SENSOR FOR IN SITU SELECTIVE DETECTION OF COMPONENTS IN A FLUID

Abstract

A sensor system for detection of an analyte in an industrial fluid in the presence of interferences which includes: a multivariable inductor-capacitor-resistor resonant transducer with multiple operationally independent outputs; a sensing material composition configured to provide different response patterns to an analyte in the fluid in the presence of interferences; and a signal processor that quantifies the analyte. Also, a sensor system for detection of an analyte that includes: a transducer with multiple operationally independent outputs; a sensing material compositions configured to provide different response patterns to an analyte in the industrial fluid in the presence of interferences; and a signal processor configured to quantify the analyte in the industrial fluid in the presence of interferences. An embodiment of the sensor system detects methane.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to, commonly assigned, U.S. Application Ser. No. 62/162,156, Entitled: ELECTRICAL RESONANT MULTIVARIABLE SENSING SYSTEM FOR IN SITU SELECTIVE DETECTION OF METHANE (attorney docket no. 281861-1). The contents of which are incorporated herein by reference in its entirety.

BACKGROUND

The subject matter of this disclosure relates generally to methane sensing and, more particularly, to a resonant multivariable sensing system for in situ selective detection of methane.

Common available types of methane sensors, such as catalytic and IR, are not only very expensive but also cannot provide selective methane detection in an unattended wireless mode at low power. Catalytic sensors operate at 250-700 degrees Celsius, need high power of 120-600 mW, are poisoned by chlorinated, silicone and sulfur compounds, and have cross-sensitivity to many gases, resulting in false readings. While IR sensors provide faster response time and long-term stability, they also have well known cross-sensitivity to other gases. Fugitive methane detection is gaining strong attention in the industrial arena. This is primarily driven by growing regulatory measures to mitigate these emissions for environmental protection and also gas monetization. However, mitigation is driven by detection. Across the economy, there are multiple sectors in which methane emissions can be reduced, from coal mines and landfills to agriculture and oil and gas development. For example, in the agricultural sector, over the last three years, the Environmental Protection Agency and the Department of Agriculture have worked with the dairy industry to increase the adoption of methane digesters through loans, incentives, and other assistance. In addition, when it comes to the oil and gas sector, reducing emissions and enhancing economic productivity are becoming quite important. For example, work is underway to advance the production of oil and gas in the Bakken while helping to reduce venting and flaring.

Accordingly, there is a need to improve upon current methane sensing methodologies.

BRIEF DESCRIPTION

Certain embodiments commensurate in scope with the originally claimed invention are summarized below. These embodiments are not intended to limit the scope of the claimed invention, but rather these embodiments are intended only to provide a brief summary of possible forms of the invention. Indeed, the invention may encompass a variety of forms that may be similar to or different from the embodiments set forth below.

In one embodiment, a methane detection system described herein comprises a single sensor for selective detection of methane, where the sensor is comprised of a multivariable inductor-capacitor-resistor (LCR) transducer with at least two operationally independent outputs, and a sensing material composition that is comprised of a methane-sensing moiety and a matrix incorporating the methane-sensing moiety, where the matrix directs interference response out of response direction to methane.

In one embodiment, a methane detection system described herein comprises a single sensor for selective detection of methane, wherein the sensor comprises a multivariable inductor-capacitor-resistor (LCR) transducer with at least two operationally independent outputs, and a sensing material composition that is comprised of a methane-sensing moiety and a matrix incorporating the methane-sensing moiety, wherein the matrix directs interference response out of response direction to methane.

In one embodiment, a sensor system for detection of an analyte in an industrial fluid in presence of interferences, the sensor system comprises: a multivariable inductor-capacitor-resistor resonant transducer with at least two operationally independent outputs; a sensing material composition configured to provide different response patterns to an analyte in the industrial fluid in the presence of interferences; and a signal processor that quantifies the analyte in the industrial fluid in the presence of interferences.

In one embodiment, a sensor system for detection of an analyte in an industrial fluid in the presence of interferences, the sensor system comprises: a transducer with at least two operationally independent outputs; a sensing material compositions configured to provide different response patterns to an analyte in the industrial fluid in the presence of interferences; and a signal processor configured to quantify the analyte in the industrial fluid in the presence of interferences.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

FIG. 1 is a block diagram representing an example multivariable sensor node, in accordance with embodiments of the present technique;

FIG. 2 is a block diagram of an impedance analyzer and resonant sensor where the impedance analyzer is integrated into a single-chip to form a resonance impedance reader module, in accordance with embodiments of the present technique;

FIG. 3 illustrates a methane-sensing supramolecular moiety incorporated into a matrix material to re-direct interference response out of response direction to methane in accordance with one embodiment;

FIG. 4A illustrates a wireless sensor node in accordance with one embodiment;

FIG. 4B illustrates an exemplary sensor network in accordance with one embodiment;

FIGS. 5A-5C illustrate a methodology for sensing analyte mixed with ambient interferences;

FIGS. 6A-6B illustrate the response of a new sensing material to methane at room temperature, in accordance with one embodiment;

FIGS. 7A-7D illustrate the experimental performance of new methane sensing material at room temperature, in accordance with one embodiment;

FIGS. 8A-8B illustrate exemplary sensor responses Fp and Zp to two replicates of exposures to methane at four concentrations, exposures to water vapor at two concentrations, exposures to toluene vapor at two concentrations, and two replicates of exposures to methane at four concentrations;

FIGS. 9A-9B illustrate exemplary sensor responses Z1 and Z2 to two replicates of exposures to methane at four concentrations, exposures to water vapor at two concentrations, exposures to toluene vapor at two concentrations, and two replicates of exposures to methane at four concentrations;

FIGS. 10A-10B illustrate exemplary sensor responses F1 and F2 to two replicates of exposures to methane at four concentrations, exposures to water vapor at two concentrations, exposures to toluene vapor at two concentrations, and two replicates of exposures to methane at four concentrations; and

FIGS. 11A-11B illustrate results of PCA analysis to two replicates of exposures to methane at four concentrations, exposures to water vapor at two concentrations, exposures to toluene vapor at two concentrations, and two replicates of exposures to methane at four concentrations.

DETAILED DESCRIPTION

One or more specific embodiments will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the present invention, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.

The term “fluid” as used herein includes gases, vapors, liquids, and emulsions that include industrial, non-industrial, and/or naturally occurring fluids. Fluids may include naturally occurring fluids such as air, hydrocarbons, water, oils, body fluids, biological fluids, and the like that occur in natural living and non-living systems.

The term “industrial fluid” as used herein includes fluids that typically may be used on an industrial site or structure. Industrial fluid includes ambient air on an industrial site or structure, compressed air, exhaled air, inhaled air, fugitive emission, biogenic emission, thermogenic emission, pollution, air pollution, water pollution, oil pollution, natural gas, water, naturally occurring fluid, synthetic fluid, lubricant, fuel, hydraulic media, drive fluid, power steering fluid, solvent, power brake fluid, drilling fluid, oil, crude oil, heat transfer fluid, insulating fluid, and the like.

The terms “industrial site” or “industrial structure” or “process area” as used herein includes a naturally occurring site or structure or area that is used for industrial applications or an artificial site or structure or area produced by any industry or industrial company that is used for industrial, environmental, recreational, residential, military, security, health, sports and other applications. Non-limiting examples of an industrial site include manufacturing facility, processing facility, disposal facility, industrial research facility, gas producing facility, oil producing facility, residential facility, sports facility, military facility, security facility, and others. In an aspect, the condition of the industrial site is based on the concentration of the external contaminant in the industrial fluid. Non-limiting examples of external contaminants include methane, ethane, hydrocarbon, ethylene, acetylene, water.

The term “analyte” as used herein includes any substance or chemical constituent that is the subject of a quantitative chemical analysis. Examples of analytes include, but are not limited to, hydrocarbon, methane, ethane, hydrocarbon, ethylene, acetylene, water, fuel, hydrogen, carbon monoxide, carbon dioxide, metals, aging products, or any combination thereof. In certain embodiments, the sensing materials of the present disclosure may be configured to detect analytes.

The term “interference” or “interferent” as used herein includes any substance or chemical constituent or physical constituent that undesirably affects quality of measurements of the analyte by reducing the accuracy, precision, or other known parameters of measurements of the analyte. Non-limiting examples of interferents and ambient environmental noise contributions include ambient temperature, ambient moisture, ambient pressure, ambient radio-frequencies, hydrocarbon, alcohol, diesel fumes, biogenic odors, biogenic volatiles and presence of interferences in a fluid. Filters (physical, chemical, and/or electronic) may be employed, based on the application specific parameters, to reduce, eliminate, or account for the presence and/or concentration of such interferents.

The term “multivariable sensor” as used herein refers to a single sensor capable of producing multiple response signals that are not substantially correlated with each other and where these individual response signals from the multivariable sensor are further analyzed using multivariate analysis tools to construct response patterns of sensor exposure to different analytes at different concentrations. In one embodiment, multivariable or multivariate signal transduction is performed on the multiple response signals using multivariate analysis tools to construct a multivariable sensor response pattern. In certain embodiments, the multiple response signals comprise a change in a capacitance and a change in a resistance of a sensing material disposed on a multivariable sensor when exposed to an analyte. In other embodiments, the multiple response signals comprise a change in a capacitance, a change in a resistance, a change in an inductance, or any combination thereof.

The term “multivariate analysis” as used herein refers to a mathematical procedure that is used to analyze more than one variable from the sensor response and to provide the information about the type of at least one environmental parameter from the measured sensor parameters and/or to quantitative information about the level of at least one environmental parameter from the measured sensor parameters. Non-limiting examples of multivariate analysis tools include canonical correlation analysis, regression analysis, nonlinear regression analysis, principal components analysis, discriminate function analysis, multidimensional scaling, linear discriminate analysis, logistic regression, or neural network analysis.

The term “sensing materials” and “sensing films” as used herein includes, but is not limited to, materials deposited onto a sensor, to perform the function of predictably and reproducibly affecting the sensor response upon interaction with the environment. In order to prevent the material in the sensor film from leaching into the liquid environment, the sensing materials are attached to the sensor surface using standard techniques, such as covalent bonding, electrostatic bonding, and other standard techniques known to those of ordinary skill in the art. Suitable sensing materials include polymer, organic, inorganic, biological, composite, and nano-composite films that change their property based on the environment in which they may be placed. A sensing material is applied onto the sensor. Non-limiting examples of sensing materials may be cryptophanes, zeolites, metal-organic frameworks, cage compounds, clathrates, inclusion compounds, semiconducting materials, metal oxides, electrospun polymer nanofibers, electrospun inorganic nanofibers, nanotubes, nanosheets, carbon nanotubes, graphene, molybdenum disulfide, electrospun composite nanofibers, and other sensor materials selected based on application specific parameters. These sensing materials may be further modified or functionalized with organic, inorganic, biological, composite, nanoparticle moieties to affect the sensitivity and selectivity of the response of these modified sensing materials to the components of interest in a fluid. As a non-limiting example, carbon nanotubes may be amide-functionalized, carboxylic acid functionalized, octadecylamine functionalized, poly(ethylene glycol) functionalized, and polyaminobenzene sulfonic acid functionalized. The carbon nanotubes can be single-walled, double-walled, and multi-walled as well known in the art. However, carbon nanotubes may produce different responses to an analyte and interferents when deposited onto a multivariable sensor.

The terms “transducer” and “sensor” as used herein refer to electronic devices such as LCR devices intended for sensing. “Transducer” is a device before it is coated with a sensing film or before it is calibrated for a sensing application. “Sensor” is a device typically after it is coated with a sensing film and after being calibrated for the sensing application.

Embodiments of the present disclosure include resonant multivariable electrical sensors and systems for in situ selective detection of methane leaks in various equipment types including across oil and gas applications such as gate and compressor stations, machine halls, valves, pressure relief valves, connectors, flanges, and others as well as along the pipelines.

In accordance with one embodiment, a resonant multivariable sensor system may include: (a) sensing material that detects methane at ambient temperature and does not need high temperature for combustion reactions; (b) multivariable sensor that recognizes the difference in response of this sensing material to methane and interferences to provide low false alarms (i.e., identifies diverse responses of the sensing material to methane versus interferences); (c) data analytics for accurate methane detection under variable interferences and ambient temperature; and, (d) a sensor reader to provide low-power wireless configuration for a sensor node.

In one embodiment, the resonant multivariable sensor system described herein includes a single sensor for selective detection of methane, where the sensor is comprised of a multivariable inductor-capacitor-resistor transducer with at least two operationally independent outputs, and a sensing material composition that is comprised of a methane-sensing moiety and a matrix incorporating the methane-sensing moiety, where the matrix directs interference response out of response direction to methane.

In one embodiment, the sensor may contain a sensing material which has its multivariable response in the presence of the gas. In one embodiment, the sensor may produce a reversible response upon exposure to a fluid. In one embodiment, the sensor may produce an non-reversible response upon exposure to a fluid.

In an embodiment, measurements of properties of fluids may be performed to determine dynamic signatures of the changes of chemical constituents in the fluid. The time scales of these dynamic signatures may vary greatly. Suitable timescale in a range of from about 0.01 second to about 200 days may be useful to determine different dynamic processes in industrial sites. Such determinations allow the identification of dynamic signatures of the leaks on industrial site, relation of the identified signature with the known leak signature from a specific industrial site component, and determination of the location of the leak based on the signature.

Measurements of properties of fluids may be performed at extreme temperature conditions. Depending on the application, these conditions may range from temperatures down to about −260 degrees Celsius and to temperatures up to about +1600 degrees Celsius. Such harsh temperature conditions with negative temperature down to about -260 degrees Celsius may be useful in relation to liquefied natural gas (LNG) and in the storage of biological and other types of samples. Harsh temperature conditions with positive temperature of up to about +1600 degrees Celsius may be useful in monitoring equipment where the temperature of operating components of the equipment can reach about +1600 degrees Celsius. Examples of equipment that operates at about 250 degrees Celsius may include downhole equipment in oil and gas production and the operations of an internal combustion engine (diesel, natural gas, hydrogen (direct combustion or fuel cells), gasoline, combinations thereof, and the like) for one or more of the fuel, the lubrication system, and the cooling/radiator system. Another example of such equipment may include an oil-filled transformer. Examples of equipment that operates at about 1000 and up to 1500 degrees Celsius include gas turbines. Examples of equipment that operates at about 1600 degrees Celsius include aircraft jet engines.

In one embodiment, the sensor contains a temperature-control unit that provides a desired temperature of the sensor. The desired temperature of the sensor may be constant or variable with a predetermined temperature-modulation pattern. The temperature of the sensor may be slightly above ambient temperature, for example from 5 to 50 degrees Celsius above ambient temperature.

In one embodiment, the temperature of the sensor may be in the range from about 100 to about 1600 degrees Celsius as provided and supported by the temperature of the equipment.

The disclosed resonant multivariable inductor-capacitor-resistor transducer produces impedance spectra that have real and imaginary spectral components. The whole real and imaginary impedance spectra can be used as sensor outputs. In addition, certain aspects of the impedance spectra, for example, the resonance peak position and the resonance peak magnitude can be used as sensor outputs. Raw measured spectral components can be processed using common multivariate analysis tools to extract sensor output features not readily observable in the raw responses. As a result, from a variety of sensor outputs that can be fully correlated, partially correlated or fully non-correlated (independent), the disclosed sensor has at least two operationally independent (non-correlated) outputs.

Embodiments of sensing materials that may be used in conjunction with the single sensor for selective detection include supramolecular moiety (e.g., cryptophanes, nanoporous metal-organic frameworks with molecular methane recognition, and others) incorporated into a matrix material inorganic or polymeric, to re-direct interference response out of response direction to methane. A supramolecular moiety may also be termed as a “cage compound”.

One specific example of methane-sensing supramolecular moiety are cryptophane A and cryptophane E. Cryptophanes are synthetic organic compounds with enforced cavity or cage of suitable size for molecular guest encapsulation. Their gas selectivity originates from size complementarity and efficient van der Waals interactions with the guest. The internal volume for Cryptophane A is 95 Angstrom̂3; the internal volume for Cryptophane E is 121 Angstrom̂3.

Another specific example of methane-sensing supramolecular moiety are metal-organic frameworks. For example, a Zirconium 1,4-dicarboxybenzene metal-organic framework UiO-66 is available from Strem Chemicals, Inc., Newburyport, Mass. Another metal-organic framework is Basolite C 300 which is Copper benzene-1,3,5-tricarboxylate available from Sigma-Aldrich, St. Louis, Mo.

Supramolecular materials provide gas recognition based on the size and shape of gas molecules of interest, so ideally only certain molecules interact with the sensing material. However, supramolecular materials when used in their native form, suffer from interferences.

Therefore, in accordance with one embodiment, a supramolecular moiety is incorporated into a matrix (e.g., inorganic or polymeric or composite). Unlike other examples of incorporation of sensing moieties into a matrix that attempt to eliminate effects of interferences, the matrix material is utilized herein to re-direct interference response into a different direction versus the response direction to methane (see e.g., FIG. 3).

Examples of a polymer matrix include polymers with glass transition temperature below 0 degrees Celsius (e.g. silicones), polymers with glass transition temperature above 0 degrees Celsius (e.g. polycarbonates) and co-polymers with more than one glass transition temperature (e.g. silicone polyimides). Examples of an inorganic matrix include porous silicon, sol-gels, and aero-gels. Examples of a composite matrix include carbon nanotubes incorporated into a polymer matrix.

FIG. 3 schematically illustrates methane- or fluid-sensing moieties in a matrix material. These sensing moieties can be dispersed or dissolved in this matrix material forming a random or non-random distribution of sensing moieties in the matrix. The relevant aspects of the sensing material are the concentration of the methane- or fluid-sensing moieties in the matrix and accessibility of methane- or fluid-molecules to the sensing moieties.

In one embodiment, the sensing material has multi-response mechanisms to different gases, such as methane as an analyte gas and interferences. In particular, the multi-response mechanisms to different gases are pronounced as changes of different properties of the sensing material. Methane can sorb into a methane-sensing supramolecular moiety and change sensor mass or capacitance or both, while other gases can change other properties such as viscoelasticity, resistance. Thus, the matrix is selected to have different effects from interference gases as compared to the methane-sensing supramolecular moiety. These different effects direct interference response into a different direction versus the response direction to methane. Other non-limiting examples of analyte gases include gases of industrial importance such as different alkanes, H₂S, NO, NO₂, NOR, CO, CO₂, H₂O and others.

Other non-limiting examples of analytes may include fluids, for example, water with dissolved analyte such as methane or other hydrocarbon. Water may be industrial water, drinking water, natural water, or well water.

The LCR sensor may be in a form of a resonator sensor such as a radio-frequency or a microwave resonant sensor. These sensors can operate at frequencies ranging from about 100 kHz to about 100 GHz. The LCR sensor also may be in a form of a resonator sensor such as quartz crystal microbalance (QCM) resonant sensor or a surface acoustic wave (SAW) resonant sensor. These sensors can operate at frequencies ranging from about 1 kHz to about 100 GHz. The LCR sensor also may be in a form of a resonator sensor such as tuning fork sensor. These sensors can operate at frequencies ranging from about 400 Hz to about 100 kHz. The LCR sensor can be in the form of electrical resonator or electromechanical resonator. Electrical resonant sensors may be radio-frequency resonators, microwave resonators, terahertz resonators, metamaterial resonators, and the like. Electromechanical resonant sensors may be thickness shear mode resonators (also known as quartz crystal microbalances, QCMs), acoustic wave devices, surface acoustic wave (SAW) devices, tuning forks, and microcantilevers.

Combining the LCR sensor with a sensing material that has different response mechanisms to different gases results in a desired multivariable sensor with a multidimensional response.

One example of a methodology for sensing methane mixed with ambient interferences is depicted in FIGS. 5A-5C. Here the real Zre(f) and imaginary Zim(f) parts of the impedance spectra Z(f) as defined in FIG. 5A is measured. Measured real Zre and imaginary Zim parts of the impedance spectrum and examples of parameters for multivariate analysis are frequency Fp and magnitude Zp of Zre, resonant F1, Z1 and anti-resonant F2, Z2 frequencies and magnitudes of Zim. During sensor operation, in situ acquisition of sensor spectra is performed as shown in FIG. 5B. In one embodiment, a processor reduces data dimensionality to principal components (PCs) (e.g. PC1, PC2) or plots individual responses. In one embodiment, a processor is configured to perform principal components analysis, or analysis of individual responses, for example. Upon this data representation, response to interferences is driven into an orthogonal direction versus analyte response as shown in FIG. 5C.

Again, as shown in FIG. 5C, in one embodiment, the multivariable sensor identifies diverse responses of the sensing material to methane versus interferences and drives a response to interferences into an orthogonal direction versus the analyte response. In one embodiment, the multivariable sensor is an inductor-capacitor-resistor (LCR) sensor that has independent outputs to recognize these different responses to gases. In one embodiment, the responses to interferences are allowed but in different directions than the analyte response. In particular, as shown in FIG. 5C, response to an analyte is in the vertical direction of the one of sensor outputs (PC2), while response to an interference is in a horizontal direction of another of sensor outputs (PC1). Such new sensing embodiment simplifies sensor design and manufacturing by bringing aspects of enhanced sensor performance to its data generation and data processing algorithms.

Data analytics of collected spectral response from the multivariable sensor allows accurate methane detection under variable interferences and ambient temperature. In one embodiment, a transfer function of a methane sensor is built with at least two variables of the sensor response that include Fp, Zp, F1, Z1, F2, Z2, or other variables extracted from the measured real Zre and imaginary Zim parts of impedance spectrum of the LCR sensor.

In one embodiment, a transfer function of a methane sensor is built with at least two variables of the sensor response that include Fp, Zp, F1, Z1, F2, Z2, or other variables extracted from the measured real Zre and imaginary Zim parts of impedance spectrum of the LCR sensor. An example of a transfer function of a methane sensor is:

[methane]=c0+c1*Fp+c2*Zp+c3*Fp*Zp,   (Eq. 1)

where [methane] is concentration of methane predicted from the LCR sensor outputs; Fp and Zp are outputs of the LCR sensor; and c0, c1, c2, and c3 are coefficients of the transfer function.

In one embodiment, a transfer function of a methane sensor with a temperature correction for ambient temperature is built with at least two variables of the sensor response that include Fp, Zp, F1, Z1, F2, Z2, or other variables extracted from the measured real Zre and imaginary Zim parts of impedance spectrum of the LCR sensor and an auxiliary input from a separate temperature sensor. An example of a transfer function of a methane sensor with an auxiliary input from a separate temperature sensor is:

[methane]=c0*T+c1*Fp*T+c2*Zp*T+c3*Fp*Zp*T+c4+c5*Fp+c6*Zp+c7*Fp*Zp   (Eq. 2)

where [methane] is concentration of methane predicted from the LCR sensor outputs and a separate temperature sensor; Fp and Zp are outputs of the LCR sensor; T is the temperature output of the temperature sensor, and c0, c1, c2, c3, c4, c5, c6, and c7 are coefficients of the transfer function.

In one embodiment, a transfer function of methane sensor with a temperature correction for ambient temperature is built with at least two variables of the sensor response that include Fp, Zp, F1, Z1, F2, Z2, or other variables extracted from the measured real Zre and imaginary Zim parts of impedance spectrum of the LCR sensor where some of the variables of the sensor response have different temperature coefficients. An example of a transfer function of a methane sensor with a temperature correction for ambient temperature is:

[methane]=c0+c1*Fp(T)+c2*Zp(T)+c3*Fp(T)*Zp(T),   (Eq. 3)

where [methane] is concentration of methane predicted from the LCR sensor outputs; Fp(T) and Zp(T) are temperature-dependent outputs of the LCR sensor; and c0, c1, c2, and c3 are coefficients of the transfer function.

Although Eqs. 1-3 show only linear terms Fp, Zp and their product Fp*Zp, the transfer functions can be not only linear, but also higher order, e.g., quadratic, cubic, and higher. As known in the art, the exact type of transfer functions depends on the concentration range of measured analyte gas (methane) and the required measurement accuracy. Further, Eqs. 1-3 show terms Fp and Zp only as examples, other terms, in addition or instead of Fp and Zp, can be used, for example, F1, Z1, F2, Z2, PC1, PC2, PC3, and others calculated from the whole resonance impedance spectra.

In one embodiment, a reduction of dimensionality of response of multivariable methane sensor is accomplished by performing a classification algorithm on the outputs of the multivariable sensor. Non-limiting examples of classification algorithms include Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM). A classification algorithm is applied to a set of outputs of the sensor that were collected at different methane concentrations at different temperatures and environmental conditions. The result of such classification algorithm is a classification model. The developed classification model is stored. In real applications, outputs from the sensor are collected in real time, the classification algorithm is performed on real-time data and compares the results with the stored classification model. Example of reduction of dimensionality using a PCA classification is shown in FIGS. 8A-11B.

In one embodiment, the multivariable methane sensors are configured as part of a wireless sensing system. In one embodiment, wireless multivariable methane sensors may be positioned inside the site at different heights based on the site layout. Such heights may be in the range from 0.1 m to about 100 m from the ground with the maximum height is limited by the height of the methane processing infrastructure.

In one embodiment, the output of the multivariable sensors can be detected using one or more sensor readers. In combination, a sensor reader with a sensor constitutes a sensor node within a low-power wireless sensing system. In one embodiment, the sensor reader operates in low-power energy harvesting mode. In one embodiment, the reader provides a low-power system to operate at powers in the range from 1 mW to 50 mW. The wireless communication is provided by ZigBee or other standard protocols. Energy harvesting mode may be provided by using solar power, for example.

In one example, the sensor reader is a single-chip impedance analyzer and is a part of a multivariable sensor node. FIG. 1 illustrates a block diagram for an example multivariable sensor node 10. The multivariable sensor node 10 may be a wireless or wired multivariable sensor node. The multivariable sensor node 50 may comprise a resonant sensor (RS) 12, an impedance analyzer or an impedance reader module (IRM) 14, a processing and communication module (PCM) 16, and a power supply (PS) 18. In certain embodiments, the multivariable sensor node 10 may comprise a modular design. Advantageously, the modular design of the multivariable sensor node 10 provides the flexibility for system upgrades. In one example, the IRM 14 may communicate to the PCM 16 using a standard interface such as SPI, I2C, etc. Non-limiting examples of such PCM 16 may comprise a Texas Instruments MSP430 microcontroller in combination with a Texas Instruments CC2420 radio transceiver. In one example, the PCM 16 may be upgraded using a single chip (Texas Instruments CC430) which combines the microcontroller and radio functions but is still accessible from the IRM 14 through the same SPI interface. In one embodiment, impedance may be measured by stepping up the excitation frequency over 5-30 MHz. At each frequency step, the IRM 14 may apply a voltage excitation signal to the sensor, and perform the measurement and digitization of the resultant current signals. The multivariable sensor node 10 may be configured to transfer the resultant impedance data to a processing unit, such as a central hub or to a smartphone or a mobile computer, for further processing.

FIG. 2 illustrates a block diagram of the sensor reader and resonant sensor integrated into a low-power single-chip to form an impedance reader module (IRM) 20. The impedance reader module 20 is configured to measure a resonance impedance of the resonant sensor 22. In the illustrated embodiment, the impedance reader comprises an impedance analyzer 24 having a voltage excitation generator containing a direct digital synthesizer (DDS) 26 and a voltage-mode digital to analog converter (DAC) 28, a receiver comprising a trans-impedance amplifier, also known as a current to voltage converter (CVC) 30, and analog to digital converter (ADC) 32; and optionally one or more filters 34. The DDS 26 generates the digital representation of the excitation carrier frequency which is converted by the DAC 28 to an analog voltage signal applied to the resonant sensor 22. The CVC converts the current flowing through the resonant sensor 22 into a voltage which is digitized by the analog to digital converter 22.

FIG. 4A illustrates a wireless sensor node 100 in accordanc with an embodiment. The sensor node 100 comprises a sensor reader 20, a power source 100, a radio communication antenna 120, and a sensor 130.

In another embodiment, a plurality of sensors forms a sensor network for mapping of methane leaks over a predetermined area of interest. FIG. 4B illustrates an exemplary sensor network in accordance with one embodiment. In the illustrated embodiment, each wireless sensor node has a multivariable sensor, sensor reader, low power wireless network communication and an energy source. An example of the energy source is a solar panel. Data from each wireless sensor is transmitted to a local network manager station. An onsite weather station provides the information about wind direction, wind speed, ambient temperature and ambient humidity. In one embodiment, the information from each sensor and weather station is sent to a central station via the long range communication (e.g., satellite) for data processing and for comparison of the processed data with the stored classification model. The stored classification model may contain information about the methane leak concentration and location in relation to wind direction, wind speed, ambient temperature, and ambient humidity. The comparison of the collected data from the sensors with the stored classification model provides information about the magnitude and location of the methane emission source inside the area of interest. The magnitude and location of the methane emission source inside the area of interest further may be displayed on the outline of the site and may be reported to a site manager or operator. Such new knowledge can be used to develop predictive maintenance algorithms.

In one embodiment, the sensor system described herein enables methane detection in upstream oil and gas operations. In another embodiment, the sensor system detects emissions from gate and compressor stations, machine halls, gate valves, pressure relief valves, control valves, connectors, flanges, casing, wellheads and others as well as along the pipelines networks especially where pipe meets and forms a connection. In another embodiment, this sensor can be used to detect emissions from oil tanks. In yet another embodiment, this sensor will be used in characterizing flare efficiency. It should also be noted that methane leak occurrence is not relegated to just upstream and midstream applications, but is also prevalent in downstream local distribution networks up to and including applications in residential homes and businesses. In yet a further embodiment, this sensor could detect dangerous emissions or leaks from home/business gas generators, water heaters, and cooking appliances.

EXAMPLES

Individual sensors have been developed with multiple outputs (called multivariable sensors). Methane sensing moiety was synthesized as cryptophane A. The matrix was selected to be silicone polyimide. A sensing film was formed on a QCM sensor with dissolving cryptophane A and silicone polyimide in dichloromethane and depositing the solution onto a 10-MHz QCM crystal. The achieved ppm sensitivity, and performed initial stability tests are shown in FIG. 6. FIG. 6A illustrates sensor response to the blank carrier gas (air) and four concentrations of methane (2222, 4444, 6666, and 8888 ppm). Different methane concentrations were generated by dilution of 10000 ppm methane concentration from a gas tank with air in different proportions at a constant flow rate of 450 mL/min. FIG. 6B illustrates repetitive exposures to these four concentrations of methane 12 hours of testing, performed as an initial stability test.

Methane-sensing materials were tested for their performance at room temperature against initial interferences such as water, toluene, methanol, and benzene vapors, the results of which are shown in FIGS. 7A-7D. FIG. 7A illustrates response diversity to methane versus two initial model interferences-water and toluene as high- and low-polarity vapors. Methane response was in one plane, while responses to interferences were designed to be in other planes. Fp and Zp are examples of independent responses of our multivariable sensor. FIG. 7B illustrates response diversity to methane versus initial polar (water, methanol) and non-polar (benzene, toluene) vapors which are serious interferences for catalytic sensors. FIG. 7C illustrates actual concentrations of methane, water, toluene, methanol, and benzene (solid lines) and predicted concentrations of methane (circles) showing that new sensing material does not have cross-sensitivity with tested interferences. FIG. 7D illustrates correlation plot of actual versus predicted methane concentrations. The accuracy of detection of methane is illustrated in FIG. 7D where the values of actual methane concentrations (X axis) are compared to the methane concentrations predicted from the sensor outputs. The data points follow the linear line with the slope of unity illustrating the accurate prediction of methane. While tested exemplary interference vapors provide a significant accuracy loss (up to 250%) to conventional catalytic sensors, the developed data analytics algorithm delivered accurate methane detection under these interferences.

Examples of reduction of dimensionality using a PCA classification are shown in FIGS. 8A-11B. The multivariable QCM sensor was coated with a methane-sensing film and was exposed to different concentrations of methane and interferences such as water and toluene. In the presented data in FIGS. 8A-11B, the sensor was exposed to two replicates of exposures to methane at four concentrations, exposures to water vapor at two concentrations, exposures to toluene vapor at two concentrations, and two replicates of exposures to methane at four concentrations.

FIGS. 8A-8B illustrate exemplary sensor responses Fp and Zp to these gases. The Fp response showed strong responses to water and toluene vapors (FIG. 8A). The Zp response showed strong responses to methane and water vapors and small response to toluene (FIG. 8B).

FIGS. 9A-9B illustrate exemplary sensor responses Z1 and Z2 to these gases. The Z1 and Z2 responses showed strong responses to methane and water vapors and small response to toluene.

FIGS. 10A-10B illustrate exemplary sensor responses F1 and F2 to these gases. The F1 response showed strong responses to water and toluene vapors and small response to methane (FIG. 10A). The F2 response showed strong responses to water and toluene vapors (FIG. 10B).

FIGS. 11A-11B illustrate results of PCA analysis to these gases. Both principal components have responses to all gases but with different proportions. The principal component 1 showed weak response to methane and strong response to water and toluene vapors (FIG. 11A). The principal component 2 showed strong response to methane and strong response to water and toluene vapors (FIG. 11B).

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 with insubstantial differences from the literal languages of the claims.

Claims

1. A sensor for selective detection of methane comprising:

a multivariable inductor-capacitor-resistor resonant transducer having a plurality of operationally independent outputs;

a sensing material composition comprising a methane-sensing moiety; and

a matrix incorporating the methane-sensing moiety, wherein the matrix directs interference response out of response direction to methane.

2. The sensor of claim 1, wherein the sensor operates at ambient temperature.

3. The sensor of claim 1, wherein the sensor operates at an elevated temperature above ambient temperature in a range from about 5 to about 50 degrees Celsius.

4. The sensor of claim 1, wherein the sensor operates with dynamic heating.

5. The sensor of claim 4, wherein the dynamic heating is configured to reduce power consumption of the sensor.

6. The sensor of claim 1, wherein the methane-sensing moiety is a cage compound.

7. The sensor of claim 1, wherein the methane-sensing moiety comprises one of: cryptophane, zeolite, metal-organic framework, and carbon nanotubes.

8. The sensor of claim 1, wherein the multivariable inductor-capacitor-resistor resonant transducer comprises an electrical resonator.

9. The sensor of claim 1, wherein the multivariable inductor-capacitor-resistor resonant transducer comprises an electromechanical resonator.

10. The sensor of claim 1, wherein the methane is in air.

11. The sensor of claim 1, wherein the methane is dissolved in water.

12. The sensor of claim 11, wherein the water comprises one of: industrial water, drinking water, natural water, and well water.

13. A sensor system for detection of an analyte in an industrial fluid in presence of interferences, the sensor system comprising:

a multivariable inductor-capacitor-resistor resonant transducer with at least two operationally independent outputs;

a sensing material composition configured to provide different response patterns to an analyte in the industrial fluid in the presence of interferences; and

a signal processor that quantifies the analyte in the industrial fluid in the presence of interferences.

14. The sensor system of claim 13, wherein the sensing material composition comprises a cage compound.

15. The sensor system of claim 13, wherein the interferences comprise one of: ambient moisture, ambient temperature, ambient pressure, ambient radio-frequencies, hydrocarbon, alcohol, diesel fumes, biogenic odors, and biogenic volatiles.

16. The sensor system of claim 13, wherein the analyte is a chemical species associated with a leak of the industrial fluid.

17. The sensor system of claim 13, wherein the industrial fluid comprises air.

18. The sensor system of claim 13, wherein the industrial fluid comprises water.

19. The sensor system of claim 18, wherein the water comprises one of: industrial water, drinking water, natural water, and well water.

20. A sensor system for detection of an analyte in an industrial fluid in the presence of interferences, the sensor system comprising:

a transducer with at least two operationally independent outputs;

a sensing material compositions configured to provide different response patterns to an analyte in the industrial fluid in the presence of interferences; and

a signal processor configured to quantify the analyte in the industrial fluid in the presence of interferences.

21. The sensor system of claim 20, wherein the interferences comprise one of: biogenic odors and biogenic volatiles.

22. The sensor system of claim 20, wherein the analyte comprises a chemical species associated with a leak of the industrial fluid.

23. The sensor system of claim 20, wherein the industrial fluid comprises air.

24. The sensor system of claim 20, wherein the industrial fluid comprises water.

25. The sensor system of claim 24, wherein the water comprises one of: industrial water, drinking water, natural water, and well water.