730, 1996.
Amrani et al., “Multi-Frequency Measurements of Organic Conducting Polymers for Sensing of Gases and Vapours”, Sensors and Actuators B: Chemical, vol. No. 33, Issue No. 1-3, pp. 137-141, Jul. 1996.
Di Natale et al., “Multicomponent Analysis of Heavy Metal Cations and Inorganic Anions in Liquids by a Non-Selective Chalcogenide Glass Sensor Array”, Sensors and Actuators B: Chemical, vol. No. 34, Issue No. 1-3, pp. 539-542, Aug. 1996.
Chinowsky et al., “Experimental Data from a Trace Metal Sensor Combining Surface Plasmon Resonance with Anodic Stripping Voltammetry”, Sensors and Actuators B: Chemical, vol. No. 35, Issue No. 1-3, pp. 37-43, Sep. 1996.
Josse et al., “AC-Impedance-Based Chemical Sensors for Organic Solvent Vapors”, Sensors and Actuators B: Chemical, vol. No. 36, Issue No. 1-3, pp. 363-369, Oct. 1996.
Wang et al., “The Application of A.C. Impedance Technique for Detecting Glycol Contamination in Engine Oil”, Sensors and Actuators B: Chemical, vol. No. 40, Issue No. 2-3, pp. 193-197, May 15, 1997.
Hammond et al., “An Acoustic Automotive Engine Oil Quality Sensor”, International Conference on Solid-State Sensors and Actuators, Chicago, pp. 1343-1346, Jun. 16-19, 1997.
Amrani et al., “An Intelligent Gas Sensing System”, Sensors and Actuators B: Chemical, vol. No. 44, Issue No. 1-3, pp. 512-516, Oct. 1997.
Di Natale et al., “Multicomponent Analysis on Polluted Waters by Means of an Electronic Tongue”, Sensors and Actuators B: Chemical, vol. No. 44, Issue No. 1-3, pp. 423-428, Oct. 1997.
Vlasov et al., “Cross-Sensitivity Evaluation of Chemical Sensors for Electronic Tongue: Determination of Heavy Metal Ions”, Sensors and Actuators B: Chemical, vol. No. 44, Issue No. 1-3, pp. 532-537, Oct. 1997.
Ehret et al., “On-line Control of Cellular Adhesion with Impedance Measurements Using Interdigitated Electrode Structures”, Medical & Biological Engineering & Computing, vol. No. 36, Issue No. 3, pp. 365-370, May 1998.
Wohltjen et al., “Colloidal Metal-Insulator-Metal Ensemble Chemiresistor Sensor”, Analytical Chemistry, vol. No. 70, Issue No. 14, pp. 2856-2859, Jul. 15, 1998.
Homola et al., “Surface Plasmon Resonance Sensors: Review”, Sensors and Actuators B: Chemical, vol. No. 54, Issue No. 1-2, pp. 3-15, Jan. 25, 1999.
Amrani et al., “Multi-Frequency Interrogation Technique Applied to Conducting Polymer Gas and Odour Sensors”, IEE Proceedings—Science, Measurement and Technology, vol. No. 146, Issue No. 2, pp. 95-101, Mar. 1999.
Legin et al., “The Features of the Electronic Tongue in Comparison with the Characterstics of the Discrete Ion-Selective Sensors”, Sensors and Actuators B: Chemical, vol. No. 58, Issue No. 1-3, pp. 464-468, Sep. 21, 1999.
Artmann, “Electronic Identification Systems: State of the Art and their Further Development”, Computers and Electronics in Agriculture, vol. No. 24, Issue No. 1-2, pp. 5-26, Nov. 1999.
Chyan et al., “Ultrapure Water Quality Monitoring by a Silicon-Based Potentiometric Sensor”, Analyst, vol. No. 125, Issue No. 1, pp. 175-178, 2000.
Basu et al., ““Smart Sensing” of Oil Degradation and Oil Level Measurements in Gasoline Engines”, SAE 2000 World congress, Detroit, Michigan, 2000-01-1366, pp. 1-7, Mar. 6-9, 2000.
Jurs et al., “Computational Methods for the Analysis of Chemical Sensor Array Data from Volatile Analytes”, Chemical Reviews, vol. No. 100, Issue No. 7, pp. 2649-2678, Jun. 7, 2000.
McQuade et al., “Conjugated Polymer-Based Chemical Sensors”, Chemical Reviews, vol. No. 100, Issue No. 7, pp. 2537-2574, Jun. 9, 2000.
Vlasov et al., “Electronic Tongue—New Analytical Tool for Liquid Analysis on the basis of Non-Specific Sensors and Methods of Pattern Recognition”, Sensors and Actuators B: Chemical, vol. No. 65, Issue No. 1-3, pp. 235-236, Jun. 30, 2000.
Rakow et al., “A Colorimetric Sensor Array for Odour Visualization”, Nature, vol. No. 406, pp. 710-713, Aug. 17, 2000.
Taton et al., “Scanometric DNA Array Detection with Nanoparticle Probes”, Science, vol. No. 289, Issue No. 5485, pp. 1757-1760, Sep. 8, 2000.
Foster et al., “Detection of Trace Levels of Water in Oil by Photoacoustic Spectroscopy”, Sensors and Actuators B: Chemical, vol. No. 77, Issue No. 3, pp. 620-624, Jul. 10, 2001.
MacDiarmid, ““Synthetic Metals”: A Novel Role for Organic Polymers (Nobel Lecture)”, Angewandte Chemie International Edition, vol. No. 40, Issue No. 14, pp. 2581-2590, Jul. 16, 2001.
Shirakawa, “The Discovery of Polyacetylene Film: The Dawning of an Era of Conducting Polymers”, Angewandte Chemie International Edition, vol. No. 40, Issue No. 14, pp. 2575-2580, Jul. 16, 2001.
Ong et al., “Design and Application of a Wireless, Passive, Resonant-Circuit Environmental Monitoring Sensor”, Sensors and Actuators A: Physical, vol. No. 93, Issue No. 1, pp. 33-43, Aug. 25, 2001.
Mourzina et al., “Development of Multisensor Systems based on Chalcogenide Thin Film Chemical Sensors for the Simultaneous Multicomponent Analysis of Metal Ions in Complex Solutions”, Electrochimica Acta, vol. No. 47, Issue No. 1-2, pp. 251-258, Sep. 1, 2001.
Gawad et al., “Micromachined Impedance Spectroscopy Flow Cytometer for Cell Analysis and Particle Sizing”, Lab on a Chip, vol. No. 1, Issue No. 1, pp. 76-82, Sep. 2001.
Heeger, “Semiconducting and Metallic Polymers: The Fourth Generation of Polymeric Materials”, The Journal of Physical Chemistry B, vol. No. 105, Issue No. 36, pp. 8475-8491, Sep. 13, 2001.
Foster-Mills et al., “Photoacoustic Spectroscopy Detects Water in Oil”, Sensors Online, pp. 1-5, Oct. 2001, Retrieved from http://archives.sensorsmag.com/articles/1001/12/pf—main.shtml on Apr. 11, 2016.
Janata, “Electrochemical Sensors and their Impedances: A Tutorial”, Critical Reviews in Analytical Chemistry, vol. No. 32, Issue No. 2, pp. 109-120, 2002.
Harpster et al., “A Passive Humidity Monitoring System for In Situ Remote Wireless Testing of Micropackages”, Microelectromechanical Systems, vol. No. 11, Issue No. 1, pp. 61-67, Feb. 2002.
Akyildiz et al., “Wireless Sensor Networks: A Survey”, Computer Networks, vol. No. 38, Issue No. 4, pp. 393-422, Mar. 15, 2002.
Haes et al., “A Nanoscale Optical Biosensor: Sensitivity and Selectivity of an Approach Based on the Localized Surface Plasmon Resonance Spectroscopy of Triangular Silver Nanoparticles”, Journal of the American Chemical Society, vol. No. 124, Issue No. 35, pp. 10596-10604, Aug. 8, 2002.
Ceresa et al., “Rational Design of Potentiometric Trace Level Ion Sensors. A Ag+-Selective Electrode with a 100 ppt Detection Limit”, Analytical Chemistry, vol. No. 74, Issue No. 16, pp. 4027-4036, Aug. 15, 2002.
Smiechowski et al., “Electrochemical Monitoring of Water-Surfactant Interactions in Industrial Lubricants”, Journal of Electroanalytical Chemistry, vol. No. 534, Issue No. 2, pp. 171-180, Oct. 18, 2002.
Related Publications (1)
Number Date Country
20140090454 A1 Apr 2014 US